Remediación de efluentes

PROCESOS DE
SEPARACIÓN AVANZADA
REMEDIACIÓN DE EFLUENTES CONTAMINADOS
MEDIANTE MÉTODOS FÍSICOS Y BIOLÓGICOS
Memoria presentada por:
Dª. María Salomé Álvarez Álvarez
para optar al grado de Doctora Internacional por la
Universidad de Vigo
Vigo, 2015
UNIVERSIDAD
DE VIGO
Título:
REMEDIACIÓN DE EFLUENTES CONTAMINADOS
MEDIANTE MÉTODOS FÍSICOS Y BIOLÓGICOS
Realizada por:
María Salomé Álvarez Álvarez
Dirigida por:
Mª Ángeles Sanromán Braga, Ana Mª Rodríguez Rodríguez y Francisco
Javier Deive Herva
Programa de Doctorado de Ingeniería Química
Departamento de Ingeniería Química
Universidad de Vigo
Vigo, 2015
Este trabajo ha sido financiado por el
Ministerio de Economía y Competitividad de
España, mediante el proyecto de código
CTM2012-31534, y la Universidad de Vigo
mediante una beca de estancia en el
extranjero
Agradecimientos
Tras unos años de incesante trabajo en los que no faltaron momentos de emoción, curiosidad,
impaciencia y desesperación llegó el momento de poner un punto y aparte a este capítulo de mi andanza por el
arduo e ilusionante mundo de la ciencia, al que llevo unida desde el día en que me pregunté por qué la hierba
era de color verde. Han sido muchas las personas que desde entonces, con sus amables consejos, han logrado que
nunca perdiera el anhelo de investigar.
En primer lugar agradecer a mis directores de tesis Ángeles, Ana y Fran todo su apoyo, sus consejos, los
medios para la realización de esta tesis y por haber propuesto el camino a seguir para llegar a este fin.
Durante estos años de tesis he tenido la oportunidad de convivir con muchos compañeros que
compartieron conmigo curiosidades y vivencias de sus pueblos, ciudades y países, gracias por todos esos buenos
ratos de café, tartas, comidas,…y una mención especial a Jose por todo el apoyo que nos brindas en el
laboratorio cuando nos tenemos que enfrentar a todas las técnicas analíticas, al doctor Esperança y al Profesor
Rebelo por su apoyo al aceptarme en su grupo del ITQB de Oeiras.
Una tesis es un trabajo que casi nunca se queda en el laboratorio y acaba formando parte de tu tiempo
libre. Geno, Chus, Mar, Elena, Xanel, Alberto gracias por escuchar pacientemente mis historietas de
laboratorio y por los consejos que en cada momento me habéis dado.
Quiero terminar estos agradecimientos recordando a las personas que más han contribuido a que esta
experiencia haya llegado a su fin.
A Javi, por haberme dado el empujón que necesitaba para emprender esta historia y otras, por tu
insistencia en que siquiera adelante, tu optimismo, paciencia, apoyo y por haberme demostrado que incluso en
lo más adverso puede sobrevivir la ilusión. Nada sería sin tu ayuda. Infinitas gracias.
A mi familia:
Al pequeñin Álex por hacer que los sábados de estos últimos meses fuesen un poco distintos.
A mis padres y a mis hermanos Lito y Rori que siempre supieron apoyar mis decisiones, por su sacrificio
y preocupación y sobre todo por saber entender y respetar mi deseo de trabajar en ciencia. Mi mayor gratitud a
vosotros.
A mis abuelos, por haberme transmitido su experiencia en la vida y sus consejos. Siempre conmigo. “Un
bo mestre é aquel que nunca deixa de ser un alumno curioso” - Justo Álvarez Fernández
Sinceramente ¡gracias!
A mi familia
“La verdadera ciencia enseña, por encima de todo, a dudar y a ser ignorante”
Miguel de Unamuno
INDEX
RESUMEN Y CONCLUSIONES
RESUMEN
3
CONCLUSIONES
9
CHAPTER 1. INTRODUCTION
1.1 ENVIRONMENTAL POLLUTION
1-3
1.2 LEGISLATION
1-5
1.3 POLLUTED EFFLUENTS
1-6
1.3.1 DYES
1-6
1.3.2 POLYCYCLIC AROMATIC HYDROCARBONS
1-8
1.3.3 EMERGING POLLUTANTS
1-9
1.3.4 HEAVY METALS
1-11
1.4 TREATMENT METHODS
1-11
1.5 REFERENCES
1-16
CHAPTER 2. BIOLOGICAL METHODS TO REMOVE POLLUTANTS
2.1 AIMS AND WORKFLOW
2-3
2.2 INTRODUCTION
2-4
2.2.1 BIOSORPTION
2-4
2.2.2 BIODEGRADATION
2-7
2.3 MATERIALS AND METHODS
2-12
2.3.1 CHEMICALS
2-12
2.3.2 CULTURE MEDIUM AND MICROORGANISMS
2-13
2.3.3 MICROBIAL ACCLIMATION
2-14
2.3.4 EFFECT OF IONIC LIQUIDS ON MICROORGANISMS
2-15
2.3.5 BIOPOLYMER PRODUCTION
2-15
2.3.6 DYE AND PAH BIOTREATMENT AT DIFFERENT SCALES
2-15
2.3.7 ANALYTICAL METHODS
2-16
2.3.8 STATISTICAL DESIGN
2-18
2.4 RESULTS AND DISCUSSION
2-19
2.4.1 MICROBIAL ADAPTATION TO IONIC LIQUIDS
2-19
2.4.2 DYES REMOVAL BY IONIC LIQUID-ADAPTED PSEUDOMONAS STRAIN
2-25
2.4.3 SIMULTANEOUS BIOTREATMENT OF PAHS AND DYES BY IONIC LIQUID-ADAPTED P. STRAIN 2-32
2.5 CONCLUSIONS
2-44
2.6 REFERENCES
2-45
CHAPTER 3. REMEDIATION OF POLLUTANTS BY AQUEOUS TWO PHASE
SYSTEMS
3.1 AIMS AND WORKFLOW
3-3
3.2 INTRODUCTION
3-4
3.3 MATERIALS AND METHODS
3-12
3.3.1 CHEMICALS
3-12
3.3.2 EXPERIMENTAL PROCEDURE
3-13
3.4 RESULTS AND DISCUSSION
3-18
3.4.1 IONIC LIQUIDS AS SEGREGATION AGENTS IN AQUEOUS SOLUTIONS OF NON-IONIC
SURFACTANTS
3-18
3.4.2 INORGANIC AND ORGANIC SALTS AS SEGREGATION AGENTS IN AQUEOUS SOLUTIONS OF
NON-IONIC SURFACTANTS
3-35
3.4.3 AQUEOUS TWO PHASE SYSTEMS FOR THE PARTITION OF DYES, PAHS, HEAVY METALS AND
EMERGING POLLUTANTS
3-49
3.5 CONCLUSIONS
3-61
3.6 REFERENCES
3-62
CHAPTER 4. CONCLUSIONS
4.1 IN RELATION TO THE BIOLOGICAL METHODS TO REMOVE POLLUTANTS
4-3
4.2 WITH REGARD TO REMEDIATION OF POLLUTANTS BY AQUEOUS TWO PHASE SYSTEMS
4-4
CHAPTER 5. QUALITY CRITERIA OF PUBLICATIONS
ANNEXES
RESUMEN Y CONCLUSIONES
Resumen y Conclusiones
RESUMEN Y CONCLUSIONES
RESUMEN
El agua es un recurso natural irreemplazable e imprescindible para la vida y el desarrollo
de todos los seres vivos del planeta. En la actualidad, una de las principales amenazas a las que
se enfrenta la población mundial es el constante deterioro de la calidad del agua, debido a la
incesante actividad industrial y el evidente cambio climático que se viene observando en las
últimas décadas. Los efectos cada vez más notables como el aumento de zonas desérticas, los
intensos periodos tormentosos y los brotes epidémicos empiezan a alertar del deterioro del
agua como un problema de ciclópeas consecuencias para nuestra supervivencia.
Una de las causas de esta problemática se centra en la constante síntesis de nuevos
compuestos químicos, con efectos desconocidos sobre el medio ambiente y con una
legislación deficiente. Por esta razón, es evidente la necesidad de adoptar soluciones a largo
plazo para resolver estos episodios de contaminación no solo en los países industrializados,
sino también mediante acciones de prevención en los países en vía de desarrollo. En este
sentido, una correcta protección del medio ambiente requiere la adopción de protocolos de
buenas prácticas que impliquen un consumo mínimo de los recursos naturales y un máximo
nivel de reciclaje. La solución a este problema empieza por la existencia de regulaciones más
estrictas a nivel nacional e internacional sobre la gestión ambiental.
En este contexto, la investigación de nuevos métodos de remediación de estos
contaminantes ha sido objeto de un inusitado interés académico e industrial. La elección de
una u otra estrategia vendrá marcada por las circunstancias específicas de cada caso concreto,
pudiendo ser más ventajoso un tratamiento ex-situ que uno in-situ dependiendo de aspectos
tales como el tipo de contaminante presente o del área contaminada. En ocasiones, la
aplicación de una única técnica de eliminación de contaminantes no permite alcanzar los
objetivos deseados, motivo por el cual se requiere la combinación de diferentes alternativas.
Teniendo en cuenta lo mencionado, a lo largo de esta tesis doctoral se abordarán
diferentes estrategias para la remediación de efluentes contaminados por diversos
compuestos de naturaleza recalcitrante como son los tintes industriales, los hidrocarburos
aromáticos policíclicos, los metales pesados y los fármacos, estos últimos en representación de
los contaminantes emergentes.
3
Resumen y Conclusiones
Los colorantes son unos de los compuestos más sintetizados en todo el mundo. Estas
sustancias son utilizadas de forma común en la industria de la alimentación, farmacéutica, del
papel, aunque son los tintes utilizados en la industria textil los responsables de la mayor
producción de efluentes contaminados debido a la gran demanda de agua que requieren sus
procesos. La presencia de tintes va a afectar no solo a la actividad fotosintética de la vida
acuática sino también al incremento de la demanda química de oxígeno. Las complejas
estructuras aromáticas de los tintes son principalmente resistentes a la acción de la luz, la
actividad biológica, el ozono y otras condiciones de degradación medioambientales. Dicha
naturaleza química es responsable de su persistencia y peligrosidad en el medioambiente,
surgiendo así una gran inquietud por la presencia de estos compuestos, ya que a menudo
contienen en sus estructuras metales, cloruros y diversos compuestos aromáticos que
desencadenan efectos mutagénicos, tóxicos y carcinogénicos en el ser humano y otras
especies vivas.
Los hidrocarburos aromáticos policíclicos (HAPs) son un grupo de compuestos tóxicos
ampliamente presentes en el medioambiente, pudiendo encontrarse en el aire, el suelo, o
incluso en las plantas y los animales como resultado tanto de procesos antropogénicos o
naturales, como por ejemplo la combustión de derivados del petróleo o las erupciones
volcánicas o incendios forestales. Se conocen unos cien HAPs diferentes al existir una gran
cantidad de isómeros, sin embargo la Agencia Estadounidense del Medioambiente (US EPA) y
la Unión Europea (UE) solo han definido como contaminantes prioritarios a dieciséis de ellos.
Se sabe que este tipo de moléculas son persistentes en la naturaleza debido a sus propiedades
físicoquímicas, como su baja solubilidad acuosa y presión de vapor, su alta lipofilicidad y su
gran estabilidad termodinámica debido al sistema conjugado  del anillo bencénico.
Los metales pesados ocupan otro nicho destacado en el deterioro del medioambiente.
Éstos se pueden encontrar en el suelo, los sedimentos o el agua y, aunque algunos de ellos son
esenciales para el desarrollo de las funciones vitales de los organismos, como por ejemplo el
cinc, hierro, manganeso o vanadio entre otros, su presencia en gran cantidad es peligrosa o
letal para los seres vivos. De forma general, estos compuestos suelen presentarse en forma de
coloides, partículas iónicas o formando parte de complejos organometálicos, aunque también
exhiben una gran afinidad por los ácidos húmicos o las arcillas orgánicas. Una propiedad
destacada de los metales pesados, que condicionará su mayor o menor grado de
contaminación, es su solubilidad en agua. Este parámetro estará influido de forma
determinante por variables como el pH, la salinidad, el tipo de especies añadidas para formar
complejos o el ambiente redox del medio en el que se encuentren.
4
Resumen y Conclusiones
Finalmente, los contaminantes emergentes engloban un amplio rango de compuestos de
naturaleza antropogénica tales como los cosméticos, los pesticidas, los productos de higiene
personal, los medicamentos, o los retardantes de llama entre otros. En los últimos años, el
desarrollo de nuevos métodos de análisis más sensibles ha permitido alertar de su presencia y
peligrosidad. El gran problema que encierran los contaminantes emergentes es la falta de
conocimiento de sus efectos a corto y largo plazo sobre la salud humana o el medioambiente,
lo que ha retardado su regulación. Otra particularidad de estos compuestos es su presencia
constante en el medioambiente como consecuencia de su elevada producción y consumo. De
todos los contaminantes emergentes recogidos en la Directiva (2000/60/CE), los productos
farmacéuticos son los más estudiados debido a su elevado consumo. Estas moléculas suelen
llegar al medioambiente en sus formas originales o metabolizadas. Diversos estudios han
demostrado la presencia de estos medicamentos en aguas superficiales y subterráneas, en
aguas residuales urbanas e incluso en aguas potables.
Por consiguiente, la presencia de estos contaminantes peligrosos exige el esfuerzo de
desarrollar técnicas eficientes y sostenibles para su eliminación del medioambiente. Esta tesis
doctoral estará enfocada a evaluar la eficacia de los métodos biológicos y físicos, tales como la
biodegradación/biosorción y los sistemas acuosos bifásicos, para la eliminación de este tipo de
contaminantes persistentes. Las tecnologías utilizadas tradicionalmente en el tratamiento de
las aguas residuales han estado divididas en dos categorías principales: así se pueden citar
procesos físico-químicos y biológicos. Los primeros están basados en la adsorción o la
descomposición de los contaminantes por medio de materiales adsorbentes o agentes
oxidantes, respectivamente. A su vez, la eliminación de contaminantes mediante métodos
biológicos puede llevarse a cabo por medio de procesos de biosorción, biodegradación o la
combinación de ambos. Por otra parte, la selección de una u otra estrategia no debería estar
exclusivamente fundamentada en la eficacia del proceso seleccionado sino que debe integrar
aspectos medioambientales y económicos para lograr su puesta en práctica a escala real.
Todas estas técnicas tienen sus ventajas e inconvenientes, por un lado los procesos físicos
suelen lograr una eliminación rápida de los contaminantes y una alta posibilidad de
regeneración del material adsorbente, sin embargo suelen ser costosos y generan gran
cantidad de residuos. De la misma manera, los procesos químicos suelen tener el
inconveniente de su elevado coste y la generación de metabolitos secundarios, los cuales
pueden llegar a ser incluso más tóxicos que el compuesto de partida. En contrapartida, estos
procesos suelen mostrar una gran eficacia a la hora de diseñar la tecnología de
descontaminación a gran escala. Por otro lado, los métodos biológicos tienen la ventaja de ser
5
Resumen y Conclusiones
viables económicamente y más respetuosos con el medioambiente que los anteriores, aunque
se suelen caracterizar por ser procesos lentos.
Una vez realizada una introducción general sobre los problemas ambientales causados
por contaminantes procedentes de la actividad industrial y doméstica, en el capítulo dos de
esta tesis doctoral se abordará la bioeliminación de contaminantes ampliamente presentes en
efluentes industriales. Por un lado, se seleccionaron los tintes Reactive Black 5 y Acid Black 48,
así como los HAPs de alto y bajo peso molecular, Fenantreno, Pireno y Benzoantraceno, por
estar presentes en aguas residuales de sectores como el textil, la curtiduría o la industria
metalúrgica. Con este fin, se estudió el efecto de la aclimatación de la bacteria Pseudomonas
stutzeri CECT 930 en presencia del líquido iónico etilsulfato de 1-etil-3-metilimidazolio,
comparando su comportamiento con el de otros microorganismos procedentes de ambientes
extremos en lo que concierne a la salinidad, a la temperatura y a la carga de hidrocarburos. Se
comprobó que esta bacteria aclimatada mostró una elevada resistencia al estrés químico
producido por diversas familias de líquidos iónicos. A tenor de estos resultados, se apostó por
aplicar este agente microbiano en la remediación de una disolución acuosa de los tintes
modelo seleccionados, como paso previo a la implementación de un proceso combinado de
eliminación de efluentes contaminados con dichos tintes y los HAPs. En todos los casos, la
viabilidad del proceso se demostró a escala matraz y reactor, y se modelaron los datos
experimentales mediante ajuste a ecuaciones matemáticas conocidas.
La obtención de estos parámetros fue crucial para permitir el diseño del esquema de
tratamiento mediante la utilización de herramientas informáticas específicas de simulación de
bioprocesos como el software SuperPro Designer. De este modo, se comprobó la viabilidad
técnica y económica de la solución final propuesta, en comparación con los procesos
convencionales sin optimizar.
En el tercer capítulo de esta tesis doctoral, se apostó por la utilización de un método
físico de tratamiento como la extracción líquido-líquido, debido a sus evidentes ventajas en
comparación con otros procesos físico-químicos de remediación. De hecho, la gran
recalcitrancia de metales pesados y fármacos, así como la eliminación incompleta de tintes y
HAPs nos animó a utilizar sistemas acuosos bifásicos, debido a sus probadas ventajas en el
tratamiento de efluentes acuosos. Esta técnica ha sido ampliamente aplicada a la separación,
recuperación y purificación de diversas especies químicas tales como compuestos orgánicos
volátiles, iones metálicos o un considerable rango de biomoléculas tales como enzimas,
antibióticos o antioxidantes, entre otros. Además de su versatilidad, los sistemas acuosos
bifásicos presentan ventajas inherentes como son los tiempos cortos requeridos para la
6
Resumen y Conclusiones
separación de fases, la baja viscosidad, la posibilidad de evitar disolventes orgánicos volátiles,
su alta capacidad de extracción, su fácil escalado o la capacidad de diseñar sistemas
biocompatibles.
Estos sistemas de extracción líquido-líquido consisten en dos fases inmiscibles, donde los
compuestos generalmente utilizados para conseguir la segregación de fases son polímeros y
sales, aunque en la actualidad han surgido los líquidos iónicos como alternativa que ha abierto
nuevos horizontes en la aplicación de este tipo de sistemas de separación. En este sentido, las
destacadas propiedades fisicoquímicas de los líquidos iónicos tales como su alta estabilidad
térmica, química y electroquímica, su despreciable inflamabilidad o su notable conductividad
iónica han favorecido su rápida incorporación en el campo de la separación y purificación de
compuestos mediante la segregación de fases en sistemas acuosos. Por otro lado, la práctica
ausencia de estudios centrados en los surfactantes ha promovido nuestro interés en su
aplicación para la eliminación de compuestos contaminantes del medio acuático mediante
sistemas acuosos bifásicos, ya que este tipo de sustancias presenta un gran interés en
diversidad de aplicaciones biotecnológicas, alimentarias y medioambientales.
Por ello, en el capítulo tres se propuso la aplicación de surfactantes no iónicos,
concretamente las familias polietilenglicol ter-octilfenil éter (Triton X) y polioxietilenglicol
sorbitán (Tween) como candidatos para formar sistemas acuosos bifásicos con líquidos iónicos
y sales para finalmente investigar su versatilidad en procesos de remediación de
contaminantes tales como tintes, HAPs, metales pesados y fármacos.
En primer lugar, se analizó la habilidad de los compuestos seleccionados para segregar
regiones de inmiscibilidad, determinándose las curvas de solubilidad y las rectas de reparto a
diferentes temperaturas. Como agentes inductores de la separación de fases en disoluciones
acuosas de los surfactantes no-iónicos, se postularon los líquidos iónicos basados en el catión
imidazolio y amonio, por ser dos de las familias más habitualmente empleadas. Además, se
investigó el papel de diferentes sales inorgánicas y orgánicas con cationes de potasio y amonio,
y se discutió su eficacia “salting out” mediante teorías ampliamente consolidadas como la
propuesta por Hofmeister, o parámetros termodinámicos característicos tales como la energía
libre de Gibbs de hidratación o la entropía de hidratación de los iones presentes. Asimismo, los
datos experimentales de las curvas de solubilidad y las rectas de reparto fueron
correlacionados experimentalmente mediante modelos empíricos de tres y cuatro parámetros,
para caracterizar más en detalle la región de inmiscibilidad.
7
Resumen y Conclusiones
Una vez valorados todos los sistemas obtenidos se procedió a la selección de aquellos
con menor impacto medioambiental para llevar a cabo la partición de los contaminantes
elegidos. Para el caso de las sales como agentes de segregación de fases se optó por la
elección de la sal orgánica citrato potásico para la partición de tintes y HAPs en disoluciones
acuosas de Tween 20 y Triton X-100, respectivamente. La sal tartrato sodio potasio fue la
seleccionada para estudiar la extracción de los iones metálicos Zn2+ y Cu2+ de muestras reales
de sedimentos marinos dragados en sistemas acuosos bifásicos basados en los surfactantes no
iónicos Triton X-100 y Tween 20. Por último, la partición de ibuprofeno y diclofenaco como
modelo de contaminantes emergentes de gran presencia en el medioambiente se llevó a cabo
con un sistema acuoso bifásico formado por el cloruro de colina como agente de segregación
de fases en una disolución acuosa de Tween 80.
En el caso de los efluentes contaminados con tintes industriales y HAPs, como ya se
había demostrado la viabilidad de su remediación por métodos biológicos, en el capítulo tres
se verificó que los sistemas propuestos permitían la mejora de los niveles de remediación
alcanzados en efluentes obtenidos tras el proceso de reacción biológica. En este sentido, se
demostró que la existencia de medios sintéticos y complejos ampliamente utilizados, así como
las biomoléculas resultantes de la reacción no interferían en la eficacia remediadora de los
ATPS considerados. Por otra parte, también se investigó la posibilidad de acoplar este tipo de
sistemas acuosos en procesos de lavado de suelos contaminados con metales pesados,
proponiendo la aplicación en un efluente real obtenido tras el lavado de sedimentos marinos.
Por último, se comprobó también la viabilidad de la utilización de ATPS basados en líquidos
iónicos de colina como una plataforma biocompatible para la eliminación y concentración de
contaminantes emergentes.
8
Resumen y Conclusiones
CONCLUSIONES
Las conclusiones obtenidas a lo largo de esta tesis doctoral se resumen a continuación:
 La utilización de microorganismos procedentes de biotopos extremos con resistencia a
la presencia de familias comunes de líquidos iónicos demostró la viabilidad de aquellos
procedentes de lugares contaminados con alta carga orgánica y salina para sobrevivir
bajo elevadas concentraciones de este tipo de disolventes.
 La bacteria Pseudomonas stutzeri se destacó como la candidata con mayor resistencia
a la presencia de contaminantes y se corroboró su adaptabilidad tras una exposición
prolongada (dos meses) a estos líquidos iónicos en un bioreactor semicontinuo.
 La respuesta adaptativa de la bacteria se reflejó en la producción de un biopolímero,
constituido fundamentalmente por unidades de glucosa, que favoreció su aplicación
en procesos de biorremediación de tintes industriales, debido a procesos de
biosorción. Por ello, se consideró su idoneidad como agente de remediación en
efluentes contaminados con tintes y HAPs para mejorar la eficacia de procesos en dos
etapas mesofílicas y termofílicas.
 EL proceso biológico se realizó satisfactoriamente, con valores superiores al 75% en
menos de dos días para ambos tintes, tanto por separado como mezclados a escala
matraz. Además, el cambio de escala en biorreactor de tanque agitado de laboratorio
permitió aumentar los niveles de remediación hasta un 80% en menos de un día en un
efluente conteniendo la mezcla de ambos tintes.
 Se propuso esta bacteria adaptada para el biotratamiento de un efluente contaminado
con el tinte Reactive Black 5 y tres HAPs, determinándose las condiciones óptimas de
operación (pH, temperatura y agitación de 7.0, 310.65K y 146 rpm, respectivamente)
mediante el uso de un plan factorial cúbico centrado en las caras, alcanzando niveles
de remediación superiores al 60%.
 La validez de estas condiciones se comprobó a escala matraz y bioreactor,
caracterizando detalladamente las cinéticas de crecimiento y biorremediación de cada
contaminante.
 Se demostró la habilidad de los cationes imidazolio y amonio al igual que diferentes
sales convencionales inorgánicas y orgánicas de potasio y amonio para lograr la
segregación de fases en disoluciones acuosas de surfactantes no iónicos
9
Resumen y Conclusiones
pertenecientes a las familias polietilenglicol ter-octilfenil éter (Triton X) y
polioxietilenglicol sorbitán (Tween).
 Se utilizaron diversas ecuaciones ampliamente descritas en la literatura para
caracterizar los datos de equilibrio y rectas de reparto.
 Se analizó el efecto del líquido iónico, del surfactante y de la temperatura sobre las
regiones de inmiscibilidad, concluyéndose que la utilización de elevadas temperaturas,
y la presencia de surfactantes de alta hidrofobicidad en la disolución acuosa conllevan
un aumento de la región bifásica.
 Se demostró la influencia del catión y del anión de las sales en su capacidad para la
segregación de fases, de acuerdo con la tendencia predicha por la clasificación de
Hofmeister. Se utilizaron funciones termodinámicas tales como la energía libre de
Gibbs de hidratación, la entropía de hidratación molar y el coeficiente-B de viscosidad
de Jones-Dole, corroborándose la siguiente secuencia para los cationes estudiados K+ >
NH4+ y para los aniones inorgánicos PO4-3 > HPO4-2 > CO3-2 > SO3-2 > SO4-2. Por otra
parte, los aniones orgánicos mostraron la siguiente secuencia: (C6H5O7)-3 > (C2O4)-2 >
(C4H4O6)-2.
 La alta eficacia de extracción de contaminantes fue superior a un 93% para los tintes
Reactive Black 5 y Acid Black 48 y a un 80% para HAPs, independientemente de la sal
orgánica utilizada. Se demostró la viabilidad de la estrategia planteada en efluentes
contaminados tratados biológicamente, considerando dos de los medios más
habitualmente empleados en este tipo de procesos. En todos los casos, los valores de
remediación fueron superiores al 92%, mejorando claramente las eficacias logradas
con el tratamiento biológico individual.
 La idoneidad de una estrategia en dos etapas para la remediación de metales pesados
de sedimentos marinos dragados con niveles superiores al 90 % para el Zn y al 80%
para el Cu, en un sistema modelo formado por el agente complejante tiocianato
potásico, la sal orgánica tartrato de sodio potasio y el surfactante no iónico Tween 20.
Se demostró la viabilidad de esta estrategia para ser acoplada tras una primera etapa
de lavado del sedimento.

La eliminación de los contaminantes emergentes ibuprofeno y diclofenaco, como
representantes de productos farmacéuticos más comúnmente utilizados, de un
efluente modelo alcanzó niveles superiores al 90% para ambos casos. Para ello, se
propuso la utilización de un sistema biocompatible formado por el líquido iónico
cloruro de colina y el surfactante no iónico Tween 80.
10
1. INTRODUCTION
1.1 ENVIRONMENTAL POLLUTION
1.2 LEGISLATION
1.3 POLLUTED EFFLUENTS
DYES
POLYCYCLIC AROMATIC HYDROCARBONS
EMERGING POLLUTANTS
HEAVY METALS
1.4 TREATMENT METHODS
1.5 REFERENCES
1-3
1-5
1-6
1-6
1-8
1-9
1-11
1-11
1-16
1.-Introduction
1.1 ENVIRONMENTAL POLLUTION
Throughout history, the planet Earth has suffered countless environmental pollution
episodes and certainly one of them caused our own life. Ironically, the great present problem
is the incessant increase pollution due to civilizations footprint in the environment. Arguably,
this impact has dawned with Industrial Revolution during the last half of the 18th century and
at the turn of the 19th century but until recently its effects as acid rain has not caused
international alarm. The industrial footprint in the environment is not only a consequence of
the produced industrial goods but it is also related to the energy that these processes require
for transforming raw materials.
Along this thesis, we will use the terms pollution and pollutant, so the meaning of these
nouns must be defined in accordance with the current legislation (in accordance with the
Spanish law “Proyecto de Real Decreto del 22 de Diciembre de 2014”): “Pollution is the direct
or indirect introduction, as a consequence of human activity, of substances or energy in
atmosphere, water or soil, that can be harmful for human health or for aquatic and land
ecosystems”.
Environmental pollution consists of five basic types, namely, air, water, soil, noise and
light. Although pollution has been traditionally studied from the point of view of the first
three, it is necessary to emphasize that most pollutants interact with more than one element
in environment, as shown in Figure 1.1.
Frequently, either by atmospheric deposition or leaching, all pollutants are sunk in
water, thus generating polluted effluents. In this sense, one of the main obstacles for their
treatment is the simultaneous existence of many different types of pollutants such as: dyes,
polycyclic aromatic hydrocarbons, pesticides, drugs, etc. (Orozco et al., 2003; Sanz, 2005; Arun
& Eyini, 2011, Kyzas & Kostoglou, 2014).
1-3
1.-Introduction
Atmosphere
Hydrosphere
POLLUTANTS
Anthroposphere
Biosphere
Lithosphere
Figure 1.1. Pollutants in the environment
The above-mentioned evidences that pollution is a problem that can spread between
different areas, from surface water to groundwater, affecting either fluvial and sea water. In
this vein, the deleterious alteration of water quality is usually the result of different activities:
 Industrial such as textile, paper, iron and steel, food, etc. This problem is obvious
because all of them entail high water consumption and the resulting generation of
wastewater containing mainly organic matter, heavy metals, detergent or industrial
oil.
 Agriculture and ranching may pollute river or aquifer water, due to spillage of waste
water from farm work and/or animal droppings.
From the reasons above, it is evident the necessity of adopting long-term permanent
solutions, not only in industrialized countries to remedy serious episodes of pollution, but also
preventive actions in developing countries. In this sense, a correct protection of the
environment requires the adoption of good practices protocols which entail minimum
resources consumption and maximum levels of recycling. Furthermore, constant world´s
population growth may cause water shortage in some areas of the planet (Gupta et al., 2012).
The solution to this problem must thus march hand in hand with the existence of more and
more strict regulations at national and transnational level.
1-4
1.-Introduction
1.2 LEGISLATION
Social awareness of quality and management of water resources was evident in 1879
when the Spanish law on water (“Ley de Aguas”) was passed, repealed when the Ley 25/1985
(August, 2nd) came into effect. It basically stated that water is a natural resource which
scantiness cannot be solved by man (“El agua es un recurso natural escaso, indispensable,
irreemplazable, no ampliable por la mera voluntad del hombre, irregular en su forma de
presentarse en el tiempo y en el espacio, fácilmente vulnerable y susceptible de usos
sucesivos”).
Therefore, the European Union, through the water framework directive (Directive
2000/60/EC, amended by Directive 2008/105/EC, established as a main target for 2015 to
achieve an environmental and chemical “good condition” for all the community water. In this
sense, two approaches have been combined: the reduction of emissions as maximum as
possible and the establishment of a minimum quality threshold. Additionally, it has the
following aims:
 Promote a sustainable use of water.
 Establish measurements for reducing wastes.
 Decrease groundwater pollution.
 Deterioration prevention, protection and improvement of aquatic systems.
 Reduce the effects of floods and droughts.
Likewise, it is worth mentioning that there are many regulations and rules (national,
regional and local) to warrant the control of emissions and to further the preservation of the
environment. Nowadays, the Spanish Ministry of Agriculture, Food and Environment has
passed the Proyecto de Real Decreto del 22 de Diciembre de 2014, establishing the criteria for
monitoring and evaluating the status of surface water and environmental quality rules which
will repeal the following decree-laws:

Real Decreto 60/2011, (January, 21st) about rules of environmental quality in the field
of water policy.

Anexes 1, 2 and 3 belonging to “Reglamento de la Administración Pública de Aguas y
de la Planificación Hidrológica” approved by Real Decreto 927/1988, (July, 29th).

“Orden de 11 de Mayo de 1988”, about quality requirements that must be kept in
surface water currents when they are used as drinking water.
1-5
1.-Introduction

“Orden de 8 de Febrero de 1988”, related to methods for measuring, frequency of
sampling and analysis of surface water used as drinking water.

“Orden de 16 de Diciembre de 1988”, related to methods and frequency of analysis or
inspection in continental water that requires protection and improvement for fish
farms development.

“Disposiciones de la Orden ARM/2656/2008, (September, 10th) where the instructions
for hydrographic planning are approved.
Within the regional sphere two main laws can be mentioned:

“Lei 8/2001” (August, 2nd) for the protection of water quality of Galician estuaries and
management of the public service of urban waste water treatment.

“Decreto de Galicia 130/1997” (May, 14th) where the regulation of fluvial fishing and
aquatic continental ecosystems is approved.
1.3 POLLUTED EFFLUENTS
Taking into account the abovementioned, most of the pollutants are present in aqueous
medium, so the research focused on the proposal of more efficient strategies to remove them
is an issue in the limelight both from an academic and industrial point of view. In this thesis,
the remediation of effluents polluted with dyes, polycyclic aromatic hydrocarbons, drugs and
heavy metals will be tackled, as they are some of the main pollutants of current concern.
1.3.1 DYES
Dyes have been used since prehistoric era as reflected in the caves of Altamira, but it
would not be until 1856 when the English chemist William Henry Perkin achieved the first
synthetic commercial dye called “Perkin´s mallow”. Since then, dyes are defined as coloured
compounds that, when applied to fibres, give permanent colour able to resist against
exposure, perspiration, light, water, chemical compounds and microbial attack (Rai et al.,
2005).
These compounds are commonly used in the food, pharmacy, textile and plastic
industries for a plethora of dying processes (Malik, 2003; Mielgo, 2002), as represented in
Figure 1.2. As can be noticed, the textile industry is a great source of polluted effluents due to
the high demand of water required in these processes (Crini, 2006). Moreover, parameters like
pH or dissolved oxygen chemical composition of the polluted effluents will be influenced by
1-6
1.-Introduction
the type of dye used (Banat et al., 1996). More than 10.000 different textile dyes, with an
estimated annual production of 8 · 105 metric tonnes, are commercially available worldwide;
about 50% of these are classified as azo dyes, which are those approached in this PhD thesis
(Leena & Selva, 2008; Szygulaa et al, 2008).
Figure 1.2. Synthetic dyes and industrial applications
Dyes are made up by molecules that have functional groups like: i) the chromophore is
an electron-acceptor and determine the colour of the dye. The most common chromophores
groups are: -N=N-, -NO2, NO,-C=N-, -C=C-, -C=O; ii) the auxochrome is an electron-donor which
is responsible for intensifying chromophore color during the synthesis of dye. Some examples
of auxochrome groups are: -NHR, -NR2, -NH2, -COOH, -OH, -SO3H (Rangabhashiyam et al.,
2013).
In relation to this, dyes can be classified according to several features, but one typical
consideration refers to their ionic character (Robinson et al., 2001). Ionic dyes are direct, acid
and reactive dyes. Non-ionic dyes refer to disperse dyes because they do not ionise in an
aqueous medium. Direct dyes are the most popular class of dyes, owing to their easy
application, wide color range and availability at moderate cost. Most direct dyes have di-azo
and tri-azo structures. Azo dyes are the largest class (60-70)% of dyes, with the greatest variety
of colours (Bae et al., 2007).
This large level of consumption causes serious risks to human health, due to the fact that
some dyes and their by-products are toxic, carcinogenic and mutagenic (Forgacs et al., 2004;
Saratale et al., 2011, Weber & Wolfe, 1986). On the other hand, the colour in water is more
visible and it affects the transparency because the human eye can detect concentrations as
1-7
1.-Introduction
low as 0.005 mg·L-1 of reactive dye in water (Shelley, 1994; Willmott et al., 1998). As a result,
the effluents polluted with dyes have been the subject of innumerable research works.
1.3.2 POLYCYCLIC AROMATIC HYDROCARBONS
Throughout time, industrial activity has caused the emission of an enormous quantity of
pollutants to the environment. Among them, polycyclic aromatic hydrocarbons (PAHs) have
occupied a prominent position. Currently, PAHs are widely spread throughout the
environment and are found in soil, plants, sediments, water, air, and animals, as a result of
both natural and anthropogenic processes (Guo et al., 2007). Regarding the former, they are
generated by natural forest fires, volcanic eruptions and natural oil seeps. However, PAHs are
more commonly generated by anthropogenic activities, mainly in combustion processes, such
as the incomplete combustion of organic materials in industry and other human activities, such
as industrial discharges, transportation, biomass burning, coal, petrol and waste incineration
(Zhao et al., 2009; Ravindra et al., 2008 ), as indicated in Figure 1.3.
Traffic
Volcanos
eruption
Industrial
activity
Oil
spillage
Figure 1.3. The main sources of PAHs
1-8
1.-Introduction
These chemicals are mainly made up of carbon and hydrogen assembled in two or more
benzene rings in linear, angular or cluster arrangements. It has been long known that this type
of compounds are persistent in the environment due to their physicochemical properties,
which include very low aqueous solubility and vapour pressure, high hydrophobicity (high log
POW), high adsorption coefficient and high thermodynamic stability of the aromatic ring. This
feature is determined by their conjugated  electron systems, which are dependent on the
number of aromatic rings and their molecular weight, making them to be weakly bioavailable
(Cao et al., 2009; Haritash & Kaushik, 2009).
More than 100 different PAHs have been identified, and they have been strictly
regulated by law in most industrialized countries. Sixteen of these hazardous molecules have
been classified as priority pollutants by the United States Environmental Protection Agency
(USA-EPA) and European Union (EU), and they are listed in Table 1.1.
Table 1.1. The 16 PAH priority pollutants defined by US-EPA and EU.
Two-ring
Three-ring
Four-ring
Five-ring
Six-ring
Naphthalene
Fluorene
Fluoranthene
Phenanthrene
Crysene
Pyrene
Benzo[g,h,i]perylene
Acenaphthene
Acenaphthylene
Anthracene
Benzo[a]anthracene
Benzo[b]fluoranthene
Benzo[k]fluoranthene
Benzo[a]pyrene
Indeno[1,2,3c,d]pyrene
Dibenzo[a,h]anthracene
In recent years, the presence and concentration of PAHs in the environmental has been
the subject of different studies due to their potential carcinogenic, mutagenic and teratogenic
effects. It seems that their genotoxic and carcinogenic character is related to the formation of
diol epoxides that covalently bound to DNA (Meehan & Bond, 1984; Whyte et al., 2000;
Delgado-Saborit et al., 2011).
1.3.3 EMERGING POLLUTANTS
Emerging pollutants (EPs) encompass a wide range of man-made chemicals such as
pesticides, cosmetics, personal and household care products, drugs, phthalates, fireretardants, among others. They are in use worldwide and are indispensable for modern society
1-9
1.-Introduction
(Thomaides et al., 2012). Statistics published by EURO-STAT in 2013 reveal that over 70% of
these chemicals bear a significant environment impact (European Commission, EUROSTAT).
These chemicals constantly reach the environment from various anthropogenic sources
and are distributed in air, sediments, soil and water. Due to EPs are compounds displaying
different origin and chemical nature, their environmental fate or possible consequences have
not been noticed. In fact, the US-EPA (United States-Environmental Protection Agency) defines
emerging pollutants as new chemicals without regulatory status which impact on environment
and human health is poorly understood (Deblonde et al., 2011). Another special feature of
these chemicals refers to their constant and continuous presence into the environment due to
their high production and consumption (Petrovic et al., 2003).
Although a list of 33 priority substances was compiled by the EU water framework
directive (2000/60/EC), the pharmaceutical compounds are the most studied due to their
common use in society. These drugs are excreted both in their original form or metabolized.
Numerous studies have demonstrated the presence of these drugs in urban waste water and
surface waters (Roberts & Thomas, 2006), sewage from hospitals (Lienert et al., 2011),
groundwater (Bendz et al., 2005) and even in drinking water (Houtman, 2010). These
chemicals may also reach the soil due to the use of waste water for irrigation (Ternes et al.,
2007).
The pharmaceutical compounds identified in the environment can be classified into
several groups: hormones, anti-inflammatory, antidepressants, beta blockers, antibiotic,
diuretics, etc. (Miège et al., 2009), as shown in Table 1.2.
Table 1.2. Pharmaceutical compounds in wastewater. (Deblonde et al., 2011)
Pharmaceutical compounds
Hormones
Anti-inflammatory and analgesic
Antidepressants
Molecules
Levonorgestrel, Progesterone, Testosterone.
Ibuprofen, Diclofenac, Indomethacine, Naproxen, Ketoprofen, Ketorolac
Fluoxetin
-blockers
Propanolol, Celiprolol, Metoprolol, Sotalol.
Antibiotics
Diuretics
Norfloxacin, Tetracyclin, Trimethoprim, Ciprofloxacin, Sulfapyridin
Furosemide, Aminotrizoic acid, Diatrizoate, Iotalamic acid.
Antiepileptics
Carbamazepine, Codeine, Ant pyrin, 4-aminoantipyrine
Lipid-regulators
Contrast agents
Bezafibrate, Acebutolol, Atenolol, Gemfibrozil.
Iopromide, Iomeprol, Iohexol, Iopamidol.
Cosmetics
Galaxolide, Tonalide
1-10
1.-Introduction
1.3.3 HEAVY METALS
The pollution in soils, sediments and water by heavy metals is one of the main
environmental problems in industrialized and developing countries due to their persistent,
carcinogenic and bioaccumulative character (DeForest et al., 2007; Rainbow, 2007).
Heavy metals are elements with high atomic weight and density over 5 g·mL-1, excluding
alkaline and alkaline earth groups. Although some of them are essential for developing vital
functions of organisms (cobalt, copper, iron, manganese, zinc, vanadium and strontium), their
excess is harmful or lethal for living beings. In this sense, heavy metals often involved in
environmental pollution problems are mainly chromium, cadmium, mercury, lead, arsenic and
antimony (Kennish, 1992).
In general terms, these compounds may be present as colloids, particle ions or being
part of organometallic complexes. In colloidal forms and particles they appear as hydroxides,
oxides, silicates, sulphur or adsorbed in minerals like clays, silica and organic matter. Also
heavy metals have a great affinity for humic acids, organic clays and oxides covered with
organic matter (McCullough et al., 1999).
The solubility of heavy metals in water is controlled by pH, salinity, type of complex
species where they are adsorbed, oxidation state of mineral phases and redox environment
(Connell & Miller, 1984). Their behaviour is a function of the organic matter content and water
chemistry, which can modify their mobility.
1.4 TREATMENT METHODS
Considering all the above-mentioned, different techniques have been explored in recent
years for removing these pollutants from soil and wastewater. These methods have been
classified into two main categories: physico-chemical (ion-exchange, adsorption, coagulationflocculation, flotation, electrochemistry) and biological, (biosorption or biodegradation). The
advantages and drawbacks of each technique have been summarized in Figure 1.4
(Subramaniam et al., 2009; Anjaneyulu et al., 2005; Adav et al., 2009; Pandey et al., 2007), and
some examples of all of them are presented in Table 1.3.
1-11
1.-Introduction
Figure 1.4. Treatment methods for the removal of pollutants
Table 1.3. Techniques applied in the remediation of different pollutants.
Techniques
Compounds
References
1.-Physico-Chemical Methods
Adsorption
Dyes
PAHs
Heavy
Metals
Emerging
Pollutants
ARS, IC
MG, MB
AS-GR, ATB-2G, IC
PHE, FA, BaA
AN
NA, FLU, PHE, PYR, FA
+2
+2
Zn , Cd
+2
Cu
+2
+2
+2
Pb , Ni , Cu
Zolgharnein et al., 2014
Kurniawan et al., 2012
Shen et al., 2009
Liu et al., 2014
He & Wang, 2011
Yuan et al., 2010
Yanagisawa et al., 2010
Li et al., 2010
Jiang et al.,2010
AMX
Putra et al., 2009
Chemical Precipitation
Dyes
PAHs
Heavy
Metals
Rred, NB-HE2R, NB-RX, BBG SRR
RGFL, BBR
MV, BF
PHE, PRY
PHE
NA, AC, FLU, PYR
+6
+2
+2
+2
Cr , Zn , Cu , Pb
+2
Hg
+2
+2
+2
Zn , Cu , Pb
Watharkar et al., 2013
Kabra et al., 2012
Bhole et al., 2004
Xi & Chen, 2014
Olivella et al., 2013
Chen et al., 2011
Chen et al., 2009
Blue et al., 2008
Álvarez et al., 2007
Filtration/Flocculation/Coagulation
Dyes
PAHs
BB3, BR46, BY2
RR-K2BP, RV-K3R, RB-KNB
CBB, CRB
PHE
AN
AN, PYR, FA
Zarei et al., 2010
Kang et al., 2007
Chakraborty et al., 2003
López-Vizcaíno et al., 2012
Poerschmann et al., 2008
Rebhun et al., 1998
1-12
1.-Introduction
+2
+3
+2
Ce , Fe , Pb
+2
+2
Zn , Cu
+2
+2
+2
Cd , Cu , Pb
Heavy
Metals
Abo-Farha et al., 2009
Borij et al.,2009
Yuan et al., 2008
Ozonation
Dyes
PAHs
Emerging
Pollutants
NcsB-G, TRww-3BS
RY (15,84), RR ( 120,239), RB160
Rred, RRB, Rblu, Rbla, RV, RY, a.o.
BaA, BbF, BeP, BkF, BaP, CHR
ACN, PHE, AN, FA
BaP, FLU
OTC
BZF
PCT
Wijannarong et al., 2013
Sancar & Balci, 2013
Sarayu et al., 2007
Bedjanian & Nguyen, 2010
Rivas et al., 2009
Miller & Olejnik, 2004
Li et al., 2008
Dantas et al., 2007
Andreozzi et al., 2003
Electrochemical Oxidation
Dyes
PAHs
Heavy
Metals
Emerging
Pollutants
RB5;
Rbla B, RGY-RNL, CR-FNG, a.o.
RR120, RR141, RR198, RO16, a.o.
NA, AC, ACN, FLU, BbF, PYR, a.o.
NA, FA, PYR
AC, AN, FLU, FA, IP, NA, PHE, a.o.
+2
Mn
+2
Cu
+2
Ni
Ibp
VIG
Iglesias et al., 2013
Chatizisymeon et al., 2006
Rajkumar & Kim, 2006
Souza et al., 2011
Muff & Søgaard, 2010
Tran et al., 2009
Shafaei et al., 2010
Camarilloa et al., 2010
Sun et al., 2009
Ciríaco et al., 2009
Özkan et al., 2004
Photocatalytic Degradation
Dyes
PAHs
Emerging
Pollutants
Rho B, Mg I
CV, AnB
MB, MO
PHE, AC, AN, BaA
PYR
NA, PHE, AN, BaA
AMX, AMP, CLX
CF
Dcf, NPX, Ibp
Li et al., 2014
Shanthi & Padmavathi, 2012
Wetchakun et al., 2012
Kou et al., 2010
Zhang et al., 2010
Woo et al., 2009
Elmolla & Chaudhuri, 2010
El-kemary et al., 2010
Méndez-Arriaga et al., 2008
Fenton/Photo-Fenton Degradation
Dyes
PAHs
Emerging
Pollutants
Rblu-RR, Rred-RR
MO, RB5, FA, LG
MG, O-II
NA, AC, ACN, FLU, PHE, AN, a.o.
BaP
FLU, PHE, ACN
PCT
ATN
Ibp
Punzi et al., 2012
Rosales et al., 2009
Rastegar et al., 2008
Da Rocha et al., 2013
Veignie et al., 2009
Beltrán et al., 1998
Trovó et al., 2012
Isarain-Chavéz et al., 2010
Skoumal et al., 2009
2.-Biological Methods
Bacterial degradation or Biosorption
Dyes
PAHs
Heavy
Metals
Emerging
Pollutants
AO52, DB71
AR88, RB5, DR81, DO3
RRB, GY, RR, Rblu, RV, RY, RO,a.o.
PHE, PYR
PHE, PYR, BaA
PYR
+6
+2
+3
+2
Cr , Cd , Fe , Ni
+2
+2
Pb , Ni
+2
+2
Cu , Pb
MET, OLA, TYL
CF, OF, MET
Liu et al., 2013
Khalid et al., 2008
Padmavathy et al., 2003
Bacosa & Inoue, 2015
Moscoso et al., 2012
Tiwari et al., 2010
Quintelas et al., 2009
Gabr et al., 2008
Pan et al., 2007
Ingerslev & Halling, 2001
Kümmerer et al., 2000
1-13
1.-Introduction
Fungal degradation or Biosorption
Dyes
PAHs
Heavy
Metals
AB, CR, TB, RBB
BG, EB
RR, RB, RO-II
PHE, PYR
NA, PHE, BaP
PHE, FA, PYR
+4
Zr
+2
Cu ;
+2
+2
Cu , Cd
Taha et al., 2014
Pryzstas et al., 2013
Ambrósio et al., 2012
Reyes-Cesar et al., 2014
Argumedo-Delia et al., 2012
Schmidt et al., 2010
Bhatti & Amin, 2013
Tseckova et al., 2010
Bhainsa & D`Sousa, 2008
Enzymatic Degradation
Dyes
PAHs
DR19, DB9
RBblu-R, Ablu25
RB4, RB160, RB171, RR11, a.o.
AN, BaP, NA, PHE
AN, PYR
AN, PRY, AC, FLU, PHE
Jamal et al., 2013
Zeng et al., 2012
Khan & Husain, 2007
Farnet et al., 2009
Eibes et al., 2006
Kraus et al., 1999
Algae Biosorption
+6
Heavy
Metals
Cr
2+
2+
Cu , Zn
+2
Pb
Cobas et al., 2014
Ajjab & Chouba, 2009
Deng et al., 2007
a.o.: among others
Sometimes, the use of one single treatment does not render possible the securing of
satisfactory results. For this reason, the combination of two or more methods can be a
suitable option to succeed in this kind of environmental problems. Literature analysis reflects
the existence of a variety of hybrid technologies for different pollutants, including physicochemical and biological technologies, as detailed below.
Fenton oxidation (Fe(II)/H2O2) pretreatment was found to improve the adsorption
capacity of granular activated carbon (GAC) due to the transformation of organic compounds
into smaller molecules that were able to pass through the micropores of GAC (Zamora et al.,
2000). Analogously, the appropriateness of the combination of a chemical and physical
method was demonstrated by Wang et al., (2002), as they observed 64% of chemical oxygen
demand (COD) reduction when UV-vis irradiation was combined with coagulation and
flocculation. Following this line, approximately 90% of COD reduction was attained by
integrating ozone and GAC adsorption for the treatment of landfill leachate. The ozonation
step allowed the formation of smaller molecules, which were more suitable to be adsorbed
than the initial molecules, and the removal of residual organic compounds and metal species in
the leachate was thus eased (Rivas et al., 2003; Oh et al., 2004).
The combination of advanced oxidation processes (AOPs) and biological treatment is
another sequential strategy leading to promising results. For instance, solar photo-Fenton was
used in combination with an aerobic biological system for the treatment of pharmaceutical
1-14
1.-Introduction
wastewater, obtaining that overall TOC degradation efficiency was over 95%, of which 33%
corresponded to the solar photochemical process and 62% to the biological treatment (Oller et
al., 2007). Other biological system configurations like biofilm reactors have also been
combined with AOPs such as H2O2/UV, TiO2/UV and photo-Fenton to treat reactive azo-dyes,
achieving 99% of removal efficiency (Kim & Park, 2008; García-Montaño et al., 2008). In the
same vain, an innovative process combining the electro-Fenton reaction followed by anaerobic
digestion and ultrafiltration as post-treatment turned out to be suitable to detoxify effluents
from olive oil processing, with removal efficiencies about 50% for COD and 95% for
monophenolic compounds (Khoufi et al., 2006, 2009). Other sequential strategies included the
use of aerobic pretreatment and GAC adsorption for reducing COD and NH3-H (65 and 95)% in
landfill leachate (Schwarzenbeck et al., 2003).
1-15
1.-Introduction
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1.-Introduction
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1.-Introduction
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1.-Introduction
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2. BIOLOGICAL METHODS TO REMOVE POLLUTANTS
2.1
AIMS AND WORKFLOW
2-3
2.2
INTRODUCTION
2-4
2.3
MATERIALS AND METHODS
2-12
2.4
RESULTS AND DISCUSSIONS
2-19
2.5
CONCLUSIONS
2-44
2.6
REFERENCES
2-45
ADAPTED FROM:
“Microbial adaptation to ionic liquids” (2015) 5, 17379-17382.
“Acclimation to ionic liquids: Enhancing the biotreatment potential of a Pseudomonas strain”.
(Under review).
“Simultaneous biotreatment of Polycyclic Aromatic Hydrocarbons and dyes in a one-step
bioreaction by an acclimated Pseudomonas strain” (2015) Accepted for publication.
2.-Biological methods to remove pollutants
2.1 AIMS AND WORKFLOW
AIMS
Biological methods are promising environmentally-friendly and cost-competitive alternatives
for the remediation of hazardous pollutants. These techniques have been applied for the last
decades to remove or decrease the toxic effect of organic and inorganic contaminants. In this
chapter, the bioelimination of dyes and PAHs will be targeted due to their demonstrated negative
effects when are present in wastewater streams. Hence, an ionic liquid-adapted Pseudomonas
stutzeri CECT 930 has been proposed for the bioremediation of different industrial pollutants. On
the one hand, azo and anthraquinone dyes Reactive Black 5 (RB5) and Acid Black 48 (AB48),
respectively, were considered as model contaminants present in textile industrial effluents. On the
other hand, hydrophobic PAHs, phenanthrene (PHE) (a three aromatic ring molecule), pyrene (PYR)
and benzo[a]anthracene (BaA) (both with four aromatic rings) were selected as models of Low
Molecular Weight (LMW) and High Molecular Weight (HMW) PAHs.
WORKFLOW
The working plan strategy to achieve the objectives described above includes:
 Screening of microorganisms from biotopes considered as extreme in terms of
salinity, temperature and hydrocarbon load, in order to investigate their ability to
thrive under the presence of high concentrations of common families of ionic liquids.
 Assessing the capacity of an ionic liquid-adapted Ps. stutzeri strain for the biological
decolourisation of an aqueous stream containing two reactive dyes RB5 and AB48,
both separately and mixed.
 Evaluating the viability of the bioremediation strategy at shake flask and bench scale
bioreactor, determining the kinetics of the process by fitting to well-known
equations.
 Assessing the ability of the adapted Ps. stutzeri strain to implement a one-step
biotreatment process for the simultaneous bioremediation of dyes and PAHs.
 Elucidating the nature of the bioremediation process, addressing the possible
existence of biosorption and biodegradation.
 Simulation of the biotreatment process for a real effluent by using commercial
software tools like SuperPro Designer.
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2.-Biological methods to remove pollutants
2.2 INTRODUCTION
The incessant growth of population and the climate change with several episodes of floods
and droughts, together with the pollution of surface water and aquifers are decreasing the
resources of drinking water around the world (Rockström, 2003; Vörösmarty et al., 2000; Kemper,
2004). The development of strategies for removing pollutants in industrial wastewater allowing its
recycling could be a safe bet for saving water and decrease wastes generation. Moreover, the
selection of specific remediation technologies should not be exclusively based on the efficiency and
must integrate environmental and economic aspects for achieving its implementation at real scale.
As already mentioned in the first chapter, technologies for treating wastewater have
traditionally been divided into two categories: physico-chemical and biological methods. As stated,
the removal of contaminants by biological methods can be accomplished by means of biosorption,
biodegradation or a combination of both (Wu et al., 2012; Chan et al., 2006).
2.2.1 BIOSORPTION
The term biosorption may be simply defined as a physico-chemical process where the
interaction occurs between substances from solution (sorbate) and biological material (biosorbent),
leading to a reduction in the solution sorbate concentration (Gadd, 2008). Furthermore, it is
noteworthy that the term biosorption includes complex mechanisms that depend on the sorbate,
the biosorbent, the presence or absence of metabolic processes (in the case of living biomass) and
environmental factors (Michalak et al., 2013). Hence, absorption, adsorption, ion exchange,
precipitation and crystallization are processes playing a decisive role in the global biosorption yield.
Absorption is the incorporation of a substance in one state into another in a different while
adsorption is the physical adherence or fixing of ions or molecules onto the surface of another
molecule (Gadd, 2008). Regarding ion exchange, it is defined as the replacement of an ion from a
solid phase in contact with a solution by another ion. Finally, precipitation and crystallization are
other possible underlying phenomena that may promote uptake capacities (Velgio & Beolchini,
1997; Aksu, 2005; Grini & Badot, 2008). It is interesting to differentiate between biosorption and
bioaccumulation, defining the first as a process where the sorbates are kept on the surface of the
cellular wall, while the latter are those substances accumulated inside the cell. These two stages
usually occur sequentially: a quick biosorption followed by a slow transport of sorbate inside the
cell (Aksu & Dönmez, 2000; Kaduková & Virčiková, 2005).
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2.-Biological methods to remove pollutants
TYPES OF BIOSORBENTS
Biosorption processes have been widely applied for the removal of organic pollutants such as
dyes, drugs, phenolics, pesticides, phthalates, hydrophobic chemicals like hydrocarbons and
inorganic compounds like metal ions or radioisotopes (Kaushik & Malik, 2009; Dhankhar & Hooda,
2011; Wang & Chen, 2006; Texier et al., 1999). A great amount of biosorbents has been
investigated as possible biological material with affinity for these hazardous pollutants in order to
open up new avenues for the treatment of wastewater. Among the possibilities, different options
can be highlighted:
 Microbial biomass: Archaea, Cyanobacteria, Bacteria, filamentous fungi, yeasts, microalgae.
 Macroalgae.
 Industrial wastes: fermentation and food wastes, activated and anaerobic sludge, etc.
 Agricultural wastes: fruit/vegetable wastes, rice straw, wheat bran, sugar beet pulp,
soybean hulls, leaves, etc.
 Natural residues: plant residues, sawdust, tree barks, weeds, peat, moss, bracken, lichens.
 Other materials: chitosan, cellulose, crustaceans, etc.
Nonetheless, the most frequently investigated biosorbents are bacteria, fungi, yeasts and algae
as reported in several reviews (Solís et al., 2012; Srinivasan & Viraraghavan, 2010; Park et al., 2010;
Chojnacka, 2010; Ahluwalia & Goyal, 2007). Some examples of these biosorbents are shown in
Figure 2.1.
Figure 2.1. Diverse biological biosorbents
2-5
2.-Biological methods to remove pollutants
The existence of functional groups like carboxyl, phosphate, hydroxyl, amino, thiol, etc. in the
cell walls of biosorbents licenses the interaction with the sorbates. For instance, archaea cell walls
composition varies from one genus to another. They may be constituted by pseudomurein,
sulfonated polysaccharide, glycoprotein, carboxyl and sulphate groups. For instance, the main cell
wall biosorptive component in cyanobacteria (blue-green algae) is peptidoglycan. Algal cell walls
have some variation in the composition but them all share cellulose as one of the main common
constituents. Other components in algal biomass include alginic acid, xylans, proteins, biopolymers,
which provide binding sites such as amino, hydroxyl, imidazole, phosphate and sulphate groups
(Davis et al., 2003). The main binding sites on bacterial walls are peptidoglycan carboxyl groups
(Gram-positive) and peptidoglycan phosphate groups (Gram-negative), proteins or polysaccharides
(Dimitriev et al., 2005). Fungal cell walls are made up by a great variety of structural components
like aminopolysaccharides as chitosan (chitin derivative), glucans, proteins, polysaccharides or
lipids. Additionally, fungal phenolic polymers possess functional groups with potential binding sites
as carboxyl, phenolic and alcoholic hydroxyl, carbonyl and methoxyl (Gadd, 2008). All in all, the
complexity of the cell wall of these biosorbents makes it difficult to elucidate the mechanisms
through which the biosorption is carried out.
FACTORS INFLUENCING BIOSORPTION
Typical features of pollutants like charge, hydrophobicity, molecular size, structure,
concentration and solubility directly affect the viability of the biosorption process. In brief, a deep
knowledge on the nature of biosorbents and pollutants to be removed will provide some hints to
select the most suitable biosorbent. Besides, physico-chemical factors such pH, temperature or
solubility have an important role in the overall biosorption processes, as indicated below.
 pH: it is perhaps the most important physico-chemical factor, since the functional groups of
biomass are activated as a result of the surface electrical charge change, and these charged
sites become available for binding pollutants through electrostatic interactions. Generally
speaking, lower and higher pH values allow removing anionic and cationic chemicals,
respectively, like dyes or anionic/cationic metal species (Das et al., 2011).
 Temperature: it is another decisive factor since it affects the growth rate of biomass as well
as the molecules kinetic energy. Nevertheless, extreme values of temperature may also
damage the physical structure of the biosorbent (Bayramoglu & Arica, 2007).
 Initial pollutant concentration and salinity: an increment of initial pollutant concentration
entails a declining biosorbent capacity by toxicity or saturation of binding sites (Anjaneya et
al., 2011). Moreover, increasing the ionic strength reduces biosorption because ions may
2-6
2.-Biological methods to remove pollutants
compete for binding positions and also causes moderate inhibition of most bacterial
activities except for halotolerant microorganisms (Meng et al., 2012).
 Pretreatment and immobilization: the pretreatment processes for increasing the adsorption
capacity of biomass include autoclaving, contacting with inorganic (acids, alkalis, CaCl2,
ZnCl2) and organic (formaldehyde, ethanol, EDTA, acetone) compounds, removal of
inhibitory groups (decarboxylation, deamination), enhancement of binding groups
(amination, phosphorylation, carboxylation), etc. Autoclaving could break the biomass
structure and expose the potential binding sites, while chemical treatment could change
the surface electrical charge of biomass and promote new electrostatic interactions (Arica
& Bayramoglu, 2007; Vijayaraghavan & Yun, 2007). In relation to the immobilisation
techniques, biomass from fungi or bacteria has been immobilised within polymeric matrix
(polystyrene or polyurethane foam (Ürek & Pazarlioglu, 2005), nylon or Luffa sponges (Iqbat
& Saeed, 2007), Ca-alginate beads (Enayatizamir et al., 2010), etc. These procedures
enhance the biosorption capacity when high amounts of toxics are present in solution, thus
avoiding the inhibition of cellular growth.
One of the key aspects in the development of efficient biosorption-based technologies is the
search of low-cost of biosorbents, easy to regenerate. Some methods widely employed for the
regeneration or desorption of the loaded biosorbent consist of a washing with distilled water,
acid/basic solutions, organic solvents (ethanol, surfactants) or complexing agents (Aksu, 2005).
The modelling and simulation of the biosorption process is a valuable tool to predict the
optimum operating conditions, in order to ease process scaling-up. Although several models have
been developed, the Langmuir and Freundlich equations are by far the most widely employed.
2.2.2 BIODEGRADATION
Biodegradation is another biological alternative for the removal of pollutants. In contrast to
biosorption, the role of removing chemicals is played mainly by enzymatic processes that are part
of the metabolic pathways of microorganisms. This remediation strategy establish a competitive
alternative due to the fact that it is eco-friendly, cost-effective and efficient when compared to
typical physico-chemical counterparts. Pollutants like simple hydrocarbons (C1-C15), alcohols,
phenols, amines, acids, amides among others are very easily biodegraded. In contrast,
polychlorinated biphenyls (PCBs), polycyclic aromatic hydrocarbons (PAHs), dyes and pesticides are
recalcitrant substances.
2-7
2.-Biological methods to remove pollutants
ANAEROBIC BIODEGRADATION
A great number of industrial wastewaters are preferably treated in anaerobic biological
processes due to the high level of chemical oxygen demand (COD) reduction, potential for energy
generation and low excess sludge production. Nevertheless, in practical applications, anaerobic
treatment undergoes a low growth rate of the microorganisms, and it requires a post aerobic
treatment of the harmful anaerobic effluent which often contains ammonium (NH4+) and hydrogen
sulphite (HS-) ions (Heijnen et al., 1991).
In the absence of molecular oxygen, alternative electron acceptors such as nitrate, sulphate,
and ferrous ions among others, can be used to oxidize aromatic compounds (Meckenstock et al.,
2000). Another viable alternative is the use of enzymes like azoreductases (NADPH-dependent
reductases or NADH-DCIP reductases) as reported elsewhere (Dhanve et al., 2008). These enzymes
are present in microorganisms such as bacteria, algae and yeasts. As an example, specific
dehalorespiring microorganisms such as Dehalococcoides strains have been reported to gain energy
from reductive dechlorination (in halogenated hydrocarbons) by substituting a halogen by a
hydrogen atom. Hence, these dehalorespiring organisms have been applied to degrade commercial
PCB mixtures (Bedard et al., 2007), polychlorinated dibenzo-p-dioxins and dibenzofurans (Bunge et
al., 2003).
AEROBIC BIODEGRADATION
Aerobic biological processes have been effectively employed for the treatment of organic
wastewaters. The main advantages of this kind of processes are: i) higher levels of removal of
soluble biodegradable organic matter than anaerobic processes, ii) easy flocculation of the
produced biomass and iii) lower content of suspended solids (Cervantes et al., 2006). In these
conditions, the oxidative degradation of pollutants can be catalysed by enzymes such as
dioxygenases, monoxygenases, proteases, phosphatases or peroxidases and phenoloxidases such
as manganese peroxidases (MnP), tyrosinases (Tyr), lignin peroxidases (LiP) and laccases (Lac)
(Kelley et al., 1990; Khlifi et al., 2010; Yang et al., 2005). Their importance can be clearly noticed in
Table 2.1.
2-8
2.-Biological methods to remove pollutants
Table 2.1. Enzymes identified in the biodegradation of pollutants by different microorganisms.
Microorganisms
Phanerochaete chrysosporium
Trametes versicolor
Several microorganisms
Phanerochaete chrysosporium
Trametes trogii
Pseudomonas diminuta
Candida tropicalis
Oscillatoria curviceps
White-rot fungi
Microbial consortia
Alishewanella sp.
Nematoloma forwardii
Trichosporon akiyoshidainum
Type of enzyme
LiP, MnP
MnP, Lac
Dehalogenases, Lac
LiP, MnP
Lac
OPH
MnP
Azoreductase, Lac
MnP, Lac, Lip
Azoreductase, Lac, LiP
NADH-DCIP reductase
LiP, MnP, Lac
MnP, Tyr
Pollutants
Several dyes
Estrogenic Chemicals
PCP, DDT, Lindane
RDX
Reactive dye
Organophosphates
Reactive dye
Reactive dye
Phenols, PAHs
Congo red-buscar
Reactive dye
PAHs
Reactive dyes
Reference
Sen et al., 2012
Tsutsumi et al., 2001
Gianfreda et al., 2002
Cameron et al., 2000
Levin et al., 2001
Chen-Goodspeed et al., 2001
Yang et al., 2003
Priya et al., 2011
Nicell, 2001
Ayet et al., 2010
Kolekar et al., 2012
Guenther et al., 1998
Pajot et al., 2011
Pentachlorophenol (PCP), Dichloro diphenyl tricholorethane (DDT), Hexahydro-1,3,5-trinitro-1,3,5-triazine (RDX), Polycyclic aromatic
hydrocarbons (PAHs), Organophosphorus hydrolase (OPH).
As an example, the potential of laccases in biodegradation is due to their non-specific
oxidation capacity and their capacity to use readily available oxygen as an electron acceptor (Telke
et al., 2011). On the other hand, the catalytic action of MnP proceeds through an initial oxidation by
H2O2 to an intermediate that promotes Mn+2 oxidation. The Mn+3 organic acid complex formed acts
as an active oxidant; in this way, MnP is able to oxidise textile dyes (Husain, 2010). A comparison
between anaerobic and aerobic processes is provided in Table 2.2.
Table 2.2. Comparison of performance in anaerobic and aerobic processes (Chan et al., 2009)
Feature
Organic removal efficiency
Effluent quality
Sludge production
Nutrient requirement
Energy requirement
Temperature sensitivity
Bioenergy and nutrient recovery
Mode of treatment
Anaerobic
High
Moderate to poor
Low
Low
Low to moderate
High
Yes
Requirement of pretreatment
Aerobic
High
Excellent
High
High
High
Low
No
Total
FACTORS INFLUENCING BIODEGRADATION
The effectiveness of a biodegradation process will be influenced by several factors such as:
 Aqueous solubility of pollutants: The hydrophobic character of many pollutants like PAHs,
PCBs or pesticides hinders their bioavailability, which makes them more resistant to
biological or chemical breakdown (Semple et al., 2003). In these cases, the use of surface-
2-9
2.-Biological methods to remove pollutants
active agents (surfactants) may solve this problem by allowing a linkage between the
hydrophobic pollutant and the water molecules (Makkar & Rockne, 2003).
 Temperature: temperature has a relevant influence on the microbial growth kinetics and
the solubility of gases, nutrients and contaminants. Although the solubility of nutrients and
contaminants is usually higher at elevated temperature, oxygen solubility is lower, which
will reduce the metabolic activity of aerobic microorganisms. Therefore, a compromise
must be reached.
 pH: as for the temperature, pH is also a crucial variable to be considered. For instance,
when biodegradation occurs “in situ”, leaching of rubble will increase the pH, resulting in
lower bioremediation yields. On the other hand, the oxidation of other materials like coal
will create an acidic environment by the release and oxidation of sulphides, which may also
hinder the achievement of high remediation efficiencies. Therefore, it is a common practice
to adjust the pH at these sites, for example by the addition of lime (Alexander, 1995).
 Nutrient availability: Growth and reproduction of microorganisms are directly related to
nutrient availability, so an excess or deficiency in nutrient load may in fact inhibit microbial
metabolism. In some cases, biodegradation of pollutants is produced under nutrient
deficient conditions (Bogan et al., 1996).
 Microbial communities: Pollutant-tolerant members within communities determine the
rate of degradation.
 Redox potential, oxygen availability, auxiliary (co)-substrates and co-contaminants are
other factors that can be directly related with an enhancement or failure of biodegradation.
Accordingly, the success of biodegradation processes does not exclusively depend on the
finding of a suitable microbial strain for the remediation, but it is also influenced by the physical
properties of the pollutants and the operating conditions at which the bioremediation process is
carried out. Generally speaking, biodegradation using a pure strain does not represent the actual
behaviour of microorganisms during bioremediation in natural sites, because this process depends
on cooperative metabolic activities of mixed microbial populations (Janbandhu & Fulekar, 2011),
and the synergies provided by different species (Zengler et al., 1999).
Although biodegradation is an environmentally friendly methodology for the treatment of
polluted sites, new xenobiotic compounds called “emerging pollutants” such as drugs, personal
care products, or ionic liquids would require the implementation of other strategies such as
bioaugmentation, biostimulation or the use of enzymes or genetically modified microorganisms (El
Fantroussi & Agathos, 2005; Nikolopoulou & Kalogerakis, 2009; Anga et al., 2005). These strategies
may improve the initial results of remediation but they cannot be considered a panacea as other
factors have to be considered like the adaptation of the inoculated microorganisms, lack of
2-10
2.-Biological methods to remove pollutants
substrate, competition between autochthonous and allochtonous microorganisms, use of organic
substrates other than the pollutant itself, predation or presence of bacteriophages (Goldstein et al.,
1985). Therefore, acclimation is an option to be considered for achieving effective biodegradation
of persistent chemicals. In this sense, the use of microorganisms from extreme environments can
be a viable start point to implement an efficient bioremediation strategy (de Carvalho et al., 2009;
Singer et al., 2005).
2-11
2.-Biological methods to remove pollutants
2.3 MATERIALS AND METHODS
2.3.1 CHEMICALS
The ionic liquids 1-ethyl-3-methylimidazolium ethylsulfate C2C1imC2SO4 (>99%) and 1-ethyl-3methylimidazolium methylsulfate C2C1imC1SO4 (>99%) were purchased from IoLiTec. 1-ethyl-3methypyridinium ethylsulfate C2PyC2SO4 (>99%) was supplied by Merck. Tributylmethyl
phosphonium methylsulfate P4441C1SO4 (>97%) was kindly donated by CYTEC industries. All of them
were subjected to vacuum (P = 2·10−1 Pa) and moderate temperature (T = 343.15 K) for several days
to remove possible traces of solvents and moisture, always prior to their use. These compounds
were kept in bottles under inert atmosphere until use, and their molecular structures are illustrated
in Figure 2.2.
The dyes RB5 and AB48 and the PAHs PHE, PYR and BaA (purity higher than 99%) used in the
bioremediation experiments were purchased from Sigma Aldrich, and their structure and main
characteristics are shown in Table 2.3. The same supplier provided the non-ionic surfactant Tween
80, benzyl benzoate, salts of the medium and chloroform. Glucose was purchased from Scharlau,
and HCl and hexane were supplied by Prolabo.
C2Py
+
+
C2C1im
P4441
-
C1SO4
Figure 2.2. Structure of the ionic liquids used
2-12
-
C2SO4
+
2.-Biological methods to remove pollutants
Table 2.3. Pollutants used for biological treatment.
Compound
Abbreviation
Phenanthrene
PHE
Pyrene
PYR
Benzo[a]anthracene
BaA
Reactive Black 5
RB5
Acid Black 48
AB48
Structure
2.3.2 CULTURE MEDIUM AND MICROORGANISMS
The composition of the Mineral Medium (MM) and Rich Medium (RM) used for the selected
microorganisms (Pseudomonas stutzeri CECT 930, acclimatized Ps. stutzeri, consortium C26b,
Staphylococcus warneri, Thermus thermophilus HB27, Anoxybacillus flavithermus, Shewanella
oneidensis, Phanerochaete chrysosporium BKM-F-1767 (ATCC 24725), Trametes versicolor,
Halobacterium salinarum) is presented in Table 2.4. Different concentrations of glucose and Tween
80 were included in the culture medium as carbon source and solubilizing agent, respectively, for
the biotreatment of dyes and PAHs simultaneously.
2-13
2.-Biological methods to remove pollutants
Table 2.4. Composition of mineral and rich media used
Mineral Medium (MM)
Rich Medium (RM)
P.s., C26b
Chemical
-1
Conc. (g·L )
T.t., A.f., St. w.
S.o.
P.c., T.v.
H. s.
-1
Chemical
Conc. (g·L )
Na2HPO4∙2H2O
6.8
Yeast extract
2.0
4.0
-
-
-
MgSO4∙7H2O
0.5
Casein peptone
5.0
8.0
-
-
10.0
Meat extract
1.0
-
-
-
-
CaCl2
0.015
NaCl
0.5
Tryptone
-
-
17.0
-
-
CuSO4
0.4
Soy peptone
-
-
3.0
-
-
NH4Cl
1.0
Malt extract
-
-
-
20.0
-
KH2PO4
3.0
Mycopeptone
-
-
-
1.0
-
-3
KI
10
H3BO3
MnSO4∙H2O
ZnSO4∙7H2O
FeCl3∙6H2O
Glucose
CaCl2
-
-
-
-
0.2
5.0·10
-3
KH2PO4
-
-
2.5
-
-
4.0·10
-3
NaCl
5.0
3.0
5.0
-
250.0
4.0·10
-3
MgSO4·7H2O
-
-
-
-
20.0
2.0·10
-3
MnCl2
-
-
-
-
0.218
FeCl2
-
-
-
-
3.58·10
Glucose
-
-
2.5
10.0
-
10.0
-3
P.s.: Ps. stutzeri; C26b: consortium C26b; St. w.: S. warneri; T.t. T. thermophilus HB27; A. f.: A. flavithermus; S.o.: S. oneidensis; P.c.:
P. chrysosporium; T.v.: T. versicolor; H.s.: H. salinarum.
The pH of the medium was adjusted according to the optimum values for each
microorganism, namely: P. chrysosporium, Tr. versicolor, consortium C26b, St. warneri, H.
salinarum, Ps. stutzeri CECT 930 and acclimatized Ps. stutzeri pH 7.2, S. oneidensis pH 7, T.
thermophilus HB27 and A. flavithermus pH 7.5. All the liquid media were sterilized by autoclaving at
394 K for 20 min. The inocula were obtained by cultivating the microorganisms in 250 mLErlenmeyer flasks capped with cellulose stoppers, containing the corresponding culture media.
They were cultivated at 310 K for the mesophiles, 333 K for A. flavithermus and 343 K for T.
thermophilus HB27. After the stationary phase was reached, the biomass was separated by
centrifugation at 5.000 g for 10 min, at 277 K. The humidity was removed by vacuum drying and the
dried cells (pellets) were stored at 253 K in Eppendorf tubes.
2.3.3 MICROBIAL ACCLIMATION
A stirred tank bioreactor (Biostat B, Sartorius, Germany) was used for microbial acclimation
and it consisted of a 5 L-jacketed glass vessel, filled with 3 L of MM. 0.2 mM of [C2C1im][C2SO4] was
added together with 10 g·L-1 of Tween 80 (polyethoxylated sorbitan oleate) as carbon source. The
2-14
2.-Biological methods to remove pollutants
temperature was maintained at 310 K by circulation of thermostated water, and the pH was
adjusted to 7.2. The bioreactor was inoculated with actively growing cells of Ps. stutzeri from 24 h
flask cultures (3% v/v). Air was supplied continuously at 0.17 vvm and the agitation was set at 200
rpm. The bioreactor was operated for two months.
2.3.4 EFFECT OF IONIC LIQUIDS ON MICROORGANISMS
Two different media were used for investigating the effect of the three selected ionic liquid
families: MM and RM with compositions detailed beforehand. The cultures were carried out in 96
well plates containing different concentrations of ionic liquids (ranging from 0.005 M to 1.5 M). The
Minimal Lethal Concentration (MLC) was ascertained by inoculating plates without the ionic liquid
pressure.
2.3.5 BIOPOLYMER PRODUCTION
The adapted Ps. stutzeri was cultured in 250 mL-Erlenmeyer flasks containing 50 mL of MM.
After 48 h, maximum biopolymer concentration was detected. Therefore, the culture medium was
centrifuged at 9300 g and 277 K to separate the cells. The supernatant was reserved for biopolymer
recovery. Afterwards, a mixture containing the supernatant and ethanol in a 1:2 ratio was kept at
277 K for 2 h and then centrifuged at 9300 g and 277 K.
2.3.6 DYE AND PAH BIOTREATMENT AT DIFFERENT SCALES
OPERATION AT FLASK SCALE
The biotreatment at small scale was carried out in 250 mL-Erlenmeyer flasks with 50 mL of
MM. The pH was initially adjusted to 7.2 and 7.0 for dyes and PAHs, respectively. The MM without
dyes and PAHs was autoclaved at 394 K for 20 min. The chemicals were sterilized by filtration
through a 20 µm filter prior to the addition to the autoclaved medium in order to avoid any
possible alteration of their chemical structure. The flasks were inoculated (3% v/v) with previously
obtained cell pellets of the adapted Ps. stutzeri, which were then incubated in an orbital shaker
(Thermo Fisher Scientific 496) at 310K and 150 rpm.
2-15
2.-Biological methods to remove pollutants
OPERATION AT BIOREACTOR SCALE
A 2-L stirred tank bioreactor (BIOSTAT®B-MO) was used for the scaling up of the process.
Temperature was maintained at 310K by circulation of thermostated water. The agitation rate was
optimized, as stated in the Results and Discussion section. Firstly, cells were grown for 12 h in flask
cultures (3% v/v) and subsequently used to inoculate it. Air was supplied continuously at 0.17 vvm.
2.3.7 ANALYTICAL METHODS
BIOPOLYMER CHARACTERIZATION
The biopolymer composition was ascertained after a preliminary acid hydrolysis step,
followed by the injection in an HPLC (Agilent 1100) equipped with a RI (refractive index) detector.
Composition determination was carried out by direct comparison with standards such as glucose,
fructose, sucrose, maltose and rhamnose.
MICROSCOPY ANALYSIS
Scanning electron microscopy (SEM) images were taken in a FEI-Quanta 200 environmental
scanning electron micro-scope using an accelerating voltage of 15 kV (Electron Microscopy Service,
C.A.C.T.I., University of Vigo).
BIOMASS DETERMINATION
Cells were harvested by centrifugation (10 min, 9300 g, and 277K), and the supernatant was
reserved for pollutant remediation analysis. Biomass concentration was measured by turbidimetry
at 600 nm in a UV-vis spectrophotometer (UV-630 Jasco), and the obtained-values were converted
to grams of cell dry weight per litre using a calibration curve as is plotted in the Figure 2.3.
0.8
Dry weight (g L-1)
0.6
0.4
0.2
0.0
0.0
0.4
0.8
1.2
1.6
Absorbance (600nm)
-1
2
Figure 2.3. Biomass concentration for adapted Ps. stutzeri (Biomass (g·L ) = 0.5663·Absorbance - 0.0401, R =
0.996)
2-16
2.-Biological methods to remove pollutants
DYES DECOLOURISATION
Dye concentrations (both independently and mixed) in the culture media were analysed by
UV-vis spectrophotometry taking into account wavelength in which the absorbance is the maxima
obtained for each dye (597 nm for RB5, 663nm for AB48 and from 547 to 713 nm for mixture of
dyes, calculated by measuring the area under the plot). Decolourisation (D) was expressed in terms
of percentage units by using the expression
𝐷(% 𝑟𝑒𝑚𝑜𝑣𝑎𝑙) = (𝐼𝑖 − 𝐼𝑓 ) ∙ 100/𝐼𝑖
(2.1)
where Ii and If are initial and final concentration of the dye solution, respectively.
Each decolourisation value was the mean of two parallel experiments. Abiotic controls were
always included. The assays were done in duplicate, and the experimental error was less than 3%.
ADSORPTION TEST
PAHs biosorption over the biomass was determined as follows. 50 mL of culture medium
were taken and centrifuged for 10 min at 5900 g and 277 K. The supernatant was withdrawn and
biomass was freeze-dried during 4 h at 233 K and 7.9·10-5 atm using a TelStarCryodes. Afterwards,
10 mL of hexane were added and ultrasounds were applied (Bransonic 3510) for 30 min. Again, the
sample was centrifuged for 10 min and 100 µL of supernatant were taken into a vial, where 10 µL of
Internal Standard (IS) were added. Samples were analysed by GC-MS as explained later on.
PAHS AND INTERMEDIATES DETERMINATION
The standards were prepared adding 10 µL of internal standard (IS) of Benzyl benzoate (20
ppm) to PAHs concentration of PAHs from 0 to 5 ppm (0, 0.1, 0.2, 0.5, 1, 2, 5). The calibration
curves were obtained by plotting the different PAHs concentrations front (PAHs Area/IS Area). The
values of the fitting parameters and regression coefficients are shown in Table 2.5.
Table 2.5. Fitting parameters and regression coefficients.
PAHs
2
Equation
R
-1
0.999
-1
0.999
-1
0.999
PHE
(PHE Area/IS Area)= 2.5209·PHE (mg·L ) + 0.0326
PYR
(PYR Area/IS Area)= 1.9569·PYR (mg·L ) + 0.0118
BaA
(BaA Area/IS Area)= 0.9457·BaA (mg·L ) + 0.0488
2-17
2.-Biological methods to remove pollutants
Aliquots (1 mL) of supernatant were added over 0.8 g of MgSO4·7H2O following 0.1 mL of HCl
1 M and 1 mL of hexane. They were shaken for 1 h and an aliquot of 100 µL was collected from the
organic phase and 10 µL of internal standard (benzyl benzoate) were added.
PAHs concentration in supernatant was analysed by using an Agilent GC 6850 gas
chromatograph equipped with a HP-5MS column (30 m x 0.25 mm; 0.25 µm, Agilent), operating
with hydrogen as carrier gas, and coupled to an Agilent MSD 5975C mass spectrometer. Injections
(1 µL) of samples were made up in split mode (10:1); GC oven was programmed under the following
conditions: 323 K for 4 min and 283 K·min-1 to 553 K for 10 min. The mass spectrometer was
operated in SIM mode.
Intermediates were detected by adding 25 mL of chloroform to 250 mL of supernatant and
the pH was adjusted to 2 in order to favour the extraction of intermediates formed during the
biodegradation process. The water content in the organic phase was removed by addition of
anhydrous sodium sulphate and subsequently filtered. The sample was then introduced in a
rotatory vacuum concentrator (RVC 2-25 CCHRIST/CHRIST CF04-50 SR), and the residue was
dissolved in chloroform. The same gas chromatograph equipment served our goal to detect the
intermediate metabolites, and 1 µL-injection of the samples was made up in split mode (2:1); GC
oven was programmed under the following conditions: 323K for 278K min, then 278K·min-1 to 553K.
The mass spectrometer was operated in SCAN mode.
2.3.8 STATISTICAL DESIGN
The statistical design was analysed through the ANalysis Of VAriance (ANOVA) by using
Design Expert® 9.0.0 software (Stat-Ease Inc., Minneapolis, USA). A second order polynomial
equation was applied to correlate the dependent and independent variables:
Yi =x0 +x1 T+x2 pH+x3 agitation+x4 T∙pH+x5 T∙ agitation + x6 pH∙ agitation +x7 T 2 +x8 pH 2 +x9 agitation2 (2.2)
where 𝑌𝑖 is the response variable (contaminant remediation) x0 is a constant, x1, x2 and x3 are the
regression coefficients for linear effects; x4, x5 and x6 are the regression coefficients for interaction
effects, and x7, x8 and x9 are the regression coefficients for quadratic effects, and T, pH and agitation
are the independent variables.
2-18
2.-Biological methods to remove pollutants
2.4 RESULTS AND DISCUSSIONS
2.4.1 MICROBIAL ADAPTATION TO IONIC LIQUIDS
The hunt for novel bacterial strains and/or engineered existing strains displaying high
tolerance under ionic liquid pressure could be crucial to implement future successful
bioremediation processes of emerging recalcitrant compounds.. Preliminary data (Deive et al.,
2011) allowed concluding that the environmental pressure caused by high petroleum hydrocarbon
load and, to a lesser extent, by high-salinity in soil, augmented the microbial capacity to actively
grow or to survive short or long periods of exposure to ionic liquids. Following this line, several
commercial families of ionic liquids made up by imidazolium, pyridinium and phosphonium cations
and sulphate anion have been proposed to select the most promising microbial strain in terms of
ionic liquid endurance. Considering the basic definition of ionic liquids as molten salts it makes
sense to test the response of marine bacteria like S. oneidensis and H. salinarum as representative
halotolerant microorganisms. In relation to the ionic liquids role as organic compounds, St. warneri,
Ps. stutzeri, and Consortium C26b are also interesting since they are bacteria commonly found in
industrial polluted areas (Moscoso et al., 2012a; Moscoso et al., 2012b). Moreover, thermophilic
microorganisms are getting increasing attention in biotechnology due to the fact that their enzymes
are better suited to operate under harsh industrial processes. For this reason, A. flavithermus and
T. thermophilus HB27 were chosen as representative thermophiles to analyse their tolerance to the
presence of ionic liquids. Finally, two white-rot fungi with demonstrated capacity to degrade
persistent contaminants were also included in this initial screening: P. chrysosporium and Tr.
versicolor. Their growth curves in the absence of ionic liquids are shown in Figure 2.4 and Figure
2.5.
2-19
2.-Biological methods to remove pollutants
Adapted Pseudomonas stutzeri
Pseudomonas stutzeri
1.2
0.9
0.9
0.6
0.6
0.3
0.3
0.0
0
2
4
Staphylococcus
6
8 warneri 0
2
Shewanella
oneidensis
6
8
4
0.0
0.9
0.9
0.6
0.6
0.3
0.3
0.0
0
2
4
6
Consortium
C26b0
8
0.0
2
4
6
8
10
Days
X Data
0.9
0.6
0.3
0.0
0
2
4
6
8
10
Days
Figure 2.4. Microbial growth in the absence of ionic liquid in RM (∆) and MM (○) of Ps. stutzeri, adapted Ps.
stutzeri, St. warneri, S. oneidensis and Consortium C26b
2-20
Absorbance
Absorbance
1.2
2.-Biological methods to remove pollutants
1.2
1.2
Anoxybacillus flavithermus
Thermus thermophilus
Absorbance
0.6
0.6
Absorbance
0.9
0.9
0.3
0.3
0.0
0.0
0
2
Phanerochaete
chrycosporium
4
6
8
0
2
6Trametes 8versicolor
4
0.9
0.9
0.6
0.6
0.3
0.3
0.0
0
2
4
Halobacterium
salinarum0
6
8
0.0
2
4
Days
6
8
10
0.9
0.6
0.3
0.0
0
2
4
6
8
10
Days
Figure 2.5. Microbial growth in the absence of ionic liquid in RM of T. thermophilus, A. flavithermus, Tr.
versicolor, P. chrysosporium and H. salinarum
The ionic liquids toxicity was evaluated by means of their minimal lethal concentration (MLC),
through microorganisms cultivation in 96-well plates in MM supplemented with glucose as carbon
source (10 g·L-1) at ionic liquids concentrations of 0.005, 0.010, 0.025, 0.05, 0.1, 0.2, 0.5, 1.0, and
1.5 M. The microbial growth was monitored by UV spectrometry at 600 nm. The analysis of the
MLC data (Table 2.6) confirmed that the microbial agents obtained from polluted locations (Ps.
stutzeri, St. warneri and Consortium C26b) and the marine bacteria (S. oneidensis) show a higher
resistance to thrive under the pressure of these neoteric solvents. The hypothesis that both
2-21
2.-Biological methods to remove pollutants
hydrocarbon load and salinity could improve the possibilities of survival is thus confirmed, in
agreement with previous findings (Deive et al., 2011). The analysis of the selected cations in terms
of toxicity reveals that phosphonium is the one leading to a greater lethal effect. The information
coming from the literature about the hazards of this family is still scarce and not conclusive,
although the initial data provided by Coutinho and coworkers allow confirming these results
(Ventura et al., 2012). In relation to the anion, a slightly higher toxicity of the C1SO4 is observed.
This seems to contradict the statement that a longer alkyl chains lead to higher toxicity (Markiewicz
et al., 2013). Nonetheless, it should be noted that the first member of a family is usually an outlier,
so that could explain this behaviour
Table 2.6. MLC values of the selected microorganisms under the pressure of different ionic liquids.
Grey colour shows growth in the tested range and white colour indicates no growth.
MLC
Ps. stutzeri
adapted Ps. stutzeri
S. oneidensis
St. warneri
Consortium C26b
C2PyC2SO4
C2C1imC1SO4
0.1 M
C2C1imC2SO4
0.1 M
25 mM
10 mM
0.1 M
1M
P4441C1SO4
1M
25 mM
H. salinarum
P.chrysosporium
T. versicolor
T. thermophilus
A. flavithermus
The comparison of the MLC values obtained with relevant literature data reveals that both
the microbial agents obtained from polluted and marine locations are highly resistant to the
studied ionic liquids, since concentration levels up to 1 M are tolerated. These values are higher to
those reported in literature (Pernak et al., 2003) for model bacteria and yeasts. Additionally, these
microorganisms were able to survive at concentrations almost similar to those reported for the
most biocompatible ionic liquids based on cholinium cations (Petkovic et al., 2009). It is necessary
to highlight that Ps. stutzeri was the bacterium leading to the highest values of biomass under the
pressure of ionic liquids. Therefore, this bacterium was selected as a viable candidate for an
acclimation process. After two months in a lab-scale bioreactor in the presence of C2C1imC2SO4 (200
µM), under controlled agitation, aeration and temperature, the microbial biomass was collected to
further investigate the existence of some kind of acclimation. The analysis of this strain revealed
MLC levels one order of magnitude higher for imidazolium and pyridinium cations, and two-fold
higher for phosphonium-based ionic liquid. Additionally, cell concentration data shown in Table 2.7
2-22
2.-Biological methods to remove pollutants
and graphically represented in Figures. A.1 to A.8 (see annex), allow concluding higher values for
the adapted Ps. stutzeri, no matter the culture medium used (both rich and mineral). This is
advantageous because the use of a mineral medium is preferred to approach future studies of
bioremediation.
Table 2.7. Microbial growth of the selected microorganisms at maximum ionic liquid
concentration in MM and RM. (-) no growth; (+) A600 = 0.1-0.4; (++) A600 = 0.4-0.6; (+++) A600
>0.6.
P.s.
P.s.a.
S.o.
St.w.
C26b
H.s.
P.c.
T.v.
T.t.
A.f.
C2PyC2SO4
MM
RM
+++
++
+++
+++
+++
+++
+++
++
++
++
+
+
+
+
++
C2C1imC1SO4
MM
RM
++
++
+++
+++
++
+++
++
+++
++
++
++
++
++
+
+
C2C1imC2SO4
MM
RM
++
++
+++
+++
++
+++
++
+++
++
+++
+
+++
+++
+
+
P4441C1SO4
MM
RM
+
+
++
++
+
++
++
+++
+
++
++
+
+
++
Hence, the results obtained suggest that acclimation is taking place, which can be due to a
phenotypic and/or genetic change. In fact, it was demonstrated that this bacterium possesses a
remarkable adaptation capacity, since the presence of organic contaminants could trigger a
permanent alteration at the gene level, by acquiring a nahH gene (responsible for encoding
catechol 2,3 - dioxygenase) (Lalucat et al., 2006). Up to date, no information has appeared in the
literature indicating the viability of ionic liquid adaptation of microorganisms. It is also interesting
to notice that the adaptation of Ps. stutzeri to imidazolium-based ionic liquids involved and
acquired resistance to the stress of other commercially available ionic liquid families.
Notwithstanding the fact that the specific mechanisms of toxicity are currently not wellunderstood, there are several research lines that point to different strategies to unravel the
microbial response to the presence of ionic liquids, such as the modification of membrane
permeability, enzyme detoxification, or the synthesis of metabolites allowing the entrapment of
the contaminant, both extracellularly and intracellularly (Khudyakov et al., 2012). In this sense, the
ionic effect related to the presence of the ionic liquid in aqueous solutions should also be taken into
account, since it could promote the observed microbial toxicity (Petkovic et al., 2011). In this
particular case, it becomes patent that the adaptation entails a clear visual change in the culture
broth, as illustrated in Figure 2.6.
2-23
2.-Biological methods to remove pollutants
Aqueous medium
Biopolymer
Biomass
Figure 2.6. Visual aspect of Ps. stutzeri wild (left) and adapted (right) in the presence of ionic liquid
The formation of a biopolymer after 24 h of cultivation of the adapted Ps. stutzeri is evident.
This response has been found to be one of the ways to protect the microbial communities from
environmental stresses (Flemming & Wingender, 2001). In this particular case, the obtained
biopolymer turned out to be a polysaccharide mainly composed of glucose, as elucidated from
HPLC analysis (see Figure.2.7).
60000
Fructose
Glucose
50000
40000
30000
20000
10000
0
0
-10000
1
2
3
4
5
6
7
8
9
10
Time (min)
Figure 2.7. HPLC chromatogram of the hydrolysed biopolymer (blue) and standard of conventional
oligosaccharides (green)
2-24
2.-Biological methods to remove pollutants
Thus, the flexible nature of prokaryotic gene expression conferred a greater acclimation to
the presence of different families of ionic liquids, by means of exopolysaccharide synthesis. The
analysis of the wild strain of Ps. stutzeri and that adapted to the presence of ionic liquids by means
of SEM microscopy makes it evident the presence of this polymer entrapping bacterial cells (Figure
2.8).
Figure 2.8. SEM images of wild (left) and ionic liquids-adapted (right) Ps. stutzeri
It should be noted that the polysaccharide expression is maintained even though the ionic
liquid is removed from the media, which points to an alteration at the gene level. Therefore, further
investigation of a global bacterial response at the transcriptome level could shed light on the
understanding of the adaptation strategies followed by microorganisms to the presence of these
emerging neoteric contaminants, and must be unavoidably tackled in future works. The synthesis of
biopolysaccharides also confers special advantages for the formation of biofilms, which allow a
higher withstanding to nutrient deprivation, pH changes, or contaminants charge swings (Koutinas
et al., 2006; Koutinas et al., 2007). Thus, the presence of these biopolymers could be beneficial for
biosorption, bioaccumulation or biomineralization strategies (Singh et al., 2006) of different kinds
of organic contaminants
2.4.2 DYES REMOVAL BY IONIC LIQUID-ADAPTED PSEUDOMONAS STRAIN
The outstanding capacity of Ps. stutzeri to be used as a remediation agent in different kind of
recalcitrant contaminants, going from pure organic compounds like PAHs to hybrid chemicals like
organophosphate pesticides, has been stressed in previous research works of our group (Moscoso
et al., 2012b; Moscoso et al., 2013). Therefore, taking into account the adaptation capacity of this
2-25
2.-Biological methods to remove pollutants
bacterium demonstrated in the last section, its potential for removing pollutants such as
anthraquinone and azo dyes has been ascertained.
DYES BIOTREATMENT BY IONIC LIQUID-ADAPTED PS. STUTZERI AT FLASK SCALE
The appropriateness of Ps. stutzeri for the biological decolourisation of an aqueous stream
containing two reactive dyes such as RB5 and AB48 (both independently and mixed) was firstly
checked at shaken flasks scale. One of the decisive challenges to be faced when designing a pointsource treatment technology is the existence of sudden changes in the dye concentration profiles
released by industries. Actually, these variations may drastically alter the outcomes of the biological
treatment, by inhibiting the microbial activity. Therefore, as dye concentrations detected in
aqueous effluents from textile industry usually range from 0.01 to 0.20 g·L-1(Pandey et al., 2007),
the influence of this parameter in the biological decolourisation was checked for both model dyes,
and the results obtained are presented in Figure 2.9.
100
Decolourisation (%)
90
80
70
60
50
0.00
0.05
0.10
0.15
0.20
0.25
-1
Dye concentration (g ·L )
Figure 2.9. Decolourisation of AB48 (□) and RB5 (○) by adapted Ps. stutzeri in shaken flasks
The results obtained evidence a great decolourisation efficiency for both dyes (>50%) no
matter the concentration used, which is promising since the strain non adapted to ionic liquids did
not show any remediation capacity. In this sense, it becomes patent that the operation at
concentration values between 0.03 and 0.06 g·L-1 entails very high levels of dye remediation, up to
90% (Figure 2.10). Therefore, the operation at industrial scale should consider the dilution of the
effluent to yield maximum values of dye removal.
2-26
2.-Biological methods to remove pollutants
Figure 2.10. Colour removal capacity of adapted Ps. stutzeri of RB5 (on the left) and AB48 (on the right)
at 0.04 g·L
-1
Additionally, the monitoring of biomass production and decolourisation levels (Figure 2.11)
during bioremediation experiments at the optimum concentration (0.04 g·L-1) for AB48, RB5 and
the mixture reveals that the stationary phase of growth is reached in less than one day of
treatment both for individual dyes and the mixture.
On the other hand, it becomes patent the ionic liquid-adapted Ps. stutzeri display the highest
decolourisation potential within 48 h, reaching levels over 75%, which points out the interest of
ionic liquids adaptation as a strategy to get more versatile microbial remediation agents. In this
vein, the comparison with literature data allows concluding the suitability of this modified strain,
since the remediation medium used is a synthetic one (with salts and glucose), contrarily to the fact
reported by other authors (Deive et al., 2010; Barragan et al., 2007). They established the necessity
of adding complex organic sources such as peptone or yeast extract to treat a dye-polluted effluent
to yield similar decolourisation values, which is disadvantageous to ease process modelling and
simulation or to carry out fundamental kinetic studies.
2-27
2.-Biological methods to remove pollutants
15
100
A
12
9
50
6
Decolourisation (%)
-1
Cell Concentration (g L )
75
25
3
0
0
B
12
75
9
50
6
25
3
0
0
C
12
75
9
50
6
25
3
0
0
0
20
40
60
80
Time (h)
Figure 2.11. Monitoring of cell growth (○) and decolourisation (∆) by ionic liquid-adapted Ps. stutzeri in
aqueous effluents polluted with: A) AB48, B) RB5, and C) mixture of dyes, at flask scale. Experimental data are
represented by symbols and solid lines are used for the proposed theoretical models
2-28
2.-Biological methods to remove pollutants
DYES BIOTREATMENT BY IONIC LIQUID-ADAPTED PS. STUTZERI AT BENCH SCALE BIOREACTOR
Once the suitability of this adapted bacterium was demonstrated at flask scale, it is necessary
to check its viability when operating at higher scale. In this sense, the feasibility of the operation in
bioreactor entails many considerations like a suitable mass transfer or optimum operating
conditions enabling an efficient removal of the dye mixture. The viability of this scale-up was
assessed by monitoring the biomass and decolourisation capacity in a stirred tank bioreactor, and
100
12
80
9
60
6
40
3
20
0
Decolourisation (%)
15
-1
Cell concentration (g L )
the results obtained are shown in Figure 2.12.
0
0
20
40
60
80
100
Time (h)
Figure 2.12. Monitoring of cell growth (○) and decolourisation (∆) by ionic liquid-adapted Ps. stutzeri in
aqueous effluents polluted with a mixture of dyes at bench scale bioreactor (310 K, 200 rpm, 0.17 vvm).
Experimental data are represented by symbols and solid lines are used for the proposed theoretical models
A visual inspection of the experimental data allows detecting an improvement both in the
remediation values and in the times required to reach the maximum, which is an important
advantage when implementing this biotreatment at industrial scale. In this sense, it is outstanding
that about 80% of decolourisation is recorded after less than one day. Although this kind of
bioreactor configuration entails advantages like an efficient control of aeration and agitation,
separately, sometimes some degree of mechanic stress could be inflicted by the impeller. However,
it seems evident that no important cell damage is recorded, since a very slight decrease in the
biomass concentration values is observed.
After demonstrating the suitability of the operation at bioreactor scale, the elucidation of the
characteristics of the remediation process was approached. The reason for the elevated
2-29
2.-Biological methods to remove pollutants
percentages of decolourisation can be linked with the nature of the remediation process. In this
sense, the production of a biopolymer by this strain once adapted to the presence of ionic liquids
promoted dye biosorption on the biomass. Additionally, a drastic decrease in pH values was
recorded (from 7.2 to 4.5), which can also help to increase the dye removal. Hence, the
improvement in dyes biosorption may be explained in terms of the electrostatic interactions
between the biomass and the dye structure (Savin & Butnaru, 2008). More specifically, the
nitrogen-containing functional groups in proteins and biomass will be easily protonated under
acidic conditions, thus leading to a net positive charge and consequently furthering an electrostatic
attraction with the negatively charged dye anions. This electrostatic behaviour has been considered
to be the primary mechanism concluded for the biosorption of different dyes (O`Mahony et al.,
2002; Bidisha et al., 2006).
Additionally, given the biosorptive nature of dye decolourisation, the monitoring of the UVvisible spectra must be tackled in order to demonstrate the absence of dye in the biotreated
effluent (Figure 2.13).
1.0
Absorbance
0.8
0.6
0.4
0.2
0.0
300
400
500
600
700
800
900
Wavelengh (nm)
Figure 2.13. UV visible spectra of untreated (solid line) and biotreated (dashed line) effluents polluted
with a mixture of AB48 and RB5
The results shown in the figure underscore the suitability of the proposed adapted Ps.
stutzeri, since the absence of the characteristic band for the dye mixture is detected once the
stationary phase of the decolourisation process is reached.
2-30
2.-Biological methods to remove pollutants
 MODELLING EXPERIMENTAL DATA
The technical implementation of the proposed process at industrial scale requires a deeper
knowledge of the biotreatment kinetics. One of the useful means to get a deeper insight into the
biological process is the description of the quantitative relationship between the biomass and the
dye decolourisation at a specific moment of the culture time t (h). A logistic model has been
proposed for the bioremediation of different contaminants (Moscoso et al., 2013a; Moscoso et al.,
2012c; Deive et al., 2010). In this way, the biomass and the remediation percentage can be defined
on the basis of the initial and maximum biomass and remediation rate as follows:
X=
Xmax
Xmax
[ln(
- 1)]-μm t
X0
1+e
D =
(2.3)
Dmax
Dmax
[ln(
- 1)] - 𝜇D t
D0
1+e
(2.4)
where X and D are the biomass (g·L-1) and pollutant remediation (%), X0 and D0 are the initial
biomass and remediation, Xmax (g·L-1) and Dmax are the maximum biomass and pollutant removal,
and µm and µD are the maximum specific growth rate and maximum specific remediation rate (h-1).
The fitting of experimental data to the proposed model was carried out by using the SOLVER
function in Microsoft EXCEL, by minimising the standard deviation, calculated as follows:
2
n
∑i DAT(zexp -ztheor )
𝜎=(
nDAT
1/2
)
(2.5)
being zexp and ztheor the experimental and theoretical data, respectively and nDAT is the number of
experimental data.
The experimental data were adequately fitted to the proposed model, as can be concluded in
the light of the regression coefficients listed in Table 2.8. The goodness of the fitting is reflected in
Figures 2.11 and 2.12, where the theoretical data are presented as solid lines.
2-31
2.-Biological methods to remove pollutants
Table 2.8. Parameters of the logistic model to characterize the kinetic growth and
dye decolourisation of ionic liquids-adapted Ps. stutzeri at flask and bioreactor scale.
Dye
-1
X0(g·L )
-1
Xmax(g·L )
-1
2
µmax(h )
R

Flask scale
RB5
0.09
6.40
0.39
0.98
0.35
AB48
0.14
6.29
0.41
0.99
0.23
RB5+AB48
0.06
6.76
0.44
0.98
0.40
0.55
0.99
0.26
Bioreactor scale
RB5+AB48
0.31
D0(%)
5.48
Dmax(%)
-1
2
µD(h )
R

Flask scale
RB5
0.11
72.43
0.31
0.98
5.05
AB48
0.17
92.71
0.29
0.99
2.82
RB5+AB48
0.06
77.05
0.28
0.99
2.67
0.31
0.99
2.43
Bioreactor scale
RB5+AB48
0.78
86.62
The analysis of the parameters confirms previous conclusions, since slightly lower maximum
biomass levels are obtained at bioreactor scale, and the maximum decolourisation percentages are
10% higher at greater scale for the dyes mixture. Additionally, it can be remarked that the
maximum specific growth rate obtained at bioreactor scale is 25% higher than that existing in
shaken flasks, probably due to the increased mass transfer provided by the mechanic agitation. The
same trend is concluded when comparing the maximum specific decolourisation rate, as it
increases by 11% when operating in stirred tank bioreactor. It is outstanding that the values
obtained are in the same order of magnitude than those reported for other microbial agents (Deive
et al., 2010).
2.4.3 SIMULTANEOUS BIOTREATMENT OF PAHS AND DYES BY IONIC LIQUID-
ADAPTED P. STRAIN
Over the last years, one of the sectors causing great environmental concerns is the leather
and textile industry, since they generate a variety of pollutants ranging from surfactants, heavy
metals, alkalis, and dyes to PAHs (Li et al., 2010). The importance of the latter two kinds of
contaminants has been underscored by current international environmental legislation (USEPA,
2-32
2.-Biological methods to remove pollutants
2008; EU-EEB, 2005) due to the fact that they unleash carcinogenic, mutagenic and toxic effects,
and are considered to bear a great recalcitrance (Simarro et al., 2011; Haritash & Kaushik, 2009;
Bae & Freeman, 2007; Zaharia & Suteu, 2013).
Albeit research works have mainly focused on the treatment of a mixture of PAHs or dyes
independently (Moscoso et al., 2012a; Álvarez et al., 2013), a lack of knowledge is detected in the
finding of suitable strategies to remediate all the contaminants when present together in the same
effluent. Attending to these requirements, considering the pollutant charge of textile and leather
waste effluents, three model PAHs of low (PHE) and high molecular weight (PYR, and BaA) and RB5
(based on the promising results mentioned beforehand) have been cherry-picked. Moreover, these
data will be employed to simulate the process to be implemented as a one-step biotreatment
strategy.
OPTIMIZATION OF MEDIUM AND TREATMENT CONDITIONS
The first point to be addressed is to find a suitable biotreatment medium allowing the
solubilisation of the hydrophobic contaminants (PAHs) and without negatively interfering in the
bioremediation of the hydrophilic pollutant (RB5). As previously demonstrated in section 2.4.1, the
acclimation of a Ps. stutzeri strain widened the proved versatility of this microbial strain for the
remediation of different kinds of pollutants (Moscoso et al., 2012a; Moscoso et al., 2012c, Moscoso
et al., 2013a). The reason for this underlies in the secretion of a biopolymer that helps to increase
its potential for the biotreatment of dyes by means of adsorption phenomena. Nevertheless, the
addition of a non-ionic surfactant to the biotreatment medium may be a double edged sword, as it
assists in increasing hydrophobic contaminants bioavailability but may solubilize the synthesized
exopolysaccharide, thus hindering dye removal.
As Tween 80 and glucose may act as carbon source in cultures of Ps. stutzeri, as previously
reported (Moscoso et al., 2012b; Álvarez et al., 2015), the combination of different concentrations
of both compounds may be crucial to reach a compromise between PAH bioavailability and
exopolysaccharide solubilisation. Since 10 g·L-1 is the carbon source concentration leading to the
highest levels of biomass, declining concentrations of Tween 80 were combined with growing
compositions of glucose ([Glucose], [Tween 80] in g·L-1 = (0.0,10), (2.5, 7.5), (5.0, 5.0 ), (7.5, 2.5),
(9.0, 1.0) and (9.9, 0.1)), and the data are presented in Figure 2.14. These data evidence the
existence of an optimum ratio (9.0, 1.0), as the PAHs are completely solubilized while the
decolourisation of the azo dye RB5 overtook 60%.
2-33
100
100
80
80
60
60
40
40
20
20
0
PAHs solubilisation (%)
Dye decolourisation (%)
2.-Biological methods to remove pollutants
0
0
2
4
6
8
10
2
0
-1
Tween concentration (g·L )
10
8
6
4
-1
Glucose concentration (g·L )
Figure 2.14. PAHs solubilization () and RB5 removal () for different concentrations of glucose and Tween
80
Once this ratio was chosen, Response Surface Methodology (RSM) based on a central
composite face-centred design was applied to optimize the contaminants degradation when using
temperature, pH and agitation as independent variables. The operation range was defined after a
previous screening, and the designed experimental 34 runs (including five replicates of the central
point to evaluate the reliability of the data) are presented in Table 2.9 together with the
bioremediation percentages. The analysis of the statistical parameters shown in Table 2.10
demonstrates that a quadratic model is significant (P<0.0001) for a suitable description of the 4
responses under study (RB5, PHE, PYR and BaA removal).
2-34
2.-Biological methods to remove pollutants
Table 2.9. Experimental conditions and remediation results of the CCF for RB5, PHE, PYR and BaA removal.
Removal (%)
Run
T (ºC)
pH
Agitation (rpm)
RB5
PHE
PYR
BaA
1
37.5
5.5
100
63.3
10.1
0
29.9
2
32.5
6.5
150
72.0
50.4
39.2
79.2
3
27.5
7.5
200
0
93.7
96.0
98.0
4
32.5
7.5
150
13.4
94.3
94.8
97.3
5
32.5
6.5
100
72.3
28.0
14.0
62.3
6
27.5
5.5
200
57.7
23.0
12.4
64.1
7
37.5
5.5
200
64.9
16.0
0
45.3
8
32.5
7.5
150
16.6
95.6
95.1
97.9
9
27.5
5.5
100
81.1
0
0
59.5
10
37.5
5.5
200
61.8
17.1
0
45.5
11
27.5
7.5
100
0
47.4
60.7
85.4
12
27.5
6.5
150
47.8
38.7
23.6
71.1
13
37.5
6.5
150
82.9
71.5
57.6
86.3
14
37.5
7.5
100
85.0
73.0
70.8
86.9
15
37.5
7.5
200
32.2
95.2
94.6
97.6
16
37.5
6.5
150
83.0
72.6
64.7
86.1
17
37.5
5.5
100
63.6
18.6
0
35.3
18
32.5
6.5
150
71.2
47.2
36.9
79.1
19
27.5
7.5
200
0
94.6
95.7
97.7
20
32.5
6.5
200
74.4
59.5
46.6
80.1
21
32.5
6.5
150
69.2
46.0
30.9
76.7
22
32.5
6.5
150
69.7
53.2
43.1
79.0
23
27.5
7.5
100
0
50.6
63.8
86.1
24
32.5
6.5
200
75.9
61.7
46.9
81.1
25
32.5
6.5
100
72.1
29.5
15.4
69.3
26
32.5
6.5
150
71.6
48.8
38.1
79.2
27
37.5
7.5
100
85.5
66.0
63.2
83.3
28
32.5
6.5
150
69.5
49.6
37.0
67.8
29
27.5
6.5
150
48.0
35.7
19.5
70.3
30
32.5
5.5
150
77.8
35.0
26.0
60.3
31
27.5
5.5
100
80.2
0
0
63.0
32
32.5
5.5
150
73.3
38.6
27.2
69.6
33
37.5
7.5
200
39.7
95.4
95.1
97.7
34
27.5
5.5
200
54.7
20.0
10.0
60.0
2-35
2.-Biological methods to remove pollutants
Table 2.10. ANOVA analysis CFC (A-Temperature; B-pH; C-Agitation).
2-36
Source
Sum of Squares
Model
A
B
C
AB
AC
BC
2
A
2
B
2
C
Residual
Lack of Fit
Pure Error
22440.08
4271.96
8234.90
1009.48
4305.33
155.13
153.64
57.14
2934.86
138.66
2332.31
2270.67
61.63
Model
A
B
C
AB
AC
BC
2
A
2
B
2
C
Residual
Lack of Fit
Pure Error
25982.91
867.11
19677.77
3202.47
37.73
372.01
556.13
52.77
352.40
920.34
1107.70
987.36
120.34
Model
A
B
C
AB
AC
BC
2
A
2
B
2
C
Residual
Lack of Fit
Pure Error
33446.78
206.27
28425.05
2194.72
55.99
71.61
632.15
37.80
1506.81
944.44
1932.44
1780.00
152.44
Model
A
B
C
AB
AC
BC
2
A
2
B
2
C
Residual
Lack of Fit
Pure Error
9661.91
189.48
7813.50
563.18
494.17
38.88
30.36
22.03
3.69
282.05
826.81
716.05
110.76
Mean Square
RB5
2493.34
4271.96
8234.90
1009.48
4305.33
155.13
153.64
57.14
2934.86
138.66
97.18
454.13
3.24
PHE
2886.99
867.11
19677.77
3202.47
37.73
372.01
556.13
52.77
352.40
920.34
46.15
197.47
6.33
PYR
3716.31
206.27
28425.05
2194.72
55.99
71.61
632.15
37.80
1506.81
944.44
80.52
356.00
8.02
BaA
1073.55
189.48
7813.50
563.18
494.17
38.88
30.36
22.03
3.69
282.05
34.45
143.21
5.83
F Value
P-value Prob > F
25.66
43.96
84.74
10.39
44.30
1.60
1.58
0.59
30.20
1.43
< 0.0001
< 0.0001
< 0.0001
0.0036
< 0.0001
0.2186
0.2207
0.4507
< 0.0001
0.2439
140.00
< 0.0001
62.55
18.79
426.35
69.39
0.82
8.06
12.05
1.14
7.64
19.94
< 0.0001
0.0002
< 0.0001
< 0.0001
0.3749
0.0091
0.0020
0.2956
0.0108
0.0002
31.18
< 0.0001
46.15
2.56
353.03
27.26
0.70
0.89
7.85
0.47
18.71
11.73
< 0.0001
0.1226
< 0.0001
< 0.0001
0.4126
0.3550
0.0099
0.4998
0.0002
0.0022
44.37
< 0.0001
31.16
5.50
226.80
16.35
14.34
1.13
0.88
0.64
0.11
8.19
< 0.0001
0.0276
< 0.0001
0.0005
0.0009
0.2987
0.3572
0.4318
0.7463
0.0086
24.57
< 0.0001
2.-Biological methods to remove pollutants
Hence, the coefficients for defining the equation of effects are shown in Table 2.11.
Table 2.11. Values of the coefficients for the equation of effects in the remediation of RB5, PHE, PRY and BaZ.
Linear effects
Pollutant
Interaction effects
Quadratic effects
x0
x1
x2
x3
x4
x5
x6
x7
x8
x9
RB5
-381.4
-8.04
186.6
0.055
3.28
-0.012
-0.026
-0.131
-23.40
0.002
PHE
-52.6
10.37
-101.7
1.69
0.307
-0.019
0.118
-0.125
8.11
-0.005
PYR
381.0
6.38
-211.3
1.26
0.374
-0.008
0.126
-0.106
16.77
-0.005
BaA
131.4
-3.50
-31.28
0.595
1.111
0.006
0.027
-0.081
0.830
-0.003
Parameters in bold are significant (P < 0.05) (Information Table 2.10).
In a visual inspection of the data compiled in this table, it becomes patent that the influence
of pH, agitation and temperature is significant for almost all the contaminants, while the interaction
and quadratic effects seem to be more dependent on the contaminant under study. In this context,
the graphical representation of the response surfaces for each contaminant at optimum agitation
rates (146 rpm) is shown in Figure 2.15.
Figure 2.15. Effect of pH and temperature in the removal of dyes and PAHs at the optimum agitation
(146 rpm)
2-37
2.-Biological methods to remove pollutants
The visualization of the data licenses to draw a distinction between azo dye and PAHs, as a
result of their completely different chemical nature, even though both of them share the presence
of condensed aromatic rings. On the one hand, maximum dye removal levels can be attained at pH
values lower than 6.5 and temperatures higher than 305.5 K. On the other hand, PAHs removal is
only feasible for pH values higher than 6.5 for all the temperatures under study. The numerical
optimization carried out by using the software Design Expert® 9.0 led to the conclusion that pH=
7.0, T= 310.5 K and agitation rates of 146 rpm led to average contaminant removal levels higher
than 60% for PHE, PYR, BaA and RB5.
MODELLING SCALING-UP
After the operating conditions and biotreatment medium were picked, the scaling-up of the
process at laboratory bioreactor was approached. Therefore, the first step was carrying out the
biological reaction at the optimum conditions, going from flask to bioreactor scale. The kinetic
model previously explained in section 2.4.2 has been applied to describe two important variables of
the process, biomass concentration and pollutant removal (Deive et al., 2010).
Table 2.12. Parameters of the logistic model to characterize the kinetic growth and
pollutant remediation by the adapted Ps. stutzeri at flask and bioreactor scale
-1
-1
-1
2
Scale
X0(g L )
Xmax(g L )
µmax(h )
R
Flask scale
0.44
3.55
0.22
0.91
Bioreactor scale
0.01
6.27
0.97
0.98
Contaminant
D0(%)
-1
2
Dmax(%)
µD(h )
R
Flask scale
RB5
0.1
73.7
0.31
0.98
PHE
8.6
70.6
0.15
0.93
PYR
6.2
56.1
0.11
0.97
BaA
0.5
64.5
0.52
0.93
Bioreactor scale
RB5
0.1
78.1
0.59
0.98
PHE
7.9
83.6
0.13
0.95
PYR
7.7
68.5
0.11
0.93
BaA
3.8
77.2
0.29
0.94
The values of the regression coefficients R2 listed in Table 2.12 (always higher than 0.9)
evidence the suitability of the proposed models to get a deep insight in the kinetic characteristics of
the process carried out at flask and bioreactor scale at the optimum conditions obtained previously.
2-38
2.-Biological methods to remove pollutants
The data presented in Figure 2.16 also makes it evident this adequate description for both the
7.5
7.5
5.0
5.0
2.5
2.5
0.0
0.0
75
75
50
50
25
25
0
Pollutant removal (%)
Pollutant Removal (%)
-1
10.0
Biomass concentration (g·L )
10.0
-1
Biomass concentration (g·L )
biomass and contaminants remediation.
0
0
20
40
Time (h)
60
0
20
40
60
80
Time (h)
Figure 2.16. Biomass concentration () and removal of RB5 (), PHE (), PYR () and BaA () in the
biotreatment processes carried out at flask (black) and bioreactor scale (blue). Dots represent the
experimental data and solid lines are employed for the modelled data
A conscious analysis of the biomass parameters points to the benefits of operating at
bioreactor scale, as both the maximum biomass concentration and specific growth rate are
enhanced by about 2 and 4 times, respectively. These results are coincident with those obtained in
the previous section (2.4.2) and follow the trends pointed in other studies tackling the scaling-up of
dye-remediation processes from flask to bench-scale bioreactors (Deive et al., 2010). These
ameliorations are also reflected in the maximum levels of pollutant removal recorded, as an
average increase of about 12% and 5% is recorded for the PAHs and RB5, respectively, when going
from flask to bioreactor scale. The reason for this boosted behavior can be attributed to the
inherent benefits of operating in this kind of stirred tank bioreactor, like the greater mass transfer
of contaminants and oxygen promoted by the Rushton impeller. In this line, it has already been well
documented the superior performance of this turbine for improving oxygen mass transfer
coefficients (Moucha et al., 2003). This is crucial for an efficient biodegradation process because
aerobic biodegradation mechanisms demand the existence of molecular oxygen as electron
2-39
2.-Biological methods to remove pollutants
acceptor, thus easing the activation of the substrate through oxygenation reactions biocatalyzed by
mono or dioxygenases (Cao et al., 2009). In this vein, GC-MS analysis reveals a complete
mineralization of the contaminants as diethylphtalate and phtalic acid have been detected as
(illustrated in Figure 2.17), in line with other studies focused on the biodegradation pathway of this
kind of contaminants (Moscoso et al., 2012a, Khanna et al., 2011), which confirms the absence of
important alterations in the metabolic routes followed to degrade these contaminants.
149
100
A)
Abundance
O
O
50
O
O
177
65
76
50
0
50
60
100 Diethyl Phthalate
(replib)
105
93
62
121
91
70
80
132
125
90
100
110
120
130
163
140
150
104
160
222
194
170
180
190
200
210
220
Mass/ charge (m/z)
230
B)
76
Abundance
O
OH
50
OH
50
O
148
74
38
0
31
36
40
44
48
30
40
50
(replib) 1,2-Benzenedicarboxylic acid
52
61
60
64
72
70
85
80
90
100
110
120
130
140
150
160
Mass/ charge (m/z)
Figure 2.17. GC-MS chromatograms of the intermediates detected in the bioremediation of PAHs and diazo
dye: A) Intermediate Diethylphthalate (mass 149) and retention time of 24.7 min (up) and B) Intermediate
Phtalic acid (mass 104) and retention time of 17.6 min
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2.-Biological methods to remove pollutants
In relation to the nature of the bioremediation process, it has been observed that PAHs and
di-azo dye RB5 behave differently. Hence, while levels of biosorption lower than 35% are recorded
for the PAHs (with just 7% for the low molecular weight PAH PHE), 60 % of the RB5 is adsorbed on
the bacterial biomass. The reason for the higher affinity of RB5 dye in relation to the PAHs lies again
in the different chemical nature of these contaminants. Thus, as explained in section 2.4.2, the ionic
character of the dye will ease the establishment of electrostatic interactions with the protonated
nitrogen-containing functional groups in microbial cells and proteins, as a consequence of the
existence of slightly acidic conditions (Bidisha et al., 2006). This fact also explains the improved
results of dye removal previously observed at acid pHs.
All in all, the optimized conditions allowed accomplishing high levels of remediation of dyes
and PAHs simultaneously.
SIMULATION OF THE PROCESS AT REAL SCALE
In order to further quantify the advancements, this one-step biotreatment process will be
likened with a traditional option including a two-stages process: a Ps. stutzeri mesophilic step to
treat the PAHs (with a duration of 150 h) followed by a thermophilic step employing A. flavithermus
to decolorize RB5 (with a total time of 12 h), in line with previous investigations (Moscoso et al.,
2015; Deive et al., 2010). Both processes are presented in Figure 2.18, with a view to ease the
analysis between the options, and they were simulated to remediate a 200,000 m 3/year polluted
effluent (with 300 mM PAHs and 0.04 mg·L-1 of azo dye) from a leather industry.
The software tool employed was SuperProDesigner v8.5 (Intelligen Inc.), as it is a simple way
to interactively analyse, on a consistent basis, the viability of both remediation alternatives at large
scale. One of the advantages provided by this program is that it enables to easily peruse the
throughput capacity and time utilization of each operation unit. Hence, on the basis of the technical
needs indicated above, time requirements, remediation yields, and biomass production, both
alternatives were simulated, and the main results for each of them are compiled in Table 2.13.
2-41
2.-Biological methods to remove pollutants
Table 2.13. Treatment capacity, remediation efficiency and scheduling summary
for the two-stages and one-stage biotreatment processes
2-steps biotreatment
1-step biotreatment
Batch time (h)
220.8
52.5
Batch number per year
309.0
51.0
Total RB5 removal (%)
78.0
79.5
Total PHE removal (%)
95.6
85.0
Total PYR removal (%)
95.6
70.9
Total BaA removal (%)
95.6
78.6
It becomes patent that the one-stage biotreatment involves a drastic cycle time reduction
from 221 h/batch to 53 h/batch, thus allowing the performance of up to 309 batches per annum,
while maintaining high levels of pollutants remediation. Additionally, this greater throughput
capacity parallels a reduction in fixed capital investment and manufacturing cost up to about 40%.
When all this information is taken together, the total costs of effluent treatment are reduced by ten
times, which makes it patent the aptness of the proposed process.
2-42
2.-Biological methods to remove pollutants
PFD 1
PFD 2
Figure 2.18. Two-step (PFD 1) vs. one-step (PFD 2) process flowsheet diagrams for the industrial biotreatment of PAHs and RB5-polluted effluents as obtained with the
software SuperProDesigner v8.5
2-43
2.-Biological methods to remove pollutants
2.5 CONCLUSIONS
First, the preliminary screening of microorganisms from extreme biotopes thriving under the
presence of common families of ionic liquids pointed to Ps. stutzeri as a suitable candidate for
bioremediation of recalcitrant pollutants. The response of this microorganism to the presence of
these neoteric contaminants after a two month-period of acclimation in a batch bioreactor led to
the production of a biopolysaccharide.
Second, the versatility of this acclimated bacterium for dye removal was checked, as a prior
step to propose it in a simultaneous biotreatment of dyes and PAHs. Two model reactive dyes (RB5
and AB48) were checked both independently and mixed. The biological process was satisfactorily
scaled-up, and values higher than 75% were attained in less than 2 days for both dyes individually
and mixed at small scale. Additionally, 80% of removal was reached in less than 1 day at stirred tank
bioreactor.
Third, this adapted bacterium was suitable proposed for the biotreatment of an effluent
polluted with a model dye and three model PAHs. A suitable medium composition and the
optimum operating conditions of pH, temperature and agitation (7.0, 310.5K and 146 rpm,
respectively) were determined after RSM optimization, and remediation levels higher than 60%
were obtained. The validity of these conditions was checked at flask and bioreactor scale and the
kinetics behavior of the pollutants removal were elucidated.
Finally, the simulation of this one-step process applied at larger scale for the remediation of
a 200,000 m3/year-effluent from a leather factory was compared with a conventional two-steps
option proving the promising potential of the proposed process in terms of economics and
throughput capacity.
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2.-Biological methods to remove pollutants
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2.-Biological methods to remove pollutants
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2-57
3. REMEDIATION OF POLLUTANTS BY AQUEOUS TWO PHASE SYSTEMS
3.1
3.2
3.3
3.4
3.5
3.6
AIMS AND WORKFLOW
INTRODUCTION
MATERIALS AND METHODS
RESULTS AND DISCUSSIONS
CONCLUSIONS
REFERENCES
3-3
3-4
3-12
3-18
3-61
3-62
ADAPTED FROM:
“Triton X surfactants to form aqueous biphasic systems: experimental and correlation”
(2012) 54, 385-392.
“On the phase behaviour of polyethoxylated sorbitan (Tween) surfactants in the
presence of potassium inorganic salts” (2012) 55, 151-158.
“Phase segregation in aqueous solutions of non-ionic surfactants using ammonium,
magnesium and iron salts” (2014) 70, 147-153.
“Influence of the addition of Tween 20 on the phase behaviour of ionic liquids-based
aqueous systems” (2014) 79, 178-183.
“Aqueous immiscibility of cholinium chloride ionic liquid and Triton surfactants” (2015)
91, 86-93.
“Environmentally benign sequential extraction of heavy metals from marine sediments”
(2014) 53, 8615-8620.
“Novel physico-biological treatment for the remediation of textile dyes-containing
industrial effluents” (2013) 146, 689-695.
“Hybrid sequential treatment of aromatic hydrocarbons-polluted effluents using nonionic surfactants as solubilizers and extractants” (2014) 162 259-265.
“Ionic liquids and non-ionic surfactants: a new marriage for aqueous segregation” (2014)
4, 32698-32700.
“A biocompatible stepping stone for the removal of emerging contaminants”. (2015) (In
Press, DOI 10.1016/j.seppur.2015.08.039)
3.-Remediation of Pollutants by Aqueous Two Phase Systems
3.1 AIMS AND WORKFLOW
AIMS
Given the existing limitations of biological methods for the remediation of different kind
of contaminants (incomplete removal of PAHs and dyes, and extreme recalcitrance of drugs
and heavy metals), we have bet in this chapter in Aqueous Two Phase Systems (ATPS) as a
possible alternative to be applied in aqueous industrial effluents. Usually, these streams
contain surfactants (employed in degreasing operations, soil washing, etc.), which can be seen
as an opportunity to remove and concentrate this kind of contaminants. Hence, model nonionic surfactants belonging to Triton and Tween families have been suggested as candidates to
be salted out by salts (inorganic and organic) and ionic liquids.
WORKFLOW
The working plan strategy to achieve the objectives described above includes:
 The selection of imidazolium and ammnonium (C2C1imC2SO4 and N1112OHCl,
respectively) cations as salting-out agents of non-ionic surfactants (Triton X-100,
Tween 80 and Tween 20) aqueous solutions.
 The selection of potassium and ammonium-based inorganic and organic salts (K3PO4,
K2CO3,
K2HPO4,
K2S2O3,
K2SO3,
(NH4)2HPO4,
(NH4)2SO4
and
K3C6H5O7·H2O,
K2C4H4O6·0.5H2O, K2C2O4·H2O, (NH4)2C4H4O6, NaKC4H4O6, respectively) as phase
segregation agents.
 The characterization of the solubility curves and tie lines through well-known empirical
equations.
 The behaviour of systems will be justified through thermodynamic parameters, the
Hofmeister series and the Hydrophilic-lipophilic balance of the surfactants.
 The selection of the most biocompatible systems to apply them for the partition of the
target pollutants.
 The assessment of the extraction efficiency for each pollutant in model solutions and
real samples.
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3-Remediation of Pollutants by Aqueous Two Phase Systems
3.2 INTRODUCTION
Aqueous Two Phase Systems (ATPS) have long been considered as a competitive and
versatile extraction technique since it was proposed by Albertsson (Albertsson, 1961). This
separation strategy has been considered as a competitive technique due to inherent
advantages such as the short process time required to trigger phase segregation, low viscosity,
little emulsion formation, absence of organic volatile solvents, high extraction efficiency, low
energy consumption, and reliable scale-up (Pei et al., 2009).
ATPS traditionally consist of two immiscible aqueous-rich phases where the compounds
usually employed to achieve the proper phase segregation include polymers and inorganic
salts. Although both solutes are water-soluble, they separate into two coexisting phases above
a given concentration: the top phase (less dense) enriched in one of the solutes and the
bottom phase (further dense) formed by another solute (Figure 3.1). Aqueous phase
segregation is a complex phenomenon depending on many factors such as hydrogen bonding,
charge interactions, steric impediments, van der Waals forces or ionic charge in saline
dissolutions among others.
Figure 3.1. Phase segregation in ATPS
Since 2003, when the ability of ionic liquids to trigger phase segregation was first
reported (Rogers et al., 2003), these systems have been used for the separation of a variety of
compounds. The ability of ionic liquids to adapt their polarities and affinities by a suitable
handling of the anion/cation design and their combinations (Seddon et al., 2006) open up new
opportunities by means of liquid-liquid equilibrium. An exhaustive literature review on this
topic has been carried out and has been summarised in Table 3.1.
3-4
3.-Remediation of Pollutants by Aqueous Two Phase Systems
Table 3. 1a. Bibliographic examples of Polymer-based ATPS.
Polymer
Segregation Agent
Aim
Reference
PEG 8000
NaOH, Na2CO3, Na2SO4, Na2HPO4,
Na3PO4, MgSO4, ZnSO4, (NH4)2SO4
Fundamental study
Hey et al., 2005
PPG 400
(NH4)2SO4, MgSO4, KCl, KC2H3O
Fundamental study
Zhao et al., 2011
PEG (3000, 6000, 8000)
K2HPO4, KH2PO4
Purification of lipase
Ooi et al., 2009
PEG (1000, 2000, 3400)
K3PO4, K2CO3, Li2SO4, ZnSO4,
(NH4)2SO4
Fundamental study
Huddlestone et al., 2003
PEG 400
Na2SO4, Mg2SO4
Fundamental study
Martins et al., 2010
PEG 6000
Na2WO4.2H2O
Fundamental study
Sadeghi & Golabiazar., 2010
PPG 400
K3C6H5O7, K2C4H4O6, K2C2O4
Fundamental study
Xie et al., 2010
PEG (9000, 6000, 4000)
K2HPO4, KH2PO4
Recovery of alkaline protease
Hotha & Banik, 1997
PEG 6000
K2HPO4, KH2PO4
Recovery of amyloglucosidase
Ramadas et al., 1996
PEG 6000
K2HPO4, Na2HPO4
Recovery of xylanase
Kulkarni et al., 1999
PEG 6000
KH2PO4, K2HPO4
Recovery of subtilin
Kuboi et al., 1994
PEG 20000
MgSO4.7H2O
Recovery of lysine
Li et al., 2000
PEG (600, 3350)
Dextrano T 500
Extraction of -amylase
Andersson et al., 1986
PEG400
Sodium phosphate and citrate
Fundamental study
De Souza et al., 2013
PEG1500, 4000, 6000
Magnesium sulfate
Extraction of dyes
de Alvarenga et al., 2015
PEG600/K3C6H5O7
[C4mim]Br
Extraction of L-Tyrosine
Hamzehzadeh & Abbasi., 2015
PEG400
Na2SO4
Extraction of polysaccharides
Zhou et al., 2014
PEG-4000
Na2C4H4O6 , NaC2H3O2
Fundamental study
Zafarani-Moatta & Tolouei, 2008
PPG
K3C6H5O7
Fundamental study
Sadeghi & Ziamajidi., 2007
3-5
3-Remediation of Pollutants by Aqueous Two Phase Systems
Table 3. 1b. Bibliographic examples of Ionic liquid-based ATPS.
3-6
Ionic liquid
Segregation Agent
Aim
Reference
C4C1imBF4
AC1imCl
Na3C6H5O7, Na2C4H4O6, NaC2H3O2
Fundamental study
Han et al., 2010a
K3PO4, K2CO3, K2HPO4
Recovery of [Aim]Cl
Deng et al., 2009
C4C1imBF4
Na2CO3, NaH2PO4
Separation of antibiotic
Han et al., 2010b
C4C1imCl
K3PO4, K2CO3, K2HPO4, KOH, Na2HPO4, NaOH
Extraction of testosterone
He et al., 2005
CnC1imCH3COOH (n=4,6,8)
K3PO4, K2CO3, K2HPO4
Fundamental study
Li et al., 2010
Pi444Tos, P444C1SO4,P4444Br
K3PO4
Separation of biomolecules
Louros et al., 2010
C2C1imC2SO4, C4C1imC1SO4, CnC1imCl (n =4,8,10)
K3PO4
Fundamental study
Naidanovic-Visak et al., 2007
CnC1imCl, CmC1imBr (n=4,6), (m=4,6,8,10)
KOH, K3PO4, K2CO3, K2HPO4
Fundamental study
Pei et al., 2007
BzC1imCl, C6imCl
K2HPO4, K3PO4, K2CO3
Fundamental study
Deive & Rodríguez, 2012
CmC1imBr (m=4,6,8)
K2HPO4
Extraction of proteins
Pei et al., 2009
CnC1imCl (n = 4, 6, 8)
K3PO4, K2CO3
Fundamental study
Deng et al., 2007
C4C1imBF4
Na2CO3, Na2SO4,(NH4)2SO4, NaH2PO4
Fundamental study
Wang et al., 2010
CnC1imC1SO4(n=1,2,4),BzC1imC1SO4, C1Py C1SO4
Na2CO3,
Fundamental study
Deive et al., 2011c
C4C1imBr
K3PO4, K2HPO4
Fundamental study
Zafarani-Moattar et al., 2007
C4C1imCl
K3PO4, K2CO3, K2HPO4
Recovery of anion TcO4
Bridges & Rogers 2008
CnC1imCl (n = 1, 2, 4, 6), AC1imCl, OHC2C1imCl
K3PO4
Fundamental study
Neves et al., 2009
C2C1imCnSOm (n=2,4,6), (m=3,4)
K3PO4, K2CO3, (NH4)2SO4
Extraction of enzymes
Deive et al., 2012
C6C1imBF4,C8imC1imCl
K2HPO4
Separation of poly- and disaccharides
Tonova et al., 2012
CmC1imBr (m=4,6,8,10)
K2HPO4.3H2O
Food colorants
Sha et al., 2015
-
3.-Remediation of Pollutants by Aqueous Two Phase Systems
Table 3.1c. Bibliographic examples of surfactants-based ATPS.
Surfactant
Segregation Agent
Aim
Reference
C10E4
CnTAB
Purification of enzyme
Rangel-Yaqui et al., 2003
C12S
DTAB
Separation of protein BSA
Zhao & Xiao, 1996
C11EO2
C12-18EO5
Purification of proteins
Linder et al., 2004
SL
C12C6C12(Me), C12CnC12(Et) (n=4, 6, 12)
Fundamental study
Lu et al., 2008
DBAB
SL
Fundamental study
Wang et al., 2008
DTAB
C11pPHCNa
Fundamental study
Jiang et al., 2009
AS, (12-3-12)
NaCl, NaBr, NaF, Na2SO4, Na3PO4
Fundamental study
Hao & Nan, 2008
Triton X-100
Na2CO3, Na2SO4, (NH4)2SO4
Separation of proteins
Xi et al., 2006
CTAB, AS
NaBr
Fundamental study
Hao et al., 2010
CTAB, AS, (12-3-12)
NaBr
Fundamental study
Nan et al., 2006
CTAB, Pluronic P105/Dextrano T500
NaAc, NaCl, NaClO4
Separation of amino acids
Svensson et al., 1997
Triton (X-100, X-102)
K3PO4, K2CO3, K2HPO4, K2S2O3,K2SO3
Fundamental study
Alvarez et al., 2012a
Triton X-100
Na3C6H5O7, MgSO4
Fundamental study
Salabat et al., 2010
DPDS, DTAB
NaCl
Tween 20, Triton X-100
(NH4)2Fe(SO4)2, MgSO4, (NH4)2HPO4, (NH4)2SO4, (NH4)2C4H4O6
Extraction of volatile organic compounds (BTEX) Weschayanwiwat et al., 2009
Fundamental study
Gutiérrez et al., 2014
Tween 20, Tween 80
K3PO4, K2CO3, K2HPO4, K2S2O3,K2SO3
Fundamental study
Álvarez et al., 2012b
Triton (X-100, X-102, X-114), Tween (20, 40, 80)
Na2CO3, Na3C6H5O7.H2O, Na2C4H4O6.0.5H2O
Extraction of antioxidant
Ulloa et al., 2012a
Triton X-102, Tween 20
Na2CO3, Na2SO4, Na2SO3, Na2SO3, Na2HPO4, NaCH3COO
Fundamental study
Ulloa et al., 2012b
Triton (X-100, X-102)
C2C1imC2SO4
Fundamental study
Álvarez et al., 2014b
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3-Remediation of Pollutants by Aqueous Two Phase Systems
Most of the ATPS phase diagrams found in literature are illustrated in an orthogonal
representation, where the vertical axis is commonly used for the solute that is enriched in the
top phase. The water concentration is omitted, so the ternary plot is transformed into the
diagram shown in Figure 3.2 (pure water becomes the origin of the orthogonal axes) .
Triangular Diagram
0
Orthogonal representation
100
10
90
20
80
30
A
70
40
10
0·W
1
50
W
0·W
10
60
Biphasic
system
E
60
70
100·W1
r
ate
50
40
D
30
M
M
E
80
20
90
10
B
D
100
0
B
Monophasic system
A
0
10
20
30
40
50
60
70
80
90
100
100·W2
100·W2
Figure 3.2. Triangular and orthogonal representation of ATPS
Vertices of equilateral triangle represent the pure components and all points on one side
of the triangular diagram denote the binary mixtures. The plotted curve (A-B-D-E) is called
solubility curve or binodal solubility curve and all mixtures of components below this curve
exhibit liquid-liquid demixing in a biphasic region, while the mixtures outside this curve are
homogeneous solutions in a monophasic region. A mixture of total composition (M) undergoes
phase separation and forms two coexisting phases with compositions D and E, which represent
the end points of a specific tie-line (TL). The tie- line length (TLL) permit to know the
composition difference between the two phases and it is usually employed to correlate trends
in the partitioning of solutes between both phases.
The proposal of ATPS makes up a novel approach with a promising potential for the
extraction of volatile organic compounds (Weschayanwiwat et al., 2009), and a wide range of
biocompounds such enzymes (Deive et al., 2012; Ventura et al., 2012; Aguilar et al., 2006),
antibiotics (Marques et al., 2013), virus (Platis & Labrou, 2006), antibodies (Rosa et al., 2007)
and antioxidants (Ulloa et al., 2012a). The aqueous environment of ATPS confers a
3-8
3.-Remediation of Pollutants by Aqueous Two Phase Systems
considerable advantage in contrast to the use of other separation methods which can
negatively affect the structural integrity of the desired bioproduct. This versatility has
encouraged us to investigate the application of this technique for the concentration and
removal of different type of contaminants, be it heavy metals, dyes or emerging contaminants.
Therefore, this PhD thesis will be focused on the exploration of the ability of ionic liquids
and organic and inorganic salts to salt out aqueous solutions of non-ionic surfactants. These
latter compounds are usually present in waste industrial effluents, as they are used in washing
operations in metallurgical, textile and tanning industry. Surface active agents are substances
which lower the surface tension of the medium in which they are dissolved. Surfactants are
typically amphiphilic molecules that exhibit a double affinity containing both hydrophilic
(polar) and lipophilic (apolar) groups. Polar groups contain heteroatoms such as oxygen,
sulphur, phosphorus or nitrogen, included in functional groups like alcohol, thiol, ether,
sulfate, polyoxyethylene oxide, amine, carboxylate, etc. On the other hand, apolar groups are
generally hydrocarbon chains like alkyl or alkylbenzene. One of the useful means to
characterize them is the Hydrophilic-Lipophilic Balance (HLB) number, which indicates the
balance of the size and weight of these two groups. This parameter varies from 0 to 20:HLB >
10 indicates a greater affinity for water (hydrophilic) and HLB < 10 represents a lower affinity
for water (lipophilic). The most accepted classification of surfactants is based on their
dissociation in water. Thus, according to the nature of the polar group they may be classified
as (Salager, 2002):
 Anionic surfactants: They are dissociated in water in an amphiphilic anion and a
cation (generally an alkaline metal like Na+ or K+ or a quaternary ammonium).
Common functional groups of anionic surfactants are ethoxylated alkylphenols,
carboxylates, napthalenesulphonates, alkylbenzenesulphonates, olefin and alkyl
sulphonates, etc. And they are used as detergents, foaming and wetting agents or
dispersants.
 Cationic surfactants: they dissociate into an amphiphilic cation and an anion (usually
a halogen) in aqueous solutions. A high proportion of these surfactants are made up
by quaternary ammonium salts, polyoxyethylene alkyl and alicyclic amines, amines
with amide linkages, etc. They are not so commonly used owing to their high-cost,
and their application is mostly as bactericides or corrosion inhibitors.
 Non-ionic surfactants: Non-ionic surfactants do not ionize in aqueous solutions due
to the fact that their hydrophilic groups are non-dissociable like alcohols, phenols,
ethers, amides or esters, resulting in polar groups like ethoxylated aliphatic alcohols,
3-9
3-Remediation of Pollutants by Aqueous Two Phase Systems
carboxylic esters and amides, polyoxyethylene fatty acid amides, polyethylene glycol
esters, nonylphenol ethoxylates, polyethoxylatest, etc. They are widely applied in
industrial cleaning, biotechnology, agrochemistry, food industry, etc.
Therefore, the latter will be studied in this chapter and the capacity of different salts and
ionic liquids to act as salting-out agents of these compounds will be investigated. Each salt
bears a different lyotropic degree leading to different salting-out abilities. These specific
effects have been traditionally analysed in the light of a recurring pattern now known as the
Hofmeister series (Hofmeister, 1908). This series allows predicting the kosmotropic/chaotropic
character of each salt, based on their interaction with water molecules. Ions are regarded as
kosmotropic and chaotropic depending on their abilities to interact with water and to change
the water structure. With a high change density, a kosmotropic ion interacts more strongly
with water than water with itself and tends to increase the water structure. The situation is
reversed in the case of a chaotropic ion. Different studies have converged upon the idea that
the ability of each ion to form hydrogen bonds with water molecules can be measured in terms
of the molar Gibbs free energy of hydration and the molar entropy of hydration (Freire et al.,
2012a).
During the last years, ionic liquids (ILs) have emerged as salts with breakthrough
possibilities. They are formed by asymmetric ions which have attractive strength cation-anion
weaker than conventional ionic salts (Seddon, 1997). The possibility of combining ILs cations
(usually organic, asymmetric and voluminous) with different anions (typically inorganics)
allows tuning these chemicals and has christened them as “design solvents”. Other remarkable
properties are their higher chemical, electrochemical, thermal stability and a notable ionic
conductivity (Baranyai et al 2004), non- flammability (Smiglak et al., 2006) or practically nonvolatile character at room temperature (Earle et al., 2006; Holbrey & Seddon, 1999), which
allow that ILs are gaining wide technological and industrial relevance in many disciplines such
as analytical chemistry, surface science, electrochemistry or biotechnology amount others
(Plechkova & Seddon, 2008). The most outstanding families are shown in Figure 3.3.
3-10
3.-Remediation of Pollutants by Aqueous Two Phase Systems
Figure 3.3. Main cations used in ILs
In summary, the advantages provided by salts and ionic liquids will be used to trigger
phase segregation in aqueous solutions of non-ionic surfactants, and the obtained biphasic
systems will be thermodynamically characterized as a prior step to apply viable examples for
the remediation of effluents contaminated with emerging pollutants, PAHs, dyes and heavy
metals.
3-11
3-Remediation of Pollutants by Aqueous Two Phase Systems
3.3 MATERIALS AND METHODS
3.3.1 CHEMICALS
The ionic liquids 1-ethyl-3-methylimidazolium alkylsulfate C2C1imCnSO4 (n: 2, 4, 6), were
purchased from IoLiTec and Merck. The ionic liquid cholinium chloride N1112OHCl, the non-ionic
surfactants belonging to the polyethoxylated sorbitan family monosubstituted with a laurate
and an oleate moiety Tween 20 and Tween 80, respectively, and the polyoxyethylene teroctylphenol family Triton X-100 and Triton X-102 made up of a 8-carbon tertiary alkyl chain
and 9-10 ethylene oxides units or 12-13 ethylene oxide units, respectively were supplied by
Sigma Aldrich an their structures are illustrated in Table 3.2. The same manufacturer provided
the non-steroidal anti-inflammatory drugs (NSAIDs) ibuprofen (>98%) and diclofenac (>98.5%).
The dyes Reactive Black 5 (RB5), Acid Black 48 (AB48) and the polycyclic aromatic
hydrocarbons phenanthrene (PHE), pyrene (PYR) and benzo[a]anthracene (BaA) (>99%) used in
bioremediation experiments were also purchased from Sigma Aldrich.
Ionic liquids purities were higher than 98% and they were subjected to vacuum (2·10−1
Pa) and moderate temperature (323.15 K) for several days to remove possible traces of
solvents and moisture. Then, they were stored in bottles under inert atmosphere until use.
Non-ionic surfactants (>99%) were used as received without further purification. Chemicals
structures and characteristics of these compounds are shown in Table 3.2.
The inorganic and organic potassium salts: K3PO4, K2CO3, K2HPO4, K2S2O3, K2SO3,
K3C6H5O7·H2O, K2C4H4O6·0.5H2O, K2C2O4·H2O and ammonium salts: (NH4)2SO4, (NH4)2HPO4,
(NH4)2C4H4O6 with mass fraction purity higher than 95% were purchased from Sigma-Aldrich.
Sodium potassium tartrate (NaKC4H4O6) was provided by Panreac. All salts were used as
received.
The complexing agent potassium thiocyanate (KSCN, Fluka, St. Gallen, Switzerland) and
the metal ions of copper (CuSO4·5H2O - Merck, Darmstadt, Germany) and zinc (ZnSO4·H2O VWR, Radnor, PA, US) were used as received without further purification.
3-12
3.-Remediation of Pollutants by Aqueous Two Phase Systems
Table 3.2. Characteristics of ionic liquids and surfactants.
Compound
Chemical structure
HLB*
CMC
1-ethyl-3-methyl imidazolium
alkylsulfate
(R: ethyl, butyl, hexyl)
-
-
Cholinium chloride
-
-
Tritón X-100 (n=9,5)
Tritón X-102 (n=12)
13,4
14,4
0,189 mM
0,267 mM
Tween 80
(w+x+y+z = 20)
15,0
0,012 mM
Tween 20
(w+x+y+z = 20)
16,7
0,06 mM
HLB: Hydrophilic-Lipophilic Balance; CMC: Critical Micellar Concentration
3.3.2 EXPERIMENTAL PROCEDURE
DETERMINATION OF SOLUBILITY CURVES
The solubility curves of the ATPS were carried out by means of the cloud point titration
method at different temperatures (from 298.15 K to 333.15 K) (Albertsson, 1986) and at
atmospheric pressure. A known amount of salt or ionic liquid was added to a surfactant
aqueous solution with known concentration under constant stirring, until detection of
turbidity. Afterwards, a drop-wise addition of ultra-pure water until a clear monophasic region
3-13
3-Remediation of Pollutants by Aqueous Two Phase Systems
was carried out. All the samples were weighed in an analytical Sartorius cubis MSA balance
(125-100-DA, ± 10-5 g). The ternary systems compositions were determined by the weight
quantification of all components within an uncertainty of ± 10-4 g. The measurements were
carried out in a jacketed glass vessel containing a magnetic stirrer connected to a temperature
controlled circulating bath (controlled to ± 0.01 K). For the jacketed cell, the temperature was
controlled with a F200 ASL digital thermometer with an uncertainly of ± 0.01 K.
In the case of ionic liquids, the solubility curve was also characterized by measuring
densities and refractive indices at different temperatures, using an Anton Paar DSA-48 digital
vibrating tube densimeter (± 2·10−4 g·cm−3) and a Dr. Kernchen ABBEMAT WR refractometer (±
4·10−5), both calibrated in accordance with the manufacturer instructions.
DETERMINATION OF TIE-LINES
The Tie-Lines (TLs) determination started with the preparation of a ternary mixture
within the biphasic region with a known mass fraction. The temperature was kept constant
and the mixture was stirred vigorously and left to settle for 24 h in order to ensure chemical
and thermodynamic equilibria.
The TLs data were obtained by the lever arm rule taking into account the relationship
between the upper phase and the overall system mass composition
wI1 =
wI2 =
wF1
R
wF2
R
1-R
- ( R ) ∙wII1
1-R
- ( R ) ∙wII2
(3.1)
(3.2)
where F, I and II represent the feed, the top phase, and the bottom phase, respectively; w1 and
w2 are the mass fraction percentage of the compound in top layer and the compound in
bottom layer, respectively; and R is the following measured ratio:
R=
Weight of the top phase
Weight of the mixture
(3.3)
In the systems with ionic liquids, the two segregated layers were split and their
composition was quantified by measuring densities and refractive indices (estimated
uncertainty of concentration ± 2%).
In parallel, the information provided by the tie-line length, TLL, and the slope of the TLs
data, S, is a useful tool to ascertain the relative distribution of both compounds between the
two aqueous phases in equilibrium. These values are calculated by means of these equations:
3-14
3.-Remediation of Pollutants by Aqueous Two Phase Systems
2
2 0.5
TLL= [(wI1 -wII1 ) +(wI2 -wII2 ) ]
wI -wII
S= w1I -w1II
2
(3.4)
(3.5)
2
where the equilibrium mass fraction (w) of the compound (1) and the compound (2), in the
upper (I) and bottom (II) phases, are represented.
MATHEMATICAL MODELLING
The SOLVER function in Microsoft EXCEL was used to adjust the parameters so that the
standard deviations were minimized. The standard deviations () were calculated by applying
the equation 2.5 (c.f. chapter 2).
ANALYTICAL DETERMINATION OF POLLUTANTS
PAH ANALYSIS
PHE, PYR and BaA concentrations (both independently and mixed) were analysed by
reversed-phase high performance liquid chromatography (HPLC) equipped with a reversed
phase C8 column (150 x 4.6 mm, 5 µm particule size, Zorbax Eclipse) with its corresponding
guard column. The HPLC system was an Agilent 1100 equipped with a quaternary pump and
photodiode array UV/Vis detector (252.4 nm). 5 µL of filtered sample (through a 0.45 µm
Teflon filter) were injected and then eluted from the column at a flow rate of 1 mL min-1, using
acetonitrile:water as mobile phase with the following ratios: (67:33) for PHE and BaA and
(65:35) for PYR. The temperature was maintained at 298.15K. The values of the fitting
parameters and regression coefficients are shown in Table 3.3.
DYE ANALYSIS
Dye remediation (both independently and mixed) were evaluated by UV-vis
spectrophotometry taking into account the maximum wavelength obtained for each dye (597
nm for RB5, 663nm for AB48 and from 547 to 713 nm for the mixture of dyes).
METAL IONS ANALYSIS
The concentrations of Cu and Zn were determined by flame atomic absorption
spectroscopy (AAS) (Agilent Technologies 200 series AA). An air-acetylene burner at
wavelengths of 324.8 and 213.9 nm, and lamp currents of 4 and 5 mA, were employed for Cu
and Zn, respectively. The parameters defining the calibration curves are indicated in Table 3.3.
The marine sediment samples were collected in the Galician coast (NW Spain). The
classification of the sediments according to the Particle Size Analysis method indicates that the
3-15
3-Remediation of Pollutants by Aqueous Two Phase Systems
dredged samples are silty clay. A prior characterization of the samples (Pazos et al., 2013)
proved that Zn and Cu were the two metal ions clearly infringing the CEDEX recommendations
for dredged marine sediments.
Metals were extracted from the marine sediments in accordance with the following
procedure: 0.25 g of soil were added to Erlenmeyer flasks together with 15 mL of aqueous
solutions of the selected non-ionic surfactant at 30% of concentration. Alternatively, 0.87 g of
KSCN were added as complexing agent when stated. 1M HCl was added to adjust the pH due
to this parameter is decisive to promote metal solubility. These mixtures were shaken at 200
rpm for 24 h at 298.15 K and 343.15 K. Then, the mixture was centrifuged at 3700 g for 5 min,
and the supernatant was kept for a second centrifugation step at 3700 g and 5 min. Metals
were determined in this supernatant. This solution was then used for ABS extraction. Milli-QPlus water leaching was performed as control. All experiments were run in triplicate.
The determination of metal concentrations in dredged sediments was performed in
accordance with EPA Methods 3010 and 3050. In brief, they were analysed in an Inductively
Coupled Plasma Optical Emission Spectrometry (Optima 4300DV Perkin Elmer). After setting
analytical conditions, and making background corrections for wavelength spectra in
accordance with the standard solution profile, sample or test solutions were introduced via the
Cross-Flow nebulizer (Scott) inside the plasma torch, equipped with an Echelle polychrometer.
The operating conditions for auxiliary gas, nebuliser gas, and cool gas (Ar) were 0.2 L·min-1,
1.10 L·min-1 and 15 L·min-1, respectively. The spectral lines for Cu and Zn were 327.393 and
206.200 nm, respectively. Calibration was carried out by using a multi-element standard
solution VI (Merck) by appropriate dilution in 2% (v/v) HNO3.
NSAIDS ANALYSIS
Ibuprofen and diclofenac were determined by HPLC measurements. HPLC-DAD (Agilent
1260 infinity) is equipped with a Kinetex Biphenyl column (4.6 x 150 mm; internal diameter 5
µm). 10 µL of sample were eluted in gradient mode for 15 min at a flow rate of 1 mL·min-1,
using a mixture water/ethanol at the following ratios: (65:30) for 10 min and (15:80) for 5 min.
Retention times for ibuprofen and diclofenac were 10.149 and 10.713 min, respectively. The
calibrations were carried out with stock solutions prepared in ethanol at a concentration of 20
mg/L, and were appropriately diluted in Milli-Q water (0.1 mg·L-1 to 10 mg·L-1). The values of
the fitting parameters and regression coefficients are shown in Table 3.3.
3-16
3.-Remediation of Pollutants by Aqueous Two Phase Systems
Table 3.3. Fitting parameters and regression coefficients for the pollutants analysed.
Pollutant
Equation
2
R
-1
0.999
PYR
-1
Concentration(mg·L ) = 0.143 · Peak area
0.999
BaA
-1
Concentration(mg·L ) = 0.0272 · Peal area
0.999
Zn
-1
0.999
PHE
Cu
Ibuprofen
Diclofenac
Concentration(mg·L ) = 0.0111 · Peak area
Concentration (mg·L ) = (Absorbance - 0.0025)/0.4971
-1
0.999
-1
0.999
Concentration ( mg·L ) = (Absorbance - 0.0007)/0.1422
Concentration ( mg·L ) = (Peak area + 2.512)/17.285
-1
Concentration (mg·L ) = (Peak area + 1.557)/157.005
0.999
3-17
3-Remediation of Pollutants by Aqueous Two Phase Systems
3.4 RESULTS AND DISCUSSION
3.4.1 IONIC LIQUIDS AS SEGREGATION AGENTS IN AQUEOUS SOLUTIONS OF
NON-IONIC SURFACTANTS
The development of new contaminant removal strategies for the treatment of industrial
waste effluents urges more efforts in the search of more competitive techniques lowering the
environmental impact and process cost. In this sense, the wide use of Triton and Tween
surfactants in a variety of industrial sectors (metallurgical, textile, or tanning) as washing or
solubilising agents has inspired this work. Then, the ability of ionic liquids to salt out aqueous
streams containing non-ionic surfactants has been the subject of this section. First of all, ionic
liquids based on the imidazolium cation were selected. More specifically, 1-ethyl-3-methyl
imidazolium ethylsulfate was firstly picked since it is produced at an industrial scale (more than
1 ton per annum), which ensures its availability when implemented at high scale. Another
factor such as the alkyl chain length will be tackled in order to elucidate the effect of the
hydrophobicity in the ionic liquid (butyl sulfate, hexyl sulfate). Additionally, the use of other
cation families with different affinity with water molecules will be taken into account
(ammonium), as they are known as more biocompatible, economical and environmentally
benign option.
ATPS WITH IMIDAZOLIUM CATION AS SEGREGATION AGENT
The immiscibility region of the systems containing the imidazolium cation and Triton X
(Triton X-100 and Triton X-102) surfactants were empirically determined at several
temperatures. The experimental data for the systems {Triton-X 100 (1) + C2C1imC2SO4 (2) + H2O
(3)} and {Triton-X 102 (1) + C2C1imC2SO4 (2) + H2O (3)} are listed in Tables A.1 and A.2,
respectively (see annex), and can be visualized in Figure 3.4. Firstly, the variation of the alkyl
chain length in the ionic liquid anion (C2SO4, C4SO4 and C6SO4) reveals that just the ethylsulfatebased ionic liquid leads to phase segregation. In this particular case, the competition of the
ionic liquid and non-ionic surfactant for the water molecules is won by C2C1imC2SO4 and two
aqueous phases are segregated: a top phase rich in the non-ionic surfactant and a bottom
ionic liquid-rich phase. Although longer alkyl chain lengths in the anion were reported to be
beneficial for increased immiscibility windows (Deive et al., 2011a), the trends here seem to be
the opposite. The reason for this lies in the role played by ionic liquids. When they act as
3-18
3.-Remediation of Pollutants by Aqueous Two Phase Systems
salting out agents (as in this case) higher hydrophilicity is desired in order to favour the
interaction with water molecules. On the contrary, when ionic liquids are salted out, a lower
capacity to establish hydrogen bonds with water will entail easier phase disengagement.
On the other hand, the data displayed for C2C1imC2SO4 (Figure 3.4) indicates that the
immiscibility window only occurs in the ternary region, while binary mixtures are completely
miscible. Therefore, these systems fall into an island-type ternary system (type 0 in Treybal
classification) (Treybal, 1963).
Water
0
10
Water
0
100
10
90
20
80
100
0
40
50
60
70
80
90
100
20
90
10
30
30
80
20
90
40
70
30
20
50
60
40
70
60
50
50
60
10
70
40
60
50
80
30
70
40
Triton X-100 0
90
20
80
30
100
C2C1imC2SO4
10
100
Triton X-102 0
0
10
20
30
40
50
60
70
80
90
100
C2C1imC2SO4
Figure 3.4. Solubility data for the systems {[Triton X-100 (1) + C2C1imC2SO4 (2) +H2O (3)} and {[Triton X102 (1) + C2C1imC2SO4 (2) +H2O (3)} at 298.15 K (), 313.15 K (), 323.15 K (), 333.15 K (). Symbols
represent experimental data, solid lines are guides to the eye.
A valuable tool to shed light on the behaviour of surfactants in these systems is the HLB,
as mentioned beforehand. In this case, lower HLB values are recorded for Triton X-100 than for
Triton X-102 (13.5 vs. 14.4, respectively). Taking this into account, it should be expected that
the use of surfactants with higher degree of hydrophobicity would entail greater immiscibility
windows. From the experimental data, it seems that this hypothesis is confirmed and Triton X100 shows weaker interactions with water molecules than Triton X-102 in the presence of
C2C1imC2SO4, thus easing phase disengagement.
Regarding the effect of temperature, a visual inspection of the results obtained at
temperatures ranging from 298.15K to 333.15K (also shown in Figure 3.4) evidences a greater
liquid-liquid demixing capacity at higher temperatures. The reason behind this behaviour lies in
the different nature of the main compounds existing in the ATPS. Thus, the non-ionic
surfactant becomes more hydrophobic at increased temperatures, due to the weakening of
hydrogen bond interactions, which eases phase segregation. On the contrary, C2C1imC2SO4
becomes more hydrophilic, which leads to a greater interplay with water molecules. These
3-19
3-Remediation of Pollutants by Aqueous Two Phase Systems
trends are in agreement with the available literature data (Table 3.4) tackling the phase
segregation in aqueous solutions of liquid polymers in the presence of organic and inorganic
salts (Govindarajan et al., 2008; Rasa et al., 2008). However, the ionic liquid-based ATPS
carried out in the presence of inorganic salts (Lv et al., 2013; Alvarenga et al., 2013) exhibit an
inverse trend, which confirms the importance of the role played by ionic liquids when
designing a new kind of ATPS.
Table 3.4. Literature data about the temperature effect on different types of Aqueous Two Phase
Systems.
Compounds
PEG 20000
PEG 10000
PEG 400
PEG 6000
PEG
(600,1000,1450,3350)
PEG 8000
PEG 4000
PEG 4000
PEG 6000
PEG 6000
PEG 4000
C2PyBr
C2C1imBF4
C4C1imBF4
C4PyBF4
POELE10
Acetone
Temperature (K)
Effect
Ref.
Polymer-based ATPS
290.15, 299.15,
Mohsen-Nia et al.,
CuSO4
Proportional
308.15 317.15
2008
295.15, 301.15,
MgSO4
Proportional
Rasa et al., 2008
305.15 311.15
MgSO4, Na2SO4
298.15, 318.15
No variation
Martins et al., 2010
MgSO4, Na2SO4,
283.15, 298.15,
No variation
Martins et al., 2008
Li2SO4, ZnSO4
313.15
295.15, 310.15,
Na3C6H5O7
Proportional
Tubio et al., 2006
323.15
MgSO4, Na2SO4
298.15, 323.15
No variation
Cunha & Aznar, 2009
283.15, 288.15,
K3PO4
Proportional
Sé & Aznar, 2002
293.15, 303.15
298.15, 308.15,
Zafarani-Moattar et al.,
Na2-Tartrate
Proportional
318.15
2008
298.15, 303.15,
(NH4)3C6H5O7
Proportional Regupathi et al., 2009
313.15 318.15
298.15, 303.15,
Sadeghi & Golabiazar,
Na2WO4.2H2O
Proportional
308.15 313.15
2010
298.15, 308.15,
Govindarajan et al.,
(NH4)3C6H5O7
Proportional
318.15
2013
Ionic liquid-based ATPS
298.15, 308.15,
NaH2PO4
Inverse
Li et al., 2013a
318.15 328.15
298.15, 303.15,
NaH2PO4, Na2HPO4
Inverse
Lv et al., 2013
308.15
288.15, 293.15,
Alvarenga et al.,
MnSO4
Inverse
303.15 308.15
2013
298.15, 308.15,
Na2C4H4O6
Inverse
Li et al., 2013b
328.15
Surfactant-based ATPS
288.15, 293.15,
K3PO4, K2CO3, KOH
Proportional
Lu et al., 2013a
303.15 308.15
Organic solvent-based ATPS
MgSO4, (NH4)2SO4,
288.15, 298.15,
Proportional
Lu et al., 2013b
Li2SO4, ZnSO4
308.15
On the other hand, the experimental solubility data for these systems were fitted to
different equations usually employed to model different types of ATPS (Merchuk et al., 1998;
Hamzehzadeh & Zafarani-Moattar, 2015; Deive & Rodríguez, 2012).
3
w1 =a∙exp(bw0.5
2 -cw2 )
3-20
(3.6)
3.-Remediation of Pollutants by Aqueous Two Phase Systems
2
w1 =a+bw0.5
2 +cw2 +dw2
(3.7)
2
w1 =exp(a+bw0.5
2 +cw2 +dw2 )
(3.8)
being w1 and w2 the mass fraction of Triton and C2C1imC2SO4, respectively. On the other hand,
a, b, c and d are the fitting parameters, which values were determined by minimizing the
standard deviation ():
Thus, the values of the parameters are listed in Tables 3.5 to 3.7 together with the
corresponding standard deviation. The analysis of the data reveals that equation (3.6) is the
one allowing the lower deviations between experimental and theoretical solubility values in
accordance with the results reported for other ionic liquid-based ATPS (Torres-Plasencia et al.,
2015).
Table 3.5. Parameters of equation (3.6) and standard deviation for {Surfactant (1) +
C2C1imC2SO4 (2) +H2O (3)}.
T/K
a
b
c

Triton X-100 (1) + C2C1imC2SO4 (2) +H2O (3)
298.15
0.6938
0.0235
5.8530
0.0103
313.15
1.1054
-0.9144
6.1110
0.0114
323.15
1.1408
-1.0296
6.8842
0.0097
333.15
1.2246
-1.2979
7.4881
0.0105
Triton X-102 (1) + C2C1imC2SO4 (2) +H2O (3)
298.15
0.3995
0.9489
5.8702
0.0094
313.15
1.2539
-0.7294
5.1879
0.0120
323.15
1.2539
-1.2087
4.9911
0.0076
333.15
1.3206
-1.4067
4.8793
0.0088
Standard deviation () was calculated by means of equation (2.5).
3-21
3-Remediation of Pollutants by Aqueous Two Phase Systems
Table 3.6. Parameters of equation (3.7) and standard deviation for {Surfactant (1) +
C2C1imC2SO4 (2) +H2O (3)}.
T/K
a
b
c
d

Triton X-100 (1) + C2C1imC2SO4 (2) +H2O (3)
298.15
0.7861
1.2815
-3.1577
0.9231
0.0074
313.15
0.7127
1.3719
-3.2771
0.9802
0.0103
323.15
0.3459
3.1895
-5.8896
2.3269
0.0099
333.15
0.4202
3.1564
-6.4327
3.0468
0.0068
Triton X-102 (1) + C2C1imC2SO4 (2) +H2O (3)
298.15
0.6528
1.7819
-3.6232
1.0664
0.0065
313.15
0.6422
1.1351
-3.4172
1.0931
0.0093
323.15
0.7733
1.1866
-3.1974
1.1369
0.0082
333.15
0.8776
0.6883
-2.5985
0.9853
0.0097
Standard deviation () was calculated by means of equation (2.5).
Table 3.7. Parameters of equation (3.8) and standard deviation for {Surfactant (1) +
C2C1imC2SO4 (2) +H2O (3)}.
T/K
a
b
c
d

Triton X-100 (1) + C2C1imC2SO4 (2) +H2O (3)
298.15
22.20
-91.84
105.64
-44.57
0.0067
313.15
4.25
-21.67
29.26
-19.39
0.0090
323.15
1.77
-10.41
15.18
-13.86
0.0096
333.15
-0.06
-0.52
0.61
-6.14
0.0053
Triton X-102 (1) + C2C1imC2SO4 (2) +H2O (3)
298.15
23.50
-93.97
104.53
-41.23
0.0044
313.15
4.71
-22.72
29.18
-17.49
0.0085
323.15
2.13
-11.37
15.33
-12.00
0.0071
333.15
1.82
-9.90
13.23
-11.00
0.0080
Standard deviation () was calculated by means of equation (2.5).
The TLs of the systems at temperatures varying from 298.15 K to 333.15 K and
atmospheric pressure were determined by using density and refractive indices measurements,
as explained in the materials and methods section. The experimental data obtained are
compiled in Table A.3 (see annex). These data demonstrated that higher concentrations of
3-22
3.-Remediation of Pollutants by Aqueous Two Phase Systems
ionic liquid in the bottom phase correlate with higher concentrations of the surfactants in the
top phase. In addition, the Othmer-Tobias correlation equation (3.9) (Othmer & Tobias, 1942),
which relates the lie line mass concentration of the top phase with the bottom phase to obtain
a linear function, was used to fit the experimental tie line data obtained for each ATPS system.
1-wI
1-wII
m
( wI 1) = n ( wII2)
1
(3.9)
2
where n and m are the fitting parameters, w is the mass fraction, subscripts 1 and 2 refer to
surfactant and ionic liquid, respectively, and superscripts I and II indicate the surfactant-rich
phase and ionic liquid-rich phase, respectively. The values of the model parameters are
presented in the Table 3.8, together with the correlation coefficient R2. The data obtained
evidences a high degree of thermodynamic consistency since the values of R2 are all higher
than 0.9.
Table 3.8. Parameters of Othmer-Tobias equation and correlation coefficient for {Surfactant
(1) + C2C1imC2SO4 (2) + H2O (3)} at several temperatures.
2
m
n
R
Triton X-100 (1) + C2C1imC2SO4 (2) + H2O (3)
0.2421
1.0199
0.946
Triton X-102 (1) + C2C1imC2SO4 (2) + H2O (3)
0.4036
1.7065
0.945
Triton X-100 (1) + C2C1imC2SO4 (2) + H2O (3)
1.1164
2.0472
0.972
Triton X-102 (1) + C2C1imC2SO4 (2) + H2O (3)
0.8725
1.9941
0.901
Triton X-100 (1) + C2C1imC2SO4 (2) + H2O (3)
0.3909
0.4935
0.993
Triton X-102 (1) + C2C1imC2SO4 (2) + H2O (3)
0.8472
1.0745
0.928
Triton X-100 (1) + C2C1imC2SO4 (2) + H2O (3)
0.9166
0.9057
0.918
Triton X-102 (1) + C2C1imC2SO4 (2) + H2O (3)
0.8189
1.0169
0.980
T = 298.15 K
T = 313.15 K
T = 323.15 K
T = 333.15 K
ATPS WITH CHOLINIUM CATION AS SEGREGATION AGENT
On the hunt for greener extraction processes, the cholinium cation emerged as a
promising candidate given its proven low environmental impact and biocompatibility (Petkovic
et al., 2011; Deive et al., 2015). Therefore, the behaviour of these ammonium-based ionic
liquids as phase promoters in aqueous solutions of non-ionic surfactants will be researched.
The segregation potential of the ionic liquid cholinium chloride (N1112OHCl) in aqueous
solutions of the non-ionic surfactants Triton X (Triton X-100 and Triton X-102) and Tween
3-23
3-Remediation of Pollutants by Aqueous Two Phase Systems
(Tween 80 and Tween 20) was explored at several temperatures (298.15, 313.15, 323.15 and
333.15 K). The experimental data are compiled in Tables A.4 to A.7 (see annex), and they are
shown as triangular representation in Figures 3.5 and 3.6 in which the binodal curve and the
SLLE phase boundary divide three clear regions: the one-liquid phase (L), the biphasic region
(L+L), and the solid-two liquid phase (S+2L).
Water
0
Water
0
100
10
90
20
30
90
20
80
L
100
10
80
L
30
70
40
70
40
60
60
50
50
50
50
60
60
L+L
40
40
L+L
70
70
30
30
80
80
20
20
90
90
10
10
S + 2L
S + 2L
100
100
Triton X-100
0
0
10
20
30
40
50
60
70
80
90
N1112OHCl
100
Triton X-102
0
0
10
20
30
40
50
60
70
80
90
100
N1112OHCl
Figure 3.5. Solubility data for the systems {[Triton X-100 (1) + N1112OHCl (2) +H2O (3)} and {[Triton X-102
(1) + N1112OHCl (2) +H2O (3)} at 298.15 K (), 313.15 K (), 323.15 K (), 333.15 K (). Symbols
represent experimental data, solid lines refer to model
Water
Water
0
0
100
10
10
100
90
90
20
20
80
80
30
30
70
70
L
L
40
40
60
50
50
60
L+L
50
60
40
70
60
50
80
L+L
20
90
10
10
S + 2L
S + 2L
100
Tween 80
0
0
10
20
30
40
50
60
70
30
80
20
90
40
70
30
80
90
100
100
N1112OHCl Tween 20
0
0
10
20
30
40
50
60
70
80
90
100
N1112OHCl
Figure 3.6. Solubility data for the systems {[Tween 80 (1) + N 1112OHCl (2) +H2O (3)} and {[Tween 20 (1) +
N1112OHCl (2) +H2O (3)} at 298.15 K (), 313.15 K (), 323.15 K (), 333.15 K (). Symbols represent
experimental data, solid curved lines refer to model
Similarly to the previous data modelling, the binodal curves were fitted to equations
(3.6), (3.7) and (3.8). The values of the parameters are listed in Tables 3.9 to 3.11, together
with the corresponding standard deviation. The analysis of these data evidences a more
3-24
3.-Remediation of Pollutants by Aqueous Two Phase Systems
suitable fitting of equations (3.8) and (3.9) for the description of the binodal data when the
cholinium-based ionic liquid is used as phase splitter, no matter the temperature or surfactant
used. These results are in agreement with our previous findings obtained for imidazoliumbased ionic liquid and other research works, since the four-parameter based equations yielded
lower deviations than the well-known Merchuk model (Hamzehzadeh & Zafarani-Moattar,
2015). The theoretical data were represented together with the experimental data in Figures
3.5 and 3.6.
Table 3.9. Parameters of equation (3.6) and standard deviation for {Surfactant (1) +
N1112OHCl (2) +H2O (3)}.
T (K)
a
b
c

Triton X-100 (1) + N1112OHCl (2) +H2O (3)
298.15
0.9312
-0.6985
98.4
0.0445
313.15
0.8827
0.5244
304.6
0.0499
323.15
0.8424
0.2149
1256.5
0.1132
333.15
1.5277
-6.4361
1000.0
0.1499
Triton X-102 (1) + N1112OHCl (2) +H2O (3)
298.15
0.9810
-1.1645
39.0
0.0229
313.15
0.9696
-1.3038
76.1
0.0309
323.15
0.9617
-1.3660
141.0
0.0280
333.15
0.9300
-1.2193
394.8
0.0271
Tween 80 (1) + N1112OHCl (2) +H2O (3)
298.15
1.0589
-1.1978
35.9
0.0280
313.15
1.0393
-1.1896
77.3
0.0380
323.15
1.0062
-0.9610
180.3
0.0380
333.15
0.9942
-0.9830
381.8
0.0637
Tween 20 (1) + N1112OHCl (2) +H2O (3)
298.15
1.0780
-1.3737
22.7
0.0210
313.15
1.0632
-1.4308
37.8
0.0217
323.15
1.0642
-1.5792
55.9
0.0274
333.15
1.0598
-1.5160
101.7
0.0346
Standard deviation () was calculated by means of equation (2.5).
3-25
3-Remediation of Pollutants by Aqueous Two Phase Systems
Table 3.10. Parameters of equation (3.7) and standard deviation for {Surfactant (1) + N1112OHCl
(2) +H2O (3)}.
T (K)
a
b
c
d

Triton X-100 (1) + N1112OHCl (2) +H2O (3)
298.15
1.0407
-2.2107
4.0081
-13.20
0.0361
313.15
1.1590
-4.5522
11.85
-39.33
0.0384
323.15
1.2083
-5.1595
10.34
-39.61
0.1323
333.15
2.6627
-6.4334
14.33
-26.63
0.1529
Triton X-102 (1) + N1112OHCl (2) +H2O (3)
298.15
0.9505
-0.8590
-0.4260
-1.9546
0.0264
313.15
1.0165
-1.4212
0.7347
-4.6391
0.0307
323.15
0.9104
-0.5652
-1.9685
-2.2100
0.0225
333.15
0.7468
1.9549
-10.85
-12.91
0.0275
Tween 80 (1) + N1112OHCl (2) +H2O (3)
298.15
1.0327
-0.9687
-0.4404
-1.7995
0.0286
313.15
1.0308
-1.2431
0.2133
-4.8094
0.0376
323.15
0.9424
-0.1166
-2.9223
-4.0314
0.0369
333.15
1.1072
-2.2280
3.1921
-4.6075
0.0508
Tween 20 (1) + N1112OHCl (2) +H2O (3)
298.15
1.0679
-1.0019
-0.4212
-0.8382
0.0258
313.15
1.0815
-1.3526
0.2350
-2.1336
0.0231
323.15
1.0261
-1.3190
-0.1442
-2.1540
0.0315
333.15
1.0406
-1.3983
0.0263
-4.3347
0.0345
Standard deviation () was calculated by means of equation (2.5).
3-26
3.-Remediation of Pollutants by Aqueous Two Phase Systems
Table 3.11. Parameters of equation (3.8) and standard deviation for {Surfactant (1) + N1112OHCl
(2) +H2O (3)}.
T (K)
a
b
c
d

Triton X-100 (1) + N1112OHCl (2) +H2O (3)
298.15
2.1218
-23.0
59.3
-119.4
0.0350
313.15
5.9199
-63.8
177.5
-402.6
0.0320
323.15
11.229
-138.6
458.4
-1453.8
0.0704
333.15
0.0002
0.0191
-14.6
-598.4
0.1501
Triton X-102 (1) + N1112OHCl (2) +H2O (3)
298.15
0.3088
-5.4383
13.1
-33.1
0.0251
313.15
0.7706
-10.838
28.1
-68.4
0.0287
323.15
1.7034
-21.247
57.9
-139.0
0.0219
333.15
2.3695
-30.542
94.3
-291.8
0.0170
Tween 80 (1) + N1112OHCl (2) +H2O (3)
298.15
0.4553
-6.6965
16.4
-36.4
0.0277
313.15
0.5733
-9.0963
25.4
-66.6
0.0368
323.15
0.6996
-12.612
40.7
-127.6
0.0363
333.15
1.0270
-17.618
66.8
-271.8
0.0268
Tween 20 (1) + N1112OHCl (2) +H2O (3)
298.15
0.2600
-3.9566
8.0117
-20.0
0.0240
313.15
0.4277
-6.8311
14.8
-35.1
0.0220
323.15
0.3907
-6.3811
15.6
-43.4
0.0297
333.15
0.6228
-9.7033
27.0
-77.5
0.0338
Standard deviation () was calculated by means of equation (2.5).
In agreement with the behaviour observed previously for imidazolium-based ATPS, the
analysis of the influence of temperature on the binodal curves allows concluding that liquidliquid demixing is eased at higher temperatures for all surfactants studied in this thesis. As
stated, the lower ability of the non-ionic surfactant to establish hydrogen bonds with water at
higher temperatures furthers the salting out effect provided by the N1112OHCl ionic liquid,
leading to greater immiscibility windows. This behaviour is coincident with the data reported
for the same and other cholinium-based ionic liquid in the presence of aqueous solutions of
polypropyleneglycol (Liu et al., 2013).
Hence, the synergic effect of a greater ability for water solvation of the N1112OHCl,
together with the higher hydrophobicity of the non-ionic surfactant leads to the observed
increased immiscibility window. Thus, Triton X family (with lower HLB value) is more easily
salted out by N1112OHCl due to the fact that it is less prone to establish hydrogen bonds with
3-27
3-Remediation of Pollutants by Aqueous Two Phase Systems
water, so the competition between the surfactant and the ionic liquid for the water molecules
is more easily won by the latter.
The TLs of the systems at 298.15, 313.15, 323.15 and 333.15 K and atmospheric pressure
were ascertained by using density and refractive indices measurements, as explained in the
materials and methods section. The experimental data obtained are compiled in Tables A.8
and A.9 (see annex) and can be visually inspected in Figures 3.8 to 3.11. These data evidenced
that higher concentrations of ionic liquids in the lower phase correlate with higher
concentrations of the surfactant in the light layer. This is a consequence of the competition
between the ionic liquid and the surfactant for the water molecules: the more amount of
N1112OHCl is present in the mixture, the lower number of water molecules are available to
solvate the surfactant. Additionally, the extraction capacity was characterized by means of the
tie-line length (TLL) and the slope (S), calculated as indicated in equations (3.4) and (3.5)
respectively.
A visual inspection of the results allowed concluding that lower TLL value led to greater S
values. On the other hand, it is also outstanding that the heavy layer is almost exclusively
constituted by a binary mixture water-ionic liquid, while surfactant concentrations in some of
the upper phases reach values higher than 90%. The comparison between surfactants makes it
possible to check that the more hydrophobic Triton X-100 and Tween 80 are able to be salted
out more easily to the upper phase than the more hydrophilic Triton X-102 or Tween 20 as can
can be noticed from the less negative S values of the latter.
3-28
3.-Remediation of Pollutants by Aqueous Two Phase Systems
Water
0
Water
0
100
10
10
90
20
80
70
90
100
50
60
70
80
90
Triton X-100
100
100
0
0
N1112OHCl
10
20
30
40
10
0
100
10
80
L
30
70
Triton X-100
80
90
30
20
10
S + 2L
0
70
40
L+L
90
10
S + 2L
100
60
50
80
20
90
50
60
70
30
80
40
70
60
40
L+L
30
100
80
L
50
50
60
20
90
90
40
60
50
10
80
100
20
40
0
70
N1112OHCl
90
70
60
Water
20
30
50
Triton X-100
Water
0
10
S + 2L
0
40
20
90
10
S + 2L
30
80
20
30
40
L+L
70
30
80
20
50
60
40
L+L
10
60
50
50
60
70
L
40
60
50
0
80
30
L
70
90
20
30
40
100
100
N1112OHCl
100
0
0
10
20
Triton X-100
30
40
50
60
70
80
90
100
N1112OHCl
Figure 3.7. TLs for the systems {[Triton X-100 (1) + N1112OHCl (2) +H2O (3)} at 298.15 K (), 313.15 K (),
323.15 K (), 333.15 K (). Symbols represent experimental data, solid curved lines refer to model
3-29
3-Remediation of Pollutants by Aqueous Two Phase Systems
Water
Water
0
0
100
10
90
20
50
60
70
80
90
100
0
10
20
30
40
10
0
100
10
L
30
70
L+L
Triton X-102
80
90
30
20
10
S + 2L
0
70
40
90
10
S + 2L
100
60
50
L+L
80
20
90
50
60
70
30
40
70
60
40
80
30
100
80
L
50
50
70
20
90
90
40
60
50
10
80
100
20
80
40
0
70
N1112OHCl
90
60
60
Water
20
30
50
Triton X-102
Water
0
10
100
0
N1112OHCl
Triton X-102
20
S + 2L
0
40
30
90
10
S + 2L
100
30
40
80
20
90
20
L+L
70
30
80
10
50
60
40
L+L
60
50
50
70
0
70
40
60
50
80
L
30
70
40
60
90
20
80
L
30
100
10
100
N1112OHCl
100
0
0
10
20
Triton X-102
30
40
50
60
70
80
90
100
N1112OHCl
Figure 3.8. TLs for the systems {[Triton X-102 (1) + N1112OHCl (2) +H2O (3)} at 298.15 K (), 313.15 K (),
323.15 K (), 333.15 K (). Symbols represent experimental data, solid curved lines refer to model
3-30
3.-Remediation of Pollutants by Aqueous Two Phase Systems
Water
0
Water
0
100
10
90
20
30
80
90
50
60
70
80
90
Tween 80
20
100
100
0
0
N1112OHCl
10
20
30
40
50
10
0
100
10
40
60
Tween 80
80
90
30
20
10
S + 2L
0
70
40
L+ L
90
10
S + 2L
100
60
50
80
20
90
50
60
70
30
80
40
70
L
60
40
L+ L
30
80
50
50
60
20
100
90
30
70
L
50
10
90
100
20
80
30
0
80
N1112OHCl
90
70
70
Water
20
40
60
Tween 80
Water
0
10
S + 2L
0
40
30
90
10
S + 2L
L+ L
80
20
30
40
70
30
L+ L
50
60
40
70
20
60
50
50
60
10
70
L
40
60
50
0
80
30
70
L
100
90
20
80
40
100
10
100
N1112OHCl
100
0
0
10
Tween 80
20
30
40
50
60
70
80
90
100
N1112OHCl
Figure 3.9. TLs for the systems {[Tween 80 (1) + N1112OHCl (2) +H2O (3)} at 298.15 K (), 313.15 K (),
323.15 K (), 333.15 K (). Symbols represent experimental data, solid curved lines refer to model
3-31
3-Remediation of Pollutants by Aqueous Two Phase Systems
Water
Water
0
10
0
100
90
20
70
L
60
50
70
80
20
0
60
70
80
90
Tween 20
20
90
10
S + 2L
100
50
100
10
S + 2L
100
0
0
N1112OHCl
10
20
30
40
10
0
100
10
70
40
60
0
Tween 20
70
80
90
40
L+ L
30
20
90
10
S + 2L
100
60
50
80
20
90
50
60
70
30
80
40
70
L
60
40
L+ L
30
100
80
50
50
60
20
90
90
30
L
50
10
80
100
20
80
30
0
70
N1112OHCl
90
70
60
Water
20
40
50
Tween 20
Water
0
30
L+ L
90
40
40
70
30
L+ L
50
60
80
30
60
50
40
20
70
L
40
60
10
80
30
50
0
90
20
80
30
40
100
10
100
N1112OHCl
0
10
S + 2L
100
0
10
20
30
40
Tween 20
50
60
70
80
90
100
N1112OHCl
Figure 3.10. TLs for the systems {[Tween 20 (1) + N1112OHCl (2) +H2O (3)} at 298.15 K (), 313.15 K (),
323.15 K (), 333.15 K (). Symbols represent experimental data, solid curved lines refer to model
The consistency of the experimental TL data was assessed by the linearization of the
Othmer-Tobias (equation 3.9). The values of the fitting parameters and the regression
coefficients are displayed in Table 3.12, and reveal the reliability of the models to
appropriately characterize the TLs, since R2 is always higher than 0.95.
3-32
3.-Remediation of Pollutants by Aqueous Two Phase Systems
Table 3.12. Parameters of Othmer-Tobias equation and correlation coefficient for {Surfactant
(1) + N1112OHCl (2) + H2O (3)} at several temperatures.
2
m
n
R
Triton X-100 (1) + N1112OHCl (2) + H2O (3)
2.8138
0.5070
0.999
Triton X-102 (1) + N1112OHCl (2) + H2O (3)
2.2656
0.2554
0.983
Tween 80 (1) + N1112OHCl (2) + H2O (3)
2.4965
0.3959
0.969
Tween 20 (1) + N1112OHCl (2) + H2O (3)
4.2347
1.5663
0.980
Triton X-100 (1) + N1112OHCl (2) + H2O (3)
2.3668
1.0000
0.999
Triton X-102 (1) + N1112OHCl (2) + H2O (3)
3.3485
1.0000
0.999
Tween 80 (1) + N1112OHCl (2) + H2O (3)
3.9707
1.0000
0.967
Tween 20 (1) + N1112OHCl (2) + H2O (3)
3.6668
1.5602
0.989
Triton X-100 (1) + N1112OHCl (2) + H2O (3)
1.0589
1.0000
0.988
Triton X-102 (1) + N1112OHCl (2) + H2O (3)
2.0798
1.0000
0.999
Tween 80 (1) + N1112OHCl (2) + H2O (3)
3.2352
1.0000
0.970
Tween 20 (1) + N1112OHCl (2) + H2O (3)
2.8284
1.0000
0.961
Triton X-100 (1) + N1112OHCl (2) + H2O (3)
0.5519
1.0000
0.976
Triton X-102 (1) + N1112OHCl (2) + H2O (3)
1.4053
1.0000
0.991
Tween 80 (1) + N1112OHCl (2) + H2O (3)
2.2298
1.0000
0.953
Tween 20 (1) + N1112OHCl (2) + H2O (3)
3.6761
1.0000
0.980
T = 298.15 K
T = 313.15 K
T = 323.15 K
T = 333.15 K
All in all, the comparison between cholinium and imidazolium-based ionic liquids
(N1112OHCl vs. C2C1imC2SO4) shown in Figure 3.11 allows to remark that the use of more
hydrophilic ionic liquids involves greater salting out potential for the four selected
temperatures, as can be inferred from the binodal curves closer to the origin. This is due to the
higher affinity of N1112OHCl for the water molecules, which makes it easier to establish hydrogen
bonds and in turn, to trigger surfactant segregation. Additionally, with the purpose of scalingup these systems, it has been demonstrated the suitability of cholinium-based ionic liquids, not
only due to its eco-friendly character, but also owing to its greater immiscible region.
3-33
3-Remediation of Pollutants by Aqueous Two Phase Systems
100
100
75
75
50
50
25
25
0
0
0
100 w1
100 w1
313.15 K
20
40
60
80
323.15
K
100
0
20
40
80
333.15
K
60
75
75
50
50
25
25
0
0
25
50
100 w2
75
0
25
50
75
100 w1
100 w1
298.15 K
0
100
100 w2
Figure 3.11. Binodal curves for imidazolium- (full symbols) and choline-based ionic liquids (void symbols)
in aqueous solutions of Triton X-100 (), Triton X-102 (), Tween 80 () and Tween 20 (☆) at different
temperatures.
3-34
3.-Remediation of Pollutants by Aqueous Two Phase Systems
3.4.2 INORGANIC AND ORGANIC SALTS AS SEGREGATION AGENTS IN
AQUEOUS SOLUTIONS OF NON-IONIC SURFACTANTS
Although liquid-liquid extraction through ATPS has gained further momentum in recent
years with the emergence of ionic liquids (Ventura et al., 2012; Freire et al., 2010), little is
known about the effect of conventional salts in aqueous solutions of non-ionic surfactants.
Taking into account the above mentioned, this section will be focused on the determination of
the immiscibility regions for non-ionic surfactant-based ATPS in the presence of inorganic and
organic salts in order to compare their salting out potential with that provided by the studied
ionic liquids. As the ATPS obtained with the salts are closer to the water vertex than those
observed with choline ionic liquid (even at high temperatures), the effect of salts will only be
studied at room temperature. On the other hand, as the salting out effect of some of the salts
could be very similar, an orthogonal representation will be given priority over the ternary plot
in order to ease the visualization of the ATPS.
INORGANIC SALTS
AS
SEGREGATION AGENTS
IN
AQUEOUS SOLUTIONS
OF
NON-IONIC
SURFACTANTS
In this section, the salting out potential of high charge density inorganic salts based on
potassium and ammonium cations (K3PO4, K2CO3, K2HPO4, K2S2O3, K2SO3, (NH4)2HPO4,
(NH4)2SO4) will be evaluated to further phase segregation in aqueous solutions of two of the
non-ionic surfactants previously mentioned, Triton X-100 and Tween 20, as examples of the
most hydrophobic and hydrophilic ones. In all cases, the solubility curves and TLs have been
determined prior to model all the experimental data with known equations, such as those
reported previously. Additionally, the salting out character has been qualitatively discussed in
the light of the Hofmeister series and quantitatively analysed based on thermodynamic
parameters.
The experimental data making up the phase diagrams of the systems involving the
selected surfactants, Triton X-100 and Tween 20, and high density charge inorganic salts,
K3PO4, K2HPO4, K2CO3, K2SO3, (NH4)2HPO4 and (NH4)2SO4 were ascertained at 298.15 K and are
listed in Tables A.10 and A.11 (see annex), and graphically compared in Figure 3.12.
3-35
3-Remediation of Pollutants by Aqueous Two Phase Systems
0,10
0,10
0,08
0,06
0,06
0,04
0,04
0,02
0,02
-1
0,08
100(w1/M1)(mol·g )
Tween 20
-1
100(w1/M1)(mol·g )
Triton X-100
0,00
0,00
0,04
0,08
100(w2/M2)(mol·g-1)
0,12
0,00
0,04
0,08
0,12
0,00
0,16
100(w2/M2)(mol·g-1)
Figure 3.12. Plot of experimental and correlated solubility data of {surfactant (1) + salt (2) +H 2O (3)} at
298.15 K. K3PO4; (), K2HPO4; () K2CO3; (), K2SO3; (), (NH4)2HPO4 (), (NH4)2SO4. Symbols represent
experimental data and solid lines refer to model
Analogously to other conventional ATPS (Han et al., 2011; Passos et al., 2013), the phase
segregation in systems containing surfactants and inorganic salts are the result of a complex
balance between the surfactant hydrophobicity and the salting-out potential of the salt to
create hydration complexes (Neves et al., 2009), as shown in Figure 3.12. Again, the more
hydrophobic or de-structuring compound Triton X-100 (HLB 13.4) is more easily salted out by
the inorganic salts than more hydrophilic Tween 20 (HLB 16.7), in line with our previous
observations for systems using ionic liquids as salting out agents, or other studies addressing
systems composed of ionic liquids and PEGs (Freire et al., 2012b; Rodríguez et al., 2009).
On the other hand, the salting-out ability of the inorganic salts can be qualitatively
evaluated by means of the Hofmeister series, which ranks the salts in accordance with the
solvation capacity of their ions (Hofmeister, 1908). Firstly, in a visual inspection of the Figure
3.12 it becomes clear the existence of a unequivocal salting-out trend for the selected anions:
PO43- > HPO42- > CO32- > SO32- when paired with cation K+ and HPO42-> SO42- when the cation is
NH4+ . Traditionally, this solvation capacity was qualitatively analysed in terms of chaotropicity
or kosmotropicity, depending on the ability of the salts and surfactants to interact with water
molecules. Kosmotropes are usually small and highly charged, while chaotropes are large and
low charged. In fact, all multivalent ions are highly hydrated and are, therefore, kosmotropic.
This is in agreement with the observed trend that trivalent anion has a higher salting out
potential than divalent anions.
Additionally, the salting-out ability can also be explained in terms of the Gibbs free
energy of hydration (∆hydG) (Marcus, 1991). Therefore, based on the data reported previously
3-36
3.-Remediation of Pollutants by Aqueous Two Phase Systems
and listed in Table 3.13, the salting-out trend cited previously matches the ∆hydG, due to the
fact that ions with a more negative ∆hydG value show a greater salting-out capacity.
Table 3.13. Molar Gibbs free energy of hydration (hydG), Jones-Dole viscosity B-coefficients (B),
and molar entropy of hydration (hydS).
Ions
+
K
-1
hydG/kJ·mol
d
-93
a
-0.008
d
-131
a
c
-440
a
d
-291
a
e
-264
a
-268
a
-219
a
-285
3-
-2765
2-
HPO4
a
0.590
b
0.382
a
0.294
a
n.a.
d
0.206
-1789
2-
-1315
2-
-1295
2-
-1080
CO3
SO3
SO4
-1
d
-1
hydS/J·K ·mol
-0.007
+
PO4
-1
a
-295
NH4
3
B/dm ·mol (25°C)
a
a (Marcus, 1994), b (Zhao et al., 2011), c (Collins, 2006), d (Zafarani-Moattar & Hamzehzadeh, 2010), e (Jenkins & Marcus,
1995). n.a. not available.
In parallel, the salting-out effect of the ions may be interpreted in the light of the JonesDole viscosity B-coefficients. This parameter provides information on the number of water
molecules that can be hydrated by a given ion, being a viable tool to analyse the potential
salting-out ability of each salt. Several reports have highlighted that ions with more positive Bcoefficients hydrated more water molecules than those presenting lower values, thus
suggesting that these ions are more kosmotropes and would exhibit a larger change in
viscosity. Hence, the reported values in Table 3.13 confirm the sequence obtained
experimentally. Moreover, the results can be interpreted in terms of the molar hydration
entropy (hydS), since different authors have stressed the narrow correlation between this
thermodynamic parameter and the salting-out effects of anions (Deive et al., 2011a; Zhao et
al., 2011). From the data presented beforehand, it is possible to conclude the same sequence
stated previously, thus confirming the validity of these thermodynamic parameters to predict
the salting-out potential of a given salt.
The use of equations (3.6), (3.7) and (3.8) served our goal to proper model the
experimental data, ant the values of the fitting parameters and standard deviations are listed
in Tables 3.14 to 3.16. The analysis of the standard deviation data () reflects the suitability of
four-parameters equations to reproduce the solubility data, in line with our previous results
using ionic liquids as salting out agents.
3-37
3-Remediation of Pollutants by Aqueous Two Phase Systems
Table 3.14. Parameters of equation (3.6) and standard deviation for {Surfactant (1) + salt (2) + H2O (3)}
at T = 298.15 K.
a
b

c
Triton X-100 (1) + K3PO4 (2) + H2O (3)
0.6059
-1.5791
3.17  10
3
Triton X-100 (1) + K2HPO4 (2) + H2O (3)
0.8129
-3.1755
2.20  10
3
0.0099
1.96  10
3
0.0175
4.61  10
3
0.0140
0.0096
Triton X-100 (1) + K2SO3 (2) + H2O (3)
0.7067
Triton X-100 (1) + K2CO3 (2) + H2O (3)
-1.9230
0.7085
-2.0807
0.0093
Triton X-100 (1) + (NH4)2HPO4 (2) + H2O (3)
0.8003
-3.6508
2.79  10
3
Triton X-100 (1) + (NH4)2SO4 (2) + H2O (3)
0.7414
-2.1638
2.79  10
3
0.0090
1.50  10
3
0.0120
1.36  10
3
0.0147
0.0114
Tween 20 (1) + K3PO4 (2) + H2O (3)
0.7944
Tween 20 (1) + K2HPO4 (2) + H2O (3)
-3.0380
0.7253
-2.4081
Tween 20 (1) + K2SO3 (2) + H2O (3)
0.8670
-2.7655
1.04  10
3
Tween 20 (1) + K2CO3 (2) + H2O (3)
0.6329
-1.4067
2.43  10
3
0.0180
1.26  10
3
0.0060
1.01  10
3
0.0073
Tween 20 (1) + (NH4)2HPO4 (2) + H2O (3)
0.6072
Tween 20 (1) + (NH4)2SO4 (2) + H2O (3)
-2.2515
0.6831
-1.8841
Standard deviation () was calculated by means of equation (2.5).
Table 3.15. Parameters of equation (3.7) and standard deviation for {Surfactant (1) + salt (2) + H 2O (3)}
at T = 298.15 K.
a
b
c
d

Triton X-100 (1) + K3PO4 (2) + H2O (3)
0.6134
0.0603
-6.8938
0.1997
0.0063
Triton X-100 (1) + K2HPO4 (2) + H2O (3)
0.6995
-0.3102
-7.3628
14.42
0.0076
Triton X-100 (1) + K2SO3 (2) + H2O (3)
1.2301
-7.0057
17.54
-75.65
0.0062
Triton X-100 (1) + K2CO3 (2) + H2O (3)
0.6942
-0.1440
-7.8539
-4.9286
0.0089
Triton X-100 (1) + (NH4)2HPO4 (2) + H2O (3)
0.5153
1.7651
-15.53
45.96
0.0094
Triton X-100 (1) + (NH4)2SO4 (2) + H2O (3)
0.7219
-0.3995
-5.9607
-4.8432
0.0038
Tween 20 (1) + K3PO4 (2) + H2O (3)
0.9143
-3.6087
6.1465
-33.80
0.0034
Tween 20 (1) + K2HPO4 (2) + H2O (3)
0.7474
-1.5924
-0.0831
-14.77
0.0057
Tween 20 (1) + K2SO3 (2) + H2O (3)
1.0807
-3.0912
0.6065
-3.1472
0.0082
Tween 20 (1) + K2CO3 (2) + H2O (3)
0.7023
-1.8124
3.3389
-49.56
0.0062
Tween 20 (1) + (NH4) 2HPO4(2) + H2O (3)
0.5635
-0.3863
-3.1373
-4.3400
0.0046
Tween 20 (1) + (NH4)2SO4 (2) + H2O (3)
0.6384
0.2136
-6.1804
5.7244
0.0064
Standard deviation () was calculated by means of equation (2.5).
3-38
3.-Remediation of Pollutants by Aqueous Two Phase Systems
Table 3.16. Parameters of equation (3.8) and standard deviation for {Surfactant (1) + salt (2) + H 2O (3)}
at T = 298.15 K.
a
b
c
d

Triton X-100 (1) + K3PO4 (2) + H2O (3)
1.2662
-30.29
130.1
-891.6
0.0086
Triton X-100 (1) + K2HPO4 (2) + H2O (3)
1.7720
-33.09
126.2
-742.5
0.0082
Triton X-100 (1) + K2SO3 (2) + H2O (3)
2.1145
-37.09
140.4
-740.7
0.0158
Triton X-100 (1) + K2CO3 (2) + H2O (3)
3.1299
-57.25
244.2
-1550.4
0.0110
Triton X-100 (1) + (NH4)2HPO4 (2) + H2O (3)
0.2900
-13.51
53.35
-531.0
0.0097
Triton X-100 (1) + (NH4)2SO4 (2) + H2O (3)
0.3912
-14.04
59.32
-537.7
0.0106
Tween 20 (1) + K3PO4 (2) + H2O (3)
1.2816
-25.90
95.49
-537.4
0.0110
Tween 20 (1) + K2HPO4 (2) + H2O (3)
0.9430
-21.74
81.37
-466.9
0.0149
Tween 20 (1) + K2SO3 (2) + H2O (3)
1.2283
-19.57
61.79
-328.4
0.0109
Tween 20 (1) + K2CO3 (2) + H2O (3)
1.5672
-34.66
147.0
-879.4
0.0053
Tween 20 (1) + (NH4)2HPO4 (2) + H2O (3)
1.6304
-30.82
107.9
-518.7
0.0076
Tween 20 (1) + (NH4)2SO4 (2) + H2O (3)
2.8351
-40.71
132.7
-519.7
0.0069
Standard deviation () was calculated by means of equation (2.5).
The parameters TLL and S, which expressions were illustrated in equations (3.4) and
(3.5), respectively, were calculated using the mass fraction of the surfactant (1) and the
inorganic salt (2), in the surfactant rich phase (I) and salt-rich phase (II) . The TLL data obtained
for each ternary system and the abovementioned parameters are given in Table A.12 (see
annex) and the complete phase diagrams obtained for the APTS are presented in Figures 3.13
and 3.14. From these data, it is clear that higher values of TLL correlate with higher salt
concentration, in line with the results reported for ionic liquid-based ATPS: as more inorganic
salt is present, the bottom phase becomes increasingly structured, thus leading to a higher
degree of mass transfer of chaotropic ions to the top phase. The slope of the tie-lines generally
increases for the systems containing the more hydrophobic surfactant Triton X-100, which
entails a higher segregation of the surfactant to the upper phase, in agreement with previous
results using ionic liquids (Deive et al., 2011c).
3-39
80
60
60
40
40
20
20
0
0
10
15
20
5
10
15
20
60
60
40
40
20
20
0
100w1
100w1
5
0
5
100w1
100w1
80
10
15
20
0
10
20
30
60
60
40
40
20
20
0
100w1
100w1
3-Remediation of Pollutants by Aqueous Two Phase Systems
0
0
5
10
15
100w2
20
0
5
10
15
20
25
100w2
Figure 3.13. Experimental and correlated phase diagram and experimental tie-lines of {Triton X-100 (1) +
salt (2) +H2O (3)} at 298.15 K. (), K3PO4; (), K2HPO4; () K2CO3; (), K2SO3; (), (NH4)2HPO4; (),
(NH4)2SO4; Stars represent TLs. Symbols represent experimental data and solid lines refer to model
3-40
3.-Remediation of Pollutants by Aqueous Two Phase Systems
60
40
40
20
20
100w1
100w1
60
0
0
5
10
15
20
5
10
15
20
40
20
20
100w1
100w1
40
0
0
5
10
15
20
5
10
15
20
40
20
20
100w1
100w1
40
0
0
0
5
10
15
20
100w2
0
5
10
15
20
25
100w2
Figure 3.14. Experimental and correlated phase diagram and experimental TLs of {Tween 20 (1) + salt (2)
+H2O (3)} at 298.15 K. (), K3PO4; (), K2HPO4; () K2CO3; (), K2SO3; (), (NH4)2HPO4; (), (NH4)2SO4;
Stars represent TLs. Symbols represent experimental data and solid lines refer to model
In addition, and similarly to previous systems, the experimental TL data were fitted to
Othmer-Tobias equation (3.9) in order to determine the thermodynamic consistency of the
experimental data, and the fitting parameters and standard deviations are shown in Table
3.17. Generally speaking, the Othmer-Tobias model fits adequately to the experimental data,
as R2 values are always higher than 0.95.
3-41
3-Remediation of Pollutants by Aqueous Two Phase Systems
Table 3.17. Parameters of Othmer-Tobias equation and correlation coefficient for
{Surfactatn (1) + Salt (2) + H2O (3)} at 298.15 K.
Triton X-100 (1) + K3PO4 (2) + H2O (3)
2
m
n
R
1.3219
8.12 · 10
-2
0.981
0.979
Triton X-100 (1) + K2HPO4 (2) + H2O (3)
1.9861
4.53 · 10
-2
Triton X-100 (1) + K2SO3 (2) + H2O (3)
4.9502
1.00 · 10
-3
0.999
1.65 · 10
-1
0.980
7.99 · 10
-3
0.992
0.975
Triton X-100 (1) + K2CO3 (2) + H2O (3)
1.0613
Triton X-100 (1) + (NH4)2HPO4 (2) + H2O (3)
2.9362
Triton X-100 (1) + (NH4)2SO4 (2) + H2O (3)
4.0612
8.29 · 10
-4
Tween 20 (1) + K3PO4 (2) + H2O (3)
2.0416
3.29 · 10
-2
0.977
1.06 · 10
-1
0.984
2.79 · 10
-2
0.933
0.999
Tween 20 (1) + K2HPO4 (2) + H2O (3)
1.4291
Tween 20 (1) + K2SO3 (2) + H2O (3)
2.2141
Tween 20 (1) + K2CO3 (2) + H2O (3)
1.5903
8.58 · 10
-2
Tween 20 (1) + (NH4)2HPO4 (2) + H2O (3)
1.0478
2.41 · 10
-1
0.993
1.07 · 10
-1
0.992
Tween 20 (1) + (NH4)2SO4 (2) +H2O (3)
ORGANIC SALTS
AS
SEGREGATION AGENTS
1.2985
IN
AQUEOUS SOLUTIONS
OF
NON-IONIC
SURFACTANTS
The last section of this chapter included the use of organic salts with potassium and
ammonium cations (K3C6H5O7·H2O, K2C4H4O6·0.5H2O, K2C2O4·H2O, (NH4)2C4H4O6, NaKC4H4O6) as
salting-out agents in aqueous solutions of the non-ionic surfactants Triton X-100 and Tween
20. In line with the biocompatibility reported for cholinium-based salts, these organic salts are
recognized as a more environmentally sustainable alternative. The solubility curves and TLs
were again determined prior to model all the experimental data and discussed on the basis of
the above mentioned parameters. The experimental data of binodal curves for the ternary
mixtures of {Triton X-100 or Tween 20 + salts + H2O} at 298.15 K are given in mas fraction in
Tables A.13 and A.14 (see annex), and graphically compared in Figure 3.15.
3-42
3.-Remediation of Pollutants by Aqueous Two Phase Systems
0,09
0,09
-1
100(w1/M1)(mol·g )
Tween 20
-1
100(w1/M1)(mol·g )
Triton X-100
0,06
0,06
0,03
0,03
0,00
0,00
0,03
0,06
0,09
0,12
0,00
0,03
100(w2/M2)(mol·g-1)
0,06
0,09
0,12
0,00
0,15
100(w2/M2)(mol·g-1)
Figure 3.15. Experimental and correlated solubility data of {surfactant (1) + salt (2) +H 2O (3)} at 298.15 K.
(), K3C6H5O7·H2O; (), K2C4H4O6·0.5H2O; (); K2C2O4·H2O, (), NaKC4H4O6; (), (NH4)2C4H4O6. Symbols
represent experimental data and solid lines refer to model
The analysis of the data in terms of surfactant HLB value reflects a similar behaviour to
that concluded previously, as the more chaotropic Triton X-100 involves greater immiscibility
windows. Another aspect that confirms the abovementioned trends is the fact that all
multivalent ions present a higher hydration capacity. Thus, potassium citrate is the salt
showing a stronger ability to form an immiscible area in the presence of aqueous mixtures of
surfactants, in line with previous studies (Freire et al., 2012a).
In general terms, having fixed the cation K, the ability of these salts to salt-out the
selected surfactants from the aqueous solution could be expressed by the following trend:
citrate > tartrate > oxalate. This salt-rank effect follows the Hofmeister series, as oxalate is the
ion with the weakest interactions with water, thus leading to a smaller biphasic region. It is
also interesting to note that the observed sequence is the same for both surfactants, which
confirms the observed behaviour.
On the other hand, the analysis of the Gibbs free energy of hydration (∆hydG) for the
anions under study reveals the following arrangement: (C6H5O7)-3 > (C2O4)-2 > (C4H4O6)-2 (values
presented in Table 3.18). These values corroborate the higher kosmotropicity found for
trivalent anion in relation to divalent anions. However, it is observed a disagreement in the
solubility curve of oxalate ion regarding what should be expected from the (∆hydG) value. In
Figure 3.15 is shown that tartrate ion of potassium-based salt entails a slightly larger
immiscible region than that obtained for the oxalate ion. This anomalous behaviour was
formerly reported with the same salts (Xie et al., 2010; Zafarani-Moattar & Hamzehzadeh,
2010) and might be due to the closer ∆hydG values of tartrate and oxalate ions.
3-43
3-Remediation of Pollutants by Aqueous Two Phase Systems
Table 3.18. Molar Gibbs energy of hydration (hydG) of selected ions.
Cations
-1
hydG/(kJ·mol )
+
a
C6H6O7
-2763
a
C2O4
-2
-1453
a
C4H4O6
Na
-365
+
-295
K
+
NH4
-1
hydG/(kJ·mol )
Anions
-285
-3
c
a
-2
b
-1102
a (Marcus, 1994), b (Zafarani-Moattar & Tolouei, 2008),c (Zafarani-Moattar & Hamzehzadeh , 2011).
It is noteworthy that different cations play a distinct role in the phase segregation. The
Hofmeister series predicts the following salting out potential: Na+ > K+ > NH4+, in line with the
data displayed in Figure 3.15. Thereby, sodium potassium tartrate salt shows a greater saltingout effect than the potassium tartrate salt and this is even more evident for the ammonium
tartrate salt. This trend is validated through the ∆hydG, and based on the data shown in Table
3.18 for the cations under study, the salting-out capacity of the salts confirms the commented
pattern. Analogously to the inorganic salts, the experimental solubility data were fitted to
equations (3.6), (3.7) and (3.8). The optimised parameters and the standard deviations () are
listed in Tables 3.19 to 3.21.
Table 3.19. Parameters of equation (3.6) and standard deviation for {Surfactant (1) + salt (2) + H 2O (3)}
at 298.15 K.
a
b

c
Triton X-100 (1) + K3C6H5O7 (2) + H2O (3)
0.6102
-0.7223
1.11  10
3
Triton X-100 (1) + K2C4H4O6 (2) + H2O (3)
0.6258
-0.9012
1.10  10
3
0.0161
2.27  10
3
0.0132
1.36  10
3
0.0131
1.45  10
3
0.0192
3.93  10
2
0.0130
3.15  10
2
0.0102
7.90  10
2
0.0118
4.75  10
3
0.0082
1.26  10
3
0.0060
Triton X-100 (1) + K2C2O4 (2) + H2O (3)
Triton X-100 (1) + NaKC4H4O6 (2) + H2O (3)
Triton X-100 (1) + (NH4)2C4H4O6 (2) + H2O (3)
Tween 20 (1) + K3C6H5O7 (2) + H2O (3)
Tween 20 (1) + K2C4H4O6 (2) + H2O (3)
Tween 20 (1) + K2C2O4 (2) + H2O (3)
Tween 20 (1) + NaKC4H4O6 (2) + H2O (3)
Tween 20 (1) + (NH4)2C4H4O6 (2) + H2O (3)
Standard deviation () was calculated by means of equation (2.5).
3-44
0.6055
0.5841
0.7107
0.7363
0.7879
0.6632
0.7179
0.5146
-0.6916
-0.9203
-0.7393
-1.8082
-2.2680
-1.4505
-1.8830
-2.2515
0.0109
3.-Remediation of Pollutants by Aqueous Two Phase Systems
Table 3.20. Parameters of equation (3.7) and standard deviation for {Surfactant (1) + salt (2) + H 2O (3)}
at 298.15 K.
a
b
c
d

Triton X-100 (1) + K3C6H5O7 (2) + H2O (3)
0.6845
-0.0057
-4.5464
-5.6888
0.0043
Triton X-100 (1) + K2C4H4O6 (2) + H2O (3)
0.7458
-0.4503
-3.6448
-5.7180
0.0079
Triton X-100 (1) + K2C2O4 (2) + H2O (3)
0.6509
0.2349
-5.9209
-11.62
0.0072
Triton X-100 (1) + NaKC4H4O6 (2) + H2O (3)
0.7004
-1.1583
0.1091
-23.11
0.0048
Triton X-100 (1) +(NH4)2C4H4O6 (2) + H2O (3)
0.6305
1.4189
-8.7819
-7.2428
0.0097
Tween 20 (1) + K3C6H5O7 (2) + H2O (3)
1.0711
-3.7285
5.3983
-15.04
0.0039
Tween 20 (1) + K2C4H4O6 (2) + H2O (3)
0.8588
-1.8009
0.2853
-3.8307
0.0050
Tween 20 (1) + K2C2O4 (2) + H2O (3)
0.8177
-1.3059
-1.5895
-4.7615
0.0055
Tween 20 (1) + NaKC4H4O6 (2) + H2O (3)
0.6564
-0.0777
-3.9647
1.3385
0.0044
Tween 20 (1) + (NH4)2C4H4O6 (2) + H2O (3)
0.9331
-1.7585
-0.0154
-4.2035
0.0043
Standard deviation () was calculated by means of equation (2.5).
Table 3.21. Parameters of equation (3.8) and standard deviation for {Surfactant (1) + salt (2) + H 2O (3)}
at 298.15 K.
Triton X-100 (1) + K3C6H5O7 (2) + H2O (3)
Triton X-100 (1) + K2C4H4O6 (2) + H2O (3)
Triton X-100 (1) + K2C2O4 (2) + H2O (3)
Triton X-100 (1) + NaKC4H4O6 (2) + H2O (3)

a
b
c
d
3.0296
-42.81
142.3
-5.55  10
2.2385
1.0964
-19.35
-32.10
-25.48
44.67
104.3
107.5
-21.98
2
2
-4.28  10
2
-6.80  10
1
-2.10  10
2
Triton X-100 (1) + (NH4)2C4H4O6 (2) + H2O (3)
1.9152
-29.57
105.5
-5.12  10
Tween 20 (1) + K3C6H5O7 (2) + H2O (3)
4.2920
-47.87
129.6
-3.35  10
2
2
Tween 20 (1) + K2C4H4O6 (2) + H2O (3)
3.7563
-40.87
105.4
-2.62  10
Tween 20 (1) + K2C2O4 (2) + H2O (3)
1.1866
-20.23
65.09
-2.96  10
Tween 20 (1) + NaKC4H4O6 (2) + H2O (3)
Tween 20 (1) + (NH4)2C4H4O6 (2) + H2O (3)
-5.1659
10.25
31.71
-94.23
-132.3
228.9
2
1
-4.54  10
2
-4.44  10
0.0088
0.0161
0.0130
0.0013
0.0163
0.0113
0.0095
0.0130
0.0014
0.0067
Standard deviation () was calculated by means of equation (2.5).
From the standard deviation values, it is possible to conclude that equation (3.8) is the
one leading to the most suitable fittings, following the results pointed for previous section of
non-ionic surfactant to form ATPS with inorganic salts. The TLL and S obtained for the different
compositions from equations (3.4) and (3.5), respectively, are given in Table A.15 (see annex)
and represented in Figures 3.16 and 3.17.
3-45
3-Remediation of Pollutants by Aqueous Two Phase Systems
60
40
40
20
20
100w1
100w1
60
0
0
5
10
15
20
5
10
15
20
40
20
20
100w1
100w1
40
0
0
5
10
15
20
5
10
15
20
25
100w2
100w1
40
20
0
0
5
10
15
20
25
100w2
Figure 3.16. Plot of experimental and correlated phase diagram and experimental tiel-lines of {Triton X100 (1) + salt (2) +H2O (3)} at 298.15 K. (), K3C6H5O7·H2O; (), K2C4H4O6·0.5H2O; (); K2C2O4·H2O, (),
NaKC4H4O6; (), (NH4)2C4H4O6. Symbols represent experimental data and solid lines refer to model
3-46
3.-Remediation of Pollutants by Aqueous Two Phase Systems
40
40
20
20
100w1
60
100w1
60
0
0
10
20
0
10
20
40
20
20
100w1
100w1
40
0
0
10
20
10
20
30
100w2
100w1
40
20
0
0
10
20
30
100w2
Figure 3.17. Plot of experimental and correlated phase diagram and experimental tiel-lines of {Tween 20
(1) + salt (2) +H2O (3)} at 298.15 K. (), K3C6H5O7·H2O; (), K2C4H4O6·0.5H2O; (); K2C2O4·H2O, (),
NaKC4H4O6; (), (NH4)2C4H4O6. Symbols represent experimental data and solid lines refer to model
The reliability of the obtained experimental data was again ascertained by means of the
Othmer-Tobias (3.9) empirical equation. As can be seen in Table 3.22, the obtained regression
coefficients are close to 1 for most of all the salts, which confirms the appropriateness of this
model, in agreement with our previous results, and with those reported for other systems
consisting of a polymer and sodium citrate (Tubio et al., 2009).
3-47
3-Remediation of Pollutants by Aqueous Two Phase Systems
Table 3.22. Parameters of Othmer-Tobias equation and correlation coefficient for
{Surfactant (1) + Salt (2) + H2O (3)} at 298.15 K.
Triton X-100 (1) + K3C6H5O7 (2) + H2O (3)
2
m
n
R
2.0045
6.82 · 10
-2
0.999
0.998
Triton X-100 (1) + K2C4H4O6 (2) + H2O (3)
2.7991
2.23 · 10
-2
Triton X-100 (1) + K2C2O4 (2) + H2O (3)
6.3449
1.19 · 10
-5
0.911
1.06 · 10
-2
0.987
4.22 · 10
-2
0.994
0.994
Triton X-100 (1) + NaKC4H4O6 (2) + H2O (3)
Triton X-100 (1) + (NH4)2C4H4O6 (2) + H2O (3)
3.0850
2.3186
Tween 20 (1) + K3C6H5O7 (2) + H2O (3)
1.7968
1.04 · 10
-1
Tween 20 (1) + K2C4H4O6 (2) + H2O (3)
1.6462
1.50 · 10
-1
0.999
7.43 · 10
-2
0.994
4.21 · 10
-2
0.994
-1
0.969
Tween 20 (1) + K2C2O4 (2) + H2O (3)
Tween 20 (1) + NaKC4H4O6 (2) + H2O (3)
Tween 20 (1) + (NH4)2C4H4O6 (2) + H2O (3)
1.7132
2.3201
1.5958
1.59 · 10
In summary, it can be concluded that the use of organic salts as salting-out agents is a
viable alternative for the application of ATPS as an eco-friendly method of separation of
pollutants in industrial effluents.
3-48
3.-Remediation of Pollutants by Aqueous Two Phase Systems
3.4.3 AQUEOUS TWO PHASE SYSTEMS FOR THE PARTITION OF DYES, PAHS,
HEAVY METALS AND EMERGING POLLUTANTS.
The suitability of ionic liquids and salts as segregation agents in aqueous solutions of the
selected non-ionic surfactants has been ascertained in this PhD thesis, so the application of
representative systems (potassium-based organic salts and ammonium-based ionic liquids) for
the removal of different pollutants commonly present in industrial effluents and sewage is
investigated in this section.
REMOVAL OF DYES AND PAHS
As already mentioned in the introduction chapter, the need to remove pollutants such as
dyes and PAHs from industrial effluents is keeping abreast of current legislation, due to their
persistence and toxicity. With this purpose, and since the biological method did not allow to
fully remove them from aqueous streams, the partition of each of these pollutants
independently and mixed will be evaluated in this section in order to elucidate the suitability
of this systems to be coupled to biological processes.
The first step for studying the behaviour of the abovementioned chemicals in the phase
segregation entails the TLs determination as indicated in section 3.3.2. For achieving this goal,
the non-ionic surfactants Triton X-100 and Tween 20 were selected as solubilisation agents for
the removal of PAHs (PHE, PYR and BaA) and reactive dyes (RB5 and AB48), respectively, owing
to they are the ones with higher and lower hydrophobic character, respectively.
One of the valuate tools to quantify the efficiency of the contaminants separation is the
extraction capacity, expressed as follows:
msurfactant
i
) ∙100
mi
E (%)= (
(3.10)
where misurfactant and mi are the pollutants mass content in the upper phase and the total
contaminant mass content, respectively. The separation efficiency at laboratory scale for PAHs
and dyes is shown in Table 3.23.
3-49
3-Remediation of Pollutants by Aqueous Two Phase Systems
Table 3.23. Extraction Efficiency, (E %), of dyes and PAHs in the top phase for different
organic salts in the presence of the non-ionic surfactants Tween 20 and Triton X-100.
Surfactant
Salting-out agent
Pollutant
Extraction Efficiency (E %)
Tween 20
K3C6H5O7
RB5
98.86 ± 1.51
Tween 20
K2C4H4O6
RB5
98.31 ± 2.50
Tween 20
K2C2O4
RB5
97.58 ± 3.76
Tween 20
K3C6H5O7
AB48
98.98 ± 1.14
Tween 20
K2C4H4O6
AB48
97.26 ± 3.74
Tween 20
K2C2O4
AB48
97.90 ± 1.53
Triton X-100
K3C6H5O7
PHE
93.36 ± 2.63
Triton X-100
K2C4H4O6
PHE
81.94 ± 6.69
Triton X-100
K2C2O4
PHE
92.42 ± 2.73
Triton X-100
K3C6H5O7
PYR
94.12 ± 3.87
Triton X-100
K2C4H4O6
PYR
88.51 ± 6.72
Triton X-100
K2C2O4
PYR
86.98 ± 2.94
Triton X-100
K3C6H5O7
BaA
98.62 ± 0.35
Triton X-100
K2C4H4O6
BaA
84.45 ± 3.56
Triton X-100
K2C2O4
BaA
91.46 ± 5.45
A visual inspection of the results allows concluding very high levels of extraction for all
the contaminants no matter the potassium organic salt used. More specifically, it is clear that
potassium citrate turned out to be the best contender, since around 90% of extraction is
yielded for all pollutants. This agrees the findings reported for the extraction of antioxidants
from microalgae (Ulloa et al., 2012a).Therefore, potassium citrate salt was selected for the
partition of a mixture of dyes and PAHs. The results of this partition are indicated in Table 3.24
and illustrated in Figure 3.18. As can be seen, the very high levels of partition are again
confirmed for both dyes and PAHs, which points out the validity of this technique to be
coupled to biological systems.
Table 3.24. Extraction Efficiency, (E %), using potassium citrate as salting out agent in
aqueous streams containing a mixture of PAHs and dyes.
3-50
Surfactant
Salting-out agent
Pollutant
Extraction Efficiency (E %)
Tween 20
K3C6H5O7
RB5
97.28 ± 0.79
Tween 20
K3C6H5O7
AB48
97.61 ± 1.71
Triton X-100
K3C6H5O7
PHE
87.39 ± 5.63
Triton X-100
K3C6H5O7
PYR
81.79 ± 5.55
Triton X-100
K3C6H5O7
BaA
83.80 ± 5.60
3.-Remediation of Pollutants by Aqueous Two Phase Systems
Figure 3.18. Partition of dyes(left) and PAHs (right) to the top-phase by means of potassium citratebased ATPS
In order to demonstrate the suitability of an ATPS stage using potassium-based salt as
biodegradable and nontoxic phase promoters, this technique was applied after the biological
treatment of dyes and PAHs. Therefore, given the incomplete degradation of these recalcitrant
pollutants after biological treatments (Table 3.25), potassium citrate was added to an obtained
effluent containing the PAH and dye mixture in the presence of the corresponding non-ionic
surfactant. This stream contains the components of the culture medium that were not used
during the biological reaction, the non-biodegraded contaminants and the synthesized
metabolites. Henceforth, this complexity demands the demonstration of the ability of the
selected organic salt to promote phase segregation and contaminants concentration in the top
phase, no matter the medium used.
In order to do that, two completely different biotreatment media widely found in
literature in biotechnological processes were used: a complex one containing peptone and
yeast extract (Deive et al, 2010) and a mineral one (composition detailed in chapter 2, section
2.3.2). The results shown demonstrated the suitability of coupling this ATPS to biologically
treated streams, since the remediation percentages found overcome 92% in all cases,
independently of the composition of the medium used. Additionally, the global removal yields
including the biological and ATPS-based treatment were higher than 98% for the 5
contaminants under study. The visual aspect of the treated effluent can be noticed in Figure
3.19
3-51
3-Remediation of Pollutants by Aqueous Two Phase Systems
Table 3.25. Treatment train of the proposed remediation process.
Pollutant
Remediation percentage
Biological treatment %
ATPS %
Total %
RB5
68.65
92.72
97.72
AB48
52.45
96.05
98.12
PHE
59.82
93.60
97.43
PYR
56.33
94.22
97.54
BaA
81.01
92.04
98.50
Total remediation percentage and biological remediation are referred to the initial amount of dye,
while ABS remediation values are referred to the concentration existing in the biotreated effluent.
Figure 3.19. Treatment train of a biological (on the left) and physical (on the right) process for PAHs and
dyes removal
The combination of a sequence of remediation techniques has already been tackled by
other researchers. Thus, Peng et al. (2008) demonstrated 90% of COD reduction in PAHscontaminated soils by combining a surfactant-based washing prior to a coagulation process.
Similarly, benzopyrene degradation levels higher than 75% were achieved by means of a
chemical and a biological treatment (ozone oxidation and aerobic biodegradation) (González et
3-52
3.-Remediation of Pollutants by Aqueous Two Phase Systems
al., 2011). The suitability of a hybrid technique based on a sequential sonolysis and
biodegradation strategy for the remediation of another azo dye (Tectilon Yellow 2G) was also
reported by Srinivasan et al. (2011), concluding an increase of the decolourization efficiency
from 46 to 66%. Given these results, a tentative flowsheet of the implemented process is
proposed in Figure 3.20.
Figure 3.20. Flowsheet of the proposed process
REMOVAL OF HEAVY METALS
Heavy metals are in the limelight due to they have been recognized as carcinogenic,
persistent and bioaccumulative contaminants (DeForest et al., 2007). One of the ecological
niches most probably to be affected by this kind of contamination are coastal and marine
sediments since more than 99% of these compounds that are entering the aquatic ecosystems
can be stored in sediments in various forms (Fu & Wang, 2011; Salomons & Stigliani, 1995). In
this sense, dredging activities involve the generation of great amounts of polluted marine
sediments that should be treated. The remediation methods are often classified into ex situ
and in situ, depending on the place where the treatment is carried out. To name a few,
washing, electrokinetic remediation or immobilization have been proposed as viable ex situ
remediation processes (Peng et al., 2009; Pazos et al., 2013). The sediment washing is a
common technique due to its inherent operational simplicity. This strategy consists of
transferring metal ions from dredged samples to aqueous solutions. The efficiency of this
process can be improved by the addition of specific compounds such as acids, chelating agents
and surfactants, which have been proved to further contaminant solubilisation, dispersion and
desorption. Therefore, the use of non-ionic surfactants (Triton X-100 and Tween 20) and KSCN
as complexation agent in acid media was considered in order to corroborate the viability of
non-ionic surfactant-based ATPS for the removal of heavy metals.
3-53
3-Remediation of Pollutants by Aqueous Two Phase Systems
In the previous section, the suitable salting-out potential of NaKC4H4O6 salt in aqueous
solutions of the selected non-ionic surfactants was demonstrated. In our case, the marine
sediments obtained from the Galician coast were mainly polluted with Zn and Cu, so a model
solution containing these heavy metals, surfactant and water was employed to study the
partition behaviour after addition of the tartrate-based salt. The remediation data were again
analysed in terms of extraction capacity, E (%), defined in equation (3.10) and the results
obtained are compiled in Table 3.26. The data reveal that heavy metal ions remain in the saltrich phase at concentrations higher than 90% for zinc and 85% for copper. This may be due to
the existence of specific interactions between metal and salt ions, so the search of a suitable
complexation agent can be a tool to allow an effective separation of the targeted
contaminants. Thus, the use of KSCN has been proposed, and the extraction values are also
shown in Table 3.26. The analysis of the data permits to conclude a quite different behaviour,
since both zinc and copper are mostly segregated to the top phase, at levels higher than 80%
and 62%, respectively.
Table 3.26. Extraction capacity, E (%) of metal ions in the top phase in the absence and
presence of KSCN.
System
E (%) without KSCN
E (%) with KSCN
11.34 ± 1.0
80.96 ± 3.3
6.19 ± 0.7
86.06 ± 4.3
Triton X-100 + NaKC4H4O6 + H2O
14.64 ± 0.8
62.80 ± 6.3
Tween 20 + NaKC4H4O6 + H2O
18.43 ± 7.9
66.19 ± 7.1
+2
Zn
Triton X-100 + NaKC4H4O6 + H2O
Tween 20 + NaKC4H4O6 + H2O
+2
Cu
The rationale behind this scenario is explained in terms of the complexation capacity of
metals in the presence of tartrate and thiocyanate ions, in accordance with the following
equilibrium, where M is the heavy metal (Zn or Cu):
(2x-2)-
-2
M+2
(aq) + xC4 H4 O6 (aq) ↔ M(C4 H4 O6 )x
-
(aq)
(x-2)-
M+2
(aq) + xSCN(aq) ↔ M(SCN)x
(aq)
Therefore, the presence of the metal-ion complex in the upper phase can be explained
based on the competition between thiocyanate or tartrate anions for the metal cations, and
the interaction of this complex with the selected non-ionic surfactant, which is the major
3-54
3.-Remediation of Pollutants by Aqueous Two Phase Systems
component in this top layer. On the one hand, taking into account the existing tartrate
interactions, it can be stated that the presence of tartrate-based complex will be intimately
influenced by the standard thermodynamic constant of formation of the metal-tartrate
complex. Thus, the order of the formation constant is Cu+2 (log K = 3) > Zn+2 (log K = 2.7) and
reveals a higher affinity of copper for the tartrate anion (Meites, 1963). This behaviour points
out the higher extraction capacity of Zn, since metal extraction is inversely proportional to the
given formation constant.
On the other hand, it seems that thiocyanate ions coordinate to copper and zinc with the
N end to form tetrahedral complexes, such as [Zn(NCS)4]−2 and [Cu(NCS)4]−2. In this sense, many
authors (Rodrigues et al., 2008; Shibukawa et al., 2001) have converged upon the idea that
these complexes are present exclusively in non-aqueous solutions, which would justify its
preferential partition to the surfactant-rich phase where hydrophobic domains exist.
The final stage of this proposal consisted of coupling the proposed ATPS to a previous
dredged sediments washing step. The data obtained were also presented in terms of
extraction capacity E (%), as can be visualized in Table 3.27. The combined heavy metals
remediation strategy yielded total remediation values about 80% or higher for both Zn and Cu,
as can be inferred from the extraction data.
Table 3.27. Extraction capacity E (%) of metals from marine dredged sediments after sequential
treatment.
System
Temp (K)
E (%) Washing
E (%) ATPS
298.15
72.3  0.0
88.8 1.1
343.15
78.0  2.7
89.3 1.8
298.15
85.6  2.7
89.4  0.7
343.15
89.4  8.1
89.3  1.4
298.15
77.0 0.1
85.7  0.8
343.15
84.1 0.1
84.9  2.4
298.15
98.4 0.2
85.3  4.6
343.15
98.4 0.1
88.8  3.5
+2
Zn
Triton X-100 + NaKC4H4O6 + H2O
Tween 20 + NaKC4H4O6 + H2O
+2
Cu
Triton X-100 + NaKC4H4O6 + H2O
Tween 20 + NaKC4H4O6 + H2O
It becomes patent that the use of Tween 20 is always preferred than Triton X-100. This
fact may be explained in terms of the different hydrophobicity of both non-ionic surfactants.
The data obtained demonstrate that thiocyanate-based complexes show a preferential
3-55
3-Remediation of Pollutants by Aqueous Two Phase Systems
interaction for the more hydrophilic Tween 20, in line with the data obtained for the model
systems containing the heavy metals (see Table 3.26). In the wake of the demonstration of the
promising remediation efficiency, a flowsheet of the proposed process is shown in Figure 3.21.
Figure 3.21. Flowsheet of the proposed non-ionic surfactant-based separation process
The presented approach involves different advantages when compared with the EPA
Method 3010 and 3050 recommended for heavy metal extraction. First of all, it is clear that
the use of room temperature do not involve any decline in the metal ions remediation levels
(see Table 3.27), which is advantageous from an economic standpoint. Additionally, the use of
this alternative avoid the use of nitric acid in the washing, which is also beneficial in terms of
environmental and health risks.
In view of the above, the non-ionic surfactant Tween 20 has been proposed to be salted
out with sodium potassium tartrate and using potassium thiocyanate as a complexing agent, in
order to propose a viable metal remediation strategy for marine sediments. This first
contribution tackles just the viability of this separation technique for metal removal, although
a deep study must be undertaken in order to search for an effective second stage to recycle
the selected components. The removal of metals and thiocyanate is not complicated, since
their precipitation could be achieved by just modifying the pH or adding compounds such as
ferric sulfate. In relation to sodium potassium tartrate, there are several strategies that could
be implemented, such as salt recovery by evaporation or reverse osmosis, or even the effluent
disposal in a sewage treatment plant, since this salt is completely biodegradable. Finally,
regarding the non-ionic surfactant, after having used it for several cycles (sediments washing ATPS), the above mentioned treatment for thiocyanate and metals removals should be
applied, and then it could be reused again.
3-56
3.-Remediation of Pollutants by Aqueous Two Phase Systems
REMOVAL OF EMERGING POLLUTANTS
Emerging contaminants are currently gaining social awareness due to their potential
deleterious effects in the environment. Nevertheless, there is still an absence of legislation and
only the Water Framework Directive (2000/60/EC) presents vague guidelines related to the
water policies in the EU. Among the emerging pollutants, non-steroidal anti-inflammatory
drugs (NSAIDs) are the most utilized group of analgesic and anti-inflammatory drugs
worldwide, due to their suitability to treat the pain triggered by common illnesses (Toledo &
Álvarez, 2015). Thus, the last report by the Spanish Ministry of Health stresses that
arylpropionic derivatives are by far the largest used pharmaceuticals (about 65.1 % of the total
drug consumption), being ibuprofen the one with higher intake rate (43.9%) and diclofenac, an
arylacetic acid derivative, the second one (Ministerio de España, 2000-2012).
This scenario has compelled to analyse the possible presence of these compounds in the
environment, as they can be excreted without having been metabolized, particularly ibuprofen
and diclofenac concentration has been detected in the inlet streams of different Waste Water
Treatment Plants (WWTPs) at concentration levels of 516 and 250 ng·L-1, recording less than
50% and 15% of removal in the outlet effluents, respectively (Rivera-Utrilla et al., 2013). Given
the observed limitations of WWTPs, new treatment strategies have been investigated such as
advanced oxidation processes or membrane technologies (Prieto-Rodríguez et al., 2012;
Petrovic et al., 2003). ATPS have emerged as a valuable separation strategy and this method
has demonstrated its capacity for the removal of NSAIDs and estrogens by using ionic liquids
(Silva et al., 2014; Dinis et al., 2015).
Therefore, the use of the ionic liquid N1112OHCl was proposed as salting out agent to
check the versatility of the ATPS investigated previously. In this sense, a non-ionic surfactant
with intermediate hydrophobicity (Tween 80) was used to implement the extraction of the
selected emerging contaminants, ibuprofen and diclofenac, at the lowest and highest
temperatures, 298.15 and 333.15 K. The efficiency of the NSAIDs removal was expressed
beforehand in equation (3.10) where misurfactant and mi is the NSAID mass content in the upper
phase and the total NSAID mass content, respectively. The impact of temperature and feed
concentration on the ibuprofen and diclofenac extraction can be noticed in Figure 3.22. In
general, it becomes patent that very high values of NSAIDs extraction to the top phase (always
greater than 90%) are recorded for the temperature range and feed concentrations employed.
3-57
3-Remediation of Pollutants by Aqueous Two Phase Systems
T = 333.15 K
T = 298.15 K
E (%)
100
Ibuprofen
95
Diclofenac
90
85
80
1
2
3
4
5
6
7
8
9
10
11
12
Feed composition (w1F, w2F)
Figure 3.23. Extraction percentage (E (%)) of ibuprofen () and diclofenac () for different feed
composition in systems Tween 80 + N1112OHCl +H2O at 298.15 and 333.15 K
However, the chemical nature of the contaminant seems to slightly impact the
extraction yields attained, since ibuprofen is generally removed at higher rates than diclofenac
Figure 3.24. This fact may be attributed to the different affinity of the contaminants for the
organic phase. Usually, one way to measure this affinity is by analysing the log K ow values. In
this particular case, log Kow for ibuprofen and diclofenac is 2.48 and 1.90, respectively (Scheytt
et al., 2005) which further demonstrates the higher migration of ibuprofen to the surfactantrich phase.
Ibuprofen
Diclofenac
Figure 3.24. Structures of the NSAIDs
Regarding the effect of N1112OHCl concentration in the feed (Figure 3.23 and Table 3.27)
when fixing the TL, it can be concluded that higher levels of ionic liquid are associated with
slightly lower NSAIDs extraction levels. In this sense, it is also outstanding that the operation at
room temperature does not jeopardize the achievement of high levels of pollutant removal (in
3-58
3.-Remediation of Pollutants by Aqueous Two Phase Systems
some cases even near to 100%), which is a clear operational advantage from an industrial point
of view, as observed in the case of heavy metals extraction. Apart from the abovementioned
benefits, the operation at feed concentrations near to the N1112OHCl vertex involves
contaminant concentration factors greater than 10 without compromising too much the
contaminant migration to the upper phase (E higher than 90%).
Table 3.27. Extraction capacity for Tween 80 (1) + N1112OHCl (2) + H2O (3) at several temperatures.
I
100 w1
I
100 w2
II
100 w1
II
100 w 2
100w2
%E
(Ibuprofen)
%E
(Diclofenac)
72.29
17.21
99.88
99.58
41.23
39.20
98.65
96.77
14.96
57.71
95.25
89.65
59.96
14.97
99.51
99.89
39.47
27.03
99.45
99.18
14.96
40.60
98.06
97.22
71.75
14.33
99.63
98.81
41.21
35.20
98.93
98.72
15.39
52.75
98.19
95.94
40.16
14.48
98.72
98.42
23.02
23.25
99.24
99.12
11.39
29.27
98.45
97.20
F
100w1
F
T = 298.15 K
95.23
72.35
0.95
8.09
0.18
0.35
68.61
49.43
T = 333.15 K
92.04
46.93
0.96
11.86
0.20
0.35
64.33
35.84
The proposed alternative could be suitably implemented for the removal of emerging
pollutants from an aqueous effluent. The process flowsheet diagram (Figure 3.25) integrates
this one-step separation strategy after a NSAIDs-polluted soil washing stage ,using an aqueous
solution of Tween 80 (5%) as solubilizing agent (point 1 in the Figure). N1112OHCl should be
added up to the concentration marked as 2 in the phase diagram is attained, leading to a an
upper phase where more than 90% of ibuprofen and diclofenac have migrated and
concentrated more than 10 times in a phase almost exclusively formed by Tween 80 (95%).
Given the interest of these data, the process should be optimized in order to analyze the
reusability of both Tween 80 and N1112OHCl. All in all, this novel process allows a one stepremoval of two of the most common emerging contaminants, which is competitive when
compared with two recent processes recently reported requiring two or even three combined
techniques (chemical, physical and biological) to yield similar levels of NSAIDs removal (Ibáñez
et al., 2013; Ávila et al., 2015).
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3-Remediation of Pollutants by Aqueous Two Phase Systems
Water
Water
0
1
100
10
90
20
80
30
70
40
60
50
50
60
3
40
>10 x
70
5x
30
2.5 x
80
20
2
1.3 x
90
10
4
100
Tw 80
Tween 80
0
0
10
20
30
40
50
60
70
80
90
100
Choline Chloride
N1112OHCl
Choline Chloride
Washing solution
2
Ibu+Dcf-polluted soil
4
Tween 80 +Ibu+Dcf
Clean soil
Water+Choline Chloride
P-1 / WSH-101
Soil Washing
Ibu+Dcf-polluted stream
1
P-2 / MSX-101
3
Mixer-Settler Extraction
Figure 3.25. Flowsheet diagram for the aqueous biphasic system-based removal of ibuprofen and
diclofenac from waste effluents obtained after soil washing with aqueous solution of Tween 80 (5%).
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3.-Remediation of Pollutants by Aqueous Two Phase Systems
3.5 CONCLUSIONS
Throughout this chapter, the viability of non-ionic surfactant-based ATPS using several
salting-out agents such as ionic liquids, inorganic and organic salts was tackled. Representative
examples of these systems were applied for the removal of pollutants of different nature like
dyes, PAHs, heavy metals or drugs. It was concluded that:
 The ability of imidazolium and cholinium cations and inorganic and organic salts for
achieving liquid-liquid demixing in aqueous solutions of non-ionic surfactants
polyethoxylated sorbitan (Tween) and polyoxyethylene octylphenol (Triton) families.
 The solubility and TL data were suitably correlated with four parameters-based
equations and Othmer-Tobias model, respectively.
 The immiscibility region was strongly influenced by both temperature and hydrophobic
character of the ionic liquid and the non-ionic surfactant.
 The Hofmeister series, the Gibbs free energy of hydration (∆hydG), the molar hydration
entropy (∆hydS) and the Jones-Dole viscosity B-coefficients allowed to corroborate the
following sequence for cations (K+ > NH4+) and with respect to inorganic anions (PO4-3 >
HPO4-2 > CO3-2 > SO3-2 > SO4-2) and for organic anions ((C6H5O7)-3 > (C2O4)-2 > (C4H4O6)-2).
 The higher extraction efficiency by means of ATPS for a model system achieved for
dyes (>93%) and PAHs (>80%) no matter the potassium organic salts used. The
feasibility of the process was demonstrated in effluents released from biological
treatments using complex and synthetic biotreatment medium, demonstrating the
suitability of this physical technique to be coupled after biological stages in sewage
treatment plants.
 The extraction efficiency in a metal removal process after a sediment washing step and
a subsequent ATPS concentration stage allowed removal levels higher than 90% for Zn
and 80% for Cu.
 The potential of a truly biocompatible platform for the remediation and concentration
of emerging contaminants such as ibuprofen and diclofenac from polluted effluents
obtained after a soil washing strategy has been demonstrated by using choline
chloride (N1112OHCl).
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3-Remediation of Pollutants by Aqueous Two Phase Systems
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3-78
4. CONCLUSIONS
4.-Conclusions
CONCLUSIONS
The main conclusions obtained in the development of this PhD thesis are summarized
below:
4.1 IN RELATION TO THE BIOLOGICAL METHODS TO REMOVE POLLUTANTS
First, the preliminary screening of microorganisms from extreme biotopes thriving under
the presence of common families of ionic liquids pointed to Ps. stutzeri as a suitable candidate
for bioremediation of recalcitrant pollutants. The response of this microorganism to the
presence of these neoteric contaminants after a two month-period of acclimation in a batch
bioreactor led to the production of a biopolysaccharide.
Second, the versatility of this acclimated bacterium for dye removal was checked, as a
prior step to propose it in a simultaneous biotreatment of dyes and PAHs. Two model reactive
dyes (RB5 and AB48) were checked both independently and mixed. The biological process was
satisfactorily scaled-up, and values higher than 75% were attained in less than 2 days for both
dyes individually and mixed at small scale. Additionally, 80% of removal was reached in less
than 1 day at stirred tank bioreactor.
Third, this adapted bacterium was suitable proposed for the biotreatment of an effluent
polluted with a model dye and three model PAHs. A suitable medium composition and the
optimum operating conditions of pH, temperature and agitation (7.0, 310.5K and 146 rpm,
respectively) were determined after RSM optimization, and remediation levels higher than
60% were obtained. The validity of these conditions was checked at flask and bioreactor scale
and the kinetics behavior of the pollutants removal were elucidated.
Finally, the simulation of this one-step process applied at larger scale for the remediation
of a 200,000 m3/year-effluent from a leather factory was compared with a conventional twosteps option proving the promising potential of the proposed process in terms of economics
and throughput capacity.
4-3
4.-Conclusions
4.2 WITH REGARD TO REMEDIATION OF POLLUTANTS BY AQUEOUS TWO PHASE SYSTEMS
First, The ability of imidazolium and cholinium cations and inorganic and organic salts for
achieving liquid-liquid demixing in aqueous solutions of non-ionic surfactants polyethoxylated
sorbitan (Tween) and polyoxyethylene octylphenol (Triton) families.
Second, the solubility and TL data were suitably correlated with four parameters-based
equations and Othmer-Tobias model, respectively.
Third, the immiscibility region was strongly influenced by both temperature and
hydrophobic character of the ionic liquid and the non-ionic surfactant.
Fourth, the Hofmeister series, the Gibbs free energy of hydration (∆hydG), the molar
hydration entropy (∆hydS) and the Jones-Dole viscosity B-coefficients allowed to corroborate
the following sequence for cations (K+ > NH4+) and with respect to inorganic anions (PO4-3 >
HPO4-2 > CO3-2 > SO3-2 > SO4-2) and for organic anions ((C6H5O7)-3 > (C2O4)-2 > (C4H4O6)-2).
Sixth, the higher extraction efficiency by means of ATPS for a model system achieved for
dyes (>93%) and PAHs (>80%) no matter the potassium organic salts used. The feasibility of the
process was demonstrated in effluents released from biological treatments using complex and
synthetic biotreatment medium, demonstrating the suitability of this physical technique to be
coupled after biological stages in sewage treatment plants.
Seventh, the extraction efficiency in a metal removal process after a sediment washing
step and a subsequent ATPS concentration stage allowed removal levels higher than 90% for
Zn and 80% for Cu.
Eighth, the potential of a truly biocompatible platform for the remediation and
concentration of emerging contaminants such as ibuprofen and diclofenac from polluted
effluents obtained after a soil washing strategy has been demonstrated by using choline
chloride (N1112OHCl).
4-4
5. QUALITY CRITERIA OF PUBLICATIONS
5.-Quality Criteria of Publications
Journal of Chemical Thermodynamics
“Triton X surfactants to form aqueous biphasic systems: experimental and correlation”
(2012) 54, 385-392.
“On the phase behaviour of polyethoxylated sorbitan (Tween) surfactants in the
presence of potassium inorganic salts” (2012) 55, 151-158.
“Phase segregation in aqueous solutions of non-ionic surfactants using ammonium,
magnesium and iron salts” (2014) 70, 147-153.
“Influence of the addition of Tween 20 on the phase behaviour of ionic liquids-based
aqueous systems” (2014) 79, 178-183.
“Aqueous immiscibility of cholinium chloride ionic liquid and Triton surfactants” (2015)
91, 86-93.
Full Journal Title: Journal of Chemical Thermodynamics
Impact factor in 2014: 2.679
ISSN: 0021-9614
Journal Country/Territory: England
Publisher: ACADEMIC PRESS LTD- ELSEVIER SCIENCE LTD
Area & Position in 2014: Q1 in Thermodynamics, 7 of 55
Journal of Chemical Thermodynamics
3
Impact factor
2
1
0
2009
2010
2011
2012
2013
2014
2015
Year
5-3
5.-Quality Criteria of Publications
Industrial & Engineering Chemistry Research
“Environmentally benign sequential extraction of heavy metals from marine sediments”
(2014) 53, 8615-8620.
Full Journal Title: Industrial & Engineering Chemistry Research
Impact factor in 2014: 2.587
ISSN: 0888-5885
Journal Country/Territory: United States
Publisher: American Chemical Society.
Area & Position in 2014: Q1 in Engineering, Chemical, 27 of 134
Industrial & Engineering Chemistry Research
3
Impact factor
2
1
0
2009
2010
2011
2012
Year
5-4
2013
2014
2015
5.-Quality Criteria of Publications
Bioresource Technology
“Novel physico-biological treatment for the remediation of textile dyes-containing
industrial effluents” (2013) 146, 689-695.
“Hybrid sequential treatment of aromatic hydrocarbons-polluted effluents using nonionic surfactants as solubilizers and extractants” (2014) 162 259-265.
“Simultaneous biotreatment of Polycyclic Aromatic Hydrocarbons and dyes in a one-step
bioreaction
by
an
acclimated
Pseudomonas
strain”
(2015)
(In
press,
DOI
10.1016/j.biortech.2015.08.125).
Full Journal Title: Bioresource Technology
Impact factor in 2014: 4.494
ISSN: 0960-8524
Journal Country/Territory: Netherlands
Publisher: Elservier Sci. Ltd
Area & Position in 2014: Q1 in Agricultural Engineering, 1 of 12
Bioresource Technology
6
Impact factor
4
2
0
2009
2010
2011
2012
2013
2014
2015
Year
5-5
5.-Quality Criteria of Publications
RSC Advances
“Ionic liquids and non-ionic surfactants: a new marriage for aqueous segregation” (2014)
4, 32698-32700.
“Microbial adaptation to ionic liquids” (2015) 5, 17379-17382.
“Acclimation to ionic liquids: Enhancing the biotreatment potential of a Pseudomonas
strain”. (Under review)
Full Journal Title: RSC Advances
Impact factor in 2014: 3.840
ISSN: 2046-2069
Journal Country/Territory: England
Publisher: Royal Society of Chemistry
Area & Position in 2011: Q1 in Chemistry, Multidisciplinary, 33 of 157
RSC Advances
4
Impact factor
3
2
1
0
2009
2010
2011
2012
Year
5-6
2013
2014
2015
5.-Quality Criteria of Publications
Separation and Purification Technology
“A biocompatible stepping stone for the removal of emerging contaminants”. (2015)
153, 91-98.
Full Journal Title: Separation and Purification Technology
Impact factor in 2014: 3.091
ISSN: 1383-5866
Journal Country/Territory: Netherlands
Publisher: ELSEVIER SCIENCE BV
Area & Position in 2014: Engineering, Chemical 16 of 134.
Separation and Purification Technology
4
Impact factor
3
2
1
0
2009
2010
2011
2012
2013
2014
2015
Year
5-7
5.-Quality Criteria of Publications
5-8
5.-Quality Criteria of Publications
PUBLICATIONS AND PATENTS IN ANNEX
 ANNEX 1. MICROBIAL ADAPTATION TO IONIC LIQUIDS (RSC ADVANCES, 2015, 5:
17379-17382.
 ANNEX 2. ACCLIMATION
POTENTIAL
OF A
TO IONIC
LIQUIDS: ENHANCING
THE
BIOTREATMENT
PSEUDOMONAS STRAIN (RSC ADVANCES, 2015, UNDER
REVIEW).
 ANNEX 3: SIMULTANEOUS BIOTREATMENT
HYDROCARBONS
DYES
AND
IN A
OF
POLYCYCLIC AROMATIC
ONE-STEP BIOREACTION
BY AN
ACCLIMATED
PSEUDOMONAS STRAIN (BIORESOURCE TECHNOLOGY, 2015, ACCEPTED
FOR
PUBLICATION).
 ANNEX 4: IONIC LIQUIDS AND NON-IONIC SURFACTANTS: A NEW MARRIAGE FOR
AQUEOUS SEGREGATION (RSC ADVANCES, 2014, 4: 32698-32700).
 ANNEX 5: AQUEOUS IMMISCIBILITY OF CHOLINIUM CHLORIDE IONIC LIQUID
TRITON SURFACTANTS (JOURNAL
OF
AND
CHEMICAL THERMODYNAMICS, 2015, 91:
86-93).
 ANNEX 6: A BIOCOMPATIBLE STEPPING STONE FOR THE REMOVAL OF EMERGING
CONTAMINANTS (SEPARATION AND PURIFICATION TECHNOLOGY, 2015, IN PRESS
DOI 10.1016/J.SEPPUR.2015.08.039).
 ANNEX 7: TRITON X SURFACTANTS
EXPERIMENTAL
AND
TO
FORM AQUEOUS BIPHASIC SYSTEMS:
CORRELATION (JOURNAL
OF
CHEMICAL THERMODYNAMICS,
2012, 54: 385-392).
 ANNEX 8: ON THE PHASE BEHAVIOUR OF POLYETHOXYLATED SORBITAN (TWEEN)
SURFACTANTS
IN THE
PRESENCE
OF
POTASSIUM INORGANIC SALTS (JOURNAL
OF
CHEMICAL THERMODYNAMICS, 2012, 55: 151-158).
5-9
5.-Quality Criteria of Publications
 ANNEX 9: NOVEL
PHYSICO-BIOLOGICAL
TREATMENT
FOR THE
REMEDIATION
OF
TEXTILE DYES-CONTAINING INDUSTRIAL EFFLUENTS (BIORESOURCE TECHNOLOGY,
2013, 146: 689-695.
 ANNEX 10: HYBRID SEQUENTIAL TREATMENT
OF
AROMATIC HYDROCARBONS-
POLLUTED EFFLUENTS USING NON-IONIC SURFACTANTS
AS
SOLUBILIZERS
AND
EXTRACTANTS (BIORESOURCE TECHNOLOGY, 2014, 162: 259-265).
 ANNEX 11: PHASE SEGREGATION
IN
AQUEOUS SOLUTIONS
SURFACTANTS USING AMMONIUM, MAGNESIUM
OF
AND IRON SALTS
NON-IONIC
(JOURNAL
OF
CHEMICAL THERMODYNAMICS, 2014, 70: 147-153.
 ANNEX 12: INFLUENCE OF THE ADDITION OF TWEEN 20 ON THE PHASE BEHAVIOUR
OF
IONIC LIQUIDS-BASED AQUEOUS SYSTEMS (JOURNAL
OF
CHEMICAL
THERMODYNAMICS, 2014, 79: 178-183).
 ANNEX 13: ENVIRONMENTALLY BENIGN SEQUENTIAL EXTRACTION
METALS
FROM
OF
HEAVY
MARINE SEDIMENTS (INDUSTRIAL & ENGINEERING CHEMISTRY
RESEARCH, 2014, 53: 8615-8620).
 ANNEX 14: PROCESO INTEGRADO
DE
REMEDIACIÓN
AROMÁTICOS POLICÍCLICOS MEDIANTE COMBINACIÓN
DE
DE
HIDROCARBUROS
BIODEGRADACIÓN
Y
SISTEMAS ACUOSOS BIFÁSICOS (SPANISH PATENT, APPLICATION NUMBER
201301068, 2013).
5-10
ANNEX 1
MICROBIAL ADAPTATION TO IONIC LIQUIDS (RSC ADVANCES, 2015, 5: 17379-17382).
RSC Advances
COMMUNICATION
Microbial adaptation to ionic liquids†
Cite this: RSC Adv., 2015, 5, 17379
M. S. Álvarez, A. Rodrı́guez,* M. A. Sanromán and F. J. Deive*
Received 12th September 2014
Accepted 29th January 2015
DOI: 10.1039/c4ra10283e
www.rsc.org/advances
One out of 10 microorganisms from extreme locations was adapted to
the presence of common families of ionic liquids, which have lately
emerged as “contaminants on the horizon”. A 10-fold higher tolerance
was concluded for the ionic liquid-resistant strain. A biopolymer was
secreted as an adaptation response.
Over the last years, environmental concerns have highlighted
the extensive and increasing importance of implementing
industrial processes using greener solvents. In particular, the
replacement of volatile organic compounds with ionic liquids
has set the pace for the achievement of truly revolutionary
processes. Although the low vapour pressure1 of ionic liquids
may reduce the air pollution with respect to the typical volatile
organic compounds, some of them show a high solubility in
water, thus becoming persistent pollutants in both the aquatic
and the soil environment.2 These neoteric solvents can be
considered as new emerging contaminants since they are
already used with some extant processes at an industrial scale in
companies such as BASF (BASIL, aluminium plating, cellulose
dissolution), and the annual production of some of them
surpasses the ton magnitude. Accordingly, ionic liquids are
assumed to gain environmental relevance and they have
recently been reported as “contaminants on the horizon”.3
Therefore, the proposal of efficient methods for their
removal falls into one of the priorities established in the current
global environmental water policies. Among the existing alternatives for pollutants remediation, biological methods stand
out as more environmentally sustainable ones and they bear a
rather positive social image, instead of their chemical and
physical counterparts. Up to date, most biological assays for
ionic liquids as pollutants have been dened under static
laboratory conditions and with the same type of microorganisms that despite their importance are usually unrealistic,
failing to reproduce the numerous abiotic and biotic processes
occurring in the environment. In general, the studies of environmental fate and toxicity of ionic liquids have shown that the
most common families present a considerable toxicity, which
varies across organisms and trophic levels.4 Generally speaking,
different ionic liquids have been reported to be highly toxic to
microorganisms due to different mechanisms: be it through
increase in osmotic pressure, a modication of membrane
uidity and structure, or an alteration of enzymatic activity.5
Since ionic liquids pose a breakthrough in the chemical
industry, the hunt of novel bacterial strains and/or engineered
existing strains for ionic liquid tolerance is critical. One solution to this problem could be placed in extreme microorganisms, which would play a role as “ionic liquids-metabolizers”.
Our preliminary data6 allowed us to conclude that the environmental pressure caused by high petroleum hydrocarbon
load and, to a lesser extent, by high-salinity in soil, augmented
the microbial capacity to actively grow or to survive short or long
periods of exposure to ionic liquids. Starting from this premise,
we have bet in this kind of microorganisms as viable candidates
for ionic liquids bioremediation. With this aim, several
commercial families of ionic liquids have been proposed as
chemical pressure in the culture medium to select the most
promising microbial strain in terms of ionic liquid endurance.
Their structure is shown in Fig. 1.
Department of Chemical Engineering, University of Vigo, 36310, Vigo, Spain. E-mail:
[email protected]; [email protected]; Fax: +34 986812383; Tel: +34 986812312
† Electronic supplementary
10.1039/c4ra10283e
information
(ESI)
This journal is © The Royal Society of Chemistry 2015
available.
See
DOI:
Fig. 1
Structure of ionic liquids used.
RSC Adv., 2015, 5, 17379–17382 | 17379
RSC Advances
Considering the basic denition of ionic liquids as molten
salts it makes sense to test the response of marine bacteria like
Shewanella oneidensis and Halobacterium salinarum as representative halotolerant microorganisms. In relation to the ionic
liquids role as organic compounds, Staphylococcus warneri,
Pseudomonas stutzeri, and Consortium C26b are also interesting
since they are bacteria commonly found in industrial polluted
areas.7,8 Moreover, thermophilic microorganisms are getting
increasing attention in biotechnology due to the fact that their
enzymes are better suited to operate under harsh industrial
processes. For this reason, Anoxybacillus avithermus and
Thermus thermophilus HB27 were chosen as representative
thermophiles to analyse their tolerance to the presence of ionic
liquids. Finally, two white-rot fungi with demonstrated capacity
to degrade persistent contaminants were also included in this
initial screening: Phanerochaete chrysosporium and Trametes
versicolor. Their growth curves in the absence of ionic liquids
are shown in ESI (Fig. S2 and S3†).
The ionic liquids toxicity was evaluated by means of their
minimal inhibitory concentration (MIC) and minimal lethal
concentration (MLC), through microorganisms cultivation in
96-well plates in mineral medium supplemented with glucose
as carbon source (10 g L1), ionic liquids concentrations 0.005,
0.010, 0.025, 0.05, 0.1, 0.2, 0.5, 1.0, and 1.5 M, and the growth
was monitored by UV spectrometry at 600 nm. Although no
differences were observed for the MIC values, the analysis of the
MLC data (listed in Table 1) conrmed that the microbial agents
obtained from polluted locations (P. stutzeri, St. warneri and
Consortium C26b) and the marine bacteria (S. oneidensis) show
a higher resistance to thrive under the pressure of these
neoteric solvents. The hypothesis that both hydrocarbon load
and salinity could improve the possibilities of survival is thus
conrmed, in agreement with our previous ndings.6 The
analysis of the selected cations in terms of toxicity reveals that
phosphonium is the one leading to a greater lethal effect. The
information coming from the literature about the hazards of
this family is still scarce and not conclusive, although the initial
data provided by Coutinho and coworkers allow conrming our
results.9 In relation to the anion, a slightly higher toxicity of the
[C1SO4] is observed. This seems to contradict the statement that
a longer alkyl chain leads to higher toxicity.10 Nonetheless, it
should be noted that the rst member of a family is usually an
Communication
outlier (not following an extrapolation of the trend presented by
the others), so that could explain this behaviour.
The comparison of the MLC values obtained with relevant
literature data reveals that both the microbial agents obtained
from polluted and marine locations are highly resistant to the
studied ionic liquids, since concentration levels up to 1 M are
tolerated. These values are higher to those reported in literature11 for model bacteria and yeasts. Additionally, these
microorganisms were able to survive at concentrations almost
similar to those reported for the most biocompatible ionic
liquids based on cholinium cations.12 It is necessary to highlight that P. stutzeri was the bacterium leading to the highest
values of biomass under the pressure of ionic liquids. Therefore, this bacterium was selected as a viable candidate for an
acclimation process. Aer two months in a lab-scale bioreactor
in the presence of [C2C1im][C2SO4] (200 mM), under controlled
agitation, aeration and temperature, the microbial biomass was
collected to further investigate the existence of some kind of
acclimation. The analysis of this strain revealed MLC levels one
order of magnitude higher for imidazolium and pyridinium
cations, and 2 times higher for phosphonium-based ionic
liquid. Additionally, cell concentration data (shown in Table 2,
and graphically represented in ESI in Fig. S4 to S11†) allow
concluding very high values for the adapted P. stutzeri, no
matter the culture medium used (both rich and mineral). This is
advantageous because the use of a mineral medium is preferred
to approach future studies of bioremediation.
Hence, the results obtained suggest that acclimation is
taking place, which can be due to a phenotypic and/or genetic
change. Up to date, no information has appeared in the literature indicating the viability of ionic liquid adaptation of
microorganisms. It is also interesting to notice that the adaptation of P. stutzeri to imidazolium-based ionic liquids involved
and acquired resistance to the stress of other commercially
available ionic liquid families. Notwithstanding the fact that the
specic mechanisms of toxicity are currently not wellunderstood, there are several research lines that point to
different strategies to unravel the microbial response to the
Table 2 Microbial growth of the selected microorganisms at
maximum ionic liquid concentration in mineral (MM) and rich medium
(RM). () no growth; (+) A600 ¼ 0.1–0.4; (++) A600 ¼ 0.4–0.6; (+++)
A600 > 0.6
[C2Py][C2SO4]
MLC values of the selected microorganisms under the pressure of different ionic liquids. White colour indicates no growth in the
tested range, and grey colour shows growth
Table 1
P. s.
P. s. a
S. o.
St. w.
C26b
H. s.
P. c.
T. v.
T. t.
A. f.
17380 | RSC Adv., 2015, 5, 17379–17382
[C2C1im][C1SO4]
[C2C1im][C2SO4]
[P4441][C1SO4]
MM
RM
MM
RM
MM
RM
MM
RM
+++
+++
+++
+++
++
++
+++
+++
++
++
+
+
+
+
++
++
+++
++
++
++
++
+++
+++
+++
++
++
++
++
+
+
++
+++
++
++
++
++
+++
+++
+++
+++
+
+++
+++
+
+
+
++
+
++
+
+
++
++
+++
++
++
+
+
++
This journal is © The Royal Society of Chemistry 2015
Communication
RSC Advances
biopolysaccharides also confers special advantages for the
formation of biolms, which allow a higher withstanding to
nutrient deprivation, pH changes, or contaminants charge
swings.15,16 Thus, the presence of these biopolymers could be
benecial for biosorption, bioaccumulation or biomineralization strategies.17
Visual aspect of P. stutzeri wild (left) and adapted (right) in the
presence of ionic liquids.
Fig. 2
SEM images of wild (left) and ionic liquids-adapted (right)
P. stutzeri.
Conclusions
In this work, a preliminary screening among microorganisms
from extreme biotopes (high temperature, hydrocarbon load
and salinity) allowed conrming the suitability of extreme
microorganism from locations with both high salt and hydrocarbon charge to thrive under the presence of common families
of ionic liquids. The existence of microbial acclimation to the
presence of ionic liquids was demonstrated for the rst time by
a two-month cultivation of P. stutzeri in a stirred tank bioreactor. Finally, the production of a biopolysaccharide was the
permanent response obtained to the continuing exposure to the
presence of ionic liquids.
Fig. 3
presence of ionic liquids, such as the modication of
membrane permeability, enzyme detoxication, or the
synthesis of metabolites allowing the entrapment of the
contaminant, both extracellular- and intracellularly.13 In this
sense, the ionic effect related to the presence of the ionic liquid
in aqueous solutions should also be taken into account, since it
could promote the observed microbial toxicity.2 In this particular case, it becomes patent that the adaptation entails a clear
visual change in the culture broth, as illustrated in Fig. 2. The
formation of a biopolymer aer 24 h of cultivation of the
adapted P. stutzeri is evident. This response has been found to
be one of the ways to protect the microbial communities from
environmental stresses.14 In this particular case, the obtained
biopolymer turned out to be a polysaccharide mainly composed
by glucose, as elucidated from HPLC analysis (see experimental
details and chromatogram Fig. S4 in ESI†).
Thus, the exible nature of prokaryotic gene expression
conferred a greater acclimation to the presence of different
families of ionic liquids, by means of exopolysaccharide
synthesis. The analysis of the wild strain of P. stutzeri and that
adapted to the presence of ionic liquids by means of SEM
microscopy (Fig. 3) makes it evident the presence of this polymer entrapping bacterial cells.
It should be noted that the polysaccharide expression is
maintained even though the ionic liquid is removed from the
media, which points to an alteration at the gene level. Therefore, further investigation of a global bacterial response at the
transcriptome level could shed light on the understanding of
the adaptation strategies followed by microorganisms to the
presence of these emerging neoteric contaminants, and must
be unavoidably tackled in future works. The synthesis of
This journal is © The Royal Society of Chemistry 2015
Notes and references
1 M. J. Earle, J. M. S. S. Esperança, M. A. Gilea, J. N. Canongia
Lopes, L. P. N. Rebelo, J. W. Magee, K. R. Seddon and
J. A. Widegren, Nature, 2006, 439, 831.
2 M. Petkovic, K. R. Seddon, L. P. N. Rebelo and C. S. Pereira,
Chem. Soc. Rev., 2011, 40, 1383.
3 S. D. Richardson and T. Ternes, Anal. Chem., 2011, 83, 4614.
4 J. Ranke, S. Stolte, R. Störmann, J. Arning and B. Jastorff,
Chem. Rev., 2007, 107, 2183.
5 (a) T. P. Pham, C. W. Cho and Y. S. Yun, Water Res., 2010, 44,
352; (b) A. Romero, A. Santos, J. Tojo and A. Rodrı́guez, J.
Hazard. Mater., 2008, 151, 268; (c) K. M. Docherty and
C. F. Kulpa, Green Chem., 2005, 7, 185.
6 F. J. Deive, A. Rodrı́guez, A. Varela, C. Rodrigues,
M. C. Leitão, J. A. M. P. Houbraken, A. B. Pereiro,
M. A. Longo, M. A. Sanromán, R. A. Samson,
L. P. N. Rebelo and C. S. Pereira, Green Chem., 2011, 13, 687.
7 F. Moscoso, I. Teijiz, F. J. Deive and M. A. Sanromán,
Bioresour. Technol., 2012, 119, 270.
8 F. Moscoso, F. J. Deive, M. A. Longo and M. A. Sanromán,
Bioresour. Technol., 2012, 104, 81.
9 S. P. M. Ventura, C. S. Marques, A. A. Rosatella,
C. A. M. Afonso, F. Gonçalves and J. A. P. Coutinho,
Ecotoxicol. Environ. Saf., 2012, 76, 162.
10 M. Markiewicz, M. Piszora, N. Caicedo, C. Jungnickel and
S. Stolte, Water Res., 2013, 47, 2921.
11 J. Pernak, K. Sobaszkiewicz and I. Mirska, Green Chem., 2003,
5, 52.
12 M. Petkovic, J. Ferguson, A. Bohn, J. Trindade, I. Martins,
M. B. Carvalho, M. C. Leitao, C. Rodrigues, H. Garcia,
R. Ferreira, K. R. Seddon, L. P. N. Rebelo and C. Silva
Pereira, Green Chem., 2009, 11, 889.
13 J. I. Khudyakov, P. D'haeseleer, S. E. Borglin,
K. M. DeAngelis, H. Woo, E. A. Lindquist, T. C. Hazen,
RSC Adv., 2015, 5, 17379–17382 | 17381
RSC Advances
B. A. Simmons and M. P. Thelen, Proc. Natl. Acad. Sci. U. S. A.,
2012, 14, E2173.
14 H. C. Flemming and J. Wingender, Water Sci. Technol., 2001,
43, 1.
15 M. Koutinas, J. Martin, L. G. Peeva, A. Mantalaris and
A. G. Livingston, Environ. Sci. Technol., 2006, 40, 595.
17382 | RSC Adv., 2015, 5, 17379–17382
Communication
16 M. Koutinas, I. I. R. Baptista, L. G. Peeva, J. R. M. Ferreira
and A. G. Livingston, Biotechnol. Bioeng., 2007, 96, 673.
17 R. Singh, D. Paul and R. K. Jain, Trends Microbiol., 2006, 14,
389.
This journal is © The Royal Society of Chemistry 2015
ANNEX 2
ACCLIMATION
TO IONIC
LIQUIDS: ENHANCING
THE
BIOTREATMENT POTENTIAL
PSEUDOMONAS STRAIN (RSC ADVANCES, 2015, UNDER REVIEW).
OF A
RSC Advances
Acclimation to ionic liquids: Enhancing the biotreatment
potential of a Pseudomonas strain
Journal:
RSC Advances
Manuscript ID:
Draft
Article Type:
Paper
Date Submitted by the Author:
Complete List of Authors:
n/a
Alvarez, Maria; University of Vigo,
Deive, Francisco; University of Vigo, ; Instituto de Tecnologia Química e
Biologica,
Sanromán, María; University of Vigo, Chemical Engineering Department
Rodriguez, Ana; University of Vigo, Chemical Engineering Department
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Acclimation to ionic liquids: Enhancing the biotreatment potential
of a Pseudomonas strain
Received 00th January 20xx,
Accepted 00th January 20xx
DOI: 10.1039/x0xx00000x
www.rsc.org/
María S Álvarez, Francisco J Deive*, M. Ángeles Sanromán, and Ana Rodríguez*
An ionic liquid-adapted Pseudomonas strain was satisfactorily proposed for the removal of two synthetic dyes widely
found in waste water effluents from textile industry: Acid Black 48 (AB48) and Reactive Black 5 (RB5). Very promising
results were obtained when the process was performed at small scale, since remediation values higher than 75% were
attained in less than 2 days for both dyes individually and mixed. The viability at higher scale (stirred tank bioreactor) was
guaranteed, as 80% of removal was reached in less than 1 day, which confirmed the suitability of the selected
hydrodynamic conditions. These results allow exploiting the presence of ionic liquids to induce an improvement of the
bioremediation potential of a well-known Pseudomonas strain. The experimental data obtained from the biological
treatment were kinetically characterised with the purpose to lay the foundations for the implementation of the bioprocess
at real scale.
8
Introduction
Nowadays, an increasing number of hazardous organic
compounds are being discharged into the environment. More
specifically, the textile industry generates polluted aqueous
streams containing different contaminants, namely,
surfactants, acids or bases, aromatics, heavy metals, salts,
suspended solids and dyes.1 The latter usually have a synthetic
origin and complex aromatic molecular structures, which make
them highly stable and recalcitrant. These molecules are
classified according to several features, but one typical
consideration is related to chromophore group.2 The most
common group of direct dyes is the azo-type, which makes up
to (60-70) % 3 of all dye waste produced, followed by the
anthraquinone type, which are extensively used for green,
4,5
blue or violet hues. Reactive dyes cannot be easily removed
by conventional waste water treatment systems because they
are stable to light, heat and oxidizing agents and display low
biodegradability. Therefore, they have been identified as
persistent compounds in textile effluents, and their impact and
6
toxicity has been addressed in numerous researches.
Hence, the search of efficient alternatives allowing the
bioremediation of this kind of polluted effluents is a subject in
the limelight. Various physico-chemical and biological
processes have been employed to remove dyes from industrial
7
waste water, for instance, ozonation, adsorption on activated
9
carbon or other adsorptive materials, electrochemical,
10
11
flocculation
and nanofiltration, but these are sometimes
inefficient, costly and not adaptable to a wide range of dye
12
13
waste water. Biological processes, such as biodegradation,
14
bioaccumulation and biosorption offer attractive options for
dye remediation due to many microorganisms such as
bacteria, yeasts, algae and fungi are able to accumulate and
15,16
degrade different dyes,
and may represent a low-cost and
environmentally friendly alternative.
Among the microbial candidates, strains belonging to
Pseudomonas genus have been highlighted in different
17
research works as viable bioremediation agents. In this line,
we have previously demonstrated the potential of
Pseudomonas stutzeri for the removal of different persistent
contaminants such as metal working fluids, pesticides or
18-20
polycyclic aromatic hydrocarbons.
However, its potential
for dyes removal was extremely low, so we have bet in
acclimation as a suitable strategy for improving its
21
bioremediation ability, as demonstrated by other authors. In
order to widen the applicability of this bacterium, imidazoliumbased ionic liquids have been chosen since they make up a
group of neoteric contaminants with recalcitrant moieties like
the nitrogen heteroatom inserted in the aromatic ring and
22
displaying high toxicity. Then, after a two-month period of
acclimation in a stirred tank bioreactor containing the ionic
liquid 1-ethyl-3-methylimidazolium ethylsulfate (C2C1imC2SO4),
23
the obtained strain was selected for assessing its potential
as dye bioremediation agent.
In this work, we have hypothesised the suitability of an
adapted P. stutzeri to enhance its bioremediation capacity
when applied to aqueous effluents polluted with two model
anthraquinone and azo dyes (AB48 and RB5). The dyes
remediation strategy followed a bottom-up methodology,
J. Name., 2013, 00, 1-3 | 1
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both from the contaminant and operation point of view: after
demonstrating the viability of the process at small scale
(shaken flasks) and with individual dyes, the operation was
performed at bench scale bioreactor and with a mixture of
dyes. The main text of the article should appear here with
headings as appropriate.
120ºC for 20 min. They were sterilised by filtration through a
20 µm filter prior to the addition to the autoclaved medium in
order to avoid any possible alteration of the chemical structure
of the dyes. The flasks were inoculated (3% v/v) with
previously obtained cell pellets, which were them incubated in
an orbital shaker (Thermo Fisher Scientific 496) at 37ºC and
150 rpm.
Experimental section
Operation at bioreactor scale
Chemicals
The dyes RB5 and AB48, which structure and main
characteristics are shown in Table 1, have been purchased
from Sigma-Aldrich. Glucose was obtained from Scharlau.
Microorganisms
Table 1
A 2-L stirred tank bioreactor (BIOSTAT®B-MO) was used for
the scaling up of the process. Temperature was maintained at
37ºC by circulation of thermostated water, and the agitation
rate was set at 200 rpm. The initial pH was adjusted to 7.2.
Firstly, cells were grown from 12 h in flask cultures (3% v/v)
and subsequently were used to inoculate it. Air was supplied
continuously at 0.17 vvm.
Analytical methods
Characteristics of the dyes used
Dye
Class
Structure
C.I.
λmax
O
NaO S
O
O
O
S
O
RB5
Di-azo
N N
O
ONa
S
O
HO
H2N
O
S
O
S
NaO
AB 48
O
O
20505
597
65005
663
O
N N
S
O
ONa
O
Anthraquinone
Biomass determination
Cells were harvested by centrifugation (10 min, 9300 g, and
4ºC), and the supernatant was reserved for decolorisation
analysis. Biomass concentration was measured by turbidimetry
at 600 nm in a UV-vis spectrophotometer (UV-630 Jasco), and
the obtained-values were converted to grams of cell dry
weight per litre using a calibration curve.
Dyes decolorisation
C.I. Color Index. λmax. (nm) Wavelength for maximum absorption
Bacterium Pseudomonas stutzeri CECT 930 was obtained
from the Spanish Type Culture Collection (ATCC 17588). This
bacterium was acclimatised for two months in a lab-scale
bioreactor in the presence of C2C1imC2SO4 (0.2mM) under
controlled agitation, aeration and temperature as previously
23
reported.
Dyes concentrations (both independently and mixed) in the
culture media were analysed by UV-vis spectrophotometry
taking into account the maxima obtained for each dye (597 nm
for RB5, 663nm for AB48 and from 547 to 713 nm for mixture
of dyes, calculated by measuring the area under the plot).
Decolorisation (D) was expressed in terms of percentage units
by using the expression:
D (% removal) = (Ii-If)·100/Ii
Dyes decolorisation assays at different scales
Bioremediation medium
-1
Minimal medium (MM) was used, composed of (g L in
distilled water): Na2HPO4·2H2O 8.5, KH2PO4 3.0, NaCl 0.5,
NH4Cl 1.0, MgSO4·7H2O 0.5, CaCl2 0.0147. MM also contained
trace elements as follows (mg L-1 in distilled water): CuSO4 0.4,
KI 1.0, MnSO4·H2O 4.0, ZnSO4·7H2O 4.0, H3BO3 5.0, FeCl3·6H2O
2.0. 10 g L-1 of glucose was also included in the culture medium
as carbon source.
Operation at small scale
The biotreatment at small scale was carried out in 250 mL
Erlenmeyer flasks with 50 mL of MM. The pH was initially
adjusted to 7.2 and the MM without dyes was autoclaved at
where Ii and If are initial and final concentration of the dye
solution, respectively.
Each decolorisation value was the mean of two parallel
experiments. Abiotic controls (without microorganisms) were
always included. The assays were done in duplicate, and the
experimental error was less than 3%.
Results and discussion
The outstanding capacity of P. stutzeri to be used as
remediation agent in different kind of recalcitrant
contaminants, going from pure organic compounds like PAHs
to hybrid chemicals like organophosphate pesticides, has been
18,19
stressed in previous research works.
Moreover, it was
demonstrated that this bacterium possesses a remarkable
adaptation capacity, since the presence of organic
2 | J. Name., 2012, 00, 1-3
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15
100
A
12
75
9
50
6
Decolorisation (%)
Dyes biotreatment by ionic liquid-adapted P. stutzeri at small
scale
industrial scale should consider the dilution of the effluent to
yield maximum values of dye biotreatment.
-1
Cell Concentration (g L )
contaminants could trigger a permanent alteration at the gene
level, by acquiring a nahH gene (responsible for encoding
24
catechol 2,3-dioxygenase). Therefore, in a previous research
23
work of our group we made use of these features to analyse
the response of the bacterium when a novel class of neoteric
contaminants like the ionic liquids was present, and the
biosynthesis of a polymer was recorded. Taking into account
these facts, a scenario where the adapted bacterium is able to
remediate another kind of pollutants such as anthraquinone
and azo dyes is envisaged.
25
3
The appropriateness of P. stutzeri for the biological
decolorisation of an aqueous stream containing two reactive
dyes such as RB5 and AB 48 (both independently and mixed)
was firstly checked at small scale (shaken flasks). One of the
decisive challenges to be faced when designing a point-source
treatment technology is the existence of sudden changes in
25,26
the dye concentration profiles released by industries.
Actually, these variations may drastically alter the outcomes of
the biological treatment, by inhibiting the microbial activity.
Therefore, as dye concentrations detected in aqueous
effluents from textile industry usually range from 0.01 to 0.2
27
g/L,
the influence of this parameter in the biological
decolorisation was checked for both model dyes, and the
results obtained are presented in Fig 1.
0
0
B
12
75
9
50
6
25
3
0
0
C
12
75
100
9
90
50
Decolorisation (%)
6
80
25
3
70
0
0
0
60
50
0.00
0.05
0.10
0.15
0.20
0.25
-1
Dye concentration (g L )
Fig 1. Decolorisation of AB48 () and RB5 () by ionic liquidadapted P. stutzeri for different dyes concentration.
The results obtained evidence a great decolorisation
efficiency for both dyes (>50%) no matter the concentration
used, which is very encouraging since the strain non adapted
to ionic liquids did not show any remediation capacity. In this
sense, it becomes patent that the operation at concentration
values between 0.03 and 0.06 g L-1 entails very high levels of
dye remediation, up to 90%. Therefore, the operation at
20
40
Time (h)
60
80
Fig 2. Monitoring of cell growth (○) and decolorisaRon (∆) by
ionic liquid-adapted P. stutzeri in aqueous effluents polluted
with: A) AB48, B) RB5, and C) mixture of dyes, at flask scale.
Experimental data are represented by symbols and solid lines
are used for the proposed theoretical models.
Additionally, the monitoring of biomass production and
decolorisation levels (Fig 2) during bioremediation
experiments at the concentration leading to maximum
-1
decolorisation levels (0.04 g L ) for AB48, RB 5 and the mixture
reveals that the stationary phase of growth is reached in less
than one day of treatment both for individual dyes and the
mixture. On the other hand, it becomes patent the ionic liquidadapted P. stutzeri display the highest decolorisation potential
within 48 h, reaching levels over 75%, which points out the
J. Name., 2013, 00, 1-3 | 3
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interest of ionic liquids adaptation as a strategy to get more
versatile microbial remediation agents. In this vein, the
comparison with literature data allows concluding the
suitability of this modified strain, since the remediation
medium used is a synthetic one (with salts and glucose),
contrarily to the fact reported by Deive et al (2010) and
28,29
Barragan et al (2007).
They established the necessity of
adding complex organic sources such as peptone or yeast
extract to treat a dye-polluted effluent to yield similar
decolorisation values, which is disadvantageous to ease
process modelling and simulation or to carry out fundamental
kinetic studies.
Dyes biotreatment by ionic liquid-adapted P. stutzeri at
bench scale bioreactor
15
100
12
80
1.0
60
6
40
3
20
0
0.8
Absorbance
9
Decolorisation (%)
-1
Cell concentration (g L )
Once the suitability of this adapted bacterium was
demonstrated at flask scale, it is necessary to check its viability
when operating at higher scale. In this sense, the feasibility of
the operation at bioreactor entails many considerations like a
suitable mass transfer or optimum operating conditions
allowing an efficient removal of the dye mixture. The viability
of this scale-up was assessed by monitoring the biomass and
decolorisation capacity of the dye mixture (initial
-1
concentration of 0.04 g L of each dye) in a stirred tank
bioreactor, and the results obtained are shown in Fig 3.
sometimes some degree of mechanic stress could be inflicted
by the impeller. However, it seems evident that no important
cell damage is recorded, since a very slight decrease in the
biomass concentration values is observed.
After demonstrating the suitability of the operation at
bioreactor scale, the elucidation of the characteristics of the
remediation process was approached. The reason for the
elevated percentages of decolorisation can be linked with the
nature of the remediation process. In this sense, the
production of a biopolymer by this strain once adapted to the
23
presence of ionic liquids, as reported recently promoted dye
biosorption on the biomass. Additionally, a drastic decrease in
pH values was recorded (from 7.2 to 4.5), which can also help
to increase the dye removal. Thus, the improvement in dyes
biosorption may be explained in terms of the electrostatic
30
interactions between the biomass and the dye structure.
More specifically, the nitrogen-containing functional groups in
proteins and biomass will be easily protonated under acidic
conditions, thus leading to a net positive charge and
consequently furthering an electrostatic attraction with the
negatively charged dye anions. This electrostatic behavior has
been considered to be the primary mechanism concluded for
31,32
the biosorption of different dyes.
Additionally, given the
biosorptive nature of dye decolorisation, the monitoring of the
UV-visible spectra must be tackled in order to demonstrate the
absence of dye in the biotreated effluent. The results shown in
Fig 4 indicate the suitability of the proposed adapted P.
stutzeri, since the absence of the characteristic band for the
dye mixture is detected once the stationary phase of the
decolorisation process is reached.
0.6
0.4
0
0
20
40
60
80
100
Time (h)
Fig 3. Monitoring of cell growth (○) and decolorisation (∆) by
ionic liquid-adapted P. stutzeri in aqueous effluents polluted
with a mixture of dyes at bench scale bioreactor (37ºC, 200
rpm, 0.17 vvm). Experimental data are represented by symbols
and solid lines are used for the proposed theoretical models.
A visual inspection of the experimental data allows
detecting an improvement both in the remediation values and
in the times required to reach the maximum, which is an
important advantage when implementing this biotreatment at
industrial scale. In this sense, it is outstanding that about 80%
of decolorisation is recorded after less than one day. Although
this kind of bioreactor configuration entails advantages like an
efficient control of aeration and agitation, separately,
0.2
0.0
300
400
500
600
700
Wavelengh (nm)
800
900
Fig 4. UV visible spectra of untreated (solid line) and
biotreated (dashed line) effluents polluted with a mixture of
AB48 and RB5.
Modelling experimental data
The technical implementation of the proposed process at
industrial scale requires a deeper knowledge of the
biotreatment kinetics. One of the useful means to get a better
insight into the biological process is the description of the
4 | J. Name., 2012, 00, 1-3
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quantitative relationship between the biomass and the dye
decolorisation at a specific moment of the culture time t (h). A
logistic model has been proposed for the bioremediation of
19,20,28
different contaminants.
In this way, the biomass and the
decolorisation percentage can be defined on the basis of the
initial and maximum biomass and decolorisation rate as
follows:
X max
X=
 


Table 2
Parameters of the logistic model to characterise the kinetic growth and
dye decolorisation of ionic liquids-adapted P. stutzeri at flask and
bioreactor scale
Dye
X max
−1  − µm t 
 ln 

 X0

D
1+ e
max
  Dmax 

−1  − µ D t 
 ln 
  D0


R2
σ
RB5
0.09
6.40
0.39
0.98
0.35
0.14
6.29
0.41
0.99
0.23
RB5+AB48
0.06
6.76
0.44
0.98
0.40
Bioreactor scale
RB5+AB48
where X and D are the biomass (g L ) and dye decolorisation
(%), X0 and D0 are the initial biomass and decolorisation, Xmax
-1
(g L ) and Dmax are the maximum biomass and decolorisation,
and µm and µD are the maximum specific growth rate and
-1
maximum specific remediation rate (h ).
The fitting of experimental data to the proposed model
was carried out by using the SOLVER function in Microsoft
EXCEL, by minimising the standard deviation, calculated as
follows:
σ
(
µmax
AB48
-1
 n DAT
 ∑ z exp − z theor
= i

nDAT


Xmax
Flask scale
1 + e 
D=
X0
1/ 2
)2 
0.31
5.48
0.55
0.99
0.26
D0(%)
Dmax(%)
µD
R2
σ
Flask scale
RB5
0.11
72.43
0.31
0.98
5.05
AB48
0.17
92.71
0.29
0.99
2.82
RB5+AB48
0.06
77.05
0.28
0.99
2.67
0.31
0.99
2.43
Bioreactor scale
RB5+AB48
0.78
86.62
Conclusions




being zexp and ztheor the experimental and theoretical data,
respectively and nDAT is the number of experimental data.
The model used suitably fitted to the experimental data
obtained, as can be concluded in the light of the regression
coefficients listed in Table 2, since all of them are higher than
0.98. This suitability can also be visually inspected in Figs 2 and
3, where the theoretical data are presented as solid lines.
The analysis of the parameters confirms previous
conclusions, since slightly lower maximum biomass levels are
obtained at bioreactor scale, and the maximum decolorisation
percentages are 10% higher at greater scale for the dyes
mixture. Additionally, it can be remarked that the maximum
specific growth rate obtained at bioreactor scale is 25% higher
than that existing in shaken flasks, probably due the increased
mass transfer provided by the mechanic agitation. The same
trend is concluded when comparing the maximum specific
decolorisation rate, as it increases by 11% when operating in
stirred tank bioreactor. It is outstanding that the values
obtained are in the same order of magnitude than those
28
reported for other microbial agents.
This study allowed checking the viability of ionic liquids
acclimation as a strategy for improving microbial versatility to
treat azo and anthraquinone dyes. The potential of the P.
stutzeri was confirmed for the typical dye concentration range
detected in waste waters released from textile factories.
Additionally, biopolymer synthesis observed in the adapted
bacterial strain, together with the low pH values furthered dye
biosorption on the biomass. The biological process was carried
out at small and bioreactor scale, obtaining promising
decolorisation levels both for each dye individually and mixed.
All the experimental data were suitably modeled with logistic
equations, allowing characterizing the kinetics of the biological
reaction in order to ease process implementation at higher
scale.
Acknowledgements
This research has been financially supported by the Spanish
Ministry of Economy and Competitiveness, Xunta de Galicia
and ERDF Funds (Projects CTM2014-52471-R and GRC
2013/003). The authors are grateful to the Spanish Ministry of
Economy and Competitiveness for the financial support of F.J.
Deive under the Ramón y Cajal program (RyC-2013-14225).
Notes and references
1
2
3
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J.S. Bae and H.S. Freeman. Dyes pigments 2007, 73, 81.
J. Name., 2013, 00, 1-3 | 5
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6 | J. Name., 2012, 00, 1-3
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ANNEX 3
SIMULTANEOUS BIOTREATMENT OF POLYCYCLIC AROMATIC HYDROCARBONS AND DYES IN
A ONE-STEP BIOREACTION BY AN ACCLIMATED PSEUDOMONAS STRAIN (BIORESOURCE
TECHNOLOGY, 2015, ACCEPTED FOR PUBLICATION).
Accepted Manuscript
Simultaneous biotreatment of Polycyclic Aromatic Hydrocarbons and dyes in a
one-step bioreaction by an acclimated Pseudomonas strain
María S. Álvarez, Ana Rodríguez, Mª Ángeles Sanromán, Francisco J. Deive
PII:
DOI:
Reference:
S0960-8524(15)01231-6
http://dx.doi.org/10.1016/j.biortech.2015.08.125
BITE 15478
To appear in:
Bioresource Technology
Received Date:
Revised Date:
Accepted Date:
31 July 2015
26 August 2015
27 August 2015
Please cite this article as: Álvarez, M.S., Rodríguez, A., Sanromán, M., Deive, F.J., Simultaneous biotreatment of
Polycyclic Aromatic Hydrocarbons and dyes in a one-step bioreaction by an acclimated Pseudomonas strain,
Bioresource Technology (2015), doi: http://dx.doi.org/10.1016/j.biortech.2015.08.125
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Simultaneous biotreatment of Polycyclic Aromatic
Hydrocarbons and dyes in a one-step bioreactionby an
acclimatedPseudomonas strain
María S. Álvarez, Ana Rodríguez, Mª Ángeles Sanromán, Francisco J. Deive*
Department of Chemical Engineering, University of Vigo, 36310, Vigo, Spain
* Corresponding author: +34 986818723; E-mail address:[email protected]
ABSTRACT
APseudomonasstutzeri strain acclimatedto the presence of neoteric contaminants
has been proposed for simultaneously remediating an effluent polluted with Polycyclic
Aromatic Hydrocarbons and a diazo dye. The pollutants chemical natureimposed a strict
control of both the medium composition and the operating conditions.pH, temperature
and agitation ratesof 7.0, 37.5 and 146 rpm, respectively, led to optimum levels of
contaminant removal (higher than 60%) after RSM optimization. The validity of these
conditions was checked at flask and bioreactor scale and the kinetics of the biotreatment
was elucidated. The simulation of this one-step process applied at larger scale for the
remediation of a 200,000 m3/year-effluent from a leather factory was compared with a
conventional two-steps option. Great reductions in treatment timesand in investment
and manufacturing costs were concluded, proving the promising potential of the
proposed process.
Keywords: Polycyclic aromatics, azo dyes, biodegradation, biosorption, process simulation
1. Introducción
Globally, during the last decades economic development has marched hand in hand
with an environmental collapse due to the thoughtless introduction of polluted-industrial
effluents. More and more regulations prompt the academic and industrial community to
come forward with competitive and environmentally friendly solutions. One of the
sectors causing great environmental concerns is the leather and textile industry, since
they generate a variety of pollutants ranging from surfactants, heavy metals, sulfides,
acids, alkalis, and dyes to Polycyclic Aromatic Hydrocarbons (PAHs) (Li et al, 2010).
The importance of the latter two kinds of contaminants has been underscored bycurrent
international environmental legislation (USEPA, 2008; EU-EEB, 2005). The health and
environmental risk ofthese aromatic compounds has been well documented, as they
involve carcinogenic, mutagenic and toxic effects, and are considered to bear a great
recalcitrance (Simarro et al, 2011; Haritash and Kaushik, 2009; Bae and Freeman, 2007;
Zaharia and Suteu, 2013). These concerns have urged the search of treatment
technologies to remove them from the environment, and a number of physico-chemical
alternatives have been successfully proposed like adsorption, ozonation,
electrochemistry and flocculation(Vecino et al, 2013; Sancar and Balci, 2013; Iglesias et
al, 2013; Devesa-Rey et al, 2012). However, economic and operational inconveniences
have favored the application of biotechnological tools to remediate PAH- and dyepolluted effluents, since they usually involve lower cost and improved social perception
(Deive et al, 2010; Moscoso et al, 2012a; Moscoso et al, 2013a).
Hitherto, research works have mainly focused on the treatment of a mixture of
PAHs or dyes independently (Moscoso et al, 2012b; Álvarez et al, 2013),while a lack of
knowledge is detected in the finding of suitable strategies to remediate all the
contaminants when present together in the same effluent. A successful outcome should
satisfy three main requirements: i) the chemical structure of the contaminants,ii) the
selected microbial agent, and iii) the operating conditions of the process (Haritash and
Kaushik, 2009; Moscoso et al, 2012a).
Attending to these demands, the aspect related to the chemical nature should be
firstly addressed to ensure that the contaminant is susceptible to be bioremediated in the
aqueous effluent. In this sense, PAHs are thermodynamically stable molecules, with
elevated hydrophobicity, so they should be solubilized by adding surfactants in order to
make them bioavailable (Yang et al, 2015). On the other hand, dyes are usually
hydrophilic and possess complex aromatic molecular structures that are classified on the
basis of the chromophore group (Robison et al, 2001). In this sense, azo dyes make up
the most common group of direct dyes, since about 60-70% of the produced dyes
belong to this category (Bae and Freeman, 2007).
In relation to the bioremediation agent, different microbial strains have been
proposed as suitable candidates to yield high levels of PAHs(Ghosh et al, 2014; Peng et
al, 2013) or dye removal (Liu et al, 2014; Manenti et al, 2014). However, a lack of
studies is detected on the finding of microorganisms able to concomitantly biotreat both
kinds of contaminants. In previous investigations, we have underscored the potential of
a Pseudomonas stutzeri strain for the degradation of PAHs (Moscoso et al, 2012a, b,
and c; Moscoso et al, 2013a; Moscoso et al, 2015), metal working fluids (Moscoso et al,
2012d), or pesticides (Moscoso et al, 2013b), and its capacity to be adapted to neoteric
solvents like ionic liquids (Álvarez et al, 2015).This fact was explained in terms of a
genetic alteration, as the acclimated strain throve under pollutant concentrations up to
10 times higher by means of the synthesis of an exopolysaccharide (Álvarez et al,
2015). Therefore, this flexible nature has encouraged us to apply it for the combined
bioremediation of both kinds of pollutants, which is the main aim of this work.
Special heed must be paid to the operating conditions selected to develop the
bioprocess once the biotreatment medium was designed. Factors like pH, temperature,
and agitation should be optimized prior to sketch the bioremediation process at real
scale. Valuable means to reach this target are computational tools like simulation
software (SuperPro Designer v8.5) and experimental designs (Design Expert 7.0),
saving time and money to reach the optimum process.
In summary, considering the pollutant charge of textile and leather waste effluents,
three model PAHs of low (phenanthrene, PHE) and high molecular weight (pyrene,
PYR, and benzo[a]anthracene, BaA) and a common azo dye (Reactive Black 5) have
been selected. This scenario raises problems related to the different nature of the
pollutants such as the degree of hydrophobicity and the carbon source, which will
compel us to optimize the biotreatment medium and propound the ideal range of
operation. Additionally, the bioprocess will be kinetically characterized both at flask
and bioreactor scaleby fitting to known models and these data will be employed to
simulate the process and will pay off in a one-step biotreatment strategy.
2. Materials and methods
2.1. Chemicals
The pollutants Reactive Black 5 (RB5), phenanthrene (PHE), pyrene (PYR) and
benzo[a]anthracene (BaA) (structures shown in Fig. S1) were acquired from SigmaAldrich, with purities higher than 99%. The same supplier provided the non-ionic
surfactant Tween 80, benzyl benzoate, salts of the medium and chloroform. Glucose
was purchased from Scharlau, andHCl and hexane weresupplied by Prolabo.
2.2. Microorganism
The bacterium Pseudomonas stutzeri CECT 930 was acquired from the Spanish
Type Culture Collection (ATCC 17588). This bacterium was acclimatized for two
months in a lab-scale bioreactor in the presence of C2C1imC2SO4 (0.2mM) under
controlled agitation, aeration and temperature as previously reported (Álvarez et al,
2015).
2.3.Bioremediation medium
Minimal medium (MM) was used, composed of (g/L in distilled water):
Na2HPO4·2H2O 8.5, KH2PO4 3.0, NaCl 0.5, NH4Cl 1.0, MgSO4·7H2O 0.5, CaCl2
0.0147. MM also contained trace elements as follows (mg/L in distilled water): CuSO4
0.4, KI 1.0, MnSO4·H2O 4.0, ZnSO4·7H2O 4.0, H3BO3 5.0, FeCl3·6H2O 2.0. Different
concentrations of glucose and Tween 80 were also included in the culture medium as
carbon source and solubilizing agent, respectively.
2.4.Biotreatment at flask scale
It was carried out in 250 mL-Erlenmeyer flasks containing 50 mL of MM. The pH
was initially adjusted to7.0 and the MM was autoclaved at 120ºC for 20 min. The dye
(0.04 g/L) and PAHs (100 M each) were sterilized by filtration through a 20 m filter
prior to the addition to the autoclaved medium in order to avoid any possible alteration
of the chemical structure of the pollutants. The flasks were inoculated (3% v/v) with
previously obtained cell pellets, which were them incubated in an orbital shaker
(Thermo Fisher Scientific 496) at 37ºC and146rpm.
2.5.Biotreatment at bioreactor scale
The scaling up of the process was carried out by operating in a 2-L bioreactor
(model BIOSTAT®B-MO), filled with 1.5 L of medium. The temperature and initial
pH was fixed at the optimum operating conditions. It was inoculated with actively
growing cells (3% v/v) and air was sparged at a continuous rate of 0.17 vvm (volumes
per minute, which involves the use of an air flowrate of 0.25 L/min).
2.6.Analytical methods
2.6.1.Biomass Determination
Cells were harvested by centrifugation (10 min, 9300 g, and 4ºC), and the
supernatant was reserved for pollutants analysis. Biomass concentration was measured
by turbidimetry at 600 nm in a UV-vis spectrophotometer (UV-630 Jasco), and the
values were converted to grams of cell dry weight per litre using a calibration curve.
(Biomass (g/L) = 0.5663·Absorbance - 0.0401, R2= 0.996).
2.6.2. Adsorption test
PAHs biosorption over the biomass was determined as follows. 50 mL of culture
medium were taken and centrifuged for 10 min at 5.900 g and 4ºC. The supernatant was
withdrawn and biomass was freeze-dried during 4 h at -40ºC and 7.9·10-5atm using a
TelStarCryodes. Afterwards, 10 mL of hexane were added and ultrasounds were applied
(Bransonic 3510) for 30 min. Again, the sample was centrifuged for 10 min and 100 µL
of supernatant were taken into a vial, where 10 µL of Internal Standard (IS) were added.
Samples were analysed by GC-MS as explained later on.
2.6.3.Dye decolourisation
Dye concentration in the culture media was analysed by UV-vis
spectrophotometry taking into account the maxima wavelength recorded for RB5 dye
(597 nm). Each decolourisation value was the mean of two parallel experiments. Abiotic
controls (without microorganisms) were always included. Decolourisation (D) was
expressed in terms of percentage units by using the expression:
D % removal =
(Ii -If )∙100
Ii
beingIi and If are the abiotic control andculture concentration of the dye, respectively.
The assays were done in duplicate, and the experimental error was less than 15%.
2.6.4. PAHs and intermediates determination
Aliquots (1 mL) of supernatant were added over 0.8 g of MgSO4·7H2O following
0.1 mL of HCl 1M and 1mL of hexane. They were shakenfor 1 h and an aliquot of 100
µL was collected from the organic phase and 10 µL of internal standard (benzyl
benzoate) were added.
PAHs concentration in supernatant was analysed using an Agilent GC 6850 gas
chromatograph equipped with a HP-5MS column (30m x 0.25mm; 0.25µm, Agilent),
operating with hydrogen as carrier gas, and coupled to an Agilent MSD 5975C mass
spectrometer. Injections (1µL) of samples were made up in split mode (10:1) split
relation; GC oven was programmed under the following conditions: 50ºC for 4 min and
10ºC/min to 280ºC for 10 min. The mass spectrometer was operated in SIM mode.
Intermediates were detected by adding 25 ml of chloroform to 250 mL of
supernatant and the pH was adjusted to 2 to favour the extraction of intermediates
formed during the biodegradation process. The water content in the organic phase was
removed by addition of anhydrous sodium sulphate and subsequently filtered. The
sample was thenintroduced in a rotatory vacuum concentrator(RVC 2-25
CCHRIST/CHRIST CF04-50 SR), and the residue was dissolved in chloroform. The
same gas chromatograph equipment served our goal to detect the intermediate
metabolites, and 1µL-injections of the samples were made up in split mode (2:1 split
relation); GC oven was programmed under the following conditions: 50ºC for 5 min,
then 5ºC/min to 280ºC for 5 min. The mass spectrometer was operated in SCAN mode.
2.6.5 Statistical design
The statistical design was analysed through the ANalysis Of VAriance (ANOVA)
using Design Expert® 9.0.0 software (Stat-Ease Inc., Minneapolis, USA). A second
order polynomial equation was applied to correlate the dependent and independent
variables:
Yi =x0 +x1 T+x2 pH+x3 agitation+x4 T∙pH+x5 T∙agitation +
x6 pH∙agitation +x7 T 2 +x8 pH 2 +x9 agitation2
where 𝑌𝑖 is the response variable (contaminant remediation) x0 is a constant, x1, x2and
x3are the regression coefficients for linear effects; x4,x5and x6are the regression
coefficients for interaction effects, and x7,x8 and x9 are the regression coefficients for
quadratic effects, and T, pH and agitation are the independent variables.
3. Results and discussion
The proposal of an efficient bioremediation process for effluents containing PAHs
and azo dyes requires the finding of a suitable medium and operating conditions.
Therefore, prior to approach the operation at bioreactor scale and simulating the
treatment of a real-scale effluent, the optimization at small scale is sketched.
3.1. Optimization of medium and treatment conditions
The first aim is to find a suitable biotreatment medium allowing the solubilisation
of the hydrophobic contaminants (PAHs) and without negatively interfering in the
bioremediation of the hydrophilic pollutants (azo dye RB5). As previously
demonstrated, the acclimation of a P. stutzeri strain to the presence of neoteric
contaminants like imidazolium-based ionic liquids triggered a permanent alteration at a
gene level that led to the synthesis of an exopolysaccharide (Álvarez et al, 2015). This
modification widened the proved versatility of this microbial strain for the remediation
of different kinds of pollutants (Moscoso et al, 2012 b; Moscoso et al, 2012d, Moscoso
et al, 2013b), as this biopolymer can help to increase its potential for the biotreatment of
dyes, by promoting dye adsorption phenomena. Therefore, the addition of a non-ionic
surfactant to the biotreatment medium is critical, as it assists in increasing hydrophobic
contaminants bioavailability but may solubilize the synthesized exopolysaccharide, thus
hindering the dye removal.
As Tween 80 and glucose may act as carbon source in cultures of P. stutzeri,as
previously reported (Moscoso et al, 2012a; Álvarez et al, 2015), the combination of
different concentrations of both compounds may be crucial to reach a compromise
between PAH bioavailability and exopolysaccharide solubilisation.Since 10 g/L is the
carbon source concentration leading to the highest levels of biomass, declining
concentrations of Tween 80 were combined with growing compositions of glucose
([Glucose], [Tween 80] in g/L = (0.0,10), (2.5, 7.5), (5.0, 5.0 ), (7.5, 2.5), (9.0, 1.0) and
(9.9, 0.1)), and the data are presented in Fig. 1. These data evidence the existence of an
optimum ratio (9.0, 1.0), as the PHE, PYR and BaA are completely solubilized while
the decolorisation of the azo dye RB5 overtook 60%.
Once this ratio was chosen, Response Surface Methodology (RSM) based on a
central composite face-centred design was applied to optimize the contaminants
remediation when using temperature, pH and agitation as independent variables. The
operation range was defined after a previous screening, and the designed experimental
34 runs (including five replicates of the central point to evaluate the reliability of the
data) are presented in the supplementary material (Table S1) together with the
bioremediation percentages. The analysis of the statistical parameters shown in Table
S2 demonstrates that a quadratic model is significant (P<0.0001) for a suitable
description of the 4 responses under study (RB5, PHE, PYR and BaA removal). Hence,
the coefficients for defining the equation of effects are shown in Table 1. In a visual
inspection of the data compiled in this table, it becomes patent that the influence of pH,
agitation and temperature is significant for almost all the contaminants, while the
interaction and quadratic effects seem to be more dependent on the contaminant under
study. In this context, the graphical representation of the response surfaces for each
contaminant at optimum agitation rates (150 rpm) isshown in Fig.2.
The visualization of the data licenses to draw a distinction between azo dye and
PAHs, as a result of their completely different chemical nature, even though both of
them share the presence of condensed aromatic rings. On the one hand, maximum dye
removal levels can be attained at pH values lower than 6.5 and temperatures higher than
32.5ºC. On the other hand, PAHs removal is only feasible for pH values higher than 6.5
for all the temperatures under study. The numerical optimization carried out by using
the software Design Expert® 9.0.0led to the conclusion that pH 7.0, T= 37.5ºC and
agitation rates of 146 rpm led toaverage contaminants removal levels higher than 60%
for PHE, PYR, BaA and RB5, respectively.
3.2 Scaling-up, modeling and simulation of the process at real scale
After the operating conditions and biotreatment medium were selected, the scalingup of the process was approached. Then, a bench-scale bioreactor will provide valuable
data prior to simulate the process at real scale. Therefore, the first step was carrying out
the biological reaction at the optimum conditions, going from flask to bioreactor scale.
A kinetic model widely applied in the characterization of bioremediation processes
allowed describing two important variables of the process, biomass concentration and
pollutant removal (Deive et al, 2010):
X
X
1 e
D
1 e
max

  X max 
1    m t 
 ln 

  X 0

Dm ax
  Dmax 

1    Dt 
ln 
D
0

 

where X and D are the biomass (g/L) and contaminant removal (%) at an specific
moment of the biotreatment (t), X0 and D0 are the initial biomass and removal, Xmax and
Dmaxare the maximum biomass and pollutant removal, and µm and µD are the maximum
specific growth rate and maximum specific remediation rate (h-1).
The values of the regression coefficients R2 listed in Table 2 (always higher than
0.9) evidence the suitability of the proposed models to get a deep insight in the kinetic
characteristics of the process carried out at flask and bioreactor scale at the optimum
conditions obtained previously. The data presented in Fig. 3 also makes it evident this
adequate description for both the biomass and contaminants remediation. A conscious
analysis of the biomass parameters points to the benefits of operating at bioreactor scale,
as both the maximum biomass concentration and specific growth rate are enhanced by
about 2 and 4 times, respectively. These results are coincident with previous studies
tackling the scaling-up of dye-remediation processes from flask to bench-scale
bioreactors (Deive et al, 2010).These ameliorations are also reflected in the maximum
levels of pollutant removal recorded, as an average increase of about 12% and 5% is
recorded for the PAHs and RB5, respectively, when going from flask to bioreactor
scale. The reason for this boosted behavior can be attributed to the inherent benefits of
operating in this kind of stirred tank bioreactor, like the greater mass transfer of
contaminants and oxygen promoted by the Rushton impeller. In this line, it has already
been well documented the superior performance of this turbine for improving oxygen
mass transfer coefficients (Moucha et al, 2003). This is crucial for an efficient
biodegradation process because aerobic biodegradation mechanisms demand the
existence of molecular oxygen as electron acceptor, thus easing the activation of the
substrate through oxygenation reactions biocatalyzed by mono or dioxygenases (Cao et
al., 2009). In this vein, GC-MS analysis confirms this hypothesis, since the three PAHs
seem to follow the same metabolic route and, after a double hydroxylation of one
aromatic ring, its cleavage is eased. Then, depending on the PAH, the stages are
repeated up to diethylphtalate and phtalic acid are obtained (c.f. Fig. S2 in
Supplementary Material) which are easily mineralized. The proposed route is in
agreement with previous results of our group and other researchers (Moscoso et al,
2012a, 2015; Khanna et al, 2011), which confirms that the acclimated P. stutzeri is able
to follow the same metabolic strategies to degrade the contaminants.
A deeper insight into the nature of the remediation process can be achieved
byapplyingthemodel reported by Marques et al. (1986), and subsequently adapted by
Deiveet al. (2010), where the remediation is presented as a function of the growth rate
and the biomass as can be seen in the following equation:





 X 
 
e t

 X
 1,0  n m ax  ln 1,0   0  1,0  e t 
D  D0  mX 0 

 X0 
 X m ax 

    


 1,0  e t 

1
,
0



X
 m ax 

 





This algorithm relates the degradation efficiency with the growth rate (m= 0), the
biomass (n = 0) or both parameters (m ≠ 0 and n ≠ 0). The data obtained are presented in
Table 3. The values of the parameters reflect that the remediationof all the contaminants
displays a greater dependence on the biomass production, since in all cases m is, at
least, more than one order of magnitude higher than n. This behaviour is in agreement
with the results reported for the remediation of this kind of contaminants independently
(Deive et al., 2010; Moscoso et al., 2012a).
This higher relationship with the biomass production may be related to the nature of
the remediation process, as usually, two subsequent stages are underlying the
contaminant removal: biosorption and metabolisation. In this sense, it has been
observed that PAHs and di-azo dye RB5 behave differently, and this behaviour is
confirmed both at flask and bioreactor scale. Thus, while levels of biosorption lower
than 35% are recorded for the PAHs (with just 7% for the low molecular weight PAH,
PHE), 60 % of the RB5 is adsorbed on the bacterial biomass. The reason for the higher
affinity of RB5 dye in relation to the PAHs liesagain in the different chemical nature of
these contaminants. Thus, the ionic character of the dye will ease the establishment of
electrostatic interactions with the protonated nitrogen-containing functional groups in
microbial cells and proteins, as a consequence of the existence of slightly acidic
conditions (Bidisha et al, 2006).This fact also explains the improved results of dye
removal previously observed at acid pHs.
All in all, the optimized conditions allowed accomplishing high levels of remediation
of dyes and PAHs simultaneously. In order to further quantify the advancements, this
one-step biotreatment process will be likened with a traditional option including a twostages process: a P. stutzeri mesophilic step to treat the PAHs (with a duration of 150 h)
followed by a thermophilic step employing Anoxybacillusflavithermusto decolorize
RB5 (with a total time of 12 h), in line with prior investigations (Moscoso et al, 2015;
Deive et al, 2010). Both processes are presented in Fig. 4, with a view to ease the
analysis between the two options, and they were simulated to remediate a 200,000
m3/year polluted effluent (with 300 mMPAHs and 0.04 mg/L ofazo dye) from a leather
industry.
The software tool employed was SuperProDesigner v8.5 (Intelligen Inc.), as it is a
simple way to interactively analyze on a consistent basis the viability of both
remediation alternatives at large scale. One of the advantages provided by this program
is that it enables to easily peruse the throughput capacity and time utilization of each
operation unit. Hence, on the basis of the technical needs indicated above, time
requirements, remediation yields, and biomass production, both alternatives were
simulated, and the main results for each of them are compiled in Table 4. It becomes
patent that the one-stage biotreatment involves a drastic cycle time reduction from 221
h/batch to 53 h/batch, thus allowing the performance of up to 309 batches per annum,
while maintaining high levels of pollutants remediation. Additionally, this greater
throughput capacity parallels a reduction in fixed capital investment and manufacturing
cost up to about 40%. When all this information is taken together, the total costs of
effluent treatment are reduced by ten times, which makes it patent the aptness of the
proposed process.Apart from that, and given the versalitity of the proposed strain in
terms of substrates utilization (complex media, metal working fluids, etc.) as already
demonstrated in previous research works (Moscoso et al, 2012d), the future search of
cheaper nutrients and other operation modes, as well as strategies including biomass
recycling will allow decreasing the total costs of the process.
Conclusion
The present manuscript has demonstrated the technical and economic superiority of
a one-step bioremediation process based on an acclimated P. stutzeri strain, to treat an
effluent polluted with 3 model PAHs and a model diazo dye.An optimum medium and
operating conditions were selected prior to demonstrate the viability of the strategy at
bioreactor scale. The kinetic parameters of the process wereobtained in order to license
its simulation with the software SuperPro Designer, recording enhancements of
treatment throughput near to 6 times while reducing the total cost in one order of
magnitude.
Acknowledgements
This work has been supported by the Spanish Ministry of Economy and
Competitiveness and EDRF funds (project CTM2014-52471-R). F. J. Deive
acknowledges Spanish Ministry of Economy and Competitiveness for funding through a
Ramón y Cajal contract.
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CAPTION TO FIGURES AND TABLES
FIGURE 1.PAHs solubilization ( ) and RB5 removal () for different concentrations of
glucose and Tween 80.
FIGURE 2.Effect of pH and temperature in the biotreatment of dyes and PAHs at the optimum
agitation (150 rpm)
FIGURE 3. Biomass concentration () and removal of RB5 (), PHE (), PYR () and BaA
() in thebiotreatment processes carried out at flask (black) and bioreactor scale (blue). Dots
represent the experimental data and solid lines are employed for the modelled data.
FIGURE 4. Two-step (PFD 1) vs. one-step (PFD 2) process flowsheet diagrams for the
industrial biotreatment of PAHs and RB5-polluted effluents as obtained with the software
SuperProDesigner v8.5.
TABLE 1.Values of the coefficients for the equation of effects in the remediation of RB5, PHE,
PYR and BaA.
TABLE 2.Parameters of the logistic model to characterize the kinetic growth and pollutant
remediation by the adapted P. stutzeri at small and bioreactor scale
TABLE 3. Parameters of the model from Marqués et al for the remediation process at bioreactor
scale
TABLE 4.Treatment capacity, remediation efficiency and scheduling summary for the twostages and one-stage biotreatmentprocesses
TABLE 1
Linear effects
Pollutant x0
x1
Interaction effects
Quadratic effects
x4
x2
x3
186.6
0.055 3.28
x5
x6
x7
x8
x9
-0.012
-0.026
-0.131 -23.40 0.002
RB5
-381.4 -8.04
PHE
-52.6
10.37 -101.7
1.69
0.307 -0.019
0.118
-0.125 8.11
-0.005
PYR
381.0
6.38
-211.3
1.26
0.374 -0.008
0.126
-0.106 16.77
-0.005
BaA
131.4
-3.50
-31.28
0.595 1.111 0.006
0.027
-0.081 0.830
-0.003
Parameters in bold are significant (P< 0.05) (c.f. Supplementary Material).
TABLE 2
Scale
X0(g L-1)
Xmax(g L-1)
µmax(h-1)
R2
Flaskscale
0.44
3.55
0.22
0.91
Bioreactorscale
0.01
6.27
0.97
0.98
Contaminant
D0(%)
Dmax(%)
µD(h-1)
R2
RB5
0.1
73.7
0.31
0.98
PHE
8.6
70.6
0.15
0.93
PYR
6.2
56.1
0.11
0.97
BaA
0.5
64.5
0.52
0.93
RB5
0.1
78.1
0.59
0.98
PHE
7.9
83.6
0.13
0.95
PYR
7.7
68.5
0.11
0.93
BaA
3.8
77.2
0.29
0.94
Flaskscale
Bioreactorscale
TABLE 3
RB5
PHE
PYR
BaA
D0 (%)
0
0
0
0.0
m (%/g)
4.6
8.7
6.2
9.4
n (%/g/h)
0.1
0.0
0.0
0.0
R2
0.90
0.89
0.90
0.96
TABLE 4
2-steps biotreatment
1-step biotreatment
Batch time (h)
220.8
52.5
Batch number per year
309
51
Total RB5 removal (%)
78.0
79.5
Total PHE removal (%)
95.6
85.0
Total PYR removal (%)
95.6
70.9
Total BaA removal (%)
95.6
78.6
100
100
80
80
60
60
40
40
20
20
0
0
0
2
4
6
8
10
2
0
Tween concentration (g/L)
10
8
6
4
Glucose concentration (g/L)
PAHs solubilisation (%)
Dye removal (%)
FIGURE 1
FIGURE 2
Pollutant Removal (%)
Biomass concentration (g/L)
10.0
7.5
7.5
5.0
5.0
2.5
2.5
0.0
0.0
75
75
50
50
25
25
0
0
20
40
Time (h)
60
0
20
40
Time (h)
60
80
0
Biomass concentration (g/L)
10.0
Pollutant removal (%)
FIGURE 3
FIGURE 4
PFD 1
PFD 2
HIGHLIGHTS
Acclimated Pseudomonas stutzeri efficiently remediates PAH and dye-polluted
effluents
Viable biotreatment medium and optimum operating conditions were determined.
Kinetics of the biotreatment and its economic advantages were ascertained
ANNEX 4
IONIC LIQUIDS AND NON-IONIC SURFACTANTS: A NEW MARRIAGE FOR AQUEOUS
SEGREGATION (RSC ADVANCES, 2014, 4: 32698-32700).
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Cite this: RSC Adv., 2014, 4, 32698
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Ionic liquids and non-ionic surfactants: a new
marriage for aqueous segregation†
M. S. Álvarez, M. Rivas, F. J. Deive,* M. A. Sanromán and A. Rodrı́guez*
Received 27th May 2014
Accepted 14th July 2014
DOI: 10.1039/c4ra04996a
www.rsc.org/advances
The aqueous nature of aqueous biphasic systems has boosted their
use in downstream stages in biotechnological processes. Since
aqueous solutions of non-ionic surfactants are widely used for
different metabolites' purification, we have demonstrated for the first
time their segregation capacity in the presence of ionic liquids.
Since 2003, when Rogers and collaborators1 rst addressed the
ability of ionic liquids to be salted out in aqueous solutions by
high charge density inorganic salts, great interest has been
devoted to ionic liquid-based aqueous biphasic systems (ABS).2
These molten salts have posed undoubted benets in a diversity
of elds, be it in electrochemistry, analytical chemistry, surface
science, catalysis, nanotechnology or biotechnology.3 This
interest emerges from their appealing features. Among them,
their structural modularity allows tuning the cation and anion
to design millions of combinations for each desired application
or task.4 In this sense, the application of ionic liquids-based ABS
has shown an enormous potential for the separation of
metabolites with industrial interest, since very oen, the
requirements for their extraction are very tough (temperature,
solvent properties, pH, etc.). The development of new downstream strategies, which usually represents more than 50–80%
of the total processing cost, urges the investigation of more
competitive alternatives to maximize product recovery and
foster the economic feasibility and robustness of biotechnological and chemical processes.5
Miscibility control in aqueous solutions of ionic liquids has
been basically tackled by using different inorganic and organic
salts, although they oen entail problems regarding metabolites stability. These handicaps have made us to hypothesize
that the use of non-ionic surfactants, widely employed in bioprocessing operations, could be a suitable strategy for achieving
phase separation in the presence of ionic liquids, the latter
acting as salting out agents. In this way, liquid–liquid equilibrium is yielded aer a complex competition between the nonionic surfactant and the ionic liquid for the water molecules.
In previous research works, we have demonstrated the ability
of surface active compounds belonging to the most commonly
used families (Triton and Tween), to be salted out by inorganic
and organic salts aiming at applying them for the separation of
metabolites and pollutants.6 In this present case, given the
advantages provided by surfactant-based ABS such as lower
interface tension, economical reasons (low cost of the reagents
and rapid phase segregation), greater immiscibility windows,
null ammability, and commercial availability of all components at bulk quantities,6 we have bet in Triton family, due to its
relevance in different biotechnological applications.7 Thus,
Triton X-100 and Triton X-102, composed by an 8-carbon
tertiary alkyl chain and 9–10 ethylene oxide units or 12–13
ethylene oxide units, respectively, have been cherry-picked for
this work (structure shown in Scheme 1).
In relation to the salting out agent, we have selected 1-ethyl3-methyl imidazolium ethylsulfate (C2MIMC2SO4) since it is
already produced at an industrial scale (more than one ton per
annum), which ensures its availability when implemented at
high scale. Besides, it can be easily synthesized in an atomefficient and halide-free way, at a reasonable cost, it shows high
Department of Chemical Engineering, Campus Lagoas Marcosende 36310, University
of Vigo, Spain. E-mail: [email protected]; [email protected]
† Electronic supplementary information (ESI) available: Materials and methods,
and tables containing solubility data. See DOI: 10.1039/c4ra04996a
32698 | RSC Adv., 2014, 4, 32698–32700
Scheme 1
Structure of the ionic liquids and non-ionic surfactants
used.
This journal is © The Royal Society of Chemistry 2014
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chemical and thermal stability, low melting points and relatively low viscosities.8 Its biocompatibility with enzymes has
also been reported in previous works for the separation of
lipases.9 Moreover, 1-ethyl-3-methyl imidazolium butylsulfate
(C2MIMC4SO4) and 1-ethyl-3-methyl imidazolium hexylsulfate
(C2MIMC6SO4) were selected in order to evaluate the inuence
of the hydrophobicity of the ionic liquid at 25 C.
Thus, in this work both the hydrophobicity of the components (ionic liquids and surfactants) and the operation
temperature to map the immiscibility region have been
screened. The variation of the alkyl chain length in the anion
(C2SO4, C4SO4 and C6SO4) revealed that just ethylsulfate-based
ionic liquid led to phase segregation, as shown in Fig. 1.
Usually, as demonstrated previously,1 phase segregation in
aqueous solutions involving ionic liquids and inorganic salts
are made up by an upper ionic liquid-rich phase and a bottom
inorganic salt-rich phase. In this particular case, the competition of the ionic liquid and non-ionic surfactant for the water
molecules is won by C2MIMC4SO4. Notwithstanding the fact
that longer alkyl chain lengths in the anion were reported to be
benecial for increased immiscibility windows,10 this just
happens when the phase segregation in aqueous solutions of
ionic liquids is triggered by high charge density salts. In this
case, ionic liquids are playing the role of salting out agents, and
therefore, the more hydrogen bonding capacity the ionic liquid
Fig. 1 Immiscibility regions for ABS composed of C2MIMC2SO4 and
Triton X-102 (upper) and Triton X-100 (down) at different temperatures: ( ) 25 C; ( ) 40 C; ( ) 50 C; ( ) 60 C. Dots represent
experimental data and solid lines represent the data obtained from
correlation.
This journal is © The Royal Society of Chemistry 2014
RSC Advances
shows, the more interaction with water molecules it displays,
thus leading to an easier phase disengagement.
Additionally, a complete characterization of the immiscibility region was carried out. In general, ABS ternary phase
diagrams are plotted in an orthogonal representation, where
pure water is located in the origin of the axes.2 This is due to the
fact that as the concentration of the salt is increased up to the
saturation limit, the coexistence of a precipitate should be taken
into account. Hence, this kind of systems is always “incomplete”. In this particular case, the presence of a liquid salt (ionic
liquid) and the liquid surfactant allows to completely characterize the immiscibility gap. The data shown in Fig. 1 reveals
that the immiscibility window occurs only in the ternary region,
while binary mixtures involved in the system are completely
miscible. Therefore, these systems fall into an island-type
ternary system (type 0 in Treybal classication).11
In addition, the Othmer–Tobias12 correlation equation,
which relates the tie line mass concentration of the top phase
with the bottom phase to obtain a linear function, was used to
t the experimental tie line data obtained for each ABS system
(listed in Table S3†):
a
1 wI1
1 wII
2
¼
b
wI1
wII
2
where a and b are the tting parameters, w is the mass fraction,
subscripts 1 and 2 refer to surfactant and ionic liquid, respectively, and superscripts I and II indicate the surfactant-rich
phase and ionic liquid-rich phase, respectively. The values of
the model parameters are presented in the ESI (Table S4†),
together with the correlation coefficient R2. The data obtained
evidences a high degree of thermodynamic consistency since
the values of R2 are all higher than 0.9.
In this study, the tunability is a term not only associated with
the ionic liquid, but also with the non-ionic surfactant, since it
provides some degree of structural modularity, by modifying its
hydrophobicity degree. A valuable tool to ascertain the hydrophobicity of a surfactant is by using the hydrophilic–lipophilic
balance (HLB), which is an empirical number varying from
0 (low hydrophilicity) to 20 (high hydrophobicity). Data from the
supplier reveals lower HLB values for Triton X-100 (13.5) than
for Triton X-102 (14.4). Taking this into account, it should be
expected that the use of surfactants with higher degree of
hydrophobicity would entail greater immiscibility windows.
From the experimental data illustrated in Fig. 1, it seems that
this hypothesis is conrmed and Triton X-100 shows weaker
interactions with water molecules than Triton X-102 in the
presence of C2MIMC2SO4, thus easing phase disengagement.
Regarding the operation temperature, a visual inspection of
the results obtained at temperatures ranging from 25 C to 60
C (also shown in Fig. 1) evidences a greater liquid–liquid
demixing capacity at higher temperatures. The reason behind
this behavior lies in the different nature of the main components existing in the ABS. Thus, the non-ionic surfactant
becomes more hydrophobic at increased temperatures, thus
weakening hydrogen bond interactions and easing phase
segregation. On the contrary, C2MIMC2SO4 becomes more
hydrophilic, which leads to a greater interplay with water
RSC Adv., 2014, 4, 32698–32700 | 32699
View Article Online
Published on 15 July 2014. Downloaded by Universidad de Vigo on 01/09/2015 11:28:12.
RSC Advances
molecules. These trends are in agreement with available literature data tackling the phase segregation in aqueous solutions
of liquid polymers in the presence of organic and inorganic
salts.13 However, the ionic liquid-based ABS carried out in the
presence of inorganic salts,14 exhibit an inverse trend, which
conrms the importance of a conscious selection for a
successful ABS composed of ionic liquids and non-ionic
surfactants. The analysis of literature data on the effect of
temperature on immiscibility gaps for aqueous systems reects
that these trends can be generalized, as shown in Table S5 in the
ESI.†
In summary, the present work has demonstrated the suitability of ionic liquids and surfactants for achieving liquid–
liquid demixing in aqueous solutions. This combination opens
up new opportunities for the separation of biotechnologically
relevant biomolecules from aqueous culture broths, where they
are usually produced, given the relevance of non-ionic surfactants in both upstream and downstream operations. A highly
hydrophobic surfactant combined with a very hydrophilic ionic
liquid, operating at elevated temperatures, will thus be a perfect
scenario for maximizing the immiscibility region. In this sense,
the proposed strategy would perfectly suit to implement an
efficient separation process in the extraction of biomolecules
from thermophilic microorganisms, where the operation
temperatures are usually higher than 50 C.
Acknowledgements
This work has been supported by the Spanish Ministry of
Economy and Competitiveness and EDERF funds (project
CTM2012-31534). F. J. Deive acknowledges Xunta de Galicia for
funding through an Isidro Parga Pondal contract.
Notes and references
1 K. E. Gutowski, G. A. Broker, H. D. Willauer,
J. G. Huddleston, R. P. Swatloski, J. D. Holbrey and
R. D. Rogers, J. Am. Chem. Soc., 2003, 125, 6632.
2 M. G. Freire, A. F. M. Claudio, J. M. M. Araújo,
J. A. P. Coutinho, I. M. Marrucho, J. N. Canongia Lopes
and L. P. N. Rebelo, Chem. Soc. Rev., 2012, 41, 4966.
3 N. V. Plechkova and K. R. Seddon, Chem. Soc. Rev., 2008, 37,
123.
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J. N. Canongia Lopes, L. P. N. Rebelo, J. W. Magee,
K. R. Seddon and J. A. Widegren, Nature, 2006, 439, 831;
(b) K. J. Baranyai, G. B. Deacon, D. R. MacFarlane,
J. M. Pringle and J. L. Scott, Aust. J. Chem., 2004, 57, 145.
5 R. Datar and C. Rosen, in Separation processes in
biotechnology, ed. J. A. Asenjo, Marcel Dekker, New York
and Basel, 1990.
6 (a) G. Ulloa, C. Coutens, M. Sánchez, J. Sineiro, J. Fábregas,
F. J. Deive, A. Rodrı́guez and M. J. Núñez, Green Chem.,
2012, 14, 1044; (b) M. S. Álvarez, F. Moscoso, A. Rodrı́guez,
M. A. Sanromán and F. J. Deive, Bioresour. Technol., 2013,
146, 689; (c) M. S. Álvarez, F. Moscoso, F. J. Deive,
M. A. Sanromán and A. Rodrı́guez, Bioresour. Technol.,
2014, 162, 259; (d) M. S. Álvarez, F. Moscoso, F. J. Deive,
M. A. Sanromán and A. Rodrı́guez, J. Chem. Thermodyn.,
2012, 55, 158; (e) M. S. Álvarez, F. Moscoso, A. Rodrı́guez,
M. A. Sanromán and F. J. Deive, J. Chem. Thermodyn., 2012,
54, 385; (f) M. S. Álvarez, E. Gutiérrez, A. Rodrı́guez,
M. A. Sanromán and F. J. Deive, Ind. Eng. Chem. Res., 2014,
53, 8615; (g) E. Gutiérrez, M. S. Álvarez, A. Rodrı́guez,
M. A. Sanromán and F. J. Deive, J. Chem. Thermodyn., 2014,
70, 147.
7 A. Singh, J. D. Van Hamme and O. P. Ward, Biotechnol. Adv.,
2007, 25, 99.
8 A. B. Pereiro, F. J. Deive, J. M. S. S. Esperança and
A. Rodrı́guez, Fluid Phase Equilib., 2010, 291, 13.
9 F. J. Deive, A. Rodrı́guez, A. B. Pereiro, J. M. M. Araújo,
M. A. Longo, M. A. Z. Coelho, J. N. Canongia Lopes,
J. M. S. S. Esperança, L. P. N. Rebelo and I. M. Marrucho,
Green Chem., 2011, 13, 390.
10 F. J. Deive, A. Rodrı́guez, I. M. Marrucho and L. P. N. Rebelo,
J. Chem. Thermodyn., 2011, 43, 1565.
11 R. E. Treybal, Liquid Extraction, McGraw-Hill, New York, 2nd
edn, 1963.
12 D. F. Othmer and P. E. Tobias, Ind. Eng. Chem., 1942, 34, 693.
13 (a) R. Govindarajan, K. Divya and M. Perumalsamy, J. Chem.
Eng. Data, 2013, 58, 315; (b) H. Rasa, M. Mohsen-Nia and
H. Modarress, J. Chem. Thermodyn., 2008, 40, 573.
14 (a) H. Lv, D. Guo, Z. Jiang, Y. Li and B. Ren, Fluid Phase
Equilib., 2013, 341, 23; (b) B. G. Alvarenga, L. S. Virtuoso,
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This journal is © The Royal Society of Chemistry 2014
ANNEX 5
AQUEOUS IMMISCIBILITY
OF
CHOLINIUM CHLORIDE IONIC LIQUID
AND
SURFACTANTS (JOURNAL OF CHEMICAL THERMODYNAMICS, 2015, 91: 86-93).
TRITON
J. Chem. Thermodynamics 91 (2015) 86–93
Contents lists available at ScienceDirect
J. Chem. Thermodynamics
journal homepage: www.elsevier.com/locate/jct
Aqueous immiscibility of cholinium chloride ionic liquid and Triton
surfactants
María S. Álvarez a, F. Patiño b, Francisco J. Deive a,⇑, M. Ángeles Sanromán a, Ana Rodríguez a,⇑
a
b
Department of Chemical Engineering, Universidade de Vigo, P.O. Box 36310, Vigo, Spain
Design in Engineering Department, Universidade de Vigo, P.O. Box 36310, Vigo, Spain
a r t i c l e
i n f o
Article history:
Received 28 May 2015
Received in revised form 18 July 2015
Accepted 21 July 2015
Available online 29 July 2015
Keywords:
Aqueous biphasic systems
Ionic liquids
Cholinium chloride
Non-ionic surfactants
Triton
a b s t r a c t
The immiscibility windows of aqueous solutions containing the ionic liquid cholinium chloride
(N1112OHCl) and the non-ionic surfactants Triton X-100 and Triton X-102 have been determined by the
cloud point method at temperatures ranging from T = (298.15 to 333.15) K. The experimental values have
been correlated by using two well-known equations. The tie-lines have been ascertained by means of
density and refractive indices measurement, and the experimental data have been modeled by the
Othmer–Tobias, Bancroft and Setschenow equations. The use of cholinium chloride involves greater
demixing capacity than other imidazolium-based ionic liquids.
Ó 2015 Elsevier Ltd. All rights reserved.
1. Introduction
Over the last years, environmental concerns have highlighted
the extensive and increasing importance of implementing industrial processes using greener solvents. Hence, a key target for
enhancing competitiveness of the chemical industry is to reduce
the environmental impact when manufacturing high value products. In particular, the replacement of volatile organic compounds
with non-flammable and tuneable ionic liquids [1] has set the pace
for the achievement of truly revolutionary processes. The last discovery involving these salts allows envisaging the prominence that
these solvents may have in the near future: a new IL-based
rechargeable battery system affording charging times of around
one minute [2]. Currently, these molten salts are already used at
an industrial scale in companies such as BASF, Institut Français
du Pétrole, Degussa, Linde, Pionics and G24i, and the annual production of some of them (mostly belonging to imidazolium family)
exceeds the ton per year [3,4]. However, these promising expectations can be jeopardized when bearing in mind the toxicity and
persistence of some cations like the imidazolium. In this sense,
the use of more biocompatible ionic liquids like those based on
the cholinium cation is the subject of more and more studies
focused on a diversity of topics that range from fundamentals to
the demonstration of their low environmental impact or their
⇑ Corresponding authors. Tel.: +34 986 81 87 23.
E-mail addresses: [email protected] (F.J. Deive), [email protected] (A. Rodríguez).
http://dx.doi.org/10.1016/j.jct.2015.07.027
0021-9614/Ó 2015 Elsevier Ltd. All rights reserved.
biocompatibility with enzymes [5,6]. These features have furthered
their application in separation processes.
Very often, conventional (liquid + liquid) extraction strategies
involve the use of volatile and toxic organic solvents. Thus, the
emergence of this kind of biocompatible ionic liquids opens up
new roads in the development of more environmentally friendly
aqueous biphasic systems (ABS) [7,8]. This separation method consists in the phase segregation of an aqueous solution containing
one hydrophilic compound when a certain amount of another
hydrophilic compound is added. Traditionally, the most common
combination was a polymer and a salt. However, since 2003, when
Rogers et al. [9] reported the ability of ionic liquids to trigger phase
disengagement, many authors have applied this kind of systems for
the separation of a variety of biomolecules like enzymes [10,11],
antioxidants [12] or alkaloids [13], among others. Several reasons
justify the interest in applying ionic liquid-based ABS in the extraction of this type of molecules, e.g. short periods of time are required
for phase disengagement, low energy demand or the possibility to
work at mild operating conditions [14].
Another step towards the building of more competitive ionic
liquid-based ABS could be the use of non-ionic surfactants, since
they provide more advantages like a low interface tension, low cost
(non-ionic surfactants are inexpensive), greater immiscibility
region and negligible flammability and volatility [15]. In this sense,
we have recently reported for the first time the ability of
imidazolium-based ionic liquids to promote phase splitting in
aqueous solutions of surface active compounds [16]. Among the
87
M.S. Álvarez et al. / J. Chem. Thermodynamics 91 (2015) 86–93
TABLE 1
Purities, provenance and characteristics of chemicals.a
Compound
Chemical structure
Triton X-100 n = 9.5
Triton X-102 n = 12
Supplier
Mass fraction purity
HLBb
CMC (ppm)b
Sigma–Aldrich
0.98
0.98
13.4
14.4
189
267
N1112OHCl
a
b
0.97
Milli-Q water was used in all the experiments.
HLB: hydrophilic lipophilic balance; CMC: critical micellar concentration. HLB and CMC were obtained from the supplier.
Water
0
10
20
TABLE 2
Phase equilibria of {Triton X-100 (1) + N1112OHCl (2) + H2O (3)} at T = (298.15 to
333.15) K and P = 101.33 kPa.a
100
90
L
80
30
70
40
60
50
50
60
L+L
40
70
30
80
20
90
Triton X-100
0
10
S+L
100
10
20
30
40
50
60
70
80
90
100
0
N1112OHCl
FIGURE 1. Phase diagrams for the systems {Triton X-100 (1) + N1112OHCl (2) + H2O
(3)} at T = 298.15 K (s), T = 313.15 K ( ), T = 323.15 K ( ), T = 333.15 K ( ) and
P = 101.33 kPa. Symbols represent experimental values, solid lines are guides to the
eye and dashed lines refer to the model.
Water
0
10
20
80
L
70
60
50
50
60
40
L+L
70
30
80
20
90
10
S+L
100
10
20
30
40
T = 323.15 K
T = 333.15 K
100 w1
100 w2
100 w2
100 w2
100 w1
74.51
69.71
64.44
59.21
54.12
49.55
45.29
38.74
34.71
29.83
28.36
26.53
25.95
25.87
24.75
23.19
21.16
18.43
14.04
7.64
0.97
0.32
0.28
0.31
0.61
0.39
0.31
0.12
0.21
0.22
0.23
0.52
3.09
6.63
11.11
16.63
23.32
31.40
42.68
53.91
68.50
88.71
(Liquid + liquid) equilibria
74.89
0.90
70.02
0.51
69.37
0.68
64.47
0.75
64.07
0.68
59.32
0.65
59.38
0.49
54.12
0.44
54.02
0.39
49.88
0.57
49.18
0.59
44.47
0.65
43.53
0.48
39.01
0.49
39.89
0.44
34.83
0.24
34.98
0.54
29.03
0.65
29.59
0.58
19.51
0.55
20.02
0.38
14.71
0.54
19.72
0.24
12.84
0.17
18.75
1.86
12.12
29.55
18.47
4.69
11.95
7.90
18.45
8.19
11.73
1.39
18.16
11.92
11.60
17.40
17.80
17.37
11.45
2.93
16.15
22.96
11.27
11.06
13.92
37.57
10.82
4.72
12.23
47.31
10.52
43.59
7.65
60.02
6.61
60.55
0.95
85.15
0.74
85.92
68.01
64.38
59.42
54.17
49.49
44.34
39.74
35.01
29.52
19.47
9.46
7.68
7.20
6.68
5.97
4.97
4.95
4.92
4.61
4.52
4.35
4.13
0.78
0.57
0.61
0.71
1.03
0.62
0.51
0.31
0.50
0.51
0.61
0.41
31.23
0.10
56.28
13.92
0.68
7.41
3.16
1.16
1.80
0.58
3.79
85.47
77.41
71.34
65.61
58.71
51.39
43.78
36.27
27.74
19.33
9.67
1.06
1.42
8.31
16.76
25.22
34.53
44.03
53.82
64.22
74.23
86.67
97.33
75.14
71.60
64.89
57.42
51.54
43.72
36.27
24.21
18.77
10.63
1.07
76.09
70.08
64.89
59.79
52.32
45.31
35.55
26.85
18.09
10.07
0.88
1.06
9.58
16.36
23.84
33.62
41.61
53.21
62.63
73.60
84.78
96.51
100 w1
100 w1
90
40
0
T = 313.15 K
100 w2
100
30
Triton X-102
T = 298.15 K
50
60
70
80
90
100
a
0
(Solid + liquid) equilibria
0.19
76.27
1.01
7.11
71.84
8.52
16.47
64.88
16.58
25.48
59.30
25.87
33.83
52.34
34.60
42.68
44.24
43.45
51.58
35.84
53.76
65.37
26.53
64.67
72.58
18.06
74.81
83.46
9.38
85.88
95.25
0.82
95.29
Standard uncertainties are ur(w) = ±0.02, u(T) = ±0.01 K; u(P) = ±2 kPa.
N1112OHCl
FIGURE 2. Phase diagrams for the systems {Triton X-102 (1) + N1112OHCl (2) + H2O
(3)} at T = 298.15 K (s), T = 313.15 K ( ), T = 323.15 K ( ), T = 333.15 K ( ) and
P = 101.33 kPa. Symbols represent experimental values, solid lines are guides to the
eye and dashed lines refer to the model.
possible surfactants, Triton X family is widely applied in the
biotechnological sector (enzyme purification, pollutants solubilization agent in bioremediation, etc.) [17,18], and has thus been
selected for the present work.
Then, in view of the above, the immiscibility regions for the systems (Triton X-100 or Triton X-102 + N1112OHCl + H2O) have been
determined at several temperatures, and the experimental data
were correlated with known equations. The tie-lines were also
ascertained in order to deeper characterize the extraction capacity.
The use of models like Othmer–Tobias, Setschenow and Bancroft
helped to elucidate the consistency of the experimental tie-line
data.
88
M.S. Álvarez et al. / J. Chem. Thermodynamics 91 (2015) 86–93
TABLE 3
Phase equilibria of {Triton X-102 (1) + N1112OHCl (2) + H2O (3)} at T = (298.15 to 333.15) K and P = 101.33 kPa.a
T = 298.15 K
100 w2
a
T = 313.15 K
100 w1
T = 323.15 K
100 w2
100 w1
(Liquid + liquid) equilibria
0.49
70.01
0.51
64.33
0.64
59.59
0.66
54.22
0.30
49.88
0.72
44.51
0.45
39.01
0.32
34.42
0.30
29.35
0.29
26.12
0.28
23.98
7.35
23.01
1.88
22.11
11.73
21.48
14.77
19.62
24.53
17.59
29.46
15.69
40.03
11.22
51.30
7.37
64.23
0.94
85.60
74.14
69.31
64.11
59.04
53.87
49.24
45.01
38.21
37.48
36.72
35.26
32.75
30.11
27.11
22.97
19.64
14.06
7.51
0.88
0.68
0.67
0.64
0.78
0.64
0.62
0.40
0.73
0.49
4.12
8.71
13.92
20.25
27.11
36.14
42.73
55.76
69.49
88.45
74.84
69.54
64.11
59.21
54.11
49.05
43.56
40.01
35.22
30.83
29.89
28.84
28.18
27.18
26.22
22.95
20.59
16.89
12.92
6.93
1.12
75.22
70.46
64.73
58.55
51.34
43.87
35.18
28.71
18.86
9.39
0.96
0.98
7.90
15.99
24.88
34.52
43.86
55.36
62.48
74.80
86.92
96.66
74.97
71.11
63.13
57.31
53.35
41.51
36.06
18.58
9.25
1.25
100 w2
(Solid + liquid) equilibria
0.70
75.85
4.74
70.12
16.09
64.44
24.75
58.65
30.05
50.63
44.37
43.16
51.58
34.41
73.78
27.46
85.76
17.81
95.78
10.29
1.05
T = 333.15 K
100 w1
100 w2
100 w1
0.52
0.89
0.38
0.34
0.57
0.61
0.49
0.65
0.33
0.34
2.64
6.05
9.44
14.32
19.73
28.23
36.73
47.76
60.51
85.08
68.14
64.51
59.81
54.48
49.66
44.48
39.54
35.03
29.28
19.70
19.49
18.31
18.29
16.34
15.89
14.86
14.42
13.08
10.62
5.97
0.88
0.44
0.48
0.32
0.72
0.45
0.37
0.52
0.48
0.75
0.22
0.59
2.63
4.21
6.97
9.95
13.92
20.05
29.71
41.75
59.69
83.88
0.97
7.72
16.94
25.04
33.76
43.40
55.21
64.29
75.82
84.53
95.53
75.89
67.94
65.14
58.08
52.17
44.55
37.13
27.65
19.18
8.67
1.01
0.83
9.77
15.00
24.77
32.67
41.74
51.64
62.82
75.37
86.65
96.76
Standard uncertainties are ur(w) = ±0.02, u(T) = ±0.01 K; u(P) = ±2 kPa.
TABLE 4
Parameters of equation (1) and standard deviation for the systems {surfactant (1) + N1112OHCl (2) + H2O (3)}.a
T/K
a
A
b
298.15
313.15
323.15
333.15
0.9312
0.8827
0.8424
1.5277
Triton X-100 (1) + N1112OHCl (2) + H2O (3)
0.6985
0.5244
0.2149
6.4361
298.15
313.15
323.15
333.15
0.9810
0.9696
0.9617
0.9300
Triton X-102 (1) + N1112OHCl (2) + H2O (3)
1.1645
1.3038
1.3660
1.2193
c
r
98.4
304.6
1256.5
1000.0
0.0445
0.0499
0.1132
0.1499
39.0
76.1
141.0
394.8
0.0229
0.0309
0.0280
0.0271
Standard deviation (r) was calculated by means of equation (3).
TABLE 5
Parameters of equation (2) and standard deviation for the systems {surfactant (1) + N1112OHCl (2) + H2O (3)}.a
a
b
d
r
298.15
313.15
323.15
333.15
2.1218
5.9199
11.229
0.0002
Triton X-100 (1) + N1112OHCl (2) + H2O (3)
23.0
59.3
63.8
177.5
138.6
458.4
0.0191
14.646
119.4
402.6
1453.8
598.4
0.0350
0.0320
0.0704
0.1501
298.15
313.15
323.15
333.15
0.3088
0.7706
1.7034
2.3695
Triton X-102 (1) + N1112OHCl (2) + H2O (3)
5.4383
13.058
10.838
28.121
21.247
57.91
30.542
94.3
33.1
68.4
139.0
291.8
0.0251
0.0287
0.0219
0.0170
T/K
a
c
Standard deviation (r) was calculated by means of equation (3).
89
100
75
75
50
50
25
25
0
0
75
75
50
50
25
25
0
100 w1
100
100 w1
100 w1
100 w1
M.S. Álvarez et al. / J. Chem. Thermodynamics 91 (2015) 86–93
0
0
25
50
75
0
25
100 w2
50
75
100
100 w2
FIGURE 3. Phase diagrams for the systems {surfactant (1) + ionic liquid (2) + H2O (3)} at P = 101.33 kPa and T = 298.15 K (black), T = 313.15 K (green), T = 323.15 K (blue) and
T = 333.15 K (red): (s) N1112OHCl; (h) C2C1imC2SO4. Void and full symbols represent systems with Triton X-100 and Triton X-102, respectively. Dashed lines are modeled data
and solid lines are guides for the eye. (For interpretation of the references to colour in this figure legend, the reader is referred to the web version of this article.)
2. Experimental
2.1. Chemicals
Triton X-100 and Triton X-102, non-ionic surfactants belonging
to the polyoxyethylene t-octylphenol family, were acquired from
Sigma–Aldrich, and used as received without further purification.
The ionic liquid N1112OHCl was also supplied by Sigma–Aldrich
(mass fraction purity > 0.97). Possible traces of solvents and moisture were removed by vacuum drying (P = 2 101 Pa) and moderate temperature (T = 323.15 K) for several days. Then, it was stored
under inert atmosphere until use. The suppliers, chemical structures, purity and characteristics of the compounds used are shown
in table 1.
refractive index measurements, after a preliminary calibration
stage, where the binodal curve data were characterized by measuring the selected physical properties the selected temperature
range. The uncertainty of the phase composition is estimated as
±2%. An Anton Paar DSA-48 digital vibrating tube densimeter with
an uncertainty of ±2 104 g cm3 was used for density measurements. It was calibrated with water and ambient air. Refractive
indices were determined by means of a Dr. Kernchen ABBEMAT
WR refractometer (uncertainty of ±4 105), after calibration with
Milli-Q water and tetrachloroethylene following the manufacturer
recommendations.
3. Results and discussion
3.1. Phase diagrams
2.2. Experimental procedure
The determination of the binodal curves was performed in a
jacketed glass vessel containing a magnetic stirrer at different temperatures from T = (298.15 to 333.15) K. The temperature was controlled with a F200 ASL digital thermometer with an uncertainty of
±0.01 K. The procedure employed was based on the cloud point
method, as previously reported [19]. In brief, water was added to
binary mixtures containing known concentrations of surfactant
and ionic liquid (ranging from 0.1:0.99 to 0.99:0.1 ratios of surfactant: ionic liquid) until the disappearance of solids. In this way, the
(S + L) region was delimited.
Then, water was added to different ternary mixtures from the
biphasic region until a transparent solution was obtained, thus
allowing to map the binodal curve. The mass of each component
was determined by means of an analytical Sartorius Cubis MSA balance (125P-100-DA, ±105 g).
Tie-line data determination was also performed in the previously described thermostated glass vessel. A ternary mixture with
known composition from the biphasic region was added to the cell,
stirred vigorously for 1 h, and left to settle for 48 h. A syringe was
used to take the samples from the immiscible phases. The composition of each layer was determined by means of density and
The phase diagrams of the systems containing (Triton X-100 or
Triton X-102 + N1112OHCl + H2O) were ascertained at temperatures
values ranging from T = (298.15 to 333.15) K and P = 101.33 kPa.
The experimental values are shown as triangular representation
in figures 1 and 2, and these data are compiled in tables 2 and 3.
As it can be observed, the binodal curve and the SLLE phase boundary divide three clear regions: the one-liquid phase (L), the biphasic region (L + L), and the solid-two liquid phase (S + L). In order to
characterize the binodal curves properly, two well-known models
were proposed [20,21]:
0:5
w1 ¼ a exp bw2 cw32 ;
ð1Þ
0:5
2
w1 ¼ exp a þ bw2 þ cw þ dw2 ;
ð2Þ
being w1 and w2 the mass fraction of triton and cholinium chloride,
respectively. On the other hand, a, b, c and d are the fitting parameters, which values were determined by minimizing the standard
deviation (r):
r¼
PnDAT i
zexp zadjust
nDAT
2 !1=2
;
ð3Þ
90
M.S. Álvarez et al. / J. Chem. Thermodynamics 91 (2015) 86–93
TABLE 6
Experimental tie-lines in mass percentage for the systems {surfactant (1) + N1112OHCl (2) + H2O (3)} at T = (298.15 to 333.15) K and P = 101.33 kPa.a
Surfactant-rich phase
100
wI1
Ionic liquid-rich phase
100
wI2
100 wII1
100 wII2
TLL
S
114.1
92.7
62.2
1.4156
1.5612
1.6995
66.85
51.89
36.54
111.4
82.5
43.6
1.3719
1.4896
1.6472
68.10
55.62
43.30
35.40
23.06
109.1
87.4
70.6
49.9
31.2
1.2733
1.3805
1.6430
1.7400
2.6718
67.55
58.34
46.03
35.27
23.71
14.29
110.0
97.3
67.0
66.6
45.1
35.0
1.3019
1.4300
1.5978
1.8570
2.5965
3.4834
113.5
103.9
78.6
1.4537
1.6053
1.8570
67.60
57.23
43.23
115.0
102.1
73.5
1.3986
1.5208
1.7612
67.44
58.01
46.03
33.44
114.2
102.3
81.5
52.4
1.3909
1.4875
1.6878
2.1378
66.82
57.55
45.62
35.56
25.94
110.8
96.7
71.4
48.4
32.0
1.3486
1.3924
1.4989
1.6616
2.2815
Triton X-100 (1) + N1112OHCl (2) + H2O (3)
T = 298.15 K
0.16
66.87
0.22
54.94
0.29
45.59
93.32
78.25
53.91
1.06
4.96
14.04
90.23
68.71
37.57
1.22
5.91
13.92
0.19
0.22
0.31
85.92
71.03
60.55
43.59
29.55
0.74
4.32
6.61
10.52
12.12
0.15
0.21
0.27
0.30
0.32
87.39
79.90
65.51
58.96
42.42
34.07
0.53
2.59
5.18
3.67
7.50
4.63
0.14
0.18
0.24
0.28
0.33
0.42
T = 313.15 K
T = 323.15 K
T = 333.15 K
Triton X-102 (1) + N1112OHCl (2) + H2O (3)
T = 298.15 K
0.16
65.06
0.27
55.81
0.30
44.77
93.69
88.45
69.49
0.72
0.88
7.51
93.69
85.60
64.23
0.72
1.12
6.93
0.15
0.27
0.30
92.89
85.08
70.35
47.76
0.77
0.94
4.49
11.23
0.16
0.19
0.24
0.28
89.15
78.75
59.69
41.75
29.71
0.85
1.15
5.97
10.62
13.08
0.18
0.22
0.26
0.31
0.37
T = 313.15 K
T = 323.15 K
T = 333.15 K
a
Standard uncertainties are ur(w) = ±0.02, u(T) = ±0.01 K; u(P) = ±2 kPa.
where zexp represents the experimental values, zadjust represents the
theoretical values, and nDAT equals the number of experimental
points. Thus, the values of the parameters are listed in tables 4
and 5, together with the corresponding standard deviation. As
expected, the deviation data justify the greater suitability of using
equation (4) for the description of the binodal results when the
cholinium-based ionic liquid is used as a phase splitter, no matter
the temperature or surfactant used. These results are in agreement
with the findings reported by Hamzehzadeh and Zaffarani [21],
where a four-parameter based equation yielded lower deviations
than the well-known Merchuk model.
The analysis of the effect of the operation temperature reveals
an increased immiscibility at higher temperatures, in agreement
with previous results of our group based on the ABS behavior of
imidazolium-based ionic liquids and the same non-ionic surfactants [16]. The reason for these trends lies in the weakening of
the hydrogen bonds between the water molecules and the hydrophilic moiety of the non-ionic surfactant at elevated temperatures,
increasing the hydrophobic character of the latter. In this scenario,
the role of choline chloride as phase segregation agents is
enhanced, thus leading to greater immiscibility windows. This
behavior is coincident with the values reported for the same and
other cholinium-based ionic liquids in the presence of aqueous
solutions of polypropyleneglycol [7].
In relation to the effect of the ionic liquid, literature data allows
us to confirm the absence of studies focused on the phase segregation of non-ionic surfactant using molten salts as segregation agents,
except for a recent work published by our group [16]. Thus, the comparison between cholinium and imidazolium-based ionic liquids
(N1112OHCl vs. C2C1imC2SO4) can be visualized in figure 3. The results
obtained for the four selected temperatures reflect that the use of
more hydrophilic ionic liquids involves greater salting out potential,
as can be inferred from the binodal curves closer to the origin. This is
due to the higher affinity of N1112OHCl for the water molecules, which
makes it easier to establish hydrogen bonds and in turn, to trigger
surfactant segregation. In this sense, the results presented in figures
1 and 2 provide the evidence of the existence of binodal curves closer
to the water vertex in the presence of Triton X-100. The rationale
91
M.S. Álvarez et al. / J. Chem. Thermodynamics 91 (2015) 86–93
Water
0
10
Water
0
10
20
30
40
50
60
70
80
90
100
0
N1112OHCl
Triton x-100
20
90
10
S+L
100
30
80
20
90
40
70
30
80
50
60
40
70
60
50
50
60
70
40
60
50
80
L
30
70
40
90
20
80
L
0
10
10
Triton X-100
20
30
40
50
100
10
90
L
30
70
70
80
90
Triton X-100
70
80
90
40
30
100
20
90
10
S+L
60
50
80
20
100
60
70
30
50
0
N1112OHCl
0
10
S+L
100
10
20
30
40
50
60
70
80
90
Triton X-100
FIGURE 4. Tie-lines for the systems {Triton X-100 (1) + N1112OHCl (2) + H2O (3)} at T = 298.15 K (s), T = 313.15 K ( ), T = 323.15 K (
Symbols represent experimental values, solid lines are guides to the eye and dashed lines refer to the model.
behind this behavior may lie in the hydrophobicity of the surfactants, since the use of more hydrophobic Triton X-100 (as inferred
from its lower HLB value – see table 1) makes it easier to trigger
(liquid + liquid) de-mixing.
3.2. Tie-lines
The tie-lines of the systems at T = (298.15, 313.15, 323.15 and
333.15) K and P = 101.33 kPa were ascertained by using measurements of density and refractive indices as explained in the materials and methods section. The experimental values obtained are
compiled in table 6, and can be visually inspected in figures 4
and 5. These data provide the evidence that higher concentrations
of ionic liquids in the lower phase correlate with higher concentrations of the surfactant in the light layer. This is a consequence of
the competition between the ionic liquid and the surfactant for
the water molecules: the greater amount of N1112OHCl present in
the mixture, the lower number of water molecules are available
to solvate the surfactant.
Additionally, the extraction capacity was characterized by
means of the tie-line length (TLL) and the slope (S), calculated as:
TLL ¼
S¼
h
wI1 wII1
wI1 wII1
;
wI2 wII2
2
2 i0:5
þ wI2 wII2
;
0
70
60
40
40
100
80
L
50
50
30
90
90
40
60
60
20
80
100
20
80
50
10
70
N1112OHCl
0
40
0
60
Water
20
30
10
S+L
100
Water
0
100
10
90
20
30
0
100
ð4Þ
ð5Þ
being [w1] and [w2] the surfactant and ionic liquid mass concentration, respectively. The superscripts I and II refer to top and bottom
phases, respectively.
100
0
N1112OHCl
), T = 333.15 K (
) and P = 101.33 kPa.
A visual inspection of the results allowed us to conclude that
the lower TLL value leads to greater S values. On the other hand,
it is also outstanding that the heavy layer is almost exclusively
constituted by a binary mixture (water + ionic liquid), while surfactant concentrations in some of the upper phases reach values
higher than 90%. The comparison between surfactants makes it
possible to check that the more hydrophobic Triton X-100 is able
to be salted out more easily to the upper phase than Triton
X-102, as can be seduced from the less negative S values of the
latter.
The application of two well-known models like Othmer–Tobias
and Bancroft equations serve our goal to get more information on
the consistency of the tie-line data [22,23].
m
1 wI1
1 wII2
¼
n
;
wI1
wII2
ð6Þ
II I r
w3
w3
¼
k
;
wII2
wI1
ð7Þ
where w1, w2 and w3 refer to the concentrations of surfactant,
N1112OHCl and water, respectively, I and II mean top and bottom
phases, respectively, and n, m, k and r are the fitting parameters.
The values of these parameters are shown in tables 7 and 8,
together with the correlation coefficient R2. Generally speaking,
both models appropriately describe the tie-line data, since the correlation coefficients are all higher than 0.96. However, it seems
that the use of Othmer-Tobias equation fits better to the experimental data (R2Othmertobias > R2Bancroft ).
92
M.S. Álvarez et al. / J. Chem. Thermodynamics 91 (2015) 86–93
Water
Water
0
40
60
30
40
50
60
70
80
90
Triton X-102
100
20
90
10
S+L
30
80
20
20
40
70
30
80
10
50
60
40
70
0
60
50
50
100
0
N1112OHCl
0
0
10
20
30
40
0
90
L
30
70
70
80
70
80
90
100
0
N1112OHCl
Triton X-102
40
30
20
90
10
S+L
60
50
80
20
100
60
70
90
0
TABLE 7
Parameters of the Othmer–Tobias equation (6) and correlation coefficient for
{surfactant (1) + N1112OHCl (2) + H2O (3)} from T = (298.15 to 333.15) K and
P = 101.33 kPa.
Surfactant
T/K
n
m
R2
Triton X-100
298.15
313.15
323.15
333.15
0.5070
1.0000
1.0000
1.0000
2.8138
2.3668
1.0589
0.5519
0.999
0.999
0.988
0.976
Triton X-102
298.15
313.15
323.15
333.15
0.2554
1.0000
1.0000
1.0000
2.2656
3.3485
2.0798
1.4053
0.983
0.999
0.999
0.991
TABLE 8
Parameters of the Bancroft equation (7) and correlation coefficient for the systems
{surfactant (1) + N1112OHCl (2) + H2O (3)} at T = (298.15 to 333.15) K and
P = 101.33 kPa.
Surfactant
T/K
k
r
R2
Triton X-100
298.15
313.15
323.15
333.15
1.5316
1.5148
1.9001
2.9375
0.4422
0.4799
0.7642
0.9580
0.990
0.999
0.991
0.987
298.15
313.15
323.15
333.15
2.3471
1.9278
2.1072
1.7525
0.5623
0.4997
0.5649
0.6072
0.957
0.999
0.996
0.991
10
S+L
100
10
20
30
40
50
60
70
80
Triton X-102
FIGURE 5. Tie-lines for the systems {Triton X-102 (1) + N1112OHCl (2) + H2O (3)} at T = 298.15 K (s), T = 313.15 K ( ), T = 323.15 K (
Symbols represent experimental values, solid lines are guides to the eye and dashed lines refer to the model.
Triton X-102
0
70
60
30
50
100
80
L
50
40
40
90
90
40
60
50
30
80
100
20
80
60
20
70
N1112OHCl
10
50
10
60
Triton X-102
100
40
0
50
Water
20
30
10
S+L
100
Water
10
70
40
60
50
80
L
30
70
90
90
20
80
L
100
10
90
20
30
0
100
10
90
100
0
N1112OHCl
), T = 333.15 K (
) and P = 101.33 kPa.
4. Conclusions
The ability of cholinium chloride ionic liquids to salt out aqueous solutions of non-ionic surfactants has been demonstrated in
this work for the first time. The immiscibility region was determined and the experimental values were suitably correlated with
well-known equations. All the data were discussed in the light of
the ionic liquid and surfactant effect, as well as the influence of
temperature. It is observed that both increase of temperature and
surfactant hydrophobicity leads to greater immiscibility regions.
Finally, it became apparent that the use of a cheap, biocompatible
and biodegradable molten salt like N1112OHCl, allowed the efficient
salting out of the surface active compounds (as can be corroborated from the TLL and S values), leading to a heavy phase exclusively composed of water and ionic liquid and a light phase with
high concentrations of surfactant.
Acknowledgements
This research has been financially supported by the Spanish
Ministry of Economy and Competitiveness, Xunta de Galicia and
ERDF Funds (Projects CTM2014-52471-R and GRC 2013/003). The
authors are grateful to the Spanish Ministry of Economy and
Competitiveness for the financial support of F.J. Deive under the
Ramón y Cajal program (RyC-2013-14225).
M.S. Álvarez et al. / J. Chem. Thermodynamics 91 (2015) 86–93
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JCT 15-357
ANNEX 6
A BIOCOMPATIBLE STEPPING STONE
(SEPARATION
AND
FOR THE
PURIFICATION
10.1016/J.SEPPUR.2015.08.039).
REMOVAL
TECHNOLOGY,
OF
EMERGING CONTAMINANTS
2015,
IN
PRESS
DOI
Separation and Purification Technology 153 (2015) 91–98
Contents lists available at ScienceDirect
Separation and Purification Technology
journal homepage: www.elsevier.com/locate/seppur
A biocompatible stepping stone for the removal of emerging
contaminants
María S. Álvarez a, José M.S.S. Esperança b, Francisco J. Deive a,⇑, Mª Ángeles Sanromán a, Ana Rodríguez a,⇑
a
b
Department of Chemical Engineering, University of Vigo, 36310 Vigo, Spain
Instituto de Tecnologia Química e Biológica António Xavier, Universidade Nova de Lisboa, Av. República, 2780-157 Oeiras, Portugal
a r t i c l e
i n f o
Article history:
Received 23 June 2015
Received in revised form 8 August 2015
Accepted 26 August 2015
Available online 28 August 2015
Keywords:
Emerging contaminants
Ibuprofen
Diclofenac
Ionic liquids
Aqueous biphasic systems
a b s t r a c t
The presence of emerging contaminants like pharmaceuticals in the environment is prompting the search
of new methods to concentrate and remove them from soils, sediments and effluents. A completely biocompatible aqueous biphasic system composed of Tween 20 or Tween 80 and the ionic liquid choline
chloride has been designed for extracting non-steroidal anti-inflammatory drugs from aqueous streams.
After an initial evaluation of the salting out potential of the selected ionic liquid at different temperatures,
the extraction capacity of these systems to be applied for ibuprofen and diclofenac removal from aqueous
streams was assessed. Very high levels of contaminant removal (higher than 90%) were reached for all the
temperature and feed concentrations used. The suitability of the proposed biocompatible aqueous biphasic systems for the treatment of drugs-polluted effluents from surfactant-based soil washing operations is
envisaged.
Ó 2015 Elsevier B.V. All rights reserved.
1. Introduction
Emerging contaminants are currently gaining social awareness
due to their potential deleterious effects in the environment.
Nonetheless, there is still an absence of legislation ruling the presence of these pollutants [1], an only the Water Framework Directive (2000/60/EC) [2] presents vague guidelines related to the
water policies in the EU. More specifically, research funds are being
invested in different international joint initiatives in order to
merge research efforts tackling efficient wastewater treatment
processes to remove these compounds [3]. Among the emerging
pollutants, non-steroidal anti-inflammatory drugs (NSAIDs) are
the most utilized group of analgesic and anti-inflammatory drugs
worldwide, due to their suitability to treat the pain triggered by
common illnesses [4]. Thus, the last report by the Spanish Ministry
of Health stresses that arylpropionic derivatives are by far the largest used pharmaceuticals (about 65.1% of the total drug consumption), being ibuprofen the one with higher intake rate (43.9%) and
diclofenac, an arylacetic acid derivative, the second one [5].
This scenario has compelled to analyze the possible presence of
these compounds in the environment, as they can be excreted
without having been metabolized. In this sense, different authors
have shed light on their presence in waste water treatment plants
⇑ Corresponding authors.
E-mail addresses: [email protected] (F.J. Deive), [email protected] (A. Rodríguez).
http://dx.doi.org/10.1016/j.seppur.2015.08.039
1383-5866/Ó 2015 Elsevier B.V. All rights reserved.
(WWTPs), and have concluded that these compounds are not effectively removed after the treatment [6]. More specifically, ibuprofen
and diclofenac concentration has been detected in the inlet
streams of different WWTPs at concentration levels of 516 and
250 ng/L, recording less than 50% and 15% of removal in the outlet
effluents, respectively [7]. In this sense, NSAIDs have also been
detected in ground waters (in the order of ppb and ppt) and sediments (in the order of ppm and ppb) due to the great development
of new analytical techniques [8,9]. It is clear that the continuous
introduction of these pollutants may seriously affect drinking
water supplies, ecosystems and human health, as reviewed by
Sirés and Brillas [10].
Given the observed limitations of WWTPs, new treatment
strategies have been investigated such as advanced oxidation processes or membrane technologies [11,12]. However, little information can be found related to the application of liquid–liquid
extraction to the removal of these contaminants. Aqueous biphasic
systems, a phase splitting typically caused by a salt in the presence
of aqueous solutions of polymers, have emerged as a valuable separation strategy. Coutinho and coworkers have demonstrated the
suitability of this method for the removal of NSAIDs and strogens
[13,14] by using ionic liquids.
In the last years, the outbreak of these neoteric solvents, with
appealing properties such as their negligible volatility and tunability [15], has boosted the implementation of ‘all-purpose’ aqueous
biphasic systems in combination with salts and polymers [16,17].
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M.S. Álvarez et al. / Separation and Purification Technology 153 (2015) 91–98
Recently, we have demonstrated the capacity of imidazoliumbased ionic liquids to trigger phase segregation in aqueous solutions of non-ionic surfactants with a number of advantages like
their low interfacial tension, rapid phase disengagement, low cost
and bulk availability of the surfactants [18]. In this work, we concluded that more hydrophilic ionic liquids were more prone to trigger liquid–liquid demixing, so the search of more hydrophilic
families could open up new opportunities to be applied in polluted
effluents obtained after surfactant-based soil washing processes,
where this kind of surface active compounds are usually employed
as contaminant solubilizers.
On the basis of the abovementioned, more hydrophilic and generally recognized biocompatible ionic liquids like choline chloride
(N1112OHCl) [19,20] have been proposed to trigger phase segregation in aqueous solutions of non-ionic surfactants. In this case,
Tween 20 and Tween 80 have been chosen since they are considered as GRAS by the US FDA and they are classified as safe food
additives in many countries (E432 and E433, respectively) [21].
The immiscibility windows of the systems were firstly investigated
at different temperatures, by characterizing the binodal curves and
tie line data. The results were discussed on the basis of the surfactant and ionic liquid hydrophobicity and operation temperature.
The extractive performance for two model NSAIDs, ibuprofen and
diclofenac, was determined in order to suggest a viable strategy
for removing them from aqueous polluted effluents.
composition was quantified by measuring densities and refractive
indices (estimated uncertainty of concentration ± 0.02%).
3.2. Ibuprofen and diclofenac extraction and quantification
For the study of NSAIDs partition, different aqueous solutions of
Tween 80 containing ibuprofen and diclofenac at concentrations of
35 mg/L were introduced in glass ampoules, since it falls in the
range usually detected in environmental samples [8,9]. Choline
chloride was added until the desired composition within the
biphasic region was reached. The mixture was vigorously stirred
and left to settle for at least 48 h at 298.15 K and 333.15 K. The layers were carefully separated in order to quantify ibuprofen and
diclofenac by HPLC measurements. HPLC-DAD (Agilent 1260 infinity) is equipped with a Kinetex Biphenyl column (4.6 150 mm;
internal diameter 5 lm). 10 lL of sample were eluted in gradient
mode for 15 min at a flow rate of 1 mL/min, using a mixture
water/ethanol at the following ratios: 65:30 for 10 min and
15:80 for the separation. Retention times for ibuprofen and diclofenac were 10.149 and 10.713 min, respectively. The calibrations
were carried out with stock solutions prepared in methanol at a
concentration of 3.5 mg/mL, and were appropriately diluted in
Milli-Q water (0.1–10 mg/L).
4. Results and discussion
2. Experimental
2.1. Chemicals
The non-ionic surfactants polyethoxylated sorbitan monolaurate (Tween 20) (>97%) and monooleate (Tween 80) (>99%), the
NSAIDs ibuprofen (>98%) and diclofenac (>98.5%) were acquired
from Sigma–Aldrich and employed as received without further
purification. Choline chloride (>99%) was also purchased from
Sigma–Aldrich and submitted to vacuum for several days at 70 °C
to ensure moisture removal prior to its use. The chemical structures of all these compounds are shown in Fig. 1.
3. Experimental procedure
3.1. Binodal curves determination
The binodal curves were ascertained in a magnetically stirred
jacketed glass cell (Fig. S1) at temperatures ranging from 298.15
to 333.15 K. The temperature was controlled with a F200 ASL digital thermometer with an uncertainty of ±0.01 K. The cloud point
method was the experimental technique for binodal data determination [18]. Briefly, binary mixtures with known compositions of
ionic liquid and surfactant were prepared in a dry chamber, and
drop-wise additions of water were carried out until the disappearance of solids, thus characterizing the S + 2L region. Afterwards,
water was added up to turbidity vanishing in order to fully map
the binodal curves. The concentration of these points was determined by weighting in an analytical Sartorius Cubis MSA balance
(125P-100-DA, ±105 g). The binodal curve was also characterized
by measuring densities and refractive indices at different temperatures, using an Anton Paar DSA-48 digital vibrating tube densimeter (±2 104 g cm3), and a Dr. Kernchen ABBEMAT WR
refractometer (±4 105), both calibrated in accordance with the
manufacture instructions.
Experimental tie-lines were calculated by preparing a ternary
mixture from the biphasic region, left under stirring for 1 h, and
afterwards, an idle period of 48 h was left in order to reach the
equilibrium. The two segregated layers were split and their
4.1. Choline chloride as segregation agent
First of all, the segregation potential of the ionic liquid
N1112OHCl in aqueous solutions of the non-ionic surfactants Tween
20 and Tween 80 was explored at several temperatures (298.15,
313.15, 323.15 and 333.15 K). The experimental data are compiled
in Tables S1 and S2 in the SI, and they can be visualized in Figs. 2
and 3. The analysis of the influence of temperature on the binodal
curves allows concluding that liquid–liquid demixing is eased at
higher temperatures for both surfactants. This is attributed to the
lower ability of the non-ionic surfactant to establish hydrogen
bonds with water at higher temperatures, which furthers the
salting out effect provided by the N1112OHCl ionic liquid. This
behavior is coincident with previous results for other systems
containing non-ionic surfactants like Triton X-100 and Triton
X-102 with the ionic liquid C2C1imC2SO4 [18].
An exhaustive literature analysis on the effect of temperature
on the immiscibility window has been carried out, and the main
results are summarized in Table S5. In order to better classify the
information, the table has been divided into the four main types
of aqueous biphasic systems found, namely, those based in polymers, ionic liquids, surfactants and organic solvents. As can be
noticed, two main behaviors can be inferred, depending on the nature of the compounds competing for the water molecules: a proportional relationship between the area of the immiscibility
window and temperature is observed when organic solvents, polymers or surfactants are salted out by inorganic or organic salts,
[22,23]. Contrarily to this, the systems composed of ionic liquids
and inorganic or organic salts display smaller biphasic regions at
higher temperatures [24,25]. The reason for these trends lies in
the weakening of the hydrogen bonds between the water molecules and the hydrophilic moiety of polymers, non-ionic surfactants and organic solvents at elevated temperatures, which leads
to an increased hydrophobicity of these compounds. On the contrary, the completely different properties of ionic liquids involve
greater interplays with water at higher temperatures. Therefore,
when non-ionic surfactant and ionic liquids are put together, a
synergic effect is observed at elevated temperatures, since a
greater ability for water solvation of the ionic liquid is summed
M.S. Álvarez et al. / Separation and Purification Technology 153 (2015) 91–98
Diclofenac
93
Ibuprofen
Tween 20 (w+x+y+z=20)
Tween 80 (w+x+y+z=20)
Choline Chloride
Fig. 1. Structures of the NSAIDs, ionic liquid and non-ionic surfactants.
to the lower affinity for the water molecules of the non-ionic surfactant, thus leading to the remarkable increase of the biphasic
region.
The comparison between surfactants Tween 20 and Tween 80
reflects the existence of greater immiscibility regions for the latter,
independently of the temperature, as a consequence of their different chemical structure. Thus, Tween 80 is more easily salted out by
N1112OHCl due to the fact that it is less prone to establish hydrogen
bonds with water, so the competition between the surfactant and
the ionic liquid for the water molecules is more easily won by the
latter. In this sense, a valuable tool to explain this effect is the
degree of hydrophobicity of the surface active compounds, as can
be inferred from their Hydrophilic/Lipophilic balance (HLB). This
is a useful parameter widely considered for measuring the aqueous
affinity of surfactants, varying between 0 and 20, from high to low
hydrophobicity, respectively. The greater hydrophobicity of Tween
80 with respect to Tween 20 (HLB = 5 vs. HLB = 16.7) makes us to
foresee its easier phase disengagement, in line with previous
results of our group for surfactant-based aqueous biphasic systems
in the presence of inorganic and organic salts, and imidazolium
ionic liquids [18,26–31].
Besides the role of the aforesaid biocompatible non-ionic surfactant in the observed water demixing behavior, the selection of
an environmentally benign ionic liquid is crucial to attain a truly
biocompatible separation platform. The results obtained with
N1112OHCl are encouraging when compared with previous aqueous
biphasic systems entailing imidazolium-based ionic liquids [18],
since much greater salting out potential is reached with the
ammonium-based solvents. The economic and environmental
gains of the implemented system can be attributed to the lower
consumption of extraction agent, the lower environmental impact
and the lower cost of reagents.
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M.S. Álvarez et al. / Separation and Purification Technology 153 (2015) 91–98
Water
0
Water
0
100
10
90
20
L
60
40
50
60
70
80
90
100
0
0
0
10
20
30
40
50
60
80
90
30
0
30
70
40
0
Tween 20
40
50
60
70
80
90
20
100
0
0
10
20
30
40
50
0:5
2
dw2
þ cw2 þ
0:5
2
w1 ¼ exp a þ bw2 þ cw þ dw2 ;
ð1Þ
ð2Þ
ð3Þ
where w1 and w2 are defined as the mass fraction of Tween and
N1112OHCl, respectively. The minimization of the following standard
deviation (r) license the calculation of a, b, c and d:
2 !1=2
PnDAT zexp zadjust
i
nDAT
60
70
80
N1112OH Cl Tween 20
All the experimental data were fitted to different common
empirical models [32,33]:
ð4Þ
In this equation, zexp and zadjust are the experimental and theoretical values, respectively, and nDAT is the number of data. Thus,
the parameters are listed in Tables 1–3, along with the optimized
standard deviation. The analysis of these data evidences a more
suitable fitting of Eq. (2), so these theoretical data were represented together with the experimental data in Figs. 2 and 3. Previous research works involving non-ionic surfactant-based aqueous
biphasic systems [26,27,32,34] reveal that this kind of polynomial
equations (Eq. (2)) is the best option to properly describe the
10
S + 2L
100
Fig. 2. Phase regions and tie-lines for the systems Tween 20 + N1112OHCl + H2O at 298.15 K (d), 313.15 K ( ), 323.15 K (
data, solid lines are guides to the eye and dashed lines refer to model.
w1 ¼ a exp ðbw2 cw32 Þ;
30
L+ L
90
10
S + 2L
40
80
20
30
50
70
30
L+ L
60
60
40
80
20
70
50
50
60
10
80
L
40
60
50
100
90
20
80
90
100
10
90
L
70
100
Water
20
r¼
70
N1112OH Cl
100
10
w1 ¼ a þ
10
S + 2L
100
Water
0:5
bw2
20
N1112OH Cl Tween 20
Tween 20
0
30
L+ L
90
10
0
30
40
80
20
S + 2L
20
50
70
30
L+ L
10
60
60
40
70
0
70
50
50
100
L
40
60
50
90
80
30
70
40
80
90
20
80
30
100
10
), 333.15 K (
90
100
N1112OH Cl
). Symbols represent experimental
immiscibility region, no matter the salting out agent under study
(organic or inorganic salts and ionic liquids). This is against the
generalized trend where exponential Merchuk-type models have
been extensively applied for the fitting of polymer/salt and ionic
liquids/salt-based aqueous biphasic systems.
4.2. Ibuprofen and diclofenac extraction
The greater immiscibility detected in the systems containing
aqueous solutions of Tween 80 suggests the possibility of getting
longer tie-lines and higher concentration factors. Therefore, the
first step was to experimentally determine the tie-lines of the
systems at different temperatures in order to define the viable
points to perform the extraction of emerging contaminants. The
experimental data are presented in Figs. 2 and 3, and are listed
in the SI (Table S3). Two useful parameters, the tie-line length
(TLL) and the slope (S), were picked to investigate the suitability
of N1112OHCl and non-ionic surfactants as a platform to remove
the abovementioned NSAIDs from wastewater:
TLL ¼
S¼
h
wI1
wI2
wI1 wII1
wII1
wII2
;
2
2 i0:5
þ wI2 wII2
;
ð5Þ
ð6Þ
95
M.S. Álvarez et al. / Separation and Purification Technology 153 (2015) 91–98
Water
0
Water
0
100
10
90
20
50
0
40
50
60
70
80
90
Tween 80
20
90
10
S + 2L
100
0
10
20
30
40
50
0
10
90
40
60
L+ L
0
40
50
60
70
80
90
Tween 80
40
L+ L
100
N1112OH Cl
20
0
Table 1
Parameters of Eq. (1) and standard deviation for Surfactant + N1112OHCl + H2O at
several temperatures.
a
b
c
r
Tween 20
298.15
313.15
323.15
333.15
1.0780
1.0632
1.0642
1.0598
1.3737
1.4308
1.5792
1.5160
22.657
37.798
55.859
101.73
0.0210
0.0217
0.0274
0.0346
Tween 80
298.15
313.15
323.15
333.15
1.0589
1.0393
1.0062
0.9942
1.1978
1.1896
0.9610
0.9830
35.862
77.331
180.30
381.77
0.0280
0.0380
0.0380
0.0637
where the superscripts I and II refer to the top and bottom phases,
respectively. The results evidence that the operation in tie-lines
with greater TLL allows triggering two immiscible layers, one of
them almost exclusively constituted by non-ionic surfactant (concentrations near to 95%) and the other one composed of the binary
mixture water–N1112OHCl. Additionally, the more hydrophobic
10
S + 2L
100
0
10
20
30
40
50
60
70
80
90
100
N1112OH Cl
Tween 80
Fig. 3. Phase regions and tie-lines for the systems Tween 80 + N1112OHCl + H2O at 298.15 K (d), 313.15 K ( ), 323.15 K (
data, solid lines are guides to the eye and dashed lines refer to model.
T/K
30
90
10
S + 2L
30
50
80
20
20
60
70
30
100
70
L
60
40
90
80
50
50
60
10
100
90
30
70
50
0
90
100
20
80
L
80
80
N1112OH Cl
100
30
70
70
Water
20
40
60
Tween 80
Water
10
10
S + 2L
100
0
N1112OH Cl
0
30
L+ L
80
20
90
30
40
70
30
L+ L
20
50
60
40
70
10
60
50
50
60
0
70
L
40
60
100
80
30
70
L
80
90
20
80
30
40
100
10
), 333.15 K (
). Symbols represent experimental
Tween 80 fosters more negative S values, which would be advantageous in terms of extraction capacity when these systems are
implemented in the treatment of a wastewater effluent obtained
from polluted-soil washing steps.
Table 2
Parameters of Eq. (2) and standard deviation for Surfactant + N1112OHCl + H2O at
several temperatures.
r
T/K
a
b
c
Tween 20
298.15
313.15
323.15
333.15
d
1.0475
1.0624
1.0460
1.0560
0.0092
1.3573
1.3072
1.3906
0.4237
0.2341
0.1377
0.0300
0.8383
2.1335
2.1522
4.3340
0.0073
0.0083
0.0167
0.0285
Tween 80
298.15
313.15
323.15
333.15
1.0483
1.0526
0.9651
1.1072
0.9580
1.2326
0.1082
2.2280
0.4346
0.2198
2.9187
3.1921
1.7981
4.8072
4.0308
22.607
0.0178
0.0252
0.0254
0.0508
96
M.S. Álvarez et al. / Separation and Purification Technology 153 (2015) 91–98
Table 3
Parameters of Eq. (3) and standard deviation for Surfactant + N1112OHCl + H2O at
several temperatures.
T/K
a
b
c
Tween 20
298.15
313.15
323.15
333.15
0.2600
0.4277
0.3907
0.6228
3.9566
6.3311
6.3811
9.7033
8.0117
14.780
15.620
26.982
Tween 80
298.15
313.15
323.15
333.15
0.4553
0.5733
0.6996
1.0270
6.6965
9.0963
12.612
17.618
16.374
25.414
40.755
66.843
r
d
19.993
35.055
43.393
77.543
0.0240
0.0220
0.0297
0.0338
36.388
66.564
127.61
271.82
0.0277
0.0368
0.0363
0.0268
The consistency of the experimental tie line data was assessed
by the linearization of the Othmer–Tobias and Bancroft equations
[35,36].
m
1 wI1
1 wII2
¼
n
;
wI1
wII2
II I r
w3
w3
¼
k
;
wII2
wI1
ð7Þ
ð8Þ
being n, m, k and r are the fitting parameters, which come from the
minimization of the sum of the squared differences between the
observed and predicted values of the dependent variable, through
an iterative procedure based on Marquardt–Levenberg algorithm,
using the Sigma Plot 11.0 software. The values of the fitting parameters and the regression coefficients are displayed in Tables 4 and 5,
and reveal the reliability of the models to appropriately characterize
the tie-lines, since R2 is always higher than 0.95.
On the basis of the binodal and tie-line data, Tween 80 was chosen to implement the extraction of the selected emerging contaminants, ibuprofen and diclofenac, at the lowest and highest
temperatures. The efficiency of the NSAIDs removal was expressed
as follows:
Eð%Þ ¼
I
mi
100
mi
ð9Þ
where miI and mi is the NSAID mass content in the upper phase and
the total NSAID mass content, respectively.
The impact of temperature and feed concentration on the
ibuprofen and diclofenac extraction can be noticed in Fig. 4. In general, it becomes patent that very high values of NSAIDs extraction
to the top phase (always greater than 90%) are recorded for the
temperature range and feed concentrations employed. However,
the chemical nature of the contaminant seems to slightly impact
the extraction yields attained, since ibuprofen is generally removed
at higher rates than diclofenac. This fact may be attributed to the
different affinity of the contaminants for the organic phase. Usually, one way to measure this affinity is by analyzing the log Kow
Table 5
Parameters of Bancroft equation and correlation coefficient for Surfactant + N1112OHCl
+ H2O at several temperatures.
Surfactant
T/K
k
r
R2
Tween 20
298.15
313.15
323.15
333.15
1.0967
1.0000
1.3719
1.2662
0.3335
0.3061
0.4453
0.3157
0.999
0.985
0.967
0.980
Tween 80
298.15
313.15
323.15
333.15
2.3952
1.3463
1.5614
1.7480
0.6394
0.3318
0.4056
0.4648
0.977
0.980
0.987
0.966
values. In this particular case, log Kow for ibuprofen and diclofenac
is 2.48 and 1.90, respectively [37], which further demonstrates the
higher migration of ibuprofen to the surfactant-rich phase.
Regarding the effect of N1112OHCl concentration in the feed
(Fig. 4 and Table S4 in SI) when fixing the tie-line, it can be concluded that higher levels of ionic liquid are associated with slightly
lower NSAIDs extraction levels. In this sense, it is also outstanding
that the operation at room temperature does not jeopardize the
achievement of high levels of pollutant removal (in some cases
even near to 100%), which is a clear operational advantage from
an industrial point of view. Apart from the abovementioned benefits, the operation at feed concentrations near to the N1112OHCl vertex involves contaminant concentration factors greater than 10
without compromising too much the contaminant migration to
the upper phase (E higher than 90%).
The proposed alternative could be suitably implemented for the
removal of emerging pollutants from an aqueous effluent. The process flowsheet diagram shown in Fig. 5 integrates this one-step
separation strategy after a NSAIDs-polluted soil washing stage,
using an aqueous solution of Tween 80 (5%) as solubilizing agent
(point 1 in both the ternary and flowsheet diagram in Fig. 5).
N1112OHCl should be added up to the concentration indicated as 2
in the ternary diagram (corresponding to the same number in
the flowsheet diagram) is attained, leading to an upper phase
where more than 90% of ibuprofen and diclofenac have migrated
and concentrated more than 10 times in a phase almost exclusively
formed by Tween 80 (95%, as indicated in point 4 in the ternary
plot). Given the interest of these data, the process should be optimized in order to analyze the reusability of both Tween 80 and
N1112OHCl. In this sense, one of the important aspects to be tackled
is to elucidate the maximum solubility of these compounds in the
Tween 80-rich phase. This would give an idea of the number of
cycles that the surfactant could be reused. All in all, this novel process allows a one step-removal of two of the most common emerging contaminants, which is competitive when compared with two
recent processes recently reported requiring two or even three
combined techniques (chemical, physical and biological) to yield
similar levels of NSAIDs removal [38,39].
5. Conclusions
Table 4
Parameters of Othmer–Tobias equation and correlation coefficient for Surfactant
+ N1112OHCl + H2O at several temperatures.
Surfactant
T/K
n
m
R2
Tween 20
298.15
313.15
323.15
333.15
1.5663
1.5602
1.0000
1.0000
4.2347
3.6668
2.8284
3.6761
0.980
0.989
0.961
0.980
Tween 80
298.15
313.15
323.15
333.15
0.3959
1.0000
1.0000
1.0000
2.4965
3.9707
3.2352
2.2298
0.969
0.967
0.970
0.953
In this work we have demonstrated the suitability of a hydrophilic and biocompatible ionic liquid, N1112OHCl, to be applied for
the removal and concentration of common drugs. The great segregation potential of the selected ionic liquid in aqueous solutions of
non-ionic surfactants such as Tween 20 and Tween 80 at different
temperatures was ascertained. The application of an aqueous system composed of ionic liquid and the most hydrophobic surfactant
to a polluted effluent containing both diclofenac and ibuprofen
revealed removal levels higher than 90%. Apart from the
undoubted environmental and economic benefits of the proposed
removal strategy (mild operating conditions, low environmental
M.S. Álvarez et al. / Separation and Purification Technology 153 (2015) 91–98
97
T = 333.15 K
T = 298.15 K
E (%)
100
95
90
85
80
Feed composition (w1F, w2F)
Fig. 4. Extraction percentage (E (%)) of ibuprofen ( ) and diclofenac ( ) for different feed composition in systems Tween 80 + N1112OHCl + H2O at 298.15 and 333.15 K. w1F
and w2F are the compositions of Tween 80 and N1112OHCl in the feed stream, respectively.
Fig. 5. Flowsheet diagram and ternary plot for the aqueous biphasic system-based removal of ibuprofen (Ibu) and diclofenac (Dcf) from waste effluents obtained after soil
washing with aqueous solution of Tween 80 (5%).
impact and price and bulk availability of Tween surfactants and
choline-based ionic liquid), the easy implementation of the process
at industrial scale urges future optimizations of the process to analyze the viability of reusing the surfactant and the ionic liquid.
Acknowledgements
This work has been supported by the Spanish Ministry of Economy and Competitiveness and EDRF funds (Project CTM201452471-R). M.S. Álvarez thanks University of Vigo for funding her
stay at the ITQB. F.J. Deive acknowledges Spanish Ministry of
Economy and Competitiveness for funding through a Ramón y
Cajal contract.
Appendix A. Supplementary material
Supplementary data associated with this article can be found, in
the online version, at http://dx.doi.org/10.1016/j.seppur.2015.08.
039.
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ANNEX 7
TRITON X SURFACTANTS
TO
FORM AQUEOUS BIPHASIC SYSTEMS: EXPERIMENTAL
CORRELATION (JOURNAL OF CHEMICAL THERMODYNAMICS, 2012, 54: 385-392).
AND
J. Chem. Thermodynamics 54 (2012) 385–392
Contents lists available at SciVerse ScienceDirect
J. Chem. Thermodynamics
journal homepage: www.elsevier.com/locate/jct
Triton X surfactants to form aqueous biphasic systems: Experiment and correlation
M.S. Álvarez, F. Moscoso, A. Rodríguez, M.A. Sanromán, F.J. Deive ⇑
Department of Chemical Engineering, Universidade de Vigo, P.O. Box 36310, Vigo, Spain
a r t i c l e
i n f o
Article history:
Received 3 March 2012
Received in revised form 2 May 2012
Accepted 18 May 2012
Available online 28 May 2012
Keywords:
Aqueous Biphasic Systems
Triton X-100
Triton X-102
Potassium Salts
Correlation
a b s t r a c t
During the last years, the extraction of biomolecules and chemicals by means of Aqueous Biphasic Systems (ABS) has triggered a renewed interest, making it necessary to characterize fully the solubility data
of this kind of system. In this study, two surfactants belonging to Triton X series (Triton X-100 and Triton
X-102) are proposed as candidates to form ABS, by adding different potassium-based salts (K3PO4,
K2HPO4, K2CO3, K2S2O3 and K2SO3) at T = 298.15 K. Several equations were used to fit the solubility data
which were previously obtained by means of the cloud point method. The different phase forming capacities were analyzed in the light of the Hofmeister series, the Effective Excluded Volume (EEV) theory and
the molar Gibbs free energy of hydration (DhydG). The Othmer–Tobias equation was proposed to correlate
the tie-line data.
Ó 2012 Elsevier Ltd. All rights reserved.
1. Introduction
The behaviour of certain organic compounds in aqueous solutions of high charge-density inorganic salts has triggered the
development of an ad hoc technology based on Aqueous Biphasic
Systems (ABS), which pursues the segregation of an upper organic
rich phase and a bottom inorganic salt-rich phase, thus leading to
two aqueous phases that become immiscible [1]. This phenomenon
is based on specific interactions between the inorganic salt and the
organic compound, and their competition for water molecules.
In general, ABS have been considered as a competitive separation technique due to inherent advantages such as the short process time required to trigger phase segregation, low viscosity,
little emulsion formation, absence of organic volatile solvents, high
extraction efficiency, low energy consumption, reliable scale-up
and biocompatible environment [2]. Due to these benefits, this
separation method has been applied to the extraction of biocompounds such as enzymes [3,4], alkaloids [5], antibiotics [6] and
antioxidants [7] or other compounds like organic pollutants [8].
The organic compounds usually employed to achieve a proper
phase segregation in ABS include polymers [9], ionic liquids (ILs)
[10] and surfactants [11]. Nowadays, non-ionic surfactants have
been widely used in many diverse fields such as food, cosmetics, textiles, detergents, biocatalysis, organic chemistry, etc. Among them,
ethylene oxide derivatives such as Triton X-100 and Triton X-102
which are made up of an 8-carbon tertiary alkyl chain and 9–10
ethylene oxide units or 12–13 ethylene oxide units, respectively,
⇑ Corresponding author. Tel.: +34 986 81 87 23.
E-mail address: [email protected] (F.J. Deive).
0021-9614/$ - see front matter Ó 2012 Elsevier Ltd. All rights reserved.
http://dx.doi.org/10.1016/j.jct.2012.05.022
present outstanding an economic importance worldwide for industrial and household products due to their detergency, biodegradability, wetting and foaming properties [12].
Taking into account the above mentioned, surfactant-based ABS
entails benefits such as a lower interface tension in comparison
with the other cases, lower cost (surfactants and inorganic salts
are inexpensive), less amount of inorganic salt required to induce
phase splitting, null flammability, experimental conveniences (all
the components are commercially available at bulk quantities),
and shorter time for phase disengagement [13].
The lyotropic degree of each salt will lead to different salting out
abilities. These specific effects have been traditionally analysed in
the light of a recurring pattern now known as the Hofmeister series.
This series allow predicting the kosmotropic/chaotropic character
of each salt, based on their interaction with water molecules. Ions
are regarded as kosmotropic and chaotropic depending on their
abilities to interact with water and to change the water structure
by shifting the equilibrium of low and high density water. With a
high charge density, a kosmotropic ion interacts more strongly with
water than water with itself and tends to increase the water structure by shifting the water equilibrium to low-density water. The
situation is reversed in the case of a chaotropic ion [14].
Therefore, in this work, the salting out potential of five high
charge density inorganic salts (K3PO4, K2CO3, K2HPO4, K2S2O3 and
K2SO3) was evaluated to assess further phase segregation in aqueous solutions of two non-ionic surfactants, Triton X-100 and Triton
X-102. In all cases, the solubility curves and tie lines were determined prior to model all the experimental data with known equations, such as those reported by Merchuk and Othmer–Tobias.
Additionally, the salting out character was qualitatively discussed
in the light of the Hofmeister series, and quantitatively analyzed
386
M.S. Álvarez et al. / J. Chem. Thermodynamics 54 (2012) 385–392
tion. The high charge density inorganic salts, K3PO4, K2CO3, K2HPO4,
K2S2O3 and K2SO3 were also purchased from Sigma-Aldrich and
used as received. The main data concerning the surfactants properties and purities of salts and surface active compounds used are
shown in table 1.
based on molar Gibbs free energy of hydration (DhydG) and
Excluded Effective Volume (EEV) data.
2. Experimental
2.1. Chemicals
2.2. Experimental procedure
The non-ionic surfactants belonging to the polyoxyethylene toctylphenol family Triton X-100 and Triton X-102 were purchased
from Sigma-Aldrich, and used as received without further purifica-
The solubility curves of the ABS were carried out by means of
the cloud point titration method at T = 298.15 K [1]. A known
TABLE 1
Samples provenance and purities.a
Compound
Chemical structure
Triton X-100 n = 9.5
Triton X-102 n = 12
K3PO4
K2CO3
K2HPO4
K2S2O3
K2SO3
a
b
c
O
H
O
Mass fraction purity
Supplier
HLBb
CMCc
P0.99
P0.99
Sigma–Aldrich
Sigma–Aldrich
13.4
14.4
189 106
267 106
P0.98
P0.99
P0.98
P0.95
0.90
Sigma–Aldrich
Sigma–Aldrich
Sigma–Aldrich
Sigma–Aldrich
Sigma–Aldrich
–
–
–
–
–
–
–
–
–
–
n
–
–
–
–
–
Deionised water was used in all the experiments.
HLB: hydrophilic lipophilic balance.
CMC: critical micellar concentration.
TABLE 2
Solubility data for {Triton X-100 (1) + salt (2) + H2O (3)} two-phase systems at T = 298.15 K.a
K3PO4
a
K2HPO4
K2S2O3
K2SO3
K2CO3
100 w2
100 w1
100 w2
100 w1
100 w2
100 w1
100 w2
100 w1
100 w2
100 w1
0.17
0.21
1.53
2.24
2.50
2.92
3.46
3.71
4.25
4.50
5.31
5.65
6.04
6.38
6.72
7.01
7.28
7.82
8.23
8.54
8.69
8.87
9.02
11.23
13.38
15.14
56.73
56.33
50.06
46.35
44.87
41.89
38.71
37.61
33.37
32.11
27.18
24.11
20.97
18.89
16.62
14.02
11.60
8.80
6.34
4.48
3.52
2.38
1.67
0.40
0.02
0.00
0.81
1.26
2.10
2.26
2.44
2.47
2.91
3.04
3.50
3.81
4.60
5.05
5.47
5.82
6.13
6.30
6.62
6.89
7.17
7.44
7.69
7.78
8.22
8.45
8.75
9.10
9.35
9.44
9.53
9.66
9.88
13.15
15.07
17.30
60.62
55.57
50.16
48.70
48.02
47.21
40.71
44.07
38.05
36.58
34.03
30.45
27.55
25.16
23.10
21.17
19.63
17.77
16.11
14.45
13.05
11.23
9.92
8.50
6.99
5.66
4.87
4.19
3.30
2.56
1.86
0.19
0.01
0.00
1.90
2.98
3.89
4.68
5.53
5.68
6.53
7.25
7.93
8.29
8.70
9.33
10.16
10.63
10.91
11.25
11.30
11.69
11.93
12.40
12.79
12.91
13.02
13.40
13.62
14.03
14.21
14.47
14.62
17.43
20.00
22.54
58.87
55.67
51.33
46.67
44.25
43.32
40.01
36.07
33.20
29.71
27.51
25.56
21.70
19.61
17.66
15.75
16.27
14.71
12.85
11.98
10.87
9.70
8.77
7.25
6.23
4.62
3.13
2.52
1.95
0.84
0.11
0.01
4.00
4.16
4.26
4.36
4.70
4.95
5.31
5.57
5.77
5.96
6.51
6.79
7.54
7.87
8.09
8.25
8.37
8.40
8.73
8.83
9.01
9.22
9.37
9.59
9.79
9.99
12.03
12.65
13.44
42.40
39.87
40.83
37.16
35.89
35.47
33.84
32.58
30.96
28.61
26.72
23.97
20.11
18.59
16.37
17.34
14.56
13.44
11.39
9.90
8.66
7.42
5.89
4.09
3.28
2.35
1.19
0.68
0.30
2.22
2.67
3.12
3.33
3.46
3.55
4.03
4.68
5.22
5.71
5.97
6.19
6.50
6.74
6.97
6.98
7.17
7.25
7.40
7.87
11.19
13.30
15.80
49.43
46.22
41.08
39.23
39.76
37.82
34.49
29.48
24.41
20.54
17.31
14.82
12.79
10.69
6.67
8.70
5.82
5.00
4.20
2.74
0.06
0.00
0.00
Standard uncertainties are u(w) = 0.0002, u(T) = 0.01 K.
387
M.S. Álvarez et al. / J. Chem. Thermodynamics 54 (2012) 385–392
0.12
100(w1M-1)/(mol.g-1)
amount of salt was added to the two different surfactant aqueous
solutions under constant stirring, until the detection of turbidity.
Afterwards, a drop-wise addition of ultra-pure water until a clear
monophasic region was carried out. All the samples were weighed
in an analytical Sartorius cubis MSA balance (125P-100-DA,
±105 g). The ternary system compositions were determined by
the weight quantification of all components within an uncertainty
of ±104 g. The measurements were carried out in a jacketed glass
vessel containing a magnetic stirrer connected to a temperature
controlled circulating bath (controlled to ±0.01 K). For the jacketed
cell, the temperature was controlled with a F200 ASL digital thermometer with an uncertainty of 0.01 K.
The tie-lines (TLs) determination started with the addition of a
ternary mixture within the immiscibility region of known mass
fraction to the ampoules, the temperature was kept constant and
the mixture was stirred vigorously and left to settle for 24 h to ensure a complete separation of the layers. The estimated uncertainty
in the determination of the surfactant and salt phases mass compositions is ±2%.
0.08
0.04
0.00
0.00
0.04
0.08
100(w2M-2)/(mol.g-1)
0.12
FIGURE 1. Plot of experimental and correlated solubility data of {Triton X-100
(1) + salt (2) + H2O (3)} at T = 298.15 K: (s) K3PO4, (h) K2HPO4, (5) K2CO3, (e)
K2SO3, (4) K2S2O3.
3. Results and discussion
Hitherto, literature analysis reveals that just few systems were
investigated previously containing Triton X-100 and Triton X-102
with different sodium and magnesium salts (sodium citrate, magnesium sulfate, sodium sulfate, sodium carbonate, sodium thiosulfate), sodium sulfite and disodium phosphate) [7,13,15,16]. The
experimental data making up the phase diagrams of the systems
involving the selected surfactants, Triton X-100 and Triton X-102,
and high density charge inorganic salts, K3PO4, K2HPO4, K2CO3,
K2S2O3 and K2SO3 were ascertained at T = 298.15 K and are listed
in tables 2 and 3, and graphically compared in figures 1 and 2. In
all cases, the top phase was rich in surfactant and poor in inorganic
salt, while the bottom phase contained most of the high charge
density salt and small quantities of surfactant.
All the experimental data obtained were correlated by means of
previously reported equations widely applied to different types of
ABS [10,17]:
0:5
w1 ¼ a expðbw2 cw32 Þ;
0:5
ð1Þ
2
w1 ¼ a þ bw2 þ cw2 þ dw2 ;
0:5
ð2Þ
2
w1 ¼ expða þ bw2 þ cw2 þ dw2 Þ;
ð3Þ
TABLE 3
Solubility data for {Triton X-102 (1) + salt (2) + H2O (3)} two-phase systems at T = 298.15 K.a
K3PO4
a
K2HPO4
K2S2O3
K2SO3
K2CO3
100 w2
100 w1
100 w2
100 w1
100 w2
100 w1
100 w2
100 w1
100 w2
100 w1
2.46
3.34
3.76
3.82
4.06
4.40
4.67
5.00
5.29
5.54
5.88
6.27
6.32
6.64
7.12
7.30
7.92
8.09
8.43
8.64
8.92
9.12
9.34
9.58
9.76
9.95
13.27
13.82
15.98
42.64
38.04
36.04
36.51
34.44
32.92
31.06
29.85
27.08
26.21
24.16
21.52
19.80
18.41
17.05
15.56
14.30
13.03
11.79
10.16
9.00
7.65
6.53
5.37
4.36
3.46
0.38
0.22
0.01
2.51
2.79
2.80
3.22
3.61
4.27
4.96
5.44
6.18
6.63
6.74
6.87
7.12
7.52
7.87
8.20
8.51
8.90
9.20
9.45
9.68
9.76
9.98
10.30
10.52
12.17
13.10
14.85
45.00
42.04
40.06
37.86
36.04
33.33
30.13
26.49
23.93
22.07
19.75
20.58
18.43
16.88
15.12
13.43
11.87
10.55
9.23
8.18
7.27
5.97
5.22
4.00
2.98
1.41
0.65
0.11
4.68
5.68
5.89
6.44
6.84
7.31
8.77
9.09
9.41
10.13
10.77
11.43
11.84
11.94
12.57
12.95
13.64
14.18
14.57
14.99
15.23
15.51
15.76
16.01
16.23
16.84
19.18
21.73
46.67
43.32
42.03
39.92
38.05
34.45
29.83
29.14
25.56
23.89
20.90
18.88
17.03
14.83
13.51
11.74
9.84
7.94
6.54
5.47
4.68
3.96
3.22
2.60
2.03
2.44
0.64
0.10
1.70
4.82
5.10
5.44
5.65
6.01
6.30
6.57
7.11
7.51
7.83
8.07
8.39
8.67
8.98
9.31
9.61
9.98
10.30
10.56
10.86
11.08
11.28
11.64
12.36
14.59
15.69
50.00
35.87
34.13
32.13
30.95
28.75
26.77
24.68
22.79
21.93
20.14
18.59
17.23
15.85
13.56
12.18
10.51
9.19
8.27
7.15
6.13
5.12
4.16
2.80
2.63
0.49
0.18
3.05
3.64
4.21
4.46
4.98
5.46
5.69
6.07
6.40
6.63
6.94
7.37
7.46
7.83
7.84
8.35
8.56
8.96
9.19
9.61
11.12
12.56
41.86
37.86
33.83
31.99
28.10
25.41
22.36
19.26
17.95
16.18
14.02
12.28
10.67
9.01
9.40
7.34
5.36
4.56
3.54
2.79
0.70
0.13
Standard uncertainties are u(w) = 0.0002, u(T) = 0.01 K.
388
M.S. Álvarez et al. / J. Chem. Thermodynamics 54 (2012) 385–392
TABLE 5
Parameters of equation (2) and Standard deviation for {Surfactant (1) + salt (2) + H2O
(3)} at T = 298.15 K.
0.12
100(w1M-1)/(mol.g-1)
a
0.08
0.04
0.00
0.00
0.04
0.08
0.12
100(w2M-2)/(mol.g-1)
FIGURE 2. Plot of experimental and correlated solubility data of {Triton X-102
(1) + salt (2) + H2O (3)} at T = 298.15 K: (s) K3PO4, (h) K2HPO4, (5) K2CO3, (e)
K2SO3, (4) K2S2O3.
where w1 is the mass fraction of surfactants, w2 is the mass fraction
of salts, and a, b, c, and d are fitting parameters. All the values of
these parameters are listed in tables 4–6 together with the Standard
deviations (r), which were calculated by applying the following
expression:
r¼
PnDAT
i
ðzexp zadjust Þ2
nDAT
!1=2
where the experimental and adjustable solubility data are represented by zexp and zadjust, respectively and nDAT is the number of
experimental data. Taking into account the obtained deviations
one can state that all the equations serve our goal to estimate
appropriately the solubility data.
The data obtained can be analyzed by starting from two main
premises: the effect of the selected high charge density inorganic
salt and the effect of the surfactant employed.
On the one hand, from the comparison of figures 1 and 2, it becomes patent that the use of the surfactant Triton X-100 involves
solubility curves closer to the origin. This means that these systems
TABLE 4
Parameters of equation (1) and Standard deviation for {Surfactant (1) + salt (2) + H2O
(3)} at T = 298.15 K.
a
b
r
c
3
0.6059
1.5791
3.1710
0.0093
0.8129
3.1755
2.20103
0.0099
0.6899
1.3830
7.11102
0.0099
0.7067
1.9230
1.96103
0.0175
0.7085
2.0807
4.61103
0.0140
0.6869
2.8352
1.78103
0.0084
1.5930
3
0.0064
3
0.5286
1.7010
0.6611
1.3849
5.2710
0.0082
0.5942
1.7150
1.33103
0.0065
0.7278
2.7595
2.72103
0.0042
Standard deviation (r) was calculated by means of equation (4).
b
c
r
d
0.6134
0.0603 6.8938
0.1997 0.0063
0.6995
0.3102 7.3628
14.4108 0.0076
0.6808
0.0721 4.8641
0.9114 0.0075
1.2301
7.0057
0.6942
0.1440 7.8539
4.9286 0.0089
0.6928
1.0255 3.7350
4.0677 0.0077
0.8044
2.9487
3.9236 21.1958 0.0064
1.0127
2.6173
0.8346
2.1768 14.5937
1.4823
7.0012
17.5337 75.6466 0.0062
3.0719 0.0076
32.0865 67.2354 0.0053
7.1264
2.1474 0.0049
Standard deviation (r) was calculated by means of equation (4).
TABLE 6
Parameters of equation (3) and Standard deviation for {Surfactant (1) + salt (2) + H2O
(3)} at T = 298.15 K.
ð4Þ
;
Triton X-100 (1) + K3PO4 (2) + H2O
(3)
Triton X-100 (1) + K2HPO4 (2) + H2O
(3)
Triton X-100 (1) + K2S2O3 (2) + H2O
(3)
Triton X-100 (1) + K2SO3 (2) + H2O
(3)
Triton X-100 (1) + K2CO3 (2) + H2O
(3)
Triton X-102 (1) + K3PO4 (2) + H2O
(3)
Triton X-102 (1) + K2HPO4 (2) + H2O
(3)
Triton X-102 (1) + K2S2O3 (2) + H2O
(3)
Triton X-102 (1) + K2SO3 (2) + H2O
(3)
Triton X-102 (1) + K2CO3 (2) + H2O
(3)
Triton X-100 (1) + K3PO4
(2) + H2O (3)
Triton X-100 (1) + K2HPO4
(2) + H2O (3)
Triton X-100 (1) + K2S2O3
(2) + H2O (3)
Triton X-100 (1) + K2SO3
(2) + H2O (3)
Triton X-100 (1) + K2CO3
(2) + H2O (3)
Triton X-102 (1) + K3PO4
(2) + H2O (3)
Triton X-102 (1) + K2HPO4
(2) + H2O (3)
Triton X-102 (1) + K2S2O3
(2) + H2O (3)
Triton X-102 (1) + K2SO3
(2) + H2O (3)
Triton X-102 (1) + K2CO3
(2) + H2O (3)
a
Triton X-100 (1) + K3PO4
(2) + H2O (3)
Triton X-100 (1) + K2HPO4
(2) + H2O (3)
Triton X-100 (1) + K2S2O3
(2) + H2O (3)
Triton X-100 (1) + K2SO3
(2) + H2O (3)
Triton X-100 (1) + K2CO3
(2) + H2O (3)
Triton X-102 (1) + K3PO4
(2) + H2O (3)
Triton X-102 (1) + K2HPO4
(2) + H2O (3)
Triton X-102 (1) + K2S2O3
(2) + H2O (3)
Triton X-102 (1) + K2SO3
(2) + H2O (3)
Triton X-102 (1) + K2CO3
(2) + H2O (3)
b
c
r
d
1.2662 30.2896 130.10
891.65 0.0086
1.7720 33.0935 126.18
742.51 0.0082
0.5955 14.0134
47.89
248.10 0.0111
140.40
740.66 0.0158
2.1145 37.093
3.1299 57.2501 244.16 1550.37 0.0110
0.5655 16.7380
60.64
433.84 0.0088
1.6424 31.9710 115.65
595.26 0.0063
3.3359 40.3343 115.32
352.30 0.0074
3.2334 43.7828 137.60
554.28 0.0070
1.2561 24.9224
646.01 0.0053
93.09
Standard deviation (r) was calculated by means of equation (4).
require less amount of salt to trigger phase segregation. The explanation of these differences between the two selected surfactants
can underlie in their different degree of hydrophobicity. A useful
means to evaluate the hydrophobic nature of a surfactant is the
hydrophilic-lipophilic balance (HLB), which is an empirical number
varying between 0 and 20. Thus, while Triton X-100 possesses a
HLB of 13.4, the value of HLB for Triton X-102 is 14.4. This fact confirms that the use of more hydrophobic or water de-structuring
(chaotropic) compounds entails solubility curves closer to the origin. The observed pattern is in agreement with what can be found
in literature. Thus, recent studies have also found [18–20] found
that the phase-forming ability of several ILs and polyethylene glycols increases with increasing alkyl-chain length, probably due to
the existence of non-favourable interactions between the saltingout inducing ions and the surfactant non-polar moieties, thus leading to an easier phase disengagement.
389
M.S. Álvarez et al. / J. Chem. Thermodynamics 54 (2012) 385–392
On the other hand, the high charge density inorganic salts used
follow the same sequence no matter the surfactant used:
K3PO4 > K2HPO4 > K2CO3 > K2SO3 > K2S2O3. Having fixed the cation
(K+) one can analyze the salting out potential of each salt by comparing with what is predicted by the Hofmeister series, since the
effects concluded in this recurring trend are often more pronounced for anions than for cations [21]. Kosmotropes are usually
small and highly charged, while chaotropes are large and low
charged. In fact, all multivalent ions are highly hydrated and are,
therefore, kosmotropic. This is in agreement with the trend observed that trivalent phosphate has a higher salting out potential
than divalent phosphate. Additionally, the observed sequence can
be also quantitatively analyzed on the basis of the lyotropic number, by means of the Gibbs energy of hydration (DhydG). Thus based
on the data reported by Marcus [22], the salting out ability of the
salts follows the order: PO43 (2765 kJ mol1) > CO32
(1315 kJ mol1) > SO32 (1295 kJ mol1), which is in agreement with our findings. This sequence allows concluding that anions leading to an easier phase disengagement possess more
negative DhydG values.
Additionally, Zafarani and Hamzehzadeh [23] focused on the
salting out effect by analyzing the viscosity B coefficients. Several
reports have highlighted that anions with more positive B coefficients hydrated more water molecules than those presenting lower
values, thus suggesting that these ions are more kosmotropes and
would exhibit a larger change in viscosity with their concentrations. Hence, three of the salts used in this work followed the trend
K3PO4 (B coefficient = 0.495 dm3 mol1) > K2HPO4 (B coefficient =
0.382 dm3 mol1) > K2CO3 (B coefficient = 0.294 dm3 mol1) [24,
25], which is in agreement with the pattern obtained in the present
work.
Another approach to analyze the salting out behaviour of each
system can be based on the analysis of the Effective Excluded Volume (EEV). This theory is based on the statistical geometry methods developed by Guan et al. [26], and states that any molecule
species in a solution is distributed at random and every system
composition on the solubility curve is a geometrically saturated
solution of one solute in the presence of another. Therefore, the
experimental data were fitted to the following equation:
w2
w1
ln V 213
þ f213 þ V 213
¼ 0;
M2
M1
where
are, respectively, the molar mass of surfactant and salt, the scaled EEV of the salt, and the volume fraction of
unfilled effective available volume after tight packaging of salt molecules into the network of surfactant molecules in aqueous solutions, which include the influence of the size of the water
molecules, respectively. The values of the EEV, f213 and Standard
deviations are presented in table 7. In general, the high values of
TABLE 7
Values of parameters of EEV and f213 using equation (1) for {Surfactant (1) + salt
(2) + H2O (3)} at T = 298.15 K.
103 V 213 /
(gmol1)
X-100
X-100
X-100
X-100
X-100
X-102
X-102
X-102
X-102
X-102
(1) + K3PO4 (2) + H2O (3)
(1) + K2HPO4 (2) + H2O (3)
(1) + K2S2O3 (2) + H2O (3)
(1) + K2SO3 (2) + H2O (3)
(1) + K2CO3 (2) + H2O (3)
(1) + K3PO4 (2) + H2O (3)
(1) + K2HPO4 (2) + H2O (3)
(1) + K2S2O3 (2) + H2O (3)
(1) + K2SO3 (2) + H2O (3)
(1) + K2CO3 (2) + H2O (3)
yI1 ¼
yF1
1R
yII1 ;
R
R
ð6Þ
xI2 ¼
xF2
1R
xII2 ;
R
R
ð7Þ
where F, I and II represent the feed, the top phase, and the bottom
phase, respectively; y1 and x2 are the mass fraction percentage of
surfactant and salt, respectively; and R is the following measured
ratio:
TABLE 8
Experimental tie–lines in mass percentage for {Surfactant (1) + salt (2) + H2O (3)} at
T = 298.15 K.
Surfactant-rich phase
Inorganic salt-rich phase
100 wI1
100 wI2
100 wII1
44.88
50.15
56.34
Triton X-100 (1) + K3PO4
2.51
0.40
1.47
0.02
0.21
0.00
47.21
55.57
60.62
1.9258
1.1510
0.4728
0.9866
1.2548
1.5488
0.1546
0.0315
0.0208
0.4783
f213
0.0574
0.2581
0.5936
0.2742
0.1589
0.2277
0.8960
0.9723
0.9837
0.6587
Standard deviation (r) was calculated by means of equation (4).
r
0.0390
0.0435
0.0463
0.0274
0.0630
0.0281
0.0315
0.0444
0.0384
0.0361
100 wII2
TLL
S
45.32
51.53
58.28
5.10
4.21
3.77
Triton X-100 (1) + K2HPO4
2.48
0.19
1.26
0.00
0.81
0.01
(2) + H2O (3)
13.15
48.22
15.07
57.26
17.30
62.80
4.40
4.02
3.68
44.25
57.71
59.57
Triton X-100 (1) + K2S2O3
5.53
0.84
1.38
0.11
1.94
0.01
(2) + H2O (3)
17.43
45.01
20.00
53.92
22.55
63.02
3.65
3.19
2.89
33.84
40.83
49.00
5.31
4.26
2.80
Triton X-100 (1) + K2SO3
1.20
0.68
0.30
(2) + H2O (3)
12.03
12.65
13.44
33.33
41.02
49.85
4.86
4.79
4.57
39.76
46.18
50.22
3.46
2.63
2.10
Triton X-100 (1) + K2CO3
0.06
0.00
0.00
(2) + H2O (3)
11.19
13.30
15.80
40.45
47.39
52.06
5.14
4.32
3.67
31.06
37.0
42.88
4.67
3.68
2.37
Triton X-102 (1) + K3PO4
0.22
0.02
0.38
(2) + H2O (3)
15.98
13.82
13.27
32.85
38.34
43.87
2.73
3.65
3.90
40.06
42.04
45.00
Triton X-102 (1) + K2HPO4
2.80
1.41
2.80
0.65
2.51
0.11
(2) + H2O (3)
12.17
39.77
13.10
42.66
14.85
46.56
4.13
4.02
3.64
29.83
39.92
42.03
Triton X-102 (1) + K2S2O3
8.77
2.44
6.44
0.64
5.89
0.10
(2) + H2O (3)
16.84
28.56
19.18
41.30
21.73
44.82
3.39
3.08
2.65
17.34
28.61
35.89
8.25
5.96
4.70
Triton X-102 (1) + K2SO3
1.47
0.76
0.57
(2) + H2O (3)
13.52
14.60
15.68
16.72
29.16
36.96
3.01
3.22
3.21
37.86
41.86
33.83
3.64
3.05
4.21
Triton X-102 (1) + K2CO3
0.70
0.13
2.79
(2) + H2O (3)
11.12
12.56
9.61
37.91
42.80
31.51
4.97
4.39
5.75
ð5Þ
M1, M2, V 213 , f213
Triton
Triton
Triton
Triton
Triton
Triton
Triton
Triton
Triton
Triton
the scaled EEV obtained for K3PO4 permit to confirm its strong salting out potential. On the contrary, sodium sulfite and sodium thiosulfite, are the inorganic salts leading to the lowest values, in
agreement with their position in the salting out sequence previously mentioned. In the same way, the higher values observed for
Triton X-100 also confirm our previous conclusions related to its
higher salting out potential.
The TLs data were obtained by the lever arm rule taking into account the relationship between the upper phase and the overall
system mass composition.
(2) + H2O (3)
11.23
13.38
15.15
R¼
M.S. Álvarez et al. / J. Chem. Thermodynamics 54 (2012) 385–392
Weight of the top phase
:
Weight of the mixture
ð8Þ
In parallel, the information provided by the tie-line length, TLL,
and the slope of the TLs data, S, is a useful tool to ascertain the relative distribution of the surfactant and the inorganic salt between
the two aqueous phases in equilibrium. These values are calculated
by means of these equations:
h
i0:5
TLL ¼ ðwI1 wII1 Þ2 þ ðwI2 wII2 Þ2 ;
S¼
ð9Þ
wI1 wII1
;
wI2 wII2
ð10Þ
100 w1
where the equilibrium mass fraction of the surfactant (1) and the
inorganic salt (2), in the upper (I) and bottom (II) phases, are represented. The TL data obtained for each ternary system and the abovementioned parameters are given in table 8, and represented in
figures 3 and 4. From these data, it is clear that higher values of
TLL correlate with higher salt concentration. The rationale behind
this behaviour can be explained in terms of interactions between
the salt and the surfactant. As more inorganic salt is present, the
bottom phase becomes increasingly structured, thus leading to a
higher degree of mass transfer of chaotropic ions to the top phase.
From the data presented, it is clear that higher values of TLL correlate with higher salt concentration. The rationale behind this
behaviour can be explained in terms of interactions between the
salt and the surfactant. As more inorganic salt is present, the bottom phase becomes increasingly structured, thus leading to a higher degree of mass transfer of chaotropic ions to the top phase. A
proper analysis of the obtained experimental data requires the
application of a correlation equation, such as that proposed by Othmer–Tobias [27]:
a
1 wI1
1 wII2
¼
b
;
wI1
wII2
ð11Þ
where a and b are the fitting parameters, w is the mass fraction,
subscripts 1, 2 and 3 refer to surfactant, salt and water, respectively,
and superscripts I and II indicate the surfactant-rich phase and saltrich phase, respectively. The use of this correlation allows relating
the TL mass concentration of both phases by means of a linear function. As can be seen in table 9, the obtained regression coefficients
very near to 1 for most of all the salts, reveal a high degree of consistency with the experimental data, thus confirming the appropriateness of this model. However, in the case of K2S2O3, this
correlation equation seems to be not so appropriate to describe
the tie-lines.
80
80
60
60
40
40
20
20
0
0
60
60
40
40
20
20
0
100 w1
390
0
10
20
30
40
100 w2
60
40
20
0
0
10
20
30
100 w2
FIGURE 3. Plot of experimental and correlated phase diagram and experimental tie-lines of {Triton X-100 (1) + salt (2) + H2O (3)}: at T = 298.15 K. Void symbols represent
experimental phase diagram, and full symbols represent tie-line data. (s) K3PO4, (h) K2HPO4, (5) K2CO3, (e) K2SO3, (4) K2S2O3.
391
M.S. Álvarez et al. / J. Chem. Thermodynamics 54 (2012) 385–392
60
40
40
20
20
0
0
40
40
20
20
100 w1
100 w1
60
0
0
10
20
30
100 w2
40
20
0
0
10
20
30
100 w2
FIGURE 4. Plot of experimental and correlated phase diagram and experimental tie-lines of {Triton X-102 (1) + salt (2) + H2O (3)}: at T = 298.15 K. Void symbols represent
experimental phase diagram, and full symbols represent tie-line data. (s) K3PO4, (h) K2HPO4, (5) K2CO3, (e) K2SO3, (4) K2S2O3.
TABLE 9
Parameters of Othmer–Tobias equation and correlation coefficient for {Surfactant
(1) + salt (2) + H2O (3)} at T = 298.15 K.
Triton X-100 (1) + K3PO4 (2) + H2O (3)
Triton X-100 (1) + K2HPO4 (2) + H2O
(3)
Triton X-100 (1) + K2S2O3 (2) + H2O
(3)
Triton X-100 (1) + K2SO3 (2) + H2O (3)
Triton X-100 (1) + K2CO3 (2) + H2O (3)
Triton X-102 (1) + K3PO4 (2) + H2O (3)
Triton X-102 (1) + K2HPO4 (2) + H2O
(3)
Triton X-102 (1) + K2S2O3 (2) + H2O
(3)
Triton X-102 (1) + K2SO3 (2) + H2O (3)
Triton X-102 (1) + K2CO3 (2) + H2O (3)
the results in the light of molar Gibbs energy of hydration, the
Hofmeister series and the EEV theory allowed concluding the
excellent salting out capacity of K3PO4, which is advantageous in
terms of economics and environmental sustainability. Also, it
was confirmed that the hydrophobicity of the surfactant plays a
major role in the phase behaviour, being the more hydrophobic
Triton X-100 leading to greater regions of immiscibility. Finally,
all the solubility data were modelled by means of previously reported equations and the tie-lines were appropriately fitted to
the Othmer–Tobías equation in most of the cases (except for
K2S2O3).
a
b
R2
r
1.3219
1.9861
0.0812
0.0453
0.9812
0.9794
0.0064
0.0079
1.9676
0.0552
0.8666
0.0249
4.9502
1.0613
2.1602
0.8747
0.0010
0.1650
81.9152
0.2647
0.9991
0.9802
0.9105
0.9981
0.0018
0.0061
0.0144
0.0009
1.6389
0.1600
0.8644
0.0196
Acknowledgements
5.4220
1.1367
0.0002
0.1535
0.9716
0.9990
0.0129
0.0010
F.J. Deive wishes to thank Xunta de Galicia for funding through
a Isidro Parga Pondal Program.
Standard deviation (r) was calculated by means of equation (4).
References
4. Conclusions
The solubility data of the systems {surfactant (Triton X-100 or
Triton X-102) + inorganic salt (K3PO4, K2HPO4, K2CO3, K2SO3 and
K2S2O3) + water} were ascertained at T = 298.15 K. The analysis of
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JCT 12-136
ANNEX 8
ON THE PHASE BEHAVIOUR OF POLYETHOXYLATED SORBITAN (TWEEN) SURFACTANTS IN
THE PRESENCE OF POTASSIUM INORGANIC SALTS (JOURNAL OF CHEMICAL
THERMODYNAMICS, 2012, 55: 151-158).
J. Chem. Thermodynamics 55 (2012) 151–158
Contents lists available at SciVerse ScienceDirect
J. Chem. Thermodynamics
journal homepage: www.elsevier.com/locate/jct
On the phase behaviour of polyethoxylated sorbitan (Tween) surfactants
in the presence of potassium inorganic salts
María S. Álvarez, Fátima Moscoso, Francisco J. Deive, M. Ángeles Sanromán, Ana Rodríguez ⇑
Department of Chemical Engineering, Universidade de Vigo, P. O. Box 36310, Vigo, Spain
a r t i c l e
i n f o
Article history:
Received 7 May 2012
Received in revised form 8 June 2012
Accepted 2 July 2012
Available online 13 July 2012
Keywords:
Aqueous Biphasic Systems
Tween 20
Tween 80
Potassium salts
Othmer-Tobías and Bancroft equations
a b s t r a c t
The salting out potential of potassium-based inorganic salts was assessed in aqueous solutions of two
non-ionic surfactants from the Tween family. New solubility data of the systems {surfactant (Tween
20/Tween 80) + inorganic salt (K3PO4/K2CO3/K2HPO4/K2S2O3/K2SO3) + H2O} were experimentally ascertained at T = 298.15 K and these data were correlated by means of several three and four parameters
empirical equations. Tie-line data were determined for the aqueous ternary systems and Ohtmer-Tobias
and Bancroft equations have been proposed to correlate these data. The phase segregation effect of the
proposed salts was investigated and compared with the sequence indicated by the Hofmeister series
and the molar Gibbs energy of hydration (DhydG) data.
Ó 2012 Elsevier Ltd. All rights reserved.
1. Introduction
In continuation of our previous research works [1–3] involving
the characterization of the solubility behaviour of surfactant aqueous solutions in the presence of inorganic and organic salts, liquidliquid equilibrium of aqueous systems containing polyethoxylated
sorbitan (Tween) surfactants were investigated at T = 298.15 K.
This kind of separation process is based on the addition of an
inorganic salt with the purpose of segregating two phases, one of
them rich in surfactant component (upper phase) and the other
enriched in the potassium-inorganic salt (bottom phase). The proposal of these systems make up a novel approach with a promising
potential for the extraction of bioactive compounds, usually produced in aqueous solutions such as culture broths [4].
Liquid-liquid extraction through Aqueous Biphasic Systems
(ABS) using inorganic salts, whose salting-out capacity can be evaluated in terms of the Hofmeister series, have gained further
momentum in recent years with the emergence of ionic liquids
[5,6]. This work is devoted to novel non-ionic surfactant-based
ABS, since these kinds of surface active compounds are widely applied in biotechnological processes due to inherent benefits such as
their low cost, biodegradability, lower interface tension and wider
immiscibility window.
The Tween family, among the most commonly used surfactants,
has been highlighted in many reports, as a non-toxic and efficient
alternative, and it was even reported to act as carbon source in
⇑ Corresponding author. Tel.: +34 986 81 87 23.
E-mail address: [email protected] (A. Rodríguez).
0021-9614/$ - see front matter Ó 2012 Elsevier Ltd. All rights reserved.
http://dx.doi.org/10.1016/j.jct.2012.07.001
culture broths [7]. Many studies entailing the use of surfactants
in separation processes have been focused on their properties to
become turbid when heated to a certain temperature known as
the cloud point. This cloud point-based extraction has been extensively used as an important separation and purification technique
of metabolites or biocompounds with industrial interest while
keeping their activities [8].
The first advantage of the strategy followed in this research
work is based on the benefits associated with the operation at mild
temperature (especially interesting for the extraction of enzymes).
Additionally, another advantage of this separation technique lies in
the prediction of the different salting-out potential provided by the
selected potassium inorganic salts agents by means of the Hofmeister series. This sequence suggests to us the selection of these specific ions due to their different ability to interact with water and to
change aqueous solution structure with the purpose to obtain two
immiscible layers. In this sense, ions such as PO43, PO42, CO32,
S2O32, and SO32, were sequenced from kosmotropic to chaotropic
depending on their abilities to further phase splitting in aqueous
solutions of surfactants.
In this work, solubility curves of aqueous solutions of Tween 20
or Tween 80 were experimentally determined by the addition of
five high charge density inorganic salts (K3PO4, K2CO3, K2HPO4, K2S2O3, K2SO3) at T = 298.15 K. Several empirical equations [9,10]
were able to fit suitably the solubility data. Ohtmer-Tobias and
Bancroft models were chosen to correlate the previous determined
tie-line data and the results are discussed in terms of standard
deviations. The potential of potassium-based inorganic salts to segregate two phases is investigated and analysed taking into account
152
M.S. Álvarez et al. / J. Chem. Thermodynamics 55 (2012) 151–158
the data provided by the Hofmeister series. This salting-out effect
is assessed and confirmed by the molar Gibbs energy of hydration.
TABLE 2
Purities and suppliers of inorganic salts.a
2. Experimental
2.1. Chemicals
Tween 20 and Tween 80, non-ionic surfactants belonging to the
polyethoxylated sorbitan family, monosubstituted with a laurate
and an oleate moiety, respectively, were supplied by Sigma-Adrich.
Their structures and the main characteristics (HLB and CMC) are
collected in table 1. The selected high charge density inorganic
salts, K3PO4, K2CO3, K2HPO4, K2S2O3, and K2SO3 used as received,
and their CAS number, purities and suppliers are shown in table 2.
2.2. Experimental procedure
The widely employed cloud point titration method [11] was
used to determine the solubility curves at T = 298.15 K. A jacketed
glass vessel containing a magnetic stirrer connected to a temperature controlled circulating bath (controlled to ±0.01 K) was used to
obtain the phase equilibrium data corresponding to the selected
systems of {surfactant (Tween 20/Tween 80) + inorganic salt
(K3PO4/K2CO3/K2HPO4/K2S2O3/K2SO3) + H2O}. The temperature
was controlled with a F200 ASL digital thermometer with an
uncertainty of ±0.01 K. Aqueous solution containing known
amounts of surfactant were used to start the solubility curve, by
the addition of a saturated aqueous solution of the inorganic salt
with known mass fraction, until the detection of a cloudy solution.
Afterwards, water was added drop wise until a clear solution was
obtained. This protocol was repeated the number of times required
to fully characterize the immiscibility window. All the samples
TABLE 1
Main properties and structure of the selected surfactants.
Surfactant
HLB
CMC/mM
Tween 20w + x + y + z = 20
16.7
0.060
Tween 80 w + x + y + z = 20
15.0
0.012
Structure
a
Inorganic salt
CAS number
K3PO4
K2CO3
K2HPO4
K2S2O3
K2SO3
7778-53-2
584-08-7
7758-11-4
10294-66-3
10117-38-1
Supplier
Mass fraction purity
Sigma-Aldrich
0.98
0.99
0.98
0.95
0.90
Deionised water was used in all the experiments.
were weighed in an analytical Sartorius cubis MSA balance
(125P-100-DA, resolution ±105 g).
The tie-lines (TLs) were determined by a gravimetric method
proposed by Merchuk [9]. In brief, a ternary mixture with known
mass fraction at the biphasic region was prepared in the above
mentioned jacketed glass vessel. The mixture was vigorously stirred and left to settle for 24 h to ensure a complete segregation of
the phases. Afterwards, top and bottom phases were separated
and weighted. The level arm rule was employed to determine each
TL composition. The estimated uncertainty in the determination of
the surfactant (top) and salt (bottom) phases mass compositions is
±2%.
3. Results and discussion
3.1. Solubility curves and correlation
The experimental solubility data of the systems {surfactant
(Tween 20/Tween 80) + inorganic salt (K3PO4/K2CO3/K2HPO4/
K2S2O3/K2SO3) + H2O} at T = 298 K are listed in tables 3 and 4 and
shown in figures 1 and 2. Up to our knowledge, this is the first approach to characterize the phase behaviour of the Tween family in
the presence of potassium-based inorganic salts.
153
M.S. Álvarez et al. / J. Chem. Thermodynamics 55 (2012) 151–158
TABLE 3
Binodal data for {Tween 20 (1) + salt (2) + H2O (3)} two-phase systems at T = 298.15 K.a
K3PO4
a
K2HPO4
K2S2O3
K2CO3
100 w2
100 w1
100 w2
100 w1
100 w2
100 w1
100 w2
100 w1
100 w2
100 w1
1.51
2.22
2.67
2.84
3.05
3.45
4.42
4.50
5.19
5.86
6.66
7.48
8.12
8.27
8.36
8.80
9.13
9.26
9.61
9.88
10.07
10.19
12.61
14.19
15.62
55.48
48.67
46.40
45.87
44.63
41.44
36.22
36.33
31.86
28.07
24.28
19.93
16.23
14.84
14.79
12.62
10.74
9.50
8.15
5.91
4.19
3.11
1.34
0.35
0.32
1.38
2.26
2.45
3.12
3.18
3.24
3.85
4.45
5.01
5.45
5.74
6.34
6.94
7.58
8.00
8.38
8.86
9.03
9.31
9.60
9.68
10.03
10.50
10.80
11.00
11.12
11.40
13.37
15.05
16.55
55.32
49.71
49.09
45.44
45.15
45.00
40.13
37.63
35.19
32.44
30.31
28.01
25.28
22.39
20.08
17.74
15.59
14.37
12.73
9.62
11.23
7.82
6.25
4.03
2.90
2.04
1.56
1.18
0.32
0.06
3.18
4.27
5.24
5.86
7.03
7.91
8.04
8.77
9.42
10.24
10.83
11.43
12.10
12.73
13.18
13.94
14.50
14.81
15.14
15.47
15.76
16.36
16.92
17.20
17.52
19.29
22.45
24.59
53.48
50.89
48.33
46.57
41.45
38.20
39.21
35.21
32.85
30.18
28.06
25.97
23.29
21.22
18.31
16.17
13.71
12.66
11.23
10.06
8.90
6.82
4.77
3.25
2.36
2.34
0.44
0.10
2.50
3.65
5.03
5.50
5.93
6.27
6.43
6.99
7.49
8.10
8.61
9.03
9.47
10.02
10.51
10.67
10.92
11.08
11.31
11.53
11.70
13.62
15.54
18.27
55.18
48.66
41.25
38.52
35.74
34.40
30.92
28.21
25.07
22.84
20.43
18.05
15.97
14.27
12.29
10.69
9.22
7.88
6.32
5.14
4.14
2.23
0.58
0.05
1.35
2.28
2.50
3.50
4.08
5.03
5.70
6.09
6.80
7.70
8.21
8.52
8.61
8.74
8.93
9.00
9.08
9.34
9.39
12.88
15.46
17.45
52.50
49.72
47.07
43.67
39.22
35.87
30.04
26.53
22.54
16.87
13.06
10.71
8.05
8.74
5.92
5.14
4.12
3.50
2.34
0.18
0.00
0.00
Standard uncertainties are u(w) = 0.0002, u(T) = 0.01 K.
TABLE 4
Binodal data for {Tween 80 (1) + salt (2) + H2O (3)} two-phase systems at T = 298.15 K.
K3PO4
a
K2SO3
K2HPO4
a
K2S2O3
K2SO3
K2CO3
100 w2
100 w1
100 w2
100 w1
100 w2
100 w1
100 w2
100 w1
100 w2
100 w1
2.22
3.64
4.18
5.28
5.36
5.87
6.30
6.72
7.35
7.58
8.17
8.37
8.54
8.68
8.81
8.96
9.07
9.20
9.99
11.17
12.01
44.42
35.54
33.63
26.70
26.16
23.02
19.57
17.22
14.84
12.59
11.14
9.68
8.33
7.02
6.02
5.26
3.65
2.10
2.98
1.02
0.49
2.97
3.68
4.50
5.35
6.00
6.05
6.44
6.92
7.06
7.77
8.20
8.51
9.00
9.20
9.34
9.63
9.74
9.88
10.23
11.07
12.77
14.04
43.75
38.50
34.62
30.28
26.29
25.75
24.38
22.36
20.30
16.29
13.30
11.40
9.84
8.79
7.58
6.73
5.72
3.80
2.53
2.67
0.91
0.18
4.73
5.94
7.58
8.82
9.52
9.71
10.14
11.10
11.72
12.43
12.90
13.09
13.62
13.70
14.06
14.69
15.54
16.92
19.00
50.39
42.42
35.24
29.86
26.78
26.24
23.81
19.84
18.19
15.43
13.21
11.44
9.76
8.36
7.43
4.71
3.21
2.55
0.82
2.96
4.05
4.96
5.80
5.83
6.26
6.91
7.22
7.31
7.50
7.83
7.83
8.10
8.65
8.97
9.31
9.40
9.61
9.85
9.99
10.34
10.54
10.68
12.30
13.19
14.24
49.90
42.68
36.41
32.45
30.26
28.57
25.56
21.22
22.63
19.37
18.10
18.19
16.12
14.01
12.81
11.98
10.80
9.23
8.06
6.92
4.87
3.82
2.82
1.67
0.81
0.76
1.50
2.30
2.58
2.78
3.10
3.96
4.67
5.17
5.80
6.07
6.96
7.15
7.49
7.83
7.95
8.13
8.58
9.47
10.48
11.33
45.89
40.50
39.42
38.30
35.44
30.53
25.14
20.72
18.57
16.12
12.42
10.83
8.65
6.44
5.23
3.49
1.65
2.15
0.71
0.26
Standard uncertainties are u(w) = 0.0002, u(T) = 0.01 K.
Analogously to ionic liquids/inorganic salts-based ABS [4–6],
the phase segregation in systems containing surfactants and inorganic salts are the result of a balance between the surfactant
hydrophobicity and the salting out potential of the salt to create
hydration complexes [12]. By comparing the data obtained in figures 1 and 2, it becomes patent that the use of Tween 80 entails
larger biphasic regions. The rationale behind this behaviour is the
hydrophobicity associated to the molar mass of the surface active
M.S. Álvarez et al. / J. Chem. Thermodynamics 55 (2012) 151–158
0.06
0.06
0.04
0.04
100w1/M1(mol/g)
100w1/M1(mol/g)
154
0.02
0.00
0.00
0.04
0.08
0.12
0.02
0.00
0.00
0.16
0.04
0.08
100w2/M2(mol/g)
0.12
0.16
100w2/M2(mol/g)
FIGURE 1. Plot of experimental and correlated solubility data of {Tween 20
(1) + salt (2) + H2O (3)} at T = 298.15 K: (s), K3PO4; (h), K2HPO4; (r) K2CO3; (}),
K2SO3; (4), K2S2O3.
FIGURE 3. Plot of experimental and correlated solubility data of {Tween 20
(1) + salt (2) + H2O (3)} at T = 298.15 K: (h) Na2CO3, (j) K2CO3; (s) Na2SO3, (d)
K2SO3; (4) Na2S2O3, (N) K2S2O3. Full symbols: experimental data; void symbols:
literature data [1].
0.06
100w1/M1(mol/g)
TABLE 6
Parameters of equation (1) and standard deviation (r) for {Surfactant (1) + salt
(2) + H2O (3)} at T = 298.15 Ka.
0.04
Tween
Tween
Tween
Tween
Tween
Tween
Tween
Tween
Tween
Tween
0.02
0.00
0.00
a
0.04
0.08
100w2/M2(mol/g)
0.12
0.16
FIGURE 2. Plot of experimental and correlated solubility data of {Tween 80
(1) + salt (2) + H2O (3)} at T = 298.15 K: (s), K3PO4; (h), K2HPO4; (r) K2CO3; (}),
K2SO3; (4), K2S2O3.
DhydG/(kJ mol1)
K+
Na+
PO43
CO32
SO32
295
365
2765
1315
1295
compound. A useful parameter to quantify the hydrophobicity of a
selected surfactant is the hydrophilic-lipophilic balance (HLB),
which is an empirical number which value varies between 0 and
20. Thus, the more hydrophobic Tween 80, as can be concluded
from the lower HLB value (see table 1), presents a lower affinity
for water and it is more easily salted out by the inorganic salts.
The same pattern has been widely described in literature with
other ABS made up with ionic liquids and PEG [13,14].
Typically, traditional polymer/salt-based ABS are decisively
influenced by the anion of the inorganic salt. Hence, a modulation
of the hydrophobic effect can be achieved by fixing a common cation
a
b
c
r
0.7944
0.7253
0.6584
0.8670
0.6329
0.6204
0.5974
0.8431
0.7989
0.6204
3.0380
2.4081
1.1371
2.7655
1.4067
2.1544
1.6975
2.2242
2.5541
2.4816
1.50 103
1.36 103
3.90 102
1.04 103
2.43 103
2.36 103
1.88 103
5.34 102
1.59 103
3.19 103
0.0120
0.0147
0.0112
0.0114
0.0180
0.0120
0.0111
0.0113
0.0104
0.0146
Standard deviation (r) was calculated by means of equation (4).
TABLE 7
Parameters of equation (2) and standard deviation (r) for {Surfactant (1) + salt
(2) + H2O (3)} at T = 298.15 Ka.
Tween
Tween
Tween
Tween
Tween
Tween
Tween
Tween
Tween
Tween
TABLE 5
Molar Gibbs energies of hydration [16] of selected ions
(DhydG).
Ions
20 + K3PO4 + H2O
20 + K2HPO4 + H2O
20 + K2S2O3 + H2O
20 + K2SO3 + H2O
20 + K2CO3 + H2O
80 + K3PO4 + H2O
80 + K2HPO4 + H2O
80 + K2S2O3 + H2O
80 + K2SO3 + H2O
80 + K2CO3 + H2O
a
20 + K3PO4 + H2O
20 + K2HPO4 + H2O
20 + K2S2O3 + H2O
20 + K2SO3 + H2O
20 + K2CO3 + H2O
80 + K3PO4 + H2O
80 + K2HPO4 + H2O
80 + K2S2O3 + H2O
80 + K2SO3 + H2O
80 + K2CO3 + H2O
a
b
c
d
r
0.9143
0.7474
0.6317
1.0807
0.7023
0.8462
0.7749
1.1921
0.9165
0.8082
3.6087
1.5924
0.1706
3.0912
1.8124
3.4030
1.9649
4.0070
2.1737
3.5828
6.1465
0.0831
3.6022
0.6065
3.3389
5.5319
0.3184
4.2914
1.2087
7.0696
33.8013
14.7750
1.6578
3.1472
49.5592
34.0307
14.6085
10.7635
3.9457
48.3449
0.0034
0.0057
0.0042
0.0082
0.0062
0.0074
0.0066
0.0065
0.0081
0.0119
Standard deviation (r) was calculated by means of equation 4.
(K+) and varying different trivalent and divalent anions. In a visual
inspection of the results, a clear salting out trend can be established
for the selected anions: PO43 > HPO42 > CO32 > SO32 > S2O32.
This series can be rationalized by bearing in mind the different solvation capacity of the selected anions. Traditionally, this solvation
capacity was qualitatively analysed in terms of chaotropicity or kosmotropicity, depending on the ability of the salts and surfactants to
interact with water molecules. The obtained sequence validates the
salting out potential predicted by the Hofmeister series [15] and it is
also corroborated by what is expected from the Gibbs energy of
hydration [16]. The values for the different anions are listed in
155
M.S. Álvarez et al. / J. Chem. Thermodynamics 55 (2012) 151–158
TABLE 8
Parameters of equation (3) and standard deviation (r) for {Surfactant (1) + salt (2) + H2O (3)} at T = 298.15 Ka.
Tween
Tween
Tween
Tween
Tween
Tween
Tween
Tween
Tween
Tween
a
20 + K3PO4 + H2O
20 + K2HPO4 + H2O
20 + K2S2O3 + H2O
20 + K2SO3 + H2O
20 + K2CO3 + H2O
80 + K3PO4 + H2O
80 + K2HPO4 + H2O
80 + K2S2O3 + H2O
80 + K2SO3 + H2O
80 + K2CO3 + H2O
a
b
c
d
r
1.2816
0.9430
1.0933
1.2283
1.5672
0.5226
0.6280
1.0799
0.0337
1.2872
25.8973
21.7405
18.6207
19.5749
34.6571
17.8991
18.1814
16.5930
8.5989
30.9587
95.49
81.37
56.73
61.79
147.01
71.7278
69.7187
48.4135
34.9879
128.5915
537.38
466.90
203.31
328.44
879.44
545.6585
472.2590
208.9651
339.6399
884.0047
0.0110
0.0149
0.0114
0.0109
0.0157
0.0126
0.0118
0.0120
0.0116
0.0144
Standard deviation (c) was calculated by means of equation (4).
TABLE 9
Experimental tie–lines in mass percentage for {Surfactant (1) + salt (2) + H2O (3)} at T = 298.15 K.
Surfactant-rich phase
100 wI1
Inorganic salt-rich phase
100 wI2
100 wII1
100 wII2
TLL
STL
14.19
15.62
12.61
45.66
50.17
35.92
3.98
3.61
4.32
13.37
15.05
16.55
35.03
51.03
47.35
4.07
3.86
3.36
19.29
22.45
24.59
29.10
41.36
51.96
3.29
2.69
2.49
13.62
18.27
15.54
34.38
57.35
49.53
4.36
3.50
4.04
12.88
15.46
17.45
36.54
45.28
51.98
4.55
3.65
3.28
11.17
9.99
12.01
33.35
20.46
45.01
4.66
4.87
4.49
11.07
14.04
12.77
18.08
35.74
26.27
4.39
3.61
3.75
16.92
15.54
19.00
34.01
21.30
51.58
3.50
3.81
3.47
12.30
13.19
14.24
17.11
28.61
36.83
3.70
4.01
3.84
10.48
11.33
9.47
38.37
46.68
23.48
4.88
4.64
4.79
Tween 20 + K3PO4 + H2O
44.63
48.67
36.33
3.05
2.22
4.50
0.35
0.32
1.34
35.19
49.71
45.44
5.01
2.26
3.12
1.18
0.32
0.06
30.18
39.21
48.33
10.84
8.04
5.24
2.34
0.44
0.10
35.74
55.18
48.66
5.93
2.50
3.65
2.23
0.05
0.58
35.87
43.67
49.72
5.03
3.50
2.28
0.18
0.00
0.00
33.63
23.02
44.42
4.18
5.87
2.22
1.02
2.98
0.49
20.30
34.62
26.29
7.06
4.50
6.00
2.67
0.18
0.91
35.24
23.81
50.39
7.58
10.14
4.73
2.55
3.21
0.82
18.19
28.57
36.41
7.83
6.26
4.96
1.67
0.81
0.76
38.30
45.89
25.14
2.78
1.50
4.67
0.71
0.26
2.15
Tween 20 + K2HPO4 + H2O
Tween 20 + K2S2O3 + H2O
Tween 20 + K2SO3 + H2O
Tween 20 + K2CO3 + H2O
Tween 80 + K3PO4 + H2O
Tween 80 + K2HPO4 + H2O
Tween 80 + K2S2O3 + H2O
Tween 80 + K2SO3 + H2O
Tween 80 + K2CO3 + H2O
table 5, and it is clear that they follow the sequence:
PO43 > CO32 P SO32, further confirming that trivalent anions
present more interplays with water molecules than divalent anions.
On the other hand, our present research line [1–3] focused on
the characterization and application of surfactant-based ABS for
the extraction of biomolecules with industrial interest, also seeks
to analyse the effect of different monovalent cations (Na+ and K+)
in the phase segregation of several non-ionic surfactants. Therefore, the influence of the cation can be noticed in figure 3, where
a comparison with experimental data presented previously [1]
for several sodium-based inorganic salts is performed. From the
data presented, it is patently clear that sodium cation involves solubility curves closer to the origin, no matter the salt used. The
greater immiscibility window correlates well with the behaviour
expected from the Hofmeister series [17]. From the analysis of
the Gibbs energy of hydration (DhydG), the values are presented
in table 5, it seems clear that Na+ possesses a larger hydration shell,
which provides a stronger salting out effect. This behaviour is in
156
M.S. Álvarez et al. / J. Chem. Thermodynamics 55 (2012) 151–158
60
40
40
20
20
100w1
100w1
60
0
0
10
20
30
0
10
20
30
X Data
40
20
20
100w1
100w1
40
0
0
10
20
30
10
20
30
40
100w2
100w1
40
20
0
0
10
20
30
40
100w2
FIGURE 4. Plot of experimental and correlated phase diagram and experimental tie-lines of {Tween 20 (1) + salt (2) + H2O (3)}: at T = 298.15 K. Void symbols represent
experimental phase diagram, and full symbols represent tie-line data. (s), K3PO4; (h), K2HPO4; (r) K2CO3; (}), K2SO3; (4), K2S2O3.
agreement with the data recently reported for other ABS made up
with inorganic salts and polymers [18].
The experimental solubility data for the ten systems were fitted
to different equations usually employed to model different types of
ABS [9,10,19]:
solubility data, in line with previous results reported for other surfactant-based ABS [3].
3.2. Tie-lines and correlation
½Tw ¼ a expðb½Salt0:5 c½Salt3 Þ;
ð1Þ
The tie-line length, TLL was also calculated from the experimental values, using the following equation:
½Tw ¼ a þ b½Salt0:5 þ c½Salt þ d½Salt2 ;
ð2Þ
TLL ¼
ð3Þ
where the equilibrium mass fraction of the surfactant (1) and the
inorganic salt (2), in the surfactant-rich phase (I) and salt-rich
phase (II), are represented. The TLL data obtained for each ternary
system and the abovementioned parameters are given in table 9.
The complete phase diagrams obtained for the ten ABS are presented in figures 4 and 5. Generally speaking, the value of the
TLL increases as the salt concentration in the bottom phase is increased in all the ten ABS studied. The reason for this lies in the
competition between the surfactant and the inorganic salt for
the water molecules. Higher amounts of inorganic salt entail an
increasingly structured salt-rich phase, which is translated in a
promotion of the surfactant mass transfer between the bottom
and upper phases.
The slopes of the tie-lines (STL) obtained from the relationship
between the top, bottom, and feed compositions of the TL are calculated as follows:
½Tw ¼ exp a þ b½Salt0:5 þ c½Salt þ d½Salt2 ;
where [Tw] is the mass fraction of Tween surfactants, [Salt] is the
mass fraction of potassium inorganic salts, and a, b, c, and d are fitting parameters. The standard deviations (r), were calculated by
applying the following expression:
r¼
2 !1=2
PnDAT zexp zadjust
i
;
nDAT
ð4Þ
where the experimental and adjustable solubility data are represented by zexp and zadjust, respectively and nDAT is the number of
experimental data points.
All the values of the fitting parameters and standard deviations
are listed in tables 6 to 8. From the deviation data, it is possible to
conclude that equation (2) is able to reproduce satisfactorily the
h
wI1 wII1
2
2 i0:5
þ wI2 wII2
;
ð5Þ
157
M.S. Álvarez et al. / J. Chem. Thermodynamics 55 (2012) 151–158
60
40
40
100w
1
100w1
60
20
20
0
10
20
10
X Data
0
20
X Data
40
100w1
100w1
40
20
20
0
10
0
20
10
X Data
100w2
20
30
100w1
40
20
0
0
10
100w2
20
30
FIGURE 5. Plot of experimental and correlated phase diagram and experimental tie-lines of {Tween 80 (1) + salt (2) + H2O (3)}: at T = 298.15 K. Void symbols represent
experimental phase diagram, and full symbols represent tie-line data. (s), K3PO4; (h), K2HPO4; (r), K2CO3; (}), K2SO3; (4), K2S2O3.
STL ¼
wI1 wII1
;
wI2 wII2
ð6Þ
where (1), (2), (I), and (II) have the same meaning as in the equation
(5). The values of the STL are also presented in Table 9. The relative
distribution of each surfactant and inorganic salt between the two
water-rich layers in equilibrium is assessed through the TL data.
The slope of the tie-lines generally increases for the systems containing the more hydrophobic surfactant Tween 80, which entails
a higher segregation of the surfactant to the upper phase, in agreement with the trend observed in the behaviour of more hydrophobic ionic liquids [20].
In addition, the experimental TL data were fitted to OthmerTobias and Brancroft [21,22] correlations for each ABS system in
order to determine the thermodynamic consistency of the experimental data:
n
1 wI1
1 wII2
¼
m
;
wI1
wII2
ð7Þ
II I r
w3
w3
¼
k
;
wII2
wI1
ð8Þ
where n, m, k, and r are the fitting parameters, w is the mass fraction,
subscripts 1, 2, and 3 refer to surfactant, salt and water, respectively,
and superscripts I and II indicate the surfactant-rich phase and saltrich phase, respectively. These correlations relate the TL mass concentration of the top phase with the bottom phase to obtain a linear
function. The fitting parameters, and the standard deviations for the
TL determined in the present work are shown in Tables 10 and 11.
Generally speaking, the Othmer-Tobias model fits better to the
experimental data than the Bancroft equation, since the correlation
factor values were all very close to the unit, revealing a high degree
of thermodynamic consistency of the related data.
TABLE 10
Parameters of Othmer-Tobias equation and correlation coefficient (R2) for {Surfactant
(1) + salt (2) + H2O (3)} at T = 298.15 K.
Tween
Tween
Tween
Tween
Tween
Tween
Tween
Tween
Tween
Tween
20 + K3PO4 + H2O
20 + K2HPO4 + H2O
20 + K2S2O3 + H2O
20 + K2SO3 + H2O
20 + K2CO3 + H2O
80 + K3PO4 + H2O
80 + K2HPO4 + H2O
80 + K2S2O3 + H2O
80 + K2SO3 + H2O
80 + K2CO3 + H2O
n
m
r
2.0419
1.4291
2.4757
2.2141
1.5903
4.7999
2.7358
4.7677
5.3282
4.5991
3.29 102
1.06 101
6.86 102
2.79 102
8.58 102
8.98 105
1.37 102
9.67 104
1.19 104
8.85 105
0.0077
0.0050
0.0015
0.0210
0.0085
0.0073
0.0092
0.0101
0.0125
0.0063
158
M.S. Álvarez et al. / J. Chem. Thermodynamics 55 (2012) 151–158
TABLE 11
Parameters of Bancroft equation and correlation coefficient (R2) for {Surfactant
(1) + salt (2) + H2O (3)} at T = 298.15 K.
Tween
Tween
Tween
Tween
Tween
Tween
Tween
Tween
Tween
Tween
20 + K3PO4 + H2O
20 + K2HPO4 + H2O
20 + K2S2O3 + H2O
20 + K2SO3 + H2O
20 + K2CO3 + H2O
80 + K3PO4 + H2O
80 + K2HPO4 + H2O
80 + K2S2O3 + H2O
80 + K2SO3 + H2O
80 + K2CO3 + H2O
k
r
r
6.0855
5.9199
3.5217
5.6622
5.6981
7.0197
4.9667
4.3247
5.5946
7.6853
0.3515
0.4257
0.2627
0.2811
0.4668
0.1903
0.3468
0.1882
0.1633
0.1932
0.0779
0.0772
0.0406
0.1478
0.0473
0.0237
0.0944
0.0902
0.0858
0.0162
4. Conclusions
The solubility curves of non-ionic surfactants Tween 20 and
Tween 80 in the presence of aqueous solutions of potassium salts
were ascertained for the first time. Several known equations were
used to suitably model the solubility data. The Othmer-Tobias and
Bancroft equations were used for satisfactory correlation of the
experimental tie line data. The Tween-based ABS is influenced by
the size of the cation (Na+ > K+) as well as the valence of the anion
(PO43 > HPO42 > CO32 > SO32 > S2O32), following the trend predicted by the Hofmeister series and the Gibbs energy of hydration.
Acknowledgements
F. J. Deive wishes to thank Xunta de Galicia for funding through
a Isidro Parga Pondal Program.
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JCT 12-288
ANNEX 9
NOVEL
PHYSICO-BIOLOGICAL
TREATMENT
FOR THE
REMEDIATION
OF
TEXTILE DYES-
CONTAINING INDUSTRIAL EFFLUENTS (BIORESOURCE TECHNOLOGY, 2013, 146: 689695.
Bioresource Technology 146 (2013) 689–695
Contents lists available at ScienceDirect
Bioresource Technology
journal homepage: www.elsevier.com/locate/biortech
Novel physico-biological treatment for the remediation of textile
dyes-containing industrial effluents
M.S. Álvarez, F. Moscoso, A. Rodríguez, M.A. Sanromán, F.J. Deive ⇑
Department of Chemical Engineering, University of Vigo, 36310 Vigo, Spain
h i g h l i g h t s
g r a p h i c a l a b s t r a c t
First anthraquinone and azo dye
aerobic degradation by an anaerobic
thermophile.
About 60% biodegradation is attained
after 12 h of cultivation of A.
flavithermus.
The organic salt potassium citrate
proved to be an effective salting out
agent.
Coupling a secondary Tween-based
ABS led to remediation values higher
than 99%.
a r t i c l e
i n f o
Article history:
Received 4 June 2013
Received in revised form 27 July 2013
Accepted 29 July 2013
Available online 6 August 2013
Keywords:
Remediation
Anoxybacillus flavithermus
Reactive Black 5
Acid Black 48
Aqueous biphasic systems
a b s t r a c t
In this work, a novel remediation strategy consisting of a sequential biological and physical process is
proposed to remove dyes from a textile polluted effluent. The decolorization ability of Anoxybacillus flavithermus in an aqueous effluent containing two representative textile finishing dyes (Reactive Black 5 and
Acid Black 48, as di-azo and antraquinone class, respectively) was proved. The decolorization efficiency
for a mixture of both dyes reached almost 60% in less than 12 h, which points out the suitability of the
selected microorganism. In a sequential stage, an aqueous biphasic system consisting of non-ionic surfactants and a potassium-based organic salt, acting as the salting out agent, was investigated. The phase segregation potential of the selected salts was evaluated in the light of different thermodynamic models, and
remediation levels higher than 99% were reached.
Ó 2013 Elsevier Ltd. All rights reserved.
1. Introduction
One of the challenges faced by environmentalists focuses on the
search of efficient wastewater treatments for industrial effluents
containing persistent organic pollutants. More specifically, in the
textile industry process water accounts for more than 90% of the
total water used and contains a mixture of different pollutants
such as, surfactants, acids or bases, heavy metals, salts, suspended
solids, and dyes (Zaharia and Suteu, 2013). Each year more
than 7 107 tons of dyestuff are produced worldwide, which is
⇑ Corresponding author. Address: Department of Chemical Engineering, Lagoas
Marcosende s/n, 36310 Vigo, Spain. Tel.: +34 986 818723; fax: +34 986 812380.
E-mail address: [email protected] (F.J. Deive).
0960-8524/$ - see front matter Ó 2013 Elsevier Ltd. All rights reserved.
http://dx.doi.org/10.1016/j.biortech.2013.07.137
translated in the production of 30,000–150,000 tons of dyes discharged into receiving watercourses (Anjaneya et al., 2011). These
compounds represent an environmental and health threat due to
their well-known problems such as carcinogenicity, toxicity and
mutagenicity (Mansour et al., 2011). Furthermore, they entail a
great environmental impact when discharged in aquatic environments, due to the reduced light penetration which hinders a proper
photosynthetic activity.
The environmental effects of dyes are strongly influenced by the
chemical structure of the chromophoric group. Thus, dyes can be
classified as polymeric, azo, anthraquinone, triphenylmethane
and heterocyclic. The most frequently used groups of dyes in
textile finishing are azo and anthraquinone, due to their superior
fixing quality, endurance against microbial degradation, and high
photolytic stability (Forss and Welander, 2011).
690
M.S. Álvarez et al. / Bioresource Technology 146 (2013) 689–695
Therefore, the typical environmental quality parameters representing this kind of effluents are usually higher than the maximum
allowable limits (i.e., total organic carbon–TOC, chemical oxygen
demand – COD, biochemical oxygen demand – BOD, total dissolved
solids, turbidity, etc.). The presence of this contaminant charge
makes it necessary to explore different technologies such as physical, biological and chemical methods. Thus, coagulation–flocculation (Ramesh Babu et al., 2007), electrochemical (Iglesias et al.,
2013), adsorption on activated carbon or other adsorptive materials (Vecino et al., 2013; Devesa-Rey et al., 2011; Zahid and El-Shafai, 2011) are recent remediation strategies followed by different
authors. Very often the use of a single method does not allow
reaching the standard quality requirements, so the combination
of different alternatives is sometimes proposed, such as the combination of electrochemical and advanced chemical oxidation (Rosales et al., 2012), surfactant-based washing and coagulation
(López-Vizcaino et al., 2012) and oxidation with ozone and aerobic
biodegradation (Russo et al., 2012).
In this work, we explore a new strategy combining biological
dye decolorization followed by a surfactant-based aqueous biphasic system separation. Biological methods possess inherent advantages such as a positive social perception, a low process economy
and an easy industrial implementation. In this particular case,
many effluents from textile industry are at moderately high temperatures, so the operation with thermophilic microorganisms
could be a viable alternative to tackle their biodegradation. Furthermore, the use of thermophiles involves reduced cooling costs,
increased solubility of most compounds (except gasses), decreased
viscosity and a lower risk of contamination (Deive et al., 2012).
Moreover, the presence of surfactants and salts in the wastewater
outfall made us to hypothesize the possible suitability of coupling
a surfactant-based aqueous biphasic system as an extraction process after the biological stage.
Dye extraction by means of aqueous biphasic systems (ABS)
can be considered an appealing alternative since this technique
has been recently proved to be an economical and efficient method for the separation of biomolecules (Luechau et al., 2009; Deive
et al., 2011; Ulloa et al., 2012), metal ions (Bulgariu and Bulgariu,
2008), and drug molecules (Mokhtarani et al., 2008). The attractiveness of this technique lies in the reduction of the utilities
requirements, in a rapid phase disengagement and in an easy
scale-up process (Martins et al., 2010; Zafarani-Moattar and Hamzehzadeh, 2010, 2011). Phase splitting can be achieved by combining two hydrophilic polymers, a polymer and a salt, or two
salts in aqueous solutions above a certain critical concentration.
The composition of the two phases is the result of a complex
competition between the polymers or salts for the water molecules and specific interactions between polymers and salts
(Albertsson, 1986).
In this study, we have screened distinct organic salts to choose
the most suitable one for promoting phase disengagement in aqueous solutions containing model dyes and non-ionic surfactants
Tween 20 and Tween 80. This step is crucial for the development
of an efficient extraction method to remove dyes from textile
industrial effluents. This stage will be included in the treatment
train proposed after the biological decolorization process using a
thermophilic microorganism recently isolated from a Galician
hot-spring.
2. Methods
2.1. Chemicals
The dyes and non-ionic surfactants Tween 20 and Tween 80,
belonging to the polyethoxylated sorbitan family, monosubstituted
with a laurate and an oleate moiety, respectively, have been purchased from Sigma–Aldrich.
Potasium citrate (K3C6H5O7H2O), potassium tartrate (K2C4H4O60.5H2O), potassium oxalate (K2C2O4H2O) were the organic salts
employed for phase segregation. All chemicals used were at least,
reagent grade or better and were supplied by Sigma–Aldrich.
2.2. Microorganism
The microorganism Anoxybacillus flavithermus was isolated by
the 13-streak plate method from a Galician hot spring (Lobios,
province of Ourense, Spain), as previously reported (Deive et al.,
2010).
2.3. Culture conditions
Basal medium for plates cultures was composed of (g L1, in distilled water): eight trypticase, four yeast extract, three sodium
chloride, and 20 agar. In all cases, the pH was adjusted to 7.5 and
the plates were incubated at 65 °C for 4 days.
Submerged aerobic cultures were carried out in 250 mL Erlenmeyer flasks with 50 mL of a basal medium without agar.
0.07 g L1 of dyes (RB5 and AB48) when used independently and
0.05 and 0.04 g L1 when mixed (RB5 and AB48, respectively) were
sterilized by filtration through a 20 lm filter prior to the addition
to the autoclaved medium (121 °C and 20 min), in order to avoid
any possible alteration of the chemical structure of the dye. The selected dye concentrations are based on previous results of the
group (Deive et al., 2010). The flasks were inoculated (3%) with
previously obtained cell pellets, which were then incubated in an
orbital shaker (Innova 44, New Brunswick Scientific,) at 65 °C
and 100 rpm.
2.4. Culture sample preparation and decolorization analysis
Cells were harvested by centrifugation (10 min, 5000g), and the
supernatant was reserved for decolorization analysis. Decolorization was measured spectrophotometrically (Unicam Helios b, Thermo Electron Corp.) from 300 to 750 nm, calculated by measuring
the area under the plot and expressed in terms of percentage.
D ð% removalÞ ¼ ðIi If Þ 100=Ii
ð1Þ
where Ii and If are initial and final area of the dye solution, respectively (Rodríguez Couto et al., 2005). Each decolorization value was
the mean of two parallel experiments that were run for 4 days. Abiotic controls (without microorganism) were always included. The
assays were done in duplicate, and the experimental error was less
than 3%.
2.5. Identification of degradation products
After the biological process, the cells were removed by centrifugation at 10,000g for 10 min. Vacuum (101 Pa) and moderate temperature (323 K) conditions were applied to a supernatant aliquot
for several days in order to reduce the water content. Afterward,
the sample was dried under nitrogen and the degradation compounds were extracted with ethyl acetate to be analyzed by GCMS
analysis.
1 lL of this organic phase was analyzed using a Focus GC Thermo Finnigan gas chromatograph equipped with a TR-5MS capillary
column (30 m 0.25 mm i.d. 0.25 lm film thickness, Thermo
Electron Corporation), operating with hydrogen carrier gas, coupled to a mass spectrometer (MS). The GC injector was operated
in splitless mode. GC oven was programmed to hold at 80 °C for
1 min, then raise the temperature by 12 °C/min to 250 °C, which
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M.S. Álvarez et al. / Bioresource Technology 146 (2013) 689–695
was held for 5 min. Degradation products were identified by comparison with the NISTS search 2.0 database of spectra.
2.6. Cell growth determination
Biomass concentration was measured by turbidimetry at
600 nm in the above mentioned spectrophotometer, and the obtained values were converted to grams of cell dry weight per liter
using a calibration curve.
2.7. Aqueous biphasic systems
The phase diagrams of the ABS were carried out by means of the
cloud point titration method (Albertsson, 1986). A known amount
of the selected organic salt was added to the different surfactantbased aqueous solutions until the detection of turbidity, followed
by the drop-wise addition of ultra-pure water until a clear monophasic region was achieved. The system was always operating under constant stirring.
The ternary system compositions were determined by the
weight quantitation of all components within an uncertainty of
±104 g. The temperature was controlled with a F200 ASL digital
thermometer with an uncertainty of ±0.01 K.
The tie-lines (TLs) determination started with the addition of a
potassium-based organic salt (citrate, oxalate and tartrate) to an
aqueous solution of Tween 20 or Tween 80, up to achieve a point
within the immiscibility region. The mixture was left to settle for
24 h to ensure a complete separation of the layers, after a vigorous
stirring at room temperature. The TLs data were determined by
solving a system of four equations: two of them consider the relationship between the upper phase and the overall system mass
composition by means of the lever arm rule and the others are
based on a correlation expression (Eq. (6)) for top and bottom
phases.
2.8. Mathematical modeling
The SOLVER function in Microsoft EXCEL was used to adjust the
parameters so that the standard deviations were minimized. The
standard deviations (r), were calculated by applying the following
expression:
r¼
PnDAT
i
ðzexp zadjust Þ2
nDAT
!1=2
ð2Þ
facultative anaerobic strain was the one leading to the most promising results. Based on these results, the viability of A. flavithermus
to decolorize an aqueous effluent containing a di-azo (RB5) and
anthraquinone (AB48) class of dyes, both independently and
mixed, is mandatory. The experimental data of cell concentration
and degradation are presented in Fig. 1. Additionally, one of the
useful means to get a better characterization of microbial process
is by describing the quantitative relationships between the selected outputs (cell concentration or dye decolorization) and the
independent variables (time of cultivation). Therefore, a logistic
model commonly used in other decolorization processes was
proposed to fit the experimental data (Deive et al., 2010):
X
h max
X¼
Ln
1þe
X max
1
X0
lt
ð3Þ
i
where X is the biomass (g L1) at a specific moment of the culture
time t (h), X0 and Xmax (g L1) are the initial and maximum biomass
concentrations and l the maximum specific growth rate (h1).
D
h max D¼
1þe
Ln
Dmax
1
D0
lD t
ð4Þ
i
where D is the dye decolorization (%) at a specific moment of the
culture time (t), D0 and Dmax the initial and maximum dye decolorization percentage, and lD the specific degradation rate. The model
was validated by analyzing the difference between the theoretical
and experimental values, in terms of the correlation coefficient R2.
All the model parameters obtained are collected in Table 1.
From the values of the correlation coefficients (R2 > 0.9 for all
cases) and the data depicted in Fig. 1, it is possible to conclude that
the logistic model is able to suitably describe the experimental
data. In general terms, it seems that the operation in the presence
of the model dyes RB5 and AB48 (both independently and mixed)
does not entail drastic alterations in the biomass profiles. On the
contrary, the analysis of the decolorization percentages indicates
that the anthraquinone dye AB48 presents a higher recalcitrance
since the maximum attained is less than 30% lower than that
reached for the cultures with RB5 (57.2% vs. 83.8%). It is also
remarkable that the presence of the di-azo dye RB5 in the mixture
involves a drastic increase in the specific degradation rate
(20 times), notwithstanding the maximum attained is almost identical. The reason for this behavior can be found in the data obtained
for the biological processes in the presence of one single dye. Thus,
where zexp and zadjust are the experimental and adjustable solubility
data, respectively and nDAT is the number of experimental data.
1.2
80
0.8
40
0.4
Decolorization (%)
60
-1
All the measurements were performed in triplicate and the data
are presented as mean ± standard deviation (SD) values. Additionally, the dyes extraction (RB5, AB48 and mixture) through surfactant-based ABS and biological degradation was analyzed by oneway ANOVA, applied to found mean values. Statistical software
SPSS ver.15 was used.
Cell Concentration (g L )
2.9. Statistical analysis
20
3. Results and discussion
0.0
3.1. Biological decolorization
0
60
120
0
180
Time (h)
In previous experiments of our group (Deive et al., 2010), a
screening of bacteria with the ability of degrading several structurally different dyes such as Poly R-478, Methyl Orange, Lissamine
Green B and Reactive Black 5 was carried out. Both aerobic and
anaerobic strains were detected, but we have observed that a
Fig. 1. Biomass concentration and dye decolorization in flask cultures of A.
flavithermus containing dyes: (s) cell concentration, (4) decolorization. Void
symbols represent AB48, and full symbols represent the mixture (AB48 and RB5).
Experimental data are denoted by symbols and the fittings to logistic models are
denoted by solid lines.
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M.S. Álvarez et al. / Bioresource Technology 146 (2013) 689–695
Table 1
Parameters defining the logistic model that characterizes the growth and the decolorization of A. flavithermus in flask cultures.
Dye
X0 (g L1)
Xmax (g L1)
lm (h1)
R2
D0 (% Rem)
Dmax (% Rem)
lD (h1)
R2
AB48
RB5
Dye mixture
0.08
0.06
0.14
0.67
0.53
0.61
0.305
0.360
0.211
0.91
0.97
0.93
5.3
2.9
0.6
57.2
83.8
57.4
0.030
0.590
0.571
0.95
0.99
0.95
while the specific degradation rate for AB48 is 0.030 h1, the value
obtained for the culture in the presence of RB5 is 0.590 h1, which
points to an easier metabolisation of the latter. Therefore, the
introduction of RB5 eases the cometabolization of AB48.
Previous works have also pointed out the higher endurance of
anthraquinone dyes to be decolorized, may be due to their higher
water solubility and greater stability of chromogenic groups (Panswad and Luangdilok, 2000). Also, the results obtained in this work
are more promising than those recently reported by Hadibarata
et al. (2012), who concluded that 24 h were necessary to lower
or similar levels of decolorization for an azo and an anthraquinone
dye (40% and 60%). It should be noted that these experiments were
carried out in the presence of only one single contaminant, instead
of a mixture of an azo and an anthraquinone class of dyes. The statistical analysis of the experimental data (ANOVA) revealed significant differences in the biological remediation depending on the
targeted dye (P < 0.001) while the ABS seems to be a more versatile
remediation method, since there are not significant differences
between them (P = 0.025).
After demonstrating the decolorization potential, a deeper analysis of the UV spectra was tackled, as a way to elucidate the true
nature of the decolorization. From the obtained spectra of RB5, it
becomes patent the presence of one band with maximum absorption at 590 nm and another band at 399 nm. The former is responsible for the dark blue color arising from aromatic rings connected
by azo groups and the latter can be associated with ‘‘benzene-like’’
structures in the molecule, as indicated by Enayatizamir et al.
(2011). After 6 h, which corresponds to the exponential growth
phase, a great reduction in the band at 590 nm is recorded, as a
consequence of the cleavage of the azo group. However, the band
at 399 nm does not show a significant reduction, but a slight increase, probably due to the formation of quinone or benzene rings
with substitution groups, in line with the results pointed by Enayatimazir et al. (2011) when studying RB5 degradation by the immobilized fungus Phanerochaete chrysosporium.
On the other hand, the analysis of the evolution in the UV visible spectra of AB48 along the cultivation time led to analogous
conclusions in relation to the chromophoric group, since a clear
reduction is recorded in the band at 642 nm. Similarly, the small
band at 487 nm is also reduced. The observed reduction could be
due to both adsorption/biosorption or biodegradation. Therefore,
the analyses of absorbance data between peaks could shed light
on the true decolorization patterns, as recently pointed out by Mitter and Corso (2013) for the degradation of AB48. The relative
absorbances of each peaks for RB5 and AB48 (A590/A399 for RB5
and A642/A487 for AB48) led to the conclusion that true biodegradation is occurring for both azo and anthraquinone dyes.
The identification of some benzene-like structures in the UV
spectra is the basis to hypothesize that the use of GCMS data could
serve our goal to get further insight in the degradation products.
Thus, the presence of 1,4-Naphthoquinone (RT (retention time)
11.34 min) and 1,4-benzenodiamine N, N dimethyl (RT 8.29 min)
in cultures of AB48 and benzoic acids (RT 8.6 min) and 4-ethylphenol (RT 10.69 min) in cultures of RB5 confirms our hypothesis of
the existence of a true degradation process.
3.2. Selection of a suitable phase segregation agent
The second step of the work consisted in analyzing the effect of
different potassium based organic salts as segregation agents in
aqueous solutions of the non-ionic surfactants Tween 80 and
Tween 20. To our knowledge, there is no information in the literature related to the application of surfactant-based ABS to extract
this kind of synthetic dyes. Very often, the use of high charge density inorganic salts has led to efficient phase splitting. However,
high concentrations of inorganic salts are not desirable in the effluent streams due to environmental problems, and the use of more
biocompatible species such as biodegradable organic salts can be
the best bet to reach a compromise between separation efficiency
and environmental sustainability. On the other hand, the presence
of surfactants in real polluted effluents made us to select a commonly used family such as polyoxyethylene sorbitan fatty esters
as representative tensoactive compounds, since they have been reported to be biodegradable (Bautista et al., 2009).
The experimental binodal curves for the ternary mixtures of the
systems composed of (Tween 20 or Tween 80) + potassium-organic
0.06
0.06
(b)
0.04
0.04
0.02
0.02
0.00
0.00
0.03
0.06
100 w2 / M2 (mol/g)
0.09
0.00
0.03
0.06
0.09
100w1 / M1(mol/g)
100 w1 / M1(mol/g)
(a)
0.00
0.12
100 w2 / M2(mol/g)
Fig. 2. Ternary phase diagrams for ABS composed of organic salts: (s) potassium citrate, (h) potassium tartrate, and (4) potassium oxalate. (a) Tween 80 and (b) tween 20.
Symbols represent experimental data and solid lines represent the fittings to correlation Eq. (6).
693
M.S. Álvarez et al. / Bioresource Technology 146 (2013) 689–695
Table 2
Correlation parameters and standard deviations of the studied systems for Eqs. (5)–(7).
a
b
1.0992
2.3694
0.7346
1.8082
2.2680
1.4505
Tween
Tween
Tween
Tween
Tween
Tween
80 + K3C6H5O7 + H2O
80 + K2C4H4O6 + H2O
80 + K2C2O4 + H2O
20 + K3C6H5O7 + H2O
20 + K2C4H4O6 + H2O
20 + K2C2O4 + H2O
0.6245
0.9140
0.5646
0.7363
0.7879
0.6632
80 + K3C6H5O7 + H2O
80 + K2C4H4O6 + H2O
80 + K2C2O4 + H2O
20 + K3C6H5O7 + H2O
20 + K2C4H4O6 + H2O
20 + K2C2O4 + H2O
d
0.6891
1.16824
0.7569
1.0711
0.8588
0.8177
e
Tween
Tween
Tween
Tween
Tween
Tween
Tween
Tween
Tween
Tween
Tween
Tween
80 + K3C6H5O7 + H2O
80 + K2C4H4O6 + H2O
80 + K2C2O4 + H2O
20 + K3C6H5O7 + H2O
20 + K2C4H4O6 + H2O
20 + K2C2O4 + H2O
h
1.8332
1.1518
1.4378
4.2920
3.7563
1.1866
i
30.48
17.67
23.99
47.87
40.87
20.23
c
r
730
588
137
393
315
790
0.0163
0.0084
0.0130
0.0130
0.0102
0.0118
f
0.9706
3.1756
0.8100
3.7285
1.8009
1.3059
salt + H2O are plotted in Fig. 2. Different models were employed for
correlating the experimental solubility data (Álvarez et al., 2012a):
0:5
w1 ¼ a exp bw2 cw32
ð5Þ
2
w1 ¼ d þ ew0:5
2 þ fw2 þ gw2
ð6Þ
2
0:5
w1 ¼ exp h þ iw2 þ jw þ kw2
ð7Þ
where w1 is the mass fraction of surfactants, w2 is the mass fraction
of organic salts, and a, b, c, d, e, f, g, h, i, j and k are fitting parameters
All the values of the fitting parameters and standard deviations
are listed in Table 2. From the deviation results it is possible to
state that Equation (6) leads to lower deviations, in line with previous results reported for other surfactant-based ABS (Álvarez
et al., 2012a,b).
A visual inspection of the experimental curves indicates that it
is possible to analyze the role of the selected potassium salts as
phase promoters in aqueous solutions of Tween 20 and Tween
80 from the point of view of the multivalent nature of the organic
anion. Thus, potassium citrate is the salt showing a stronger ability
to form an immiscible area in the presence of the aqueous-surfactants mixtures. This trend indicates that the anions with higher valence (C6H5O7)3 possess a stronger salting-out effect than divalent
anions, due to they are able to be hydrated by more water molecules (Freire et al., 2012).
In general terms, the ability of potassium-based salts to salt-out
the selected surfactants from the aqueous solution could be expressed by the following trend: citrate > tartrate > oxalate. This
salt-rank effect follows the Hofmeister series, which order ions
according to their water structuring capacity. In this sense, it is
possible to conclude that oxalate is the ion with the weakest interactions with water, thus leading to a smaller biphasic region. It is
also interesting to note that the observed sequence is the same
for both surfactants, which confirms the observed behavior.
The segregation ability can be analyzed attending to statistical
geometry methods developed by Guan et al. (1993), which is based
on a random distribution of molecules species in a solution, considering that every system composition on the binodal is a geometrically saturated solution of one solute in the presence of another.
Therefore, the Effective Excluded Volume (EEV) was studied by
using the following equation:
r
g
0.0978
1.0580
3.2850
5.3983
0.2853
1.5895
14.5578
2.75912
5.13842
15.0346
3.8307
4.7615
j
102.5
53.99
81.71
129.6
105.4
65.09
0.0041
0.0067
0.0062
0.0039
0.0050
0.0055
r
k
395.4
239.2
419.5
335.3
262.9
296.2
0.0142
0.0085
0.0148
0.0113
0.0095
0.0130
Table 3
EEV parameters and volumetric fraction (f213) of the ternary mixtures obtained from
Eq. (8).
Tween
Tween
Tween
Tween
Tween
Tween
80 + K3C6H5O7 + H2O
80 + K2C4H4O6 + H2O
80 + K2C2O4 + H2O
20 + K3C6H5O7 + H2O
20 + K2C4H4O6 + H2O
20 + K2C2O4 + H2O
103 V 231 (g mol1)
f213
r
0.0950
0.0119
0.0058
0.0655
0.0058
0.0031
0.9519
0.9920
0.9960
0.9623
0.9957
0.9976
0.0655
0.1103
0.1305
0.0765
0.1795
0.1519
w2
w1
ln V 213
þ f213 þ V 213
¼0
M2
M1
ð8Þ
where V 213 is the scaled EEV of the salt, f213 is the volume fraction of
unfilled effective available volume after tight packaging of salt
molecules into the network of surfactant molecules in aqueous
solutions, which includes the influence of the size of the water
molecules, M1, and M2 are the molar mass of surfactant and potassium-based organic salt, respectively.
The values of the obtained parameters and standard deviations
are presented in Table 3, and allow confirming the salting out
potential observed in Fig. 2, since the more water structuring
potassium citrate presents the higher values of EEV, no matter
the surfactant employed. In the same line, potassium oxalate is
the salt leading to the lowest EEV values, in agreement with its
smallest immiscibility region.
3.3. Treatment train combining biodegradation and ABS
After having demonstrated the suitable phase segregation of
potassium citrate, the next step included the investigation of its
extraction capacity, in comparison with the rest of the selected organic salts. Since Tween 20 is the non-ionic surfactant allowing
more suitable phase splitting, a greater interaction with the
selected class of dyes is expected. One of the valuable tools to
elucidate the effectiveness of a separation process is the extraction
capacity:
E ð%Þ ¼
Tween 20 mi
100
mi
ð9Þ
where miTween 20 and mi are the dye (RB5 and AB48) mass content in
the upper phase and the total contaminant mass content,
respectively.
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M.S. Álvarez et al. / Bioresource Technology 146 (2013) 689–695
Table 4
Experimental tie-lines (TLLs) in mass percentage for the studied systems.
Surfactant-rich phase
Organic salt-rich phase
100w1
100w1
100w2
100w2
TLL
Table 5
Treatment train of the proposed remediation process.
E%
Tween 20 + K3H5C6O7 + H2O + RB5
47.84
4.68
1.38
55.26
2.43
0.32
61.26
1.04
0.06
20.06
22.69
24.99
48.95
58.57
65.72
3.02
2.71
2.55
97.1
99.6
99.9
Tween 20 + K2H4C4O6 + H2O + RB5
46.52
4.73
1.79
52.73
3.01
0.49
55.39
2.36
0.14
20.60
23.30
25.39
47.46
56.04
59.85
2.82
2.57
2.40
95.4
99.6
99.9
Tween 20 + K2C2O4 + H2O + RB5
47.15
4.01
1.79
54.29
1.83
0.32
59.85
0.51
0.08
15.67
18.16
19.75
46.83
56.39
62.79
3.89
3.30
3.11
93.2
99.7
99.8
Tween 20 + K3H5C6O7 + H2O + AB48
49.24
4.24
1.24
55.51
2.36
0.33
58.63
1.57
0.10
20.27
22.62
24.46
50.60
58.78
62.85
2.99
2.72
2.56
97.7
99.3
99.9
Tween 20 + K2H4C4O6 + H2O + AB48
47.73
4.38
2.03
51.25
3.40
0.57
56.83
2.05
0.20
20.30
23.01
24.89
48.39
54.34
61.07
2.87
2.58
2.48
92.9
99.3
99.5
Tween 20 + K2C2O4 + H2O + AB48
49.65
3.27
0.97
56.37
1.24
0.21
58.55
0.75
0.05
16.63
18.64
20.29
50.48
58.78
61.68
3.64
3.23
2.99
96.9
97.2
99.7
Tween 20 + K3H5C6O7 + H2O + Mixture AB48 and RB5
42.63
6.23
1.43
19.99
43.45
51.11
3.66
0.36
22.47
54.12
54.93
2.52
0.09
24.60
59.12
2.99
2.69
2.48
96.7
98.8
99.8
Besides, the separation efficiency at laboratory scale was also
ascertained by means of useful parameters such as the tie-line
length, TLL, and the slope of the TL data, S. These parameters indicate the relative distribution of the surfactant and the organic salt
between the two segregated aqueous phases. These values were
calculated by applying the following equations to the experimental
data:
2
2 0:5
TLL ¼ f wTw20
wsalt
þ ðwTw20
wsalt
1
1
2
2 Þ g
S¼
20
wTw
wsalt
1
1
Tw 20
w2
wsalt
2
Dye
Remediation percentage
Biological treatment (%)
ABS (%)
Total (%)
RB5
AB48
Dyes mixture
83.8
57.2
57.4
99.8
99.0
99.5
99.97
99.57
99.79
S
ð10Þ
ð11Þ
where the equilibrium mass percentage of Tween 20 (w1) and organic salt (w2), in the surfactant-rich phase (Tw 20) and salt-rich
phase (salt), are represented. The data shown in Table 4 reveal that
extremely high separation percentages (>93%) can be obtained, no
matter the organic salt and dye employed. Besides, a slight increase
in the extraction capacity is recorded when the TLL is higher, similar
to the findings reported by Gonçalves Lacerda et al. (2009).
Total remediation percentage and biological remediation are referred to the initial
amount of dye, while ABS remediation values are referred to the concentration
existing in the biotreated effluent.
Therefore, it becomes patent that the use of potassium citrate
entails not only an efficient contaminant extraction, but also the
stronger salting out capacity. These facts made us to bet in this
organic salt to approach the final remediation step with the biologically treated effluent.
The final stage of this work consisted of implementing a treatment train consisting of a biodegradation step followed by an
ABS (Tween 20 + potassium citrate) to extract the non-degraded
contaminant charge. The remediation percentages obtained for
each stage are listed in Table 5. A visual inspection of the results
indicates that the proposed remediation strategy is valid not only
for single contaminants but also for a mixture of them. Besides,
the versatility of the technique allows treating both anthraquinone
and dia-azo dyes with efficiencies close to 100%. The flowsheet
proposed can be visualized in Fig. 3. These remediation levels indicate that the efficiency of the proposed strategy is quite higher
than recent works tackling different treatments for the remediation of persistent organic contaminants (López-Vizcaino et al.,
2012; Russo et al., 2012). More specifically, Srinivasan et al.
(2011) also reported the suitability of a hybrid technique based
on a sequential sonolysis and biodegradation strategy for the
remediation of another azo dye (Tectilon Yellow 2G), since they
were able to increase the decolorization efficiency from 46% to
66%.
4. Conclusion
The anthraquinone and di-azo dye remediation strategy proposed in this work is based on the combination of two treatments
based on the biological and physical removal of contaminants. The
first step consisted of a dye biodegradation process by means of a
thermophilic bacterium isolated from a northwestern Spain hot
spring, and allowed achieving circa 60% degradation in less than
12 h for AB48 and a mixture of AB48 and RB5. By coupling ABS
as a second step a total remediation yield higher than 99% is
achieved, which confirms the suitability of this hybrid strategy to
remediate textile dyes-polluted effluents.
Fig. 3. Flowsheet of the proposed process.
M.S. Álvarez et al. / Bioresource Technology 146 (2013) 689–695
Acknowledgements
This project was funded by the Spanish Ministry of Science and
Innovation (CTQ2008-03059/PPQ). Francisco J. Deive wants to
thank Xunta de Galicia for funding through an Isidro Parga Pondal
contract.
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ANNEX 10
HYBRID SEQUENTIAL TREATMENT OF AROMATIC HYDROCARBONS-POLLUTED EFFLUENTS
USING NON-IONIC SURFACTANTS AS SOLUBILIZERS AND EXTRACTANTS (BIORESOURCE
TECHNOLOGY, 2014, 162: 259-265).
Bioresource Technology 162 (2014) 259–265
Contents lists available at ScienceDirect
Bioresource Technology
journal homepage: www.elsevier.com/locate/biortech
Hybrid sequential treatment of aromatic hydrocarbon-polluted
effluents using non-ionic surfactants as solubilizers and extractants
M.S. Álvarez a, F. Moscoso a,b, A. Rodríguez a, M.A. Sanromán a, F.J. Deive a,⇑
a
b
Department of Chemical Engineering, Universidade de Vigo, 36310 Vigo, Spain
Instituto de Tecnología Química e Biológica, Universidade Nova de Lisboa, 2780-256 Oeiras, Portugal
h i g h l i g h t s
Physico-biological treatment based on ATPS led to very high remediation levels.
Treatment train was able to remove more than 97% of PAHs.
The organic salt potassium citrate proved to be an effective salting out agent.
Concomitant use of non-ionic surfactants as solubilizers and extractants.
a r t i c l e
i n f o
Article history:
Received 6 February 2014
Received in revised form 25 March 2014
Accepted 28 March 2014
Available online 5 April 2014
Keywords:
Effluent treatment
Phenanthrene
Pyrene
Benzoanthracene
ATPS
a b s t r a c t
A treatment train combining a biological and a physical approach was investigated for the first time in
order to remediate polycyclic aromatic hydrocarbons (PAHs)-polluted effluents. Given the hydrophobic
nature of these contaminants, the presence of non-ionic surfactants is compulsory to allow their bioavailability. The presence of these surfactants also entails an advantage in order to ease contaminant removal
by the formation of aqueous two-phase systems (ATPS). The segregation ability of environmentally
benign salts such as potassium tartrate, citrate, and oxalate was discussed for extracting phenanthrene
(PHE), pyrene (PYR), and benzo[a]anthracene (BaA). The biological remediation efficiency reached circa
60% for PHE and PYR, and more than 80% for BaA. The coupling of ATPS subsequent stage by using potassium citrate allowed increasing the total PAH remediation yields higher than 97% of PAH removal. The
viability of the proposed solution was investigated at industrial scale by using the software tool SuperPro
Designer.
Ó 2014 Elsevier Ltd. All rights reserved.
1. Introduction
The current life standards and environmental awareness urge
petrochemical industry to give its attention to sustainable growth.
The rapid industrialisation has resulted in the depletion of natural
resources and has caused an adverse impact on ecosystems. Nowadays, there is a growing interest in the effect of hazardous wastes
generated from petrochemical processes, since complex mixtures
of hydrocarbons are often identified on soil–water environment
as common contaminants. Among them, PAHs stand out as highly
toxic, mutagenic, genotoxic and carcinogenic compounds (Simarro
et al., 2011; Zhong et al., 2011). They are introduced in the environment through different natural and anthropogenic activities, and
16 of them are considered as priority pollutants (Zhao et al.,
⇑ Corresponding author. Address: Department of Chemical Engineering, Lagoas
Marcosende s/n, 36310 Vigo, Spain. Tel.: +34 986 818723; fax: +34 986 812380.
E-mail address: [email protected] (F.J. Deive).
http://dx.doi.org/10.1016/j.biortech.2014.03.158
0960-8524/Ó 2014 Elsevier Ltd. All rights reserved.
2009; Bautista et al., 2009) by the US Environmental Protection
Agency (USEPA).
Their environmental risks are mostly due to their inherent
properties such as persistence, low vapour pressure, high hydrophobicity and thermodynamic stability of the aromatic ring (Cao
et al., 2009). The presence of pollutants with a recalcitrant nature
in the environment has promoted an intense research effort
focused on the development of more effective technologies for
the removal of contaminants from industrial locations and polluted
effluents. These treatments have been classified in three main categories: physical (volatilisation, photolysis, adsorption, filtration
and electro remediation), chemical (oxidation, photocatalysis and
coagulation–flocculation) and biological (biosorption or biodegradation) (Kim and Lee, 2007; Janbandhu and Fulekar, 2011).
Usually, the application of one single technique does not allow
achieving high remediation levels, and a promising strategy consist
in combining biological and physical methods to form efficient
treatment trains. The foundation for betting in this approach is
260
M.S. Álvarez et al. / Bioresource Technology 162 (2014) 259–265
motivated in the fact that persistent hydrophobic compounds possess a limited bioavailability (Kanaly and Harayama, 2000). To
overcome this drawback, the addition of surface active compounds
appears as one of the acceptable strategies licensing the increase of
solubility by decreasing the interfacial surface tension between the
hydrophobic contaminant and the soil–water interphase. Among
the typical surfactants used, Tween and Triton families have been
highlighted in many studies, as a non-toxic and efficient alternative (Bautista et al., 2009; Ulloa et al., 2012a). Therefore, the biological treatment is facing a biphasic medium composed of an organic
phase (PAH and surfactant) and an aqueous phase (with salts and
organic matter dissolved). This biphasic nature made us to bet in
the coupling of a physical method based on a liquid–liquid equilibrium strategy to yield high levels of PAH removal.
The liquid–liquid equilibrium makes up one of the appealing
alternatives (García-Chavez et al., 2012; Domanska et al., 2006)
and the extraction by means of aqueous two-phase systems (ATPS)
has become a viable, sustainable and competitive alternative in the
field of separation processes. The reasons behind this attractiveness are the low energy consumption and costs, rapid phase disengagement, and high efficiencies in the separation of different kinds
of biomolecules and chemicals (Freire et al., 2012).
The basis of this strategy lies in merging two hydrophilic polymers, or salts, or a polymer and a salt, with water, leading to a
biphasic system over a certain concentration (Deive et al.,
2011a,b,c). This segregation is the result of a competitive interplay
between salts or polymers and the water molecules and multifarious interactions between polymers and salts. One of the novel
alternatives of ATPS are based on the use of non-ionic surfactants
such as Tween and Triton families (Ulloa et al., 2012b; Álvarez
et al., 2012a,b).
In this work, we propose a feasible treatment train composed of
a previous biodegradation process by the use of Pseudomonas stutzeri, followed by a non-ionic surfactant-based ATPS. The search for
more sustainable process included the analysis of the solubility
curves obtained for three potassium organic salts (citrate, oxalate
and tartrate) in the presence of aqueous solutions of Triton surfactants. Merchuk, Othmer–Tobias and Bancroft equations served our
goal to properly characterise the proposed ATPS. Finally, a commercial software (SuperPro Designer) was used to simulate the
treatment of a PAHs-polluted industrial effluent.
2. Methods
2.3. Biodegradation reaction
Biodegradation cultures were carried out in 250-mL Erlenmeyer
flasks with 50 mL of medium containing 1% w/v of the non-ionic
surfactant Triton X-100, and 2% v/v of acetone (used as solvent to
prepare the contaminant solution, due to its low solubility in aqueous media), following the strategy proposed by Moscoso et al.
(2012). The flasks, capped with cellulose stoppers, were inoculated
(3%) with previously obtained cell pellets, and incubated in the
darkness for 15 days in an orbital shaker (INNOVA 4400 New
Brunswick) at 37 °C, initial pH 8.0, and 150 rpm. Samples were
withdrawn daily to monitor PAH biodegradation and cell density.
All samples were analysed in triplicate, and the biodegradation
experiments were repeated to check the reproducibility. The presented data in tables and figures are mean values. All the material
used was made of glass in order to avoid contaminants losses due
to sorption.
2.4. Aqueous two-phase systems
The phase diagrams of the ATPS were carried out by means of
the cloud point titration method (Albertsson, 1986) at 25 °C. A
given amount of organic salt, determined by weighing, was added
to the different surfactant-based aqueous solutions until the detection of turbidity, and then followed by the drop-wise addition of
ultra-pure water until a clear monophasic region was achieved.
The system was always operating under constant stirring. The ternary system compositions were determined by the weight quantification of all components within an uncertainty of ±104 g. The
temperature was controlled with a F200 ASL digital thermometer
with an uncertainty of ±0.01 K.
The tie-lines (TLs) determination started with the addition of a
potassium-based organic salt (citrate, oxalate and tartrate) to an
aqueous solution of Triton X-100 or Triton X-102, up to achieve a
point within the immiscibility region. The mixture was left to settle for 24 h to ensure a complete separation of the layers, after a
vigorous stirring at room temperature. The estimated uncertainty
in the determination of Triton and salt phase mass compositions
is less than 2 104. The TLs data were determined by solving a
system of four equations: two of them consider the relationship
between the upper phase and the overall system mass composition
by means of the lever arm rule and the others are based on Merchuk expression for top and bottom phases.
2.1. Chemicals
2.5. Analytical methods
Phenanthrene (PHE), pyrene (PYR) and benzo[a]anthracene
(BaA) (purity higher than 99%) used in degradation experiments
were purchased from Sigma Aldrich (Germany). PAHs stock solutions were 5 mM in acetone.
Potasium
citrate
(K3C6H5O7H2O),
potassium
tartrate
(K2C4H4O60.5H2O), potassium oxalate (K2C2O4H2O), and the
non-ionic surfactants Triton X-100 and Triton X-102 were supplied
by Sigma Aldrich (Germany). All chemicals used were at least,
reagent grade or better.
2.2. Microorganism and culture conditions
Bacterium P. stutzeri CECT 930 was obtained from the Spanish
Type Culture Collection (ATCC 17588). Culture medium was composed of (g/L): Na2HPO42H2O 8.5, KH2PO4 3.0, NaCl 0.5, NH4Cl
1.0, MgSO47H2O 0.5, CaCl2 14.7 103. This medium also contained trace elements as follows (mg/L): CuSO4 0.4, KI 1.0,
MnSO4H2O 4.0, ZnSO47H2O 4.0, H3BO3 5.0, FeCl36H2O 2.0.
2.5.1. Cell growth determination
Biomass concentration was measured by turbidimetry at
600 nm in a Unicam Hekios b spectrophotometer, and the
obtained-values were converted to grams of cell dry weight per
litre using a calibration curve.
2.5.2. PAH analysis
PHE, PYR and BaA concentrations in the culture media were
analysed by reversed-phase high performance liquid chromatography (HPLC) equipped with a reversed phase C8 column
(150 4.6 mm, 5 lm particule size, Zorbax Eclipse) with its corresponding guard column. The HPLC system was an Agilent 1100
equipped with a quaternary pump and photodiode array UV/Vis
detector (252.4 nm). 5 lL of filtered cultivation media (through a
0.45 lm Teflon filter) were injected and then eluted from the column at flow rate of 1 mL min1 using acetonitrile:water (67:33) as
mobile phase. The temperature was maintained at 25 °C.
M.S. Álvarez et al. / Bioresource Technology 162 (2014) 259–265
261
w1 ¼ a expðbw2 cw32 Þ
ð1Þ
free energy of hydration (DhydG) data. These values allow
elucidating the possible formation of water–ion complexes and
its consequent phase disengagement. Thus, for the studied
anions the sequence is: (C6H5O7)3 (2763 kJ mol1) < (C2O4)2
(1453 kJ mol1) < (C4H4O6)2
(1102 kJ mol1)
(ZafaraniMoattar and Hamzehzadeh, 2011; Zafarani-Moattar and Tolouei,
2008).
On the one hand, these values suggest a greater interaction with
water molecules of the trivalent potassium-based organic salt. On
the other hand, the Gibbs free energies of oxalate and tartrate are
very similar, which would be translated in the existence of similar
solubility curves. These hypotheses were confirmed by analysing
the ATPS data illustrated in Fig. 2, where the experimental data
are represented together with those obtained from the Eq. (2).
The analysis of the obtained data confirms that potassium citrate is by far the salt leading to greater immiscibility windows both
for Triton X-100 and Triton X-102. In general, the higher salting out
potential of this salt can be explained based on its multivalent nature. It has been already reported that trivalent ions such as phosphate or citrate show a higher interaction with water molecules
than divalent ions, which ultimately entail the existence of solubility curves more close to the origin (Freire et al., 2012; Deive et al.,
2011a,b,c; Shahriari et al., 2012). The salting out capacity of the
selected organic salts can also be analysed in the light of the Effective Excluded Volume (EEV) theory (Guan et al., 1993). Therefore,
the experimental data were fitted to the following equation:
2
w1 ¼ d þ ew0:5
2 þ fw2 þ gw2
ð2Þ
lnðV 213 w2 =M 2 þ f213 Þ þ V 213 w1 =M1 ¼ 0
2.5.3. Modelling and simulation
The experimental data were fitted to the proposed equations
through SOLVER function in Microsoft EXCEL. Flowsheet simulation was carried out by means of SuperPro DesignerÒ v8.5 (Intelligen Inc.).
3. Results and discussion
The implementation of a sequential strategy consisting of biodegradation and surfactant-based ATPS will demand the search
of effective phase segregation agents, following the flow chart presented in Fig. 1. Usually, inorganic salts have been proposed as
effective salting out compounds. However, in this work we have
bet in organic salts, since high concentrations of inorganic salts
are not desirable in the effluent streams due to environmental
problems (Ulloa et al., 2012a). Therefore, three organic salts such
as potassium citrate, potassium tartrate and potassium oxalate
were chosen as biodegradable and nontoxic phase promoters in
aqueous solutions of the non-ionic surfactants Triton X-100 and
Triton X-102. One of the key steps to properly characterise the
phase segregation of the selected systems is based on finding a
suitable fitting equation. Due to this, different empirical equations
(Merchuk et al., 1998; Han et al., 2012; Deive et al., 2011a,b,c) were
used to model the experimental data:
0:5
0:5
2
w1 ¼ expðh þ iw2 þ jw2 þ kw2 Þ
ð3Þ
where w1 is the mass fraction of surfactants, w2 is the mass fraction
of salts, and a, b, c, d, e, f, g, h, i, j and k are fitting parameters. All the
values of these parameters are listed in Table 1 together with the
standard deviations. From these data it is possible to conclude that
Eq. (2) is the one allowing a better estimation of the experimental
solubility curves. Therefore, this empirical model will be selected
for further investigation.
3.1. Effect of potassium organic salts
The addition of an appropriate amount of the selected potassium based-organic salts allows triggering phase segregation due
to the competition of the salt for the water molecules in the presence of the non-ionic surfactant. One of the tools existing to predict
the salting out behaviour of the selected salts is the molar Gibbs
ð4Þ
where M1, M2 are the molar mass of Triton and organic salt, respectively, V 213 the scaled EEV of the salt, and f213 the volume fraction of
unfilled effective available volume after tight packaging of salt molecules into the network of surfactant molecules in aqueous solutions, which includes the influence of the size of the water
molecules. The values of the EEV, f213 and standard deviations are
listed in Table 2. Generally speaking, the analysis of the data
obtained allows confirming the previous conclusions related to
the high ability to form hydrogen bonds of potassium citrate, since
the scaled EEV obtained for this salt in the presence of both Triton
surfactants are quite higher than those observed for the other
organic salts employed. Furthermore, the analysis of the results in
terms of the salted out surfactant suggest that Triton X-100 is the
one leading to higher EEV values. This is in agreement with its
greater immiscibility regions and hydrophobic nature, as can be
inferred from its lower hydrophilic–lipophilic balance. Thus, this
empirical number, which varies between 0 and 20, is 13.4 and
15.0 for Triton X-100 and Tween 80, respectively (Ulloa et al.,
2012a).
3.2. Extraction capacity of the target PAHs
Once the suitability of the selected potassium organic salts to
segregate the Triton-rich phase from the aqueous surfactant solution was demonstrated, the next step tackled the investigation of
the PAH removal from a model aqueous effluent, as a prior stage
to implement the process at real scale. The most common way to
do that is by determining the tie lines (TLs) data, by means of
the lever arm rule. To do that, the relationship between the upper
phase and the overall system mass composition was ascertained:
Fig. 1. Flow chart of experimental design.
yI1 ¼ ðyF1 =RÞ ½ð1 RÞ=RyII1
ð5Þ
xI2 ¼ ðxF2 =RÞ ½ð1 RÞ=R xII2
ð6Þ
where F, I and II represent the feed, the top phase, and the bottom
phase, respectively; y1 and x2 are the mass fraction percentage of
262
M.S. Álvarez et al. / Bioresource Technology 162 (2014) 259–265
Table 1
Fitting parameters of empirical equations and standard deviations.
a
b
c
r
0.011
0.016
0.013
0.005
0.007
0.009
Triton
Triton
Triton
Triton
Triton
Triton
X-100 + K3C6H5O7 + H2O
X-100 + K2C4H4O6 + H2O
X-100 + K2C2O4 + H2O
X-102 + K3C6H5O7 + H2O
X-102 + K2C4H4O6 + H2O
X-102 + K2C2O4 + H2O
0.6102
0.6258
0.6055
0.8612
0.7754
0.5194
0.7223
0.9012
0.6916
2.6958
2.3463
1.1337
1110
1010
2270
464
473
889
d
e
f
g
r
Triton
Triton
Triton
Triton
Triton
Triton
X-100 + K3C6H5O7 + H2O
X-100 + K2C4H4O6 + H2O
X-100 + K2C2O4 + H2O
X-102 + K3C6H5O7 + H2O
X-102 + K2C4H4O6 + H2O
X-102 + K2C2O4 + H2O
0.6845
0.7458
0.6509
0.6686
0.9798
0.5070
0.0057
0.4503
0.2349
0.4612
2.6133
0.1408
4.5464
3.6448
5.9209
7.0182
1.0677
3.4158
5.6888
5.7180
11.6201
11.8276
3.0028
5.1367
0.004
0.008
0.007
0.004
0.004
0.004
h
i
j
k
r
Triton
Triton
Triton
Triton
Triton
Triton
X-100 + K3C6H5O7 + H2O
X-100 + K2C4H4O6 + H2O
X-100 + K2C2O4 + H2O
X-102 + K3C6H5O7 + H2O
X-102 + K2C4H4O6 + H2O
X-102 + K2C2O4 + H2O
3.0296
2.2385
1.0964
2.2251
3.3740
3.0296
42.81
32.10
25.48
28.13
39.17
42.81
142.3
104.3
107.5
77.92
106.6
142.3
555.3
428.5
679.6
257.5
310.9
555.3
0.009
0.016
0.013
0.004
0.005
0.009
0.100
0.100
100w1 / M1 (mol/g)
0.075
0.075
0.050
0.050
0.025
0.025
0.000
0.00
0.03
0.06
0.09
0.00
0.03
100w2 / M2 (mol/g)
0.06
100w1 / M1 (mol/g)
Triton X-102
Triton X-100
0.000
0.12
0.09
100w2 / M2 (mol/g)
Fig. 2. Experimental solubility curves for the systems {Triton-X + Organic Salt + H2O}: (s), K3C6H5O7; (h), K2C4H4O6; (4) K2C2O4.
Table 2
Parameters of EEV, f213 and standard deviations.
Triton
Triton
Triton
Triton
Triton
Triton
X-100 + K3C6H5O7 + H2O
X-100 + K2C4H4O6 + H2O
X-100 + K2C2O4 + H2O
X-102 + K3C6H5O7 + H2O
X-102 + K2C4H4O6 + H2O
X-102 + K2C2O4 + H2O
103 V 213 / (g/mol)
f213
r
1.9028
1.4270
1.0635
1.2720
0.1476
0.0356
0.0339
0.0351
0.2316
0.2533
0.8859
0.9706
0.0366
0.0358
0.0414
0.0238
0.0362
0.0573
Triton and organic salt, respectively; and R is the ratio (Weight of
the top phase: Weight of the mixture).
The evaluation of the thermodynamic consistency of the TL data
was carried out by the application of known correlation equations
(Othmer and Tobias, 1942; Li et al., 2010) such as the Othmer–
Tobias and Bancroft models:
n
1 wI1 =wI1 ¼ m 1 wII2 =wII2
r
wII3 =wII2 ¼ k wI3 =wI1
ð7Þ
ð8Þ
where n, m, k and r are the fitting parameters, w is the mass fraction,
subscripts 1, 2 and 3 refer to Triton, potassium organic salt and
water, respectively, and superscripts I and II indicate the Triton-rich
phase and organic salt-rich phase, respectively.
The values of the parameters obtained for both models are presented in Table 3 together with the correlation coefficients. The
analysis of the data reveals that the fitting parameters obtained
from Othmer–Tobias equation lead to a more satisfactory
Table 3
Parameters of Othmer–Tobias and Bancroft equations and correlation coefficients.
Triton
Triton
Triton
Triton
Triton
Triton
Triton
Triton
Triton
Triton
Triton
Triton
X-100 + K3C6H5O7 + H2O
X-100 + K2C4H4O6 + H2O
X-100 + K2C2O4 + H2O
X-102 + K3C6H5O7 + H2O
X-102 + K2C4H4O6 + H2O
X-102 + K2C2O4 + H2O
X-100 + K3C6H5O7 + H2O
X-100 + K2C4H4O6 + H2O
X-100 + K2C2O4 + H2O
X-102 + K3C6H5O7 + H2O
X-102 + K2C4H4O6 + H2O
X-102 + K2C2O4 + H2O
m
n
R2
2.0055
2.8000
6.6197
1.2744
2.1494
1.6034
0.0681
0.0222
0.00001
0.2254
0.0746
0.1394
0.9991
0.9982
0.8156
0.9972
0.9803
0.9371
k
r
R2
3.9965
4.0225
5.7828
3.4377
3.5021
3.5417
0.5046
0.3615
0.0780
0.7822
0.4354
0.6325
0.8423
0.8395
0.5153
0.9055
0.9999
0.6631
M.S. Álvarez et al. / Bioresource Technology 162 (2014) 259–265
263
90% of extraction is yielded for all the PAHs. This in agreement with
the findings reported recently, for the extraction of antioxidants
from microalgae (Ulloa et al., 2012a). Besides, taking into account
previous results related to the salting out potential of each salt,
potassium citrate will be cherry-picked for the implementation
of a strategy based on the maximum concentration of these pollutants from a real effluent.
3.3. Treatment train combining biological degradation and ATPS
Fig. 3. PAHs extraction percentage for the systems composed of Triton X-100 and
potassium organic salts: Black bars represent PHE; grey bars represent PYR, and
white bars represent BaA.
Table 4
PAHs decontamination percentages by using a sequential biological and physical
treatment.
Biological treatment
Physical treatment (ATPS)
Total
PHE
PYR
BaA
59.8
93.6
97.4
56.5
94.2
97.5
81.0
92.0
98.5
description of the experimental TL data. In agreement with it, the
Othmer–Tobias equation turned out to be more suitable to fit the
experimental data in an ATPS made up with a liquid polymer
and an organic salt (Tubio et al., 2009). Besides, Triton X-100 is
the surfactant allowing a better theoretical description, which
makes it a suitable candidate to further investigate the extraction
of PAHs from polluted effluents (Álvarez et al., 2012a).
Once the systems were properly characterised, the non-ionic
surfactant Triton X-100 was selected as the solubilisation agent
for the removal of PHE, PYR and BaA, as model PAHs of low and
high molecular weight, from an aqueous effluent. This approach
is the indispensable requirement prior to implement the process
with a real effluent. Therefore, the extraction efficiency for each
pollutant was evaluated as follows:
Eð%Þ ¼ mTriton
=mi 100
i
ð9Þ
where mTriton
and mi are the PAH mass content in the upper phase
i
and the total contaminant mass content, respectively. The values
for extraction yield of PHE, PYR and BaA for each of the selected
potassium organic salts-based ATPS are plotted in Fig. 3.
A visual inspection of the results allows concluding very high
levels of extraction for all the contaminants (>80%) no matter the
potassium organic salt used. More specifically, it is clear that
potassium citrate turned out to be the best contender, since around
The previous results obtained encouraged us to implement an
environmentally benign two-stage strategy train combining an
extraction process after a biodegradation treatment. A previous
research work from our group allowed confirming the promising
potential of P. stutzeri as bioremediation agent for the degradation
of effluents containing PHE (Moscoso et al., 2012). Maximum degradation levels higher than 90% both at flask and stirred tank bioreactor scale were attained, so this bacterial strain was selected for
approaching a strategy combining biological and physical treatment in order to decontaminate an industrial-polluted effluent
containing PHE, PYR and BaA. The data obtained after a first stage
of biological treatment point that the degradation levels of the contaminants are lower than those reported for cultures carried out in
the presence of PHE, as shown in Table 4. Despite the reduction in
the maximum degradation yields attained, the values are still very
promising, since a minimum of 56% is obtained in all cases, and the
levels are higher for the more accessible structures of BaA and PHE.
Different authors have claimed that simultaneous bacteria-mediated PAH removal was strongly influenced by their different bioavailabilities, which are a direct consequence of their varied
structures (Moscoso et al., 2012; Russo et al., 2012).
Next, potassium citrate was added to the biologically treated
effluent containing the PAH mixture in the presence of Triton X100, and the extraction data listed in Table 4 indicate that again,
very high remediation capacity is attained. Levels higher than
92% are reached for all the contaminants present in the biodegraded effluent, which confirms the behaviour observed previously for
the model aqueous systems.
Overall, the total remediation values after the train treatment
reached levels higher than 97%, which is higher than recent alternatives. Thus, the combination of a sequence of removal techniques has already been tackled by other researchers (Peng et al.,
2008), who demonstrated 90% of COD reduction in PAHs-contaminated soils by combining a surfactant-based washing prior to a
coagulation process. Similarly, benzopyrene degradation levels
higher than 75% were achieved by means of a chemical and a biological treatment (ozone oxidation and aerobic biodegradation)
(González et al., 2011).
3.4. Simulation of the proposed treatment train
Finally, the last step of the research work included the
simulation of the proposed process to treat 200000 m3/year of a
Fig. 4. Process flowsheet diagram for the treatment train combining biological and physical separation of 200,000 m3/year of a PAH-polluted effluent.
264
M.S. Álvarez et al. / Bioresource Technology 162 (2014) 259–265
Table 5
Composition and flow rate of the main streams in a treatment train of a PAH-polluted effluent.
Components
PHE
PYR
BaA
Triton X-100
Potassium citrate
Biomass
S-1
S-2
S-4
S-5
Kg/batch
g/L
Kg/batch
g/L
Kg/batch
g/L
Kg/batch
g/L
18.5
21.0
23.7
10400
0
0
0.018
0.020
0.022
10.0
0
0
7.4
9.1
4.7
2600
0
0
0.007
0.009
0.005
2.5
0
0
0
0
0
195213
157603
0
0
0
0
749
604
0
0.051
0.063
0.030
4718
155484
27.1
0
0
0
9.3
307
0.053
PAHs-polluted effluent from a metallurgical industry. One of the
most powerful tools to evaluate the viability of scaling-up this kind
of environmental processes is the use of process simulators, which
allow saving time and costly laboratory analysis. Simulation of
integrated processes enables to efficiently elucidate the influence
of different crucial process parameters on a consistent basis in a
short period of time. In this case, SuperPro DesignerÒ was selected
for the design of a treatment plant which flowsheet is shown in
Fig. 4. This software is advantageous since it presents a great database of chemicals and units of operations specifically designed for
this kind of biotechnological processes.
The simulation of the process allowed obtaining the mass flowrates and compositions of the main components and streams, as
shown in Table 5. From the data presented, it is clear the high
remediation values reached in the outlet stream (S-5). 4 bioreactors (350 m3), 1 clarifier-settler (100 m3) and 2 ATPS-extraction
units (53 m3 mixer and 420 m3 settler) are required to reach the
presented values, according to the simulation results. The plant
operates at atmospheric pressure, room temperature and
0.17 vvm of aeration (bioreactor). All these data will be of
undoubted interest for proposing a preliminary economic evaluation of a real plant, although the necessity of additional operation
units depending on the characteristics of the polluted effluent
could affect the final economic viability assessment.
4. Conclusions
The viability of a sequential biological and physical treatment to
remediate PAHs-polluted effluents was demonstrated at laboratory
scale. The suitability of Triton X-100 as bioavailability enhancer
and contaminant extractant was proved after a thermodynamic
analysis based on the phase behaviour of the different potassium
organic salts. The remediation yields obtained after a combined
biological–physical treatment reached values higher than 97%,
which are clearly higher than the levels obtained by a one-step biotreatment (lower than 60% and 80% for the three studied PAHs).
Finally, the process was simulated for the treatment of a
200,000 m3/year industrial effluent.
Acknowledgements
This project was funded by the Spanish Ministry of Economy
and Competitiveness (CTM2012-31534). Francisco J. Deive wants
to thank Xunta de Galicia (Spain) for funding through a Isidro Parga
Pondal contract. F. Moscoso thanks FCT-Portugal for a SFRH/BPD/
86887/2012 postdoctoral grant.
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ANNEX 11
PHASE SEGREGATION
AMMONIUM,
IN
AQUEOUS SOLUTIONS
MAGNESIUM
AND
IRON
THERMODYNAMICS, 2014, 70: 147-153.
OF
NON-IONIC SURFACTANTS USING
SALTS
(JOURNAL
OF
CHEMICAL
J. Chem. Thermodynamics 70 (2014) 147–153
Contents lists available at ScienceDirect
J. Chem. Thermodynamics
journal homepage: www.elsevier.com/locate/jct
Phase segregation in aqueous solutions of non-ionic surfactants using
ammonium, magnesium and iron salts
E. Gutiérrez, M.S. Álvarez, F.J. Deive, M.A. Sanromán, A. Rodríguez ⇑
Department of Chemical Engineering, Universidade de Vigo, P.O. Box 36310, Vigo, Spain
a r t i c l e
i n f o
Article history:
Received 30 September 2013
Received in revised form 14 October 2013
Accepted 24 October 2013
Available online 7 November 2013
Keywords:
Non ionic-surfactants
Aqueous Biphasic Systems
Tie-lines
Inorganic and organic salts
a b s t r a c t
Aqueous Biphasic Systems (ABS) are suggested as a separation technique for a great diversity of compounds, making it necessary to characterise fully the solubility data of these kinds of systems. In this
study, the non-ionic surfactants Tween 20 and Triton X-100 are proposed as candidates to form ABS, with
different inorganic and organic salts ((NH4)2Fe(SO4)2, MgSO4, (NH4)2HPO4, (NH4)2SO4, (NH4)2C4H4O6) at
T = 298.15 K. All the solubility data were obtained by means of the cloud point method and the saltingout ability of salts was also evaluated in terms of the Gibbs free energy of hydration (DhydG) and molar
entropy of hydration (DhydS). The Merchuk equation and some variations of this model have been used
for correlating the solubility curve. The extraction capacity k was evaluated in terms of tie-lines (TL) data.
Ó 2013 Published by Elsevier Ltd.
1. Introduction
Globally, there is an increasing interest in the development of
efficient yet cleaner and more eco-friendly industrial processes.
In this sense, Aqueous Biphasic Systems (ABS) have long been considered as a competitive and versatile separation technique that
can be applied to the extraction of volatile organic compounds
[1], metallic ions [2], and biocompounds such as enzymes [3,4],
antibiotics [5] and antioxidants [6], among other relevant
compounds. In the last decade, this separation technique has been
the subject of an unprecedented interest of the scientific community, mainly due to the emergence of new classes of neoteric solvents [7]. Thus, different combinations of aqueous solutions
containing polymers or ionic liquids, or surfactants have been used
to yield phase segregation in this kind of ABS. In addition, from an
economic point of view, the polymer–salt combination is more
advantageous than the polymer–polymer and ionic liquid + salt
systems.
The appeal of this technique has made bet on the characterisation and application of extraction processes involving both ionic
liquids and surfactants [3,4,6,8–14]. This work is devoted to the
study non-ionic surfactants-based ABS, since this kinds of surface
active compounds are widely applied in biotechnological processes
due to inherent benefits such as their low cost and biodegradability
[15,16]. These molecules consist of hydrophilic and lipophilic
groups and the balance between strength and size of polar and
non-polar groups is called Hydrophilic–Lipophilic Balance (HLB)
⇑ Corresponding author. Tel.: +34 986 81 87 23.
E-mail address: [email protected] (A. Rodríguez).
0021-9614/$ - see front matter Ó 2013 Published by Elsevier Ltd.
http://dx.doi.org/10.1016/j.jct.2013.10.033
[17]. Moreover, the non-ionic surfactants polyethoxylated sorbitan
(Tween) and polyoxyethylene toctylphenol (Triton) families have
been highlighted in many reports as a non-toxic and efficient
alternative, and they were even reported to act as carbon source
in culture broths [18] or to avoid enzyme deactivation [19].
Hofmeister initially classified the different salting-out potential
of salts according to their ability to precipitate or solubilise different solutes in water [20], and nowadays most of the authors have
converged upon the idea that the segregation capacity of a given
salt is a entropy-driven process which results from the formation
of ion + water complexes [7]. According to this, several magnesium, ammonium and iron salts (MgSO4), (NH4)2SO4, (NH4)2HPO4,
(NH4)2Fe(SO4)2, (NH4)2C4H4O6) have been proposed as phase promoters in aqueous solutions of two non-ionic surfactants, due to
their successful use as benign media to purify enzymes or as contaminant extractants [4,14]. On the one hand, the rationale behind
2
this selection is based on the fact that the anions SO2
4 and HPO4
are ranked as two outstanding salting-out ions, while NHþ
and
4
Mg2+ cations are examples of strong or weak salting-in agents,
respectively. On the other hand, ammonium tartrate was chosen
since it is a biodegradable, non-toxic organic salt that can be discharged into biological wastewater treatment plants [21] and its
high valence may lead to more competitive interactions in the
systems.
In this work, the capacity to trigger phase segregation of systems
composed of aqueous solutions of Tween 80 or Triton X-100
in the presence of the above mentioned salts was investigated.
The solubility curves, determined by the cloud point method, were
the tools employed to describe the salting-out capacity of each
system. The salting-out character of each salt was qualitatively
148
E. Gutiérrez et al. / J. Chem. Thermodynamics 70 (2014) 147–153
TABLE 1
Purities and suppliers of chemicals.a
a
Inorganic salt
Supplier
Mass fraction purity
Triton X-100
Tween 20
(NH4)2Fe(SO4)2
MgSO4
(NH4)2HPO4
(NH4)2SO4
(NH4)2C4H4O6
Sigma Aldrich
Sigma Aldrich
Merck
Merck
Sigma Aldrich
Sigma Aldrich
Alfa Aesar
0.99
0.99
0.99
0.99
0.99
0.99
0.98
TABLE 2
Binodal data for {Triton-X 100 (1) + salt (2) + H2O (3)} at atmospheric pressure and
T = 298.15 K.a
(NH4)2Fe(SO4)2 MgSO4
Deionised water was used in all the experiments.
discussed in the light of the Hofmeister series, and quantitatively
analysed based on molar Gibbs free energy of hydration and molar
entropy of hydration. The experimental data were modelled by
several empirical equations [13,22,23]. Additionally, the extraction
capacity k was evaluated from the tie-line data and the agreement
between experimental and correlated results was interpreted in
terms of regression coefficients.
2. Experimental
(NH4)2HPO4
(NH4)2SO4
(NH4)2C4H4O6
100
w2
100
w1
100
w2
100
w1
100
w2
100
w1
100
w2
100
w1
100
w2
100
w1
1.86
2.88
3.32
3.94
4.53
4.84
5.08
5.20
5.31
5.68
5.82
6.88
6.99
7.49
8.08
8.21
8.41
8.76
13.74
14.02
45.54
40.19
35.21
32.07
29.88
26.97
25.45
24.24
24.07
21.83
18.94
16.40
14.40
13.02
11.39
9.90
7.38
5.22
0.15
0.11
0.89
0.94
1.23
1.57
2.29
2.45
2.49
3.04
3.71
4.28
4.43
4.77
4.79
5.26
5.69
6.00
6.23
6.59
6.89
7.17
7.47
7.95
11.61
13.46
15.57
51.84
51.43
49.60
46.88
42.84
41.10
39.60
36.41
31.82
27.48
26.02
24.32
22.53
19.09
16.10
13.98
12.20
9.79
7.72
6.22
4.48
3.30
0.03
0.00
0.00
1.26
1.60
2.06
3.20
3.26
3.29
3.88
4.16
4.16
4.41
4.89
5.34
5.77
5.95
6.22
7.18
7.47
7.69
7.95
8.06
11.35
14.64
15.98
52.69
49.87
46.11
40.11
36.13
37.36
33.13
31.14
31.14
29.21
25.06
23.50
18.93
17.55
15.68
12.32
10.39
8.62
6.68
5.12
0.40
0.00
0.00
0.33
2.17
3.01
3.68
4.15
4.17
4.69
5.00
5.42
5.79
6.16
6.72
7.00
7.48
7.76
8.00
8.18
8.69
8.89
13.28
15.19
16.42
65.53
53.11
47.29
41.47
39.31
38.47
35.28
32.35
28.88
26.76
24.05
19.29
16.59
14.02
12.16
10.16
8.47
5.11
3.71
0.06
0.00
0.00
2.80
4.09
5.60
6.13
6.43
6.86
7.11
7.33
7.52
7.85
8.03
8.25
8.57
8.85
8.91
9.40
9.59
9.80
10.07
10.64
10.76
10.89
14.58
17.43
19.14
63.00
53.09
43.26
42.47
39.48
36.81
34.54
33.00
31.89
30.29
29.10
27.28
25.39
22.33
20.79
18.75
16.02
13.58
11.50
8.50
6.05
4.62
0.58
0.02
0.00
2.1. Chemicals
The non-ionic surfactants belonging to the polyethoxylated sorbitan monolaurate (Tween 20) and polyoxyethylene t-octylphenol
(Triton X-100) were purchased from Sigma–Aldrich, and used
without further purification. Triton X-100 possesses a Critical
Micellar Concentration (CMC) of 0.189 mM and a HLB of 13.4.
The CMC and HLB for Tween 20 are 0.060 and 16.7, respectively
[13,14]. The high charge density salts MgSO4, (NH4)2SO4, (NH4)2HPO4, (NH4)2Fe(SO4)2, (NH4)2C4H4O6 (mass fraction purities
P0.99) were used as received, and their purities and suppliers
are listed in table 1.
a
Standard uncertainties are u(w) = 0.0002, u(T) = 0.01 K.
TABLE 3
Binodal data for {Tween 20 (1) + salt (2) + H2O (3)} at atmospheric pressure and
T = 298.15 K.a
2.2. Experimental procedure
The solubility curves were built by applying the cloud point
titration method at T = 298.15 K following the procedure described
previously [24]. An aqueous solution of surfactant was introduced
in a jacketed glass vessel connected to a temperature controlled
(by means of a F200 ASK digital thermometer with an uncertainty
of ±0.01 K) circulating bath (controlled to ±0.01 K) and magnetic
stirring. Known amounts of salt were added to the Triton X-100
or Tween 20 aqueous solutions until the detection of turbidity.
Then, drops of deionised water were added in order to get a transparent solution, which is indicative that the monophasic region
was reached. The uncertainty in the weight quantification of all
components in the ternary systems is ±104 g.
The procedure followed to experimentally ascertain the tielines (TLs) consisted of adding a selected salt to a non-ionic surfactant aqueous solution until turbidity is reached. Similar to the solubility curves determination, the temperature was controlled at
T = 298.15 K, and the mixture was left to settle for 24 h in order
to ensure chemical and thermodynamic equilibria, after a vigorous
stirring. The lever arm rule was employed to determine each TL.
The estimated uncertainty in the determination of the surfactant
and salt phase mass compositions is less than 0.05%.
3. Results and discussion
a
(NH4)2Fe(SO4)2
MgSO4
(NH4)2HPO4
(NH4)2SO4
(NH4)2C4H4O6
100
w2
100
w1
100
w2
100
w1
100
w2
100
w1
100
w2
100
w1
100
w2
100
w1
1.33
1.47
1.91
3.07
3.49
4.06
4.44
4.73
5.56
6.00
6.80
7.25
7.63
8.05
8.46
9.20
9.53
10.14
10.73
11.31
11.91
14.70
17.19
19.12
50.93
49.95
47.21
42.80
39.00
36.33
34.63
32.80
30.24
28.62
25.16
23.42
21.70
19.80
17.94
15.71
14.20
12.26
9.64
7.31
4.77
1.69
0.33
0.07
0.83
1.16
1.53
1.61
2.23
2.62
3.37
4.08
4.59
4.93
5.24
5.91
6.47
6.90
7.59
8.16
8.69
9.28
9.85
10.40
11.39
11.87
13.85
15.25
53.62
50.16
47.22
46.35
42.26
39.54
34.96
30.75
28.25
26.59
24.43
21.87
19.72
17.49
14.56
11.97
9.38
6.46
4.45
2.69
1.56
1.65
0.34
0.08
1.13
2.21
2.37
3.20
3.64
3.91
4.45
4.79
5.39
6.08
6.40
6.89
7.53
8.02
8.87
9.36
9.69
10.12
10.49
10.87
13.61
16.08
19.21
47.75
42.89
41.86
39.56
37.19
36.73
33.69
31.59
29.05
26.24
24.41
22.23
19.48
16.86
13.20
11.66
10.29
8.19
6.40
4.09
1.11
0.13
0.00
0.44
1.01
2.26
2.50
2.97
3.88
4.53
5.28
5.78
6.16
6.55
6.56
7.17
7.72
8.24
8.63
9.05
9.54
9.81
10.20
10.64
10.88
11.30
11.55
12.21
12.70
15.56
17.58
19.69
59.78
56.10
50.68
53.00
49.21
44.53
40.08
37.64
35.47
33.30
31.74
31.64
28.91
25.97
23.33
21.36
19.29
16.61
14.84
13.24
11.47
9.29
7.91
6.47
4.85
3.43
0.72
0.13
0.01
3.18
6.00
6.57
7.25
7.67
8.14
9.44
9.91
10.29
10.69
11.60
12.13
12.58
12.90
13.39
13.85
14.33
15.05
15.53
16.11
16.52
16.96
17.34
17.88
20.30
23.57
26.22
55.40
49.10
45.61
42.64
42.02
40.40
35.54
33.97
32.49
30.12
27.93
26.26
24.68
23.09
20.98
19.25
17.27
14.93
13.66
11.80
10.49
8.47
6.86
5.54
3.13
0.73
0.16
Standard uncertainties are u(w) = 0.0002, u(T) = 0.01 K.
3.1. Solubility data and correlation
The experimental data of binodal curves for the ternary
mixtures of {Triton X-100 or Tween 20 + MgSO4, or (NH4)2SO4,
or (NH4)2HPO4, or (NH4)2Fe(SO4)2, or (NH4)2C4H4O6 + H2O}
at T = 298.15 K are given in mass fraction in tables 2 and 3.
149
E. Gutiérrez et al. / J. Chem. Thermodynamics 70 (2014) 147–153
TABLE 4
Parameters of equation (1) and standard deviation for {non-ionic surfactant (1) + salt (2) + H2O (3)} at T = 298.15 K.
a
Triton X-100 (1) + (NH4)2Fe(SO4)2 (2) + H2O (3)
Triton X-100 (1) + MgSO4 (2) + H2O (3)
Triton X-100 (1) + (NH4)2HPO4 (2) + H2O (3)
Triton X-100 (1) + (NH4)2SO4 (2) + H2O (3)
Triton X-100 (1) + (NH4)2C4H4O6 (2) + H2O (3)
Tween 20 (1) + (NH4)2Fe(SO4)2 (2) + H2O (3)
Tween 20 (1) + MgSO4 (2) + H2O (3)
Tween 20 (1) + (NH4)2HPO4 (2) + H2O (3)
Tween 20 (1) + (NH4)2SO4 (2) + H2O (3)
Tween 20 (1) + (NH4)2C4H4O6 (2) + H2O (3)
b
3
4.0595
2.7497
3.6508
2.1638
0.7393
3.2512
3.9398
2.2515
1.8841
0.2433
0.8089
0.6731
0.8003
0.7414
0.7107
0.7482
0.7689
0.6072
0.6831
0.5146
r
c
1.85 10
4.30 103
2.79 103
2.71 103
1.45 103
7.45 102
1.49 103
1.26 103
1.01 103
3.40 102
0.0108
0.0052
0.0096
0.0090
0.0192
0.0069
0.0067
0.0060
0.0073
0.0057
TABLE 5
Parameters of equation (2) and standard deviation for {non-ionic surfactant (1) + salt (2) + H2O (3)} at T = 298.15 K.
Triton X-100 (1) + (NH4)2Fe(SO4)2 (2) + H2O (3)
Triton X-100 (1) + MgSO4 (2) + H2O (3)
Triton X-100 (1) + (NH4)2HPO4 (2) + H2O (3)
Triton X-100 (1) + (NH4)2SO4 (2) + H2O (3)
Triton X-100 (1) + (NH4)2C4H4O6 (2) + H2O (3)
Tween 20 (1) + (NH4)2Fe(SO4)2 (2) + H2O (3)
Tween 20 (1) + MgSO4 (2) + H2O (3)
Tween 20 (1) + (NH4)2HPO4 (2) + H2O (3)
Tween 20 (1) + (NH4)2SO4 (2) + H2O (3)
Tween 20 (1) + (NH4)2C4H4O6 (2) + H2O (3)
a
b
c
d
r
0.7298
0.5761
0.5153
0.7219
0.6305
0.9983
0.6155
0.5635
0.6384
0.9331
1.7717
0.5424
1.7651
0.3995
1.4189
4.6725
0.5383
0.3863
0.2136
1.7585
1.3484
10.9088
15.5341
5.9607
8.7819
8.6752
5.3695
3.1373
6.1804
0.0154
3.1882
24.7027
45.9566
4.8432
7.2428
26.2292
13.9583
4.3400
5.7244
4.2035
0.0104
0.0078
0.0094
0.0038
0.0097
0.0038
0.0055
0.0046
0.0064
0.0043
TABLE 6
Parameters of equation (3) and standard deviation for {non-ionic surfactant (1) + salt (2) + H2O (3)} at T = 298.15 K.
Triton X-100 (1) + (NH4)2Fe(SO4)2 (2) + H2O (3)
Triton X-100 (1) + MgSO4 (2) + H2O (3)
Triton X-100 (1) + (NH4)2HPO4 (2) + H2O (3)
Triton X-100 (1) + (NH4)2SO4 (2) + H2O (3)
Triton X-100 (1) + (NH4)2C4H4O6 (2) + H2O (3)
Tween 20 (1) + (NH4)2Fe(SO4)2 (2) + H2O (3)
Tween 20 (1) + MgSO4 (2) + H2O (3)
Tween 20 (1) + (NH4)2HPO4 (2) + H2O (3)
Tween 20 (1) + (NH4)2SO4 (2) + H2O (3)
Tween 20 (1) + (NH4)2C4H4O6 (2) + H2O (3)
a
b
c
a
b
r
49.2245
5.3922
33.3144
6.6649
3.5999
12.8450
12.9256
47.3367
7.6195
22.5707
5.5603
2.9359
4.9889
3.0108
2.4295
5.2724
4.9808
5.5765
4.0153
5.4522
585.07
1558.64
979.39
1537.94
2689.47
3686.72
2533.88
1558.10
3270.09
3143.68
0.0453
0.0480
0.0431
0.0487
0.0919
0.1248
0.0987
0.0458
0.1089
0.1244
2.4378
2.5730
2.5203
2.7384
3.3037
3.7183
3.1886
3.0730
3.5708
4.4344
0.0109
0.0065
0.0096
0.0098
0.0173
0.0046
0.0057
0.0060
0.0045
0.0042
Three-, four- and five-parameter equations have been successfully
used to correlate the solubility data for the above ABS [9,22,23]:
3
w1 ¼ A expðBw0:5
2 Cw2 Þ;
ð1Þ
2
w1 ¼ A þ Bw0:5
2 þ Cw2 þ Dw2 ;
ð2Þ
w1 ¼ A exp Bwa2 Cwb2 ;
ð3Þ
where w1 is the mass composition of Tween 20 or Triton X-100, w2
is the mass composition of organic and inorganic salts, and A, B, C, D,
a and b are the fitting parameters. The optimised parameters and
the standard deviations (r) are listed in tables 4–6. These values
were calculated by applying the following expression:
r¼
2 !1=2
PnDAT zexp zadjust
i
;
nDAT
ð4Þ
where the experimental and adjustable solubility data are represented by zexp and zadjust, respectively and nDAT is the number of
experimental data points. A visual inspection of the data indicates
that the proposed equations serve our goal to describe the segregation behaviour of the non-ionic surfactants in the presence of the
salts used. More specifically, the analysis in terms of standard
deviations reveals that four-parameter equation is the one that better correlates the experimental data.
The salting-out ability of the salts is investigated and analysed
by the binodal curves plotted in figures 1 and 2. The analysis of
the data represented in the figures reflects that the selected salts
entail quite different phase segregation behaviour, which follows
the same order no matter the non-ionic surfactant under study:
(NH4)2Fe(SO4)2 > MgSO4 > (NH4)2HPO4 > (NH4)2SO4 > (NH4)2C4H4O6.
In this work, this effect has been qualitatively evaluated by means
of the different solvation capacity of the selected ions [20]. It is
interesting to notice that the salt containing the tetravalent cation
with (2SO4)2 is the one leading to the binodal curve closer to
the origin, due to the greater interactions established in the
system after addition of smaller amount of salt. On the contrary,
the rest of the salts with lower valence led to smaller immiscibility
regions.
Moreover, recent literature references [6–14,21] have often related the salting-out potential with the Gibbs free energy of hydration (DhydG), because ions with a more negative DhydG value show
a better salting-out ability. Therefore, based on the data reported
previously [25–27] and listed in table 7, the salting-out ability of
the ions show the following order: cations: Fe2+ > Mg2+ > NHþ
4 ; an2
2
ions: HPO2
>
C
H
O
>
SO
.
Notwithstanding
the
sequence
for
4
4
4
6
4
cations is in agreement with our findings, the behaviour observed
150
E. Gutiérrez et al. / J. Chem. Thermodynamics 70 (2014) 147–153
TABLE 7
Molar Gibbs free energies of hydration (DhydG), Jones–Dole viscosity B-coefficients (B),
and molar entropy of hydration (DhydS).
DhydG/kJ mol1
Ions
2+
Fe
Mg2+
NHþ
4
HPO2
4
SO2
4
C4 H4 O2
6
A
b
c
d
FIGURE 1. Plot of experimental and correlated solubility data of {Triton X-100
(1) + salt (2) + H2O (3)} at T = 298.15 K: (r), (NH4)2Fe(SO4)2; (s), MgSO4; (}),
(NH4)2HPO4; (4), (NH4)2SO4; (h), (NH4)2C4H4O6.
for anions fails to reproduce the salting-out potential of tartrate,
which confirms the necessity of performing the experimental
determination of the immiscibility curves. The salting-out ability
of the different salts is apparently governed by the cation, but
when the anion has a significantly favourable hydration, the influence of the cation or the anion will depend on a complex balance of
competitive interactions [28].
This anomalous trend has also been reported for the same salt
in aqueous solutions of polyethylene glycols [27,29], which confirms the necessity of experimentally determining the binodal
curves as a prior step to design and optimise a given ABS. In parallel, the results can be interpreted in terms of the molar hydration
entropy (DhydS), since different authors have stressed the narrow
correlation between this thermodynamic parameter and the
salting-out effects [9,27]. From the data presented in table 7, it is
possible to conclude the same sequence stated previously, thus
confirming the validity of both thermodynamic parameters to
predict the salting-out potential of a given salt.
In the same way, the salting-out effect of the ions may be interpreted in the light of the Jones–Dole viscosity B-coefficients [30].
This parameter provides information on the number of water molecules that can be hydrated by a given ion, being a viable tool to
analyse the potential salting-out ability of each salt. The reported
B/dm3 mol1 (25 °C)
DhydS/J K1 mol1
d
1840
1830a
285a
1789b
381a
350a
131a
291a
0.412
0.385d
0.008d
0.382d
1080a
0.206d
219a
1102c
n.a.
n.a.
Ref. [25].
Ref. [26].
Ref. [27].
Ref. [29], n.a. (not available).
TABLE 8
Experimental tie-lines in mass composition for {non-ionic surfactant (1) + salt
(2) + H2O (3)} at T = 298.15 K.
Non-ionic surfactant-rich phase
100
wI1
100
wI2
Salt-rich phase
100
wII1
100
TLL
wII2
35.21
45.54
24.07
Triton X-100 (1) + (NH4)2Fe(SO4)2 (2) + H2O (3)
3.32
0.15
13.74
1.86
0.11
14.02
5.31
1.18
11.71
36.58
47.03
34.88
41.10
51.84
51.43
Triton X-100 (1) + MgSO4 (2) + H2O (3)
2.45
0.03
11.61
0.89
0.00
13.46
0.94
0.00
15.57
42.08
53.34
53.47
33.13
37.36
49.87
Triton X-100 (1) + (NH4)2HPO4 (2) + H2O
3.88
0.40
3.29
0.00
1.60
0.00
47.29
39.31
53.11
Triton X-100 (1) + (NH4)2SO4 (2) + H2O
3.01
0.00
4.15
0.06
2.17
0.00
33.00
36.81
42.47
FIGURE 2. Plot of experimental and correlated solubility data of {Tween 20
(1) + salt (2) + H2O (3)} at T = 298.15 K: (r), (NH4)2Fe(SO4)2; (s), MgSO4; (}),
(NH4)2HPO4; (4), (NH4)2SO4; (h), (NH4)2C4H4O6.
a
(3)
11.35
14.64
15.98
33.57
39.04
51.91
(3)
15.19
13.28
16.42
48.83
40.30
54.99
Triton X-100 (1) + (NH4)2C4H4O6 (2) + H2O (3)
7.33
0.58
14.58
6.86
0.02
17.43
6.13
0.00
19.14
33.22
38.28
44.42
Tween 20 (1) + (NH4)2Fe(SO4)2 (2) + H2O
1.91
1.69
1.47
0.33
1.33
0.07
(3)
14.70
17.19
19.12
47.28
52.05
53.89
46.35
50.16
53.62
Tween 20 (1) + MgSO4 (2) + H2O (3)
1.61
1.65
11.87
1.16
0.34
13.85
0.83
0.08
15.25
45.86
51.41
55.44
37.19
42.89
47.75
Tween 20 (1) + (NH4)2HPO4 (2) + H2O (3)
3.64
1.11
13.61
2.21
0.13
16.08
1.13
0.00
19.21
37.43
44.95
51.06
50.68
56.10
59.78
Tween 20 (1) + (NH4)2SO4 (2) + H2O (3)
2.26
0.72
15.56
1.01
0.13
17.58
0.44
0.01
19.69
51.70
58.37
62.79
47.21
49.95
50.93
42.02
45.61
55.40
Tween 20 (1) + (NH4)2C4H4O6 (2) + H2O
7.67
3.13
6.57
0.73
3.18
0.16
(3)
20.30
23.57
26.22
40.89
47.99
59.85
values are collected in table 7, and confirm the sequence obtained
experimentally, since the Jones–Dole viscosity B-coefficients for
Fe2+ is higher than that obtained for Mg2+, which in turn is higher
than the value for NHþ
4 . In the same vein, two of the anions used
can be also analysed when the cation NHþ
4 is fixed, and follow
the order established by the Jones–Dole viscosity B-coefficient
(HPO2
4 ).
E. Gutiérrez et al. / J. Chem. Thermodynamics 70 (2014) 147–153
151
FIGURE 3. Plot of experimental and correlated phase diagram and experimental tie-lines of {Triton X-100 (1) + salt (2) + H2O (3)}: at T = 298.15 K. Void symbols represent
experimental phase diagram, and full symbols represent tie-line data. (r), (NH4)2Fe(SO4)2; (s), MgSO4; (}), (NH4)2HPO4; (4), (NH4)2SO4; (h), (NH4)2C4H4O6.
Finally, the experimental solubility data can be analysed in
terms of the hydrophobicity of the selected surfactants, by means
of the Hydrophilic–Lipophilic Balance (HLB). This is an empirical
number varying from 0 to 20, from very hydrophobic to very
hydrophilic, respectively. In this way, it seems clear that the use
of the more hydrophobic Triton X-100 entails less concentration
of salt to promote phase separation, as can be inferred from the
data illustrated in figures 1 and 2. The presence of more hydrophobic compounds leads to binodal curves closer to the origin, probably due to the existence of less interaction between the salts and
the polar moiety of the non-ionic surfactant, as previously reported
for other ABS [6,31].
3.2. Tie-lines and correlation
A complete thermodynamic characterisation of the phase segregation behaviour needs the determination of the tie-lines for each
system, since it gives an idea of the amount of surfactant-rich
phase obtained in relation to the salt-rich phase. Hence, the tielines for these binodal curves were calculated on the basis of the
lever arm rule applied to the relationship between the mass phase
composition and the overall system composition [22]. The mathematical expressions used are shown below:
wI1 ¼
wF1
1R
wII1 ;
R
R
ð5Þ
152
E. Gutiérrez et al. / J. Chem. Thermodynamics 70 (2014) 147–153
FIGURE 4. Plot of experimental and correlated phase diagram and experimental tie-lines of {Tween 20 (1) + salt (2) + H2O (3)}: at T = 298.15 K. Void symbols represent
experimental phase diagram, and full symbols represent tie-line data. (r), (NH4)2Fe(SO4)2; (s), MgSO4; (}), (NH4)2HPO4; (4), (NH4)2SO4; (h), (NH4)2C4H4O6.
TABLE 9
Parameters of equation (9) and standard deviation for {non-ionic surfactant (1) + salt
(2) + H2O (3)} at T = 298.15 K.
Triton X-100 (1) + (NH4)2Fe(SO4)2 (2) + H2O (3)
Triton X-100 (1) + MgSO4 (2) + H2O (3)
Triton X-100 (1) + (NH4)2HPO4 (2) + H2O (3)
Triton X-100 (1) + (NH4)2SO4 (2) + H2O (3)
Triton X-100 (1) + (NH4)2C4H4O6 (2) + H2O (3)
Tween 20 (1) + (NH4)2Fe(SO4)2 (2) + H2O (3)
Tween 20 (1) + MgSO4 (2) + H2O (3)
Tween 20 (1) + (NH4)2HPO4 (2) + H2O (3)
Tween 20 (1) + (NH4)2SO4 (2) + H2O (3)
Tween 20 (1) + (NH4)2C4H4O6 (2) + H2O (3)
k
b
R2
0.0544
0.1078
0.0702
0.0618
0.0444
0.1133
0.1027
0.1281
0.1875
0.0694
0.4659
2.8696
1.1836
1.2731
0.7351
3.1235
2.6078
3.3605
7.4938
1.7233
0.999
0.999
0.994
0.997
0.984
0.989
0.996
0.977
0.982
0.999
wI2 ¼
wF2
1R
wII2 ;
R
R
ð6Þ
where F, I and II represent the feed, the top phase, and the bottom
phase, respectively; w1 and w2 are the mass fraction of non-ionic
surfactant and salt, respectively; and R is the following measured
ratio:
R¼
Weight of the top phase
:
Weight of the mixture
ð7Þ
The lie-lines lengths (TLL) for the different compositions were
calculated by the equivalent expression of the tie-lines obtained
from the relationship between the top, bottom and feed composition according to:
E. Gutiérrez et al. / J. Chem. Thermodynamics 70 (2014) 147–153
TLL ¼
h
2 2 i1=2
wI1 wII1 þ wI2 wII2
;
ð8Þ
where the equilibrium mass fraction of the non-ionic surfactant (1)
and the organic or inorganic salt (2), in the upper (I) and bottom (II)
phases, are represented. The numerical data of the tie-lines obtained for each ternary system are given in table 8, and are graphically shown in figures 3 and 4. From the data obtained, it seems
clear that the increase in the salt concentration in the bottom phase
leads to more non-ionic surfactant being salted out to the top phase,
which in turn is associated with an increase in the TLL. Furthermore,
the evaluation of the extraction capacity reveals that ammonium
tartrate is the salt leading to lower concentrations of surfactant in
the top phase, no matter what surfactant is used. Contrary to this,
magnesium sulfate and ammonium sulfate are the salts able to salt
out more Triton X-100 and Tween 20 to the top phase, respectively.
A two-parameter equation derived from the binodal theory [32]
was used for the correlation of the experimental tie-line data.
ln
wI2
wII2
¼ b þ k wII1 wI1 ;
ð9Þ
in which the fitting parameters k and b are the salting-out coefficient and the constant related to the activity coefficient, respectively. The values of the fitting parameters are listed in table 9,
together with the corresponding correlation coefficients (R2).
As can be seen, this equation provides good reliability for the
correlation of the tie line data. The increase of the fitting parameter
k, points a greater salting-out ability of salts [28,32]. On the basis of
these values, conclusions similar to those observed for the TLLs are
obtained, since ammonium tartrate and ammonium or magnesium
sulfate are the salts providing the lowest and highest extraction
capacity, respectively.
4. Conclusions
The solubility data of the systems {(Triton X-100 or Tween
20) + (NH4)2Fe(SO4)2 or MgSO4 or (NH4)2HPO4 or (NH4)2SO4 or
(NH4)2C4H4O6 + H2O} were ascertained at T = 298.15 K by the cloud
point method and were successfully correlated by different
empirical equations. The analysis of the data in terms of decisive
thermodynamic functions such as DhydG and DhydS, as well as of
the Jones–Dole viscosity B-coefficient led to the conclusion that
the iron salt is the one providing a greater immiscibility region.
This study marks the first time that this salt is used as a saltingout agent, and demonstrates its suitability as phase segregation
promoter. The efficiency of the extraction was investigated by
means of tie-line data, and ammonium sulfate turned out to be
the salt leading to higher values of the parameter k.
E. Gutierrez thanks University of Vigo for funding through a master
grant. F. J. Deive wishes to thank Xunta de Galicia for funding
through an Isidro Parga Pondal contract.
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Acknowledgements
This work has been supported by the Spanish Ministry of Economy and Competitiveness and FEDER funds (IPT-310000-2010-17).
153
JCT 13-575
ANNEX 12
INFLUENCE
OF THE
ADDITION
OF
TWEEN 20
ON THE
PHASE BEHAVIOUR
OF IONIC
LIQUIDS-BASED AQUEOUS SYSTEMS (JOURNAL OF CHEMICAL THERMODYNAMICS, 2014,
79: 178-183).
J. Chem. Thermodynamics 79 (2014) 178–183
Contents lists available at ScienceDirect
J. Chem. Thermodynamics
journal homepage: www.elsevier.com/locate/jct
Influence of the addition of Tween 20 on the phase behaviour
of ionic liquids-based aqueous systems
María S. Álvarez, Ana Mateo, Francisco J. Deive ⇑, M. Ángeles Sanromán, Ana Rodríguez ⇑
Department of Chemical Engineering, Universidade de Vigo, P.O. Box 36310, Vigo, Spain
a r t i c l e
i n f o
Article history:
Received 21 July 2014
Received in revised form 1 August 2014
Accepted 2 August 2014
Available online 9 August 2014
Keywords:
Ionic liquids
Aqueous biphasic systems
Tween 20
Imidazolium
Potassium salts
a b s t r a c t
The addition of a non-ionic surfactant (Tween 20) to 1-ethyl-3methyl imidazolium alkylsulfate
(C2C1imCnSO4)-based aqueous biphasic systems was investigated in this work. The solubility curves of
the systems {(C2C1imCnSO4 (n = 2 and 8) + Tween 20) + high charge density salt (K3PO4/K2CO3/
K2HPO4) + H2O} were carried out at T = 298.15 K. The obtained experimental data were correlated by
using three empirical equations. The molar Gibbs free energy of hydration (DhydG) is a valuable parameter to analyse the segregation capacity provided by the inorganic salts. Additionally, the efficiency of the
separation capacity was discussed in terms of the salting out potential of the selected salts and the presence of Tween 20. Othmer–Tobias and Bancroft equations have been used to correlate the experimental
tie-line data.
Ó 2014 Elsevier Ltd. All rights reserved.
1. Introduction
Currently, the hunt for more competitive and greener processes
has led to the application of ionic liquids, neoteric solvents with
outstanding properties such as their negligible volatility and tunability [1,2], in a range of fields such as electrochemistry, analytical
chemistry, or chemical engineering, to name a few [3]. In this
sense, one of the areas that has benefitted from the presence of
these molten salts is the separation of compounds with industrial
interest by means of (liquid + liquid) equilibrium [4–6].
Among the existing alternatives, the segregation of phases in
systems containing aqueous solutions of ionic liquids is a field with
booming interest since 2003, when Gutowski and co-workers published the first work on the capacity of hydrophilic ionic liquids to
form aqueous biphasic systems (ABS) [7]. This separation strategy
generally consists in adding an inorganic salt to aqueous solutions
of ionic liquids, thus triggering phase disengagement: an upper
aqueous phase mainly composed of ionic liquid and a lower aqueous layer rich in inorganic salt [8]. In this work, 1-ethyl-3-methyl
imidazolium ethylsulfate and octylsulfate have been selected as
model ionic liquids to be salted out in aqueous solutions, since they
are reasonably cheap, they can be easily synthesized in an atomefficient and halide-free way, they display relatively low viscosities
and melting points [9], and they belong to one of the most
⇑ Corresponding authors. Tel.: +34 986 81 87 23.
E-mail addresses: [email protected] (F.J. Deive), [email protected] (A. Rodríguez).
http://dx.doi.org/10.1016/j.jct.2014.08.004
0021-9614/Ó 2014 Elsevier Ltd. All rights reserved.
representative families (1-ethyl-3methyl imidazolium) already
synthesized at levels higher than one ton per year [10].
Different types of ABS have been described in the literature,
such as those exclusively formed by a polymer and a salt, a polymer and a polymer, an ionic liquid and a salt, an ionic liquid and
a polymer, and a surfactant and a salt [11–15]. The latter has been
reported to be advantageous in terms of cost, availability at bulk
quantities, biodegradability, lower interface tension, mild operation temperatures and wider immiscibility window. Thus, in this
study, the phase segregation capacity of different potassium-based
inorganic salts was studied in aqueous mixtures of ionic liquids
and a model non-ionic surfactant (Tween 20). This family is commonly used in industrial biotechnology, for instance in enzyme
production processes or bioremediation studies, and has been
reported to act even as nutrient in culture media [16,17].
In recent research works, the suitability of this kind of non-ionic
surfactants (Tween 20) to extract biomolecules [18], industrial
dyes [19] and metals [20] has already demonstrated. Additionally,
the applicability of C2C1imCnSO4 has also been concluded for
enzyme separation [8,21]. Therefore, in this study we intend to
shed light on the ABS behaviour of both compounds mixed (nonionic surfactants and ionic liquids at ratios 25:75 and 75:25,
respectively) in the presence of the inorganic salts K3PO4, K2HPO4
and K2CO3. These salts are well-known salting out agents, as predicted by the Hofmeister series, so their segregation capacity was
discussed in this work by analysing the solubility curves at
T = 298.15 K and the molar Gibbs free energy of hydration (DhydG).
Three empirical equations were employed to correlate the
179
M.S. Álvarez et al. / J. Chem. Thermodynamics 79 (2014) 178–183
experimental solubility data. The segregation capacity was also
evaluated in terms of tie-line data, tackling the slope (S) and the
tie-line length (TLL). Othmer–Tobias and Bancroft models were
used to evaluate the consistency of the tie-line data [22].
2. Experimental
2.1. Chemicals
The ionic liquids 1-ethyl-3-methyl imidazolium ethylsulfate
(C2C1imC2SO4) and 1-ethyl-3-methyl imidazolium octylsulfate
(C2C1imC8SO4) were provided by Merck. They were subjected to
vacuum of P = 101 Pa and temperature T = 330.15 K for several
days in order to remove moisture and possible traces of organic
volatile compounds. The inorganic salts K3PO4, K2HPO4 and
K2CO3 were supplied by Sigma–Aldrich and were used as received,
without further purification. The non-ionic surfactant Tween 20,
polyethoxylated sorbitan monolaurate was purchased from
Sigma–Aldrich. All the data concerning chemicals purities and
provenance are shown in table 1.
2.2. Experimental procedure
The solubility curves were empirically ascertained by means of
the cloud point titration method [23] in a jacketed glass vessel
containing a magnetic stirrer at atmospheric pressure,
101.33 kPa. The temperature was controlled at T = 298.15 K with
a F200 ASL digital thermometer with an uncertainty of T = ±0.01
K. Known amounts of Tween 20 and C2C1imCnSO4 in aqueous solution were introduced to a vessel, and the immiscibility window
was mapped by sequential additions of inorganic salt (K3PO4,
K2HPO4 and K2CO3) and water, until the detection of a turbid and
clear solution, respectively.
Additionally, a mixture with known mass fraction at the biphasic region was prepared to determine the tie-lines (TLs), in accordance with the protocol defined by Merchuk [24]. Briefly, after a
vigorous stirring, the mixture was left to settle for 24 h to allow a
complete phase separation. Top and bottom layers were then separated and weighted, and the level arm rule was used to determine
each TL composition. The estimated uncertainty associated with
the determination of the surfactant (top) and salt (bottom) phases
mass compositions is ±2%. All the samples were weighed in an analytical Sartorius Cubis MSA balance (125P-100-DA, ±105 g).
are collected in tables 2 and 3, and they are plotted in figures 1
and 2.
The data obtained can be analysed in the light of the surfactant
addition, the alkyl chain length in the ionic liquid anion, and the
salting out potential of the selected inorganic salt. These factors
have proven to be crucial to achieve phase segregation. The formation of an upper (ionic liquid/surfactant)-rich phase is observed, in
line with previous investigations by Wang et al. (2004), who concluded that ionic liquids and surfactants form an organised moiety:
not only does the ionic liquid act as a specific solvent, but also as a
co-surfactant [25]. Additionally, it becomes patent that the
increase of the surfactant concentration from 25% to 75% entails
immiscibility regions closer to the origin, no matter the ionic liquid
or inorganic salt employed. The comparison of the obtained data
with the systems containing the pure surfactant (100% Tween
20) and ionic liquid (100% C2C1imCnSO4), reported by Álvarez
et al. [22] and Deive et al. [26], respectively, reveals a close agreement with the observed trends. The explanation of this behaviour
may be related to the increased hydrophobicity provided by the
non-ionic surfactant. Thus, the competition between the mixture
ionic liquid/surfactant and the inorganic salt for the water molecules is easily won when the first is more hydrophobic, due to they
establish weaker hydrogen-bonds with water. This is coincident
with other types of ABS involving ionic liquids and PEG [14,27].
The observed trends pose undoubted advantages related to the
process economy and supply logistics.
TABLE 2
Binodal data in mass fraction for {(C2C1imC2SO4 + Tween 20) (1) + salt (2) + H2O (3)}
two-phase systems for different surfactants concentrations at T = 298.15 K,
P = 101.33 kPa.a
K3PO4
100 w2
3. Results and discussion
3.1. Phase diagrams determination and correlation
Up to our knowledge, this is the first time that the ABS resulting
from the combination of the non-ionic surfactant Tween 20 with an
ionic liquid and a high charge density inorganic salt is investigated.
Thus, (liquid + liquid) de-mixing was characterised for the systems
{(C2C1imCnSO4 (n = 2 and 8) + Tween 20) + (K3PO4 or K2CO3 or
K2HPO4) + H2O} at T = 298.15 K. The experimental solubility data
TABLE 1
Purities and suppliers of chemicals.a
a
Chemical
Supplier
Mass fraction purity
K3PO4
K2CO3
K2HPO4
Tween 20
C2C1imCnSO4
Sigma–Aldrich
0.98
0.99
0.98
0.98
0.98
Merck
Deionised water was used in all the experiments.
a
K2HPO4
100 w1
2.83
3.53
4.32
4.84
5.40
5.92
6.24
6.74
7.06
7.49
8.00
8.13
8.50
8.89
9.17
9.31
9.66
9.97
10.02
51.43
46.06
42.48
38.73
35.58
32.41
28.34
25.66
22.72
20.79
17.14
15.28
13.39
10.87
9.37
8.00
5.66
3.45
2.54
10.36
10.57
10.80
10.95
11.02
11.01
11.03
11.05
11.09
11.00
10.94
11.01
11.37
11.71
25.96
23.03
20.14
17.57
15.32
13.41
8.21
6.99
5.61
11.32
9.74
13.41
3.17
1.96
100 w2
K2CO3
100 w1
100 w2
25% C2C1imC2SO4 + 75% Tween 20
2.21
53.74
4.07
6.95
19.35
4.98
7.63
15.85
5.96
8.06
12.32
6.45
8.43
10.21
6.93
8.65
8.36
7.33
8.89
6.56
7.43
9.24
4.75
7.69
9.44
3.58
8.01
3.44
47.75
7.16
4.33
41.93
7.92
4.98
37.26
8.64
5.48
33.48
8.31
5.84
31.17
8.87
6.15
28.62
9.20
6.43
26.21
7.00
22.15
75% C2C1imC2SO4 + 25% Tween 20
8.30
32.48
8.65
8.88
28.56
8.75
9.15
25.10
9.02
9.38
23.11
9.04
9.49
18.68
9.07
9.64
17.10
9.02
9.45
20.29
9.07
9.69
15.19
9.22
9.70
12.49
9.09
9.65
9.90
9.54
9.78
7.53
8.37
9.79
5.88
9.90
9.99
3.02
100 w1
46.42
38.90
33.71
28.43
24.77
20.48
18.54
16.22
13.26
23.22
10.99
6.40
9.09
3.24
1.80
29.98
26.16
22.55
20.32
17.37
15.02
13.42
10.76
8.73
4.83
33.38
2.58
Standard uncertainties are u(w) = ± 0.0002, u(T) = ± 0.01 K; u(P) = ± 0.03 kPa.
180
M.S. Álvarez et al. / J. Chem. Thermodynamics 79 (2014) 178–183
0.16
TABLE 3
Binodal data in mass fraction for {(C2C1imC8SO4 + Tween 20) (1) + salt (2) + H2O (3)}
two-phase systems for different surfactants concentrations at T = 298.15 K,
P = 101.33 kPa.a
a
100 w1
1.09
1.36
1.65
2.35
3.26
4.18
4.90
5.59
6.21
6.58
6.99
7.21
7.65
8.17
8.65
8.97
9.11
9.56
9.93
14.86
17.11
17.14
51.60
49.93
48.32
45.21
42.12
37.57
32.91
29.49
26.88
23.79
21.66
19.78
16.58
13.78
11.34
9.30
7.29
5.25
3.13
0.27
0.02
0.02
2.56
2.99
3.94
3.98
4.65
5.32
6.53
6.84
7.89
8.55
9.27
10.18
10.71
11.10
12.30
12.73
13.42
13.96
16.53
18.20
19.52
51.46
49.10
45.15
44.21
40.84
37.87
31.61
29.03
25.48
23.07
19.13
16.60
13.88
11.90
9.60
7.10
4.37
2.58
2.55
1.14
0.54
100 w2
0.12
K2CO3
100 w1
25% C2C1imC8SO4 + 75% Tween 20
1.32
48.26
1.90
44.99
2.43
40.74
3.67
37.81
4.01
35.96
4.66
32.28
5.81
27.60
6.12
25.44
6.15
23.16
6.66
20.33
7.10
18.13
7.50
15.79
7.63
12.50
8.26
9.77
8.67
7.02
9.23
3.97
100 w2
2.91
3.44
4.22
4.81
5.58
5.76
6.22
6.75
7.13
7.56
7.95
8.38
8.67
8.99
75% C2C1imC8SO4 + 25% Tween 20
3.52
49.85
6.67
3.97
43.44
8.48
4.69
38.29
9.83
5.64
35.96
10.95
6.05
33.11
12.11
7.08
30.34
13.23
7.65
27.85
13.78
8.27
24.95
15.07
9.15
22.11
16.28
9.93
19.26
17.14
10.12
17.17
5.02
11.46
13.13
18.10
12.24
9.89
18.65
13.56
6.42
19.19
14.15
4.21
14.53
2.06
100 w1
45.23
41.97
36.77
31.65
27.73
24.79
22.39
18.48
15.74
12.89
9.47
6.85
4.80
2.58
w1/M1
100 w2
K2HPO4
0.08
0.04
0.00
0.00
0.08
0.16
0.24
0.32
w2/M2
FIGURE 1. Plot of experimental and correlated solubility data of {(C2C1imC2SO4 +
Tween 20) (1) + salt (2) + H2O (3)} at T = 298.15 K, P = 101.33 kPa: (s), K3PO4; (4),
K2HPO4; (h) K2CO3. Blue, 100% Tween 20, reference [22]; Black, 75% Tween 20; Red,
25% Tween 20; Cyan, 0% Tween 20, reference [26] (For interpretation of the
references to color in this figure legend, the reader is referred to the web version of
this article.).
0.15
41.61
35.27
31.72
29.29
25.99
22.61
21.22
17.44
13.59
9.58
47.75
7.87
4.87
3.11
Standard uncertainties are u(w) = ± 0.0002, u(T) = ± 0.01 K; u(P) = ± 0.03 kPa.
Secondly, the comparison of the immiscibility gap for the two
selected ionic liquids shown in figures 1 and 2, makes it evident
the higher tendency of the octylsulfate-based ionic liquids for
ABS formation, in agreement with the data reported by Deive
et al. [26]. Although generally speaking, the self-aggregation of
ionic liquids has been more evident for long alkyl chains in the
cation [28], the present data evidence the existence of similar phenomena for the anion, thus confirming analogous hydrophobicity
effects to those discussed for the surfactants.
The analysis of the salting out ability of the salts under study
evidences the following sequence for the selected anions (given
2
2
that the cation is fixed): PO3
4 > HPO4 > CO3 . Seemingly, the
obtained data follow the Hofmeister series, an ion classification
on the basis of the salting out potential of salts. As expected, trivalent anions entail more interplay with water than the divalent
anions, a fact that can also be interpreted in the light of the molar
Gibbs free energy of hydration (DhydG), which confirms the
observed trends. Thus, the lower values of this thermodynamic
parameter are related with an easier capacity to trigger phase
0.10
w1/M1
K3PO4
0.05
0.00
0.00
0.05
0.10
0.15
0.20
w2/M2
FIGURE 2. Plot of experimental and correlated solubility data of {(C2C1imC8SO4 +
Tween 20) (1) + salt (2) + H2O (3)} at T = 298.15 K, P = 101.33 kPa: (s), K3PO4; (4),
K2HPO4; (h) K2CO3. Blue, 100% Tween 20, reference [22]; Black, 75% Tween 20; Red,
25% Tween 20; Cyan, 0% Tween 20, reference [26] (For interpretation of the
references to color in this figure legend, the reader is referred to the web version of
this article.).
1
disengagement. In this sense, the value for PO3
)
4 (2765 kJ mol
2
1
is higher than that reported for HPO4 (1789 kJ mol ), and in
turn, this is higher than the one concluded for CO2
(1315 kJ 3
mol1) [29,11]. Therefore, it is confirmed that trivalent phosphate
anion will be more akin to interact with water molecules, thus
leading to greater immiscibility window.
The experimental data were correlated by means of the following empirical equations [22]:
0:5
½S þ IL ¼ a expðb½Salt
½S þ IL ¼ a þ b½Salt
0:5
3
c½Salt Þ;
ð1Þ
2
þ c½Salt þ d½Salt ;
½S þ IL ¼ expða þ b½Salt
0:5
ð2Þ
2
þ c½Salt þ d½Salt Þ
ð3Þ
being [S + IL] the mass fraction of the mixture surfactant and ionic
liquid, [Salt] the mass fraction of the potassium-based inorganic
181
M.S. Álvarez et al. / J. Chem. Thermodynamics 79 (2014) 178–183
TABLE 4
Parameters of equation (1) and standard deviation for {(C2C1imCnSO4 + Tween 20) (1) + salt (2) + H2O (3)}.a
Ionic liquid and non ionic surfactant (1)
25%
25%
25%
25%
25%
25%
75%
75%
75%
75%
75%
75%
a
C2C1imC2SO4 + 75%
C2C1imC2SO4 + 75%
C2C1imC2SO4 + 75%
C2C1imC2SO4 + 75%
C2C1imC2SO4 + 75%
C2C1imC2SO4 + 75%
C2C1imC2SO4 + 25%
C2C1imC2SO4 + 25%
C2C1imC2SO4 + 25%
C2C1imC2SO4 + 25%
C2C1imC2SO4 + 25%
C2C1imC2SO4 + 25%
Tween
Tween
Tween
Tween
Tween
Tween
Tween
Tween
Tween
Tween
Tween
Tween
20
20
20
20
20
20
20
20
20
20
20
20
Salt (2)
H2O (3)
K3PO4
K2HPO4
K2CO3
K3PO4
K2HPO4
K2CO3
K3PO4
K2HPO4
K2CO3
K3PO4
K2HPO4
K2CO3
a
H2O
H2O
H2O
H2O
H2O
H2O
H2O
H2O
H2O
H2O
H2O
H2O
b
0.5926
0.6438
0.7953
0.5043
0.5669
0.5403
3.62 104
3.39 104
1.39 105
0.9271
0.9458
0.6779
r
c
0.7042
0.9296
1.3934
0.6015
1.5655
0.6748
25.92
31.45
38.29
3.4794
3.6147
1.5453
3
2.19 10
2.65 103
2.70 103
2.15 103
2.15 103
3.08 103
3.05 103
3.06 103
2.97 103
6.43 102
5.40 102
2.5 102
0.0108
0.0101
0.0234
0.0088
0.0133
0.0096
0.0323
0.0370
0.0402
0.0083
0.0138
0.0137
Standard deviation (r) was calculated by means of equation (4).
salts, and a, b, c, and d the fitting parameters. The values of these
parameters are collected in tables 4–6, together with the standard
deviations (r), which were calculated by means of the following
equation:
r¼
PnDAT
i
ðzexp zadjust Þ2
nDAT
3.2. Tie-lines determination and correlation
After having determined the phase behaviour of the systems
under study, the composition of the top and bottom phases were
determined by simple mass balances, using the correlation equation (2), since it turned out to be the most suitable one to describe
the ABS. Two typical parameters used to describe the phase separation are the tie-line length (TLL) and the slope of the tie lines (S),
which expressions are given below:
!1=2
ð4Þ
;
h
i
2
2 0:5
TLL ¼ ðwI1 wII1 Þ þ ðwI2 wII2 Þ
;
where the experimental and adjustable solubility data are represented by zexp and zadjust, respectively and nDAT is the number of
experimental data.
From the deviations obtained (tables 4–6), it seems evident that
the four parameters-based equation (2) is the one able to describe
in a more suitable manner the experimental solubility data, which
is also in agreement with previous works on surfactant-based ABS
[22].
S¼
ð5Þ
wI1 wII1
;
wI2 wII2
ð6Þ
where the equilibrium mass fraction of the mixture surfactant and
ionic liquid (1) and the inorganic salt (2), in the mixture surfactant
TABLE 5
Parameters of equation (2) and standard deviation for {(C2C1imCnSO4 + Tween 20) (1) + salt (2) + H2O (3)}.a
a
Ionic liquid and non ionic surfactant (1)
Salt (2)
H2O (3)
a
b
c
d
r
25%
25%
25%
25%
25%
25%
75%
75%
75%
75%
75%
75%
K3PO4
K2HPO4
K2CO3
K3PO4
K2HPO4
K2CO3
K3PO4
K2HPO4
K2CO3
K3PO4
K2HPO4
K2CO3
H2O
H2O
H2O
H2O
H2O
H2O
H2O
H2O
H2O
H2O
H2O
H2O
0.6733
0.5870
2.9120
0.4640
0.5994
0.6741
4.7343
3.6752
3.9760
0.9759
1.3973
1.3351
0.1377
1.0519
23.84
1.0872
1.1560
0.2456
31.50
10.58
11.72
2.8731
7.2962
6.0976
6.3151
8.8087
68.40
7.2829
1.2571
5.9496
79.36
2.2994
2.8037
1.2789
13.81
11.21
5.6113
6.3900
235.87
5.7882
39.4900
5.1655
238.9
2.9561
2.9755
2.7035
28.56
21.33
0.0055
0.0095
0.0162
0.0047
0.0100
0.0040
0.0321
0.0327
0.0358
0.0053
0.0097
0.0038
C2C1imC2SO4 + 75%
C2C1imC2SO4 + 75%
C2C1imC2SO4 + 75%
C2C1imC2SO4 + 75%
C2C1imC2SO4 + 75%
C2C1imC2SO4 + 75%
C2C1imC2SO4 + 25%
C2C1imC2SO4 + 25%
C2C1imC2SO4 + 25%
C2C1imC2SO4 + 25%
C2C1imC2SO4 + 25%
C2C1imC2SO4 + 25%
Tween
Tween
Tween
Tween
Tween
Tween
Tween
Tween
Tween
Tween
Tween
Tween
20
20
20
20
20
20
20
20
20
20
20
20
Standard deviation (r) was calculated by means of equation (4).
TABLE 6
Parameters of equation (3) and standard deviation for {(C2C1imCnSO4 + Tween 20) (1) + salt (2) + H2O (3)}.a
a
Ionic liquid and non ionic surfactant (1)
Salt (2)
H2O (3)
a
b
c
d
r
25%
25%
25%
25%
25%
25%
75%
75%
75%
75%
75%
75%
K3PO4
K2HPO4
K2CO3
K3PO4
K2HPO4
K2CO3
K3PO4
K2HPO4
K2CO3
K3PO4
K2HPO4
K2CO3
H2O
H2O
H2O
H2O
H2O
H2O
H2O
H2O
H2O
H2O
H2O
H2O
1.2464
0.6922
1.5000
2.1431
1.2992
2.9208
36.91
17.02
17.25
1.1674
1.8826
5.6250
26.48
19.88
7.7352
39.57
31.46
48.26
276.68
51.82
54.16
19.30
25.33
56.64
107.02
88.45
10.91
151.81
131.36
184.23
690.16
27.13
27.63
57.14
69.48
141.01
650.98
657.32
356.39
788.59
794.04
998.90
1919.2
37.49
37.23
259.74
254.56
294.99
0.0106
0.0091
0.0240
0.0079
0.0095
0.0090
0.0323
0.0375
0.0383
0.0085
0.0121
0.0091
C2C1imC2SO4 + 75%
C2C1imC2SO4 + 75%
C2C1imC2SO4 + 75%
C2C1imC2SO4 + 75%
C2C1imC2SO4 + 75%
C2C1imC2SO4 + 75%
C2C1imC2SO4 + 25%
C2C1imC2SO4 + 25%
C2C1imC2SO4 + 25%
C2C1imC2SO4 + 25%
C2C1imC2SO4 + 25%
C2C1imC2SO4 + 25%
Tween
Tween
Tween
Tween
Tween
Tween
Tween
Tween
Tween
Tween
Tween
Tween
20
20
20
20
20
20
20
20
20
20
20
20
Standard deviation (r) was calculated by means of equation (4).
182
M.S. Álvarez et al. / J. Chem. Thermodynamics 79 (2014) 178–183
TABLE 7
Experimental tie-lines in mass fraction for {(C2C1imCnSO4 + Tween 20) (1) + salt (2) + H2O (3)} at T = 298.15 K, P = 101.33 kPa.
a
Surfactant and ionic liquid-rich phase
Inorganic salt-rich phase
100wI1
100wI2
100wII1
TLL
S
25.66
35.58
51.43
6.74
5.40
2.83
{(25% C2C1imC2SO4 + 75% Tween 20) (1) + K3PO4 (2) + H2O (3)}
1.49
12.81
0.50
14.19
0.12
15.71
25.78
36.50
52.88
3.62
3.77
4.01
31.17
51.52
41.93
5.84
2.70
4.33
{(25% C2C1imC2SO4 + 75% Tween 20) (1) + K2HPO4 (2) + H2O (3)}
1.75
11.04
0.02
14.14
0.83
12.42
32.68
52.76
43.18
3.99
4.24
4.04
24.77
33.71
46.42
6.93
5.96
4.07
{(25% C2C1imC2SO4 + 75% Tween 20) (1) + K2CO3 (2) + H2O (3)}
0.02
14.00
0.00
16.10
0.00
18.31
25.74
35.20
48.61
3.50
3.33
3.22
26.88
37.57
49.93
6.21
4.18
1.65
{(25% C2C1imC8SO4 + 75% Tween 20) (1) + K3PO4 (2) + H2O (3)}
1.49
12.81
0.27
14.86
0.02
17.11
26.24
38.80
50.71
3.85
3.49
3.12
42.57
53.03
32.28
2.49
0.05
4.66
{(25% C2C1imC8SO4 + 75% Tween 20) (1) + K2HPO4 (2) + H2O (3)}
0.06
14.04
0.02
16.23
0.17
12.42
44.05
55.46
33.03
3.68
3.27
4.14
27.73
36.77
45.23
5.58
4.22
2.91
{(25% C2C1imC8SO4 + 75% Tween 20) (1) + K2CO3 (2) + H2O (3)}
0.39
11.80
0.60
13.02
0.80
14.29
28.16
37.22
45.83
4.41
4.11
3.96
11.32
18.64
25.96
11.00
10.65
10.36
{(75% C2C1imC2SO4 + 25% Tween 20) (1) + K3PO4 (2) + H2O (3)}
0.53
12.94
0.18
13.69
0.06
14.37
11.55
18.71
26.23
5.93
6.07
6.23
15.19
20.29
25.10
9.69
9.45
9.15
{(75% C2C1imC2SO4 + 25% Tween 20) (1) + K2HPO4 (2) + H2O (3)}
0.41
11.96
0.28
12.84
0.06
14.16
15.78
20.45
25.54
4.36
4.70
4.99
17.37
22.55
29.98
9.07
9.02
8.65
{(75% C2C1imC2SO4 + 25% Tween 20) (1) + K2CO3 (2) + H2O (3)}
0.39
11.52
0.24
11.96
0.24
13.11
17.55
23.26
30.07
6.94
6.82
6.67
44.21
49.10
51.46
3.98
2.99
2.56
{(75% C2C1imC8SO4 + 25% Tween 20) (1) + K3PO4 (2) + H2O (3)}
2.55
16.53
1.14
18.20
0.54
19.52
43.51
50.31
53.67
3.32
3.15
3.00
24.95
35.96
49.09
8.27
5.64
3.25
{(75% C2C1imC8SO4 + 25% Tween 20) (1) + K2HPO4 (2) + H2O (3)}
0.16
19.00
0.17
20.50
0.17
22.52
27.23
38.47
52.31
2.20
2.54
2.68
25.99
35.27
47.75
12.11
8.48
5.02
{(75% C2C1imC8SO4 + 25% Tween 20) (1) + K2CO3 (2) + H2O (3)}
0.17
26.28
0.41
29.96
0.58
34.02
28.21
39.33
52.85
2.15
2.10
2.07
100wII2
Standard uncertainties are u(w) = ±0.0002, u(T) = ±0.01 K; u(P) = ±0.03 kPa.
TABLE 8
Parameters of Othmer–Tobias equation and correlation coefficient for {(C2C1imCnSO4 + Tween 20) (1) + salt (2) + H2O (3)}.
Ionic liquid and non ionic surfactant (1)
25%
25%
25%
25%
25%
25%
75%
75%
75%
75%
75%
75%
C2C1imC2SO4 + 75%
C2C1imC2SO4 + 75%
C2C1imC2SO4 + 75%
C2C1imC2SO4 + 75%
C2C1imC2SO4 + 75%
C2C1imC2SO4 + 75%
C2C1imC2SO4 + 25%
C2C1imC2SO4 + 25%
C2C1imC2SO4 + 25%
C2C1imC2SO4 + 25%
C2C1imC2SO4 + 25%
C2C1imC2SO4 + 25%
Tween
Tween
Tween
Tween
Tween
Tween
Tween
Tween
Tween
Tween
Tween
Tween
20
20
20
20
20
20
20
20
20
20
20
20
Salt (2)
K3PO4
K2HPO4
K2CO3
K3PO4
K2HPO4
K2CO3
K3PO4
K2HPO4
K2CO3
K3PO4
K2HPO4
K2CO3
H2O (3)
H2O
H2O
H2O
H2O
H2O
H2O
H2O
H2O
H2O
H2O
H2O
H2O
a
4.8034
2.9881
3.0636
2.9468
2.7407
3.5071
6.4927
3.0714
4.4180
1.4434
4.9037
2.6085
R2
b
4
3.02 10
4.21 103
1.20 102
9.62 103
9.71 103
2.23 103
2.70 105
1.16 102
5.42 104
1.21 101
2.39 103
1.96 101
0.993
0.992
0.995
0.999
0.995
0.999
0.999
0.975
0.969
0.987
0.997
0.998
183
M.S. Álvarez et al. / J. Chem. Thermodynamics 79 (2014) 178–183
TABLE 9
Parameters of Bancroft equation and correlation coefficient for {(C2C1imCnSO4 + Tween 20) (1) + salt (2) + H2O (3)}.
Ionic liquid and non ionic surfactant (1)
Salt (2)
H2O (3)
k
r
R2
25%
25%
25%
25%
25%
25%
75%
75%
75%
75%
75%
75%
K3PO4
K2HPO4
K2CO3
K3PO4
K2HPO4
K2CO3
K3PO4
K2HPO4
K2CO3
K3PO4
K2HPO4
K2CO3
H2O
H2O
H2O
H2O
H2O
H2O
H2O
H2O
H2O
H2O
H2O
H2O
5.4648
6.3676
4.3301
4.9035
5.4668
5.7246
5.4038
4.6320
5.7850
4.4427
2.4806
1.9311
0.2046
0.3131
0.3398
0.3414
0.3854
0.3015
0.1113
0.2908
0.1990
0.6359
0.2829
0.4172
0.995
0.988
0.993
0.999
0.990
0.998
0.996
0.966
0.950
0.977
0.985
0.996
C2C1imC2SO4 + 75%
C2C1imC2SO4 + 75%
C2C1imC2SO4 + 75%
C2C1imC2SO4 + 75%
C2C1imC2SO4 + 75%
C2C1imC2SO4 + 75%
C2C1imC2SO4 + 25%
C2C1imC2SO4 + 25%
C2C1imC2SO4 + 25%
C2C1imC2SO4 + 25%
C2C1imC2SO4 + 25%
C2C1imC2SO4 + 25%
Tween
Tween
Tween
Tween
Tween
Tween
Tween
Tween
Tween
Tween
Tween
Tween
20
20
20
20
20
20
20
20
20
20
20
20
and ionic liquid-rich phase (I) and potassium salt-rich phase (II), are
represented.
The TLL and S data obtained for each system are compiled in
table 7, together with the layers compositions. From the data presented, it is evident that higher salt concentrations are correlated
with greater TLL values, which is due to the fact that as more salt
is added to the aqueous solution of Tween 20 and C2C1imCnSO4,
more surfactant and ionic liquid is segregated to the upper phase.
The analysis of the TL consistency was carried out by fitting the
experimental data to Othmer–Tobias and Brancroft [30,22]
equations:
a
1 wI1
1 wII2
¼b
;
I
II
w1
w2
ð7Þ
II I r
w3
w3
¼
k
;
wII2
wI1
ð8Þ
being a, b, k and r the fitting parameters, w the mass fraction, subscripts 1, 2 and 3 the mixture (Tween 20 + C2C1imCnSO4), the potassium-based salt and water, respectively, and superscripts I and II
the (ionic liquid and surfactant) rich-phase and salt-rich phase,
respectively. In tables 8 and 9, all the parameters defining both
models are collected, together with the correlation factor R2. In general terms, on the basis of the values of this correlation coefficient, it
is possible to conclude that Othmer–Tobias model entails a better
description of the experimental data for both ionic liquids, no matter the salt employed, in agreement with previous works on this
topic [22].
4. Conclusions
In this work, the suitability of a non-ionic surfactant to assist in
the formation of ionic-liquid based ABS has been demonstrated.
The increase of surfactant concentration has led to greater immiscibility windows, which is advantageous from both an extraction
an economic point of view. It was confirmed that the Hofmeister
series and the molar Gibbs free energy of hydration are valuable
tools to predict the salting out potential of the selected inorganic
salts in systems involving mixtures of ionic liquids and surfactants.
All the experimental solubility data were suitably correlated by
means of three and four parameter-based empirical equations.
Additionally, Othmer–Tobias model served our goal to properly
describe the TL data.
Acknowledgements
This work has been supported by the Spanish Ministry of Economy and Competitiveness and EDERF funds (project CTM201231534).
References
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[4] A. Rodríguez, J. Canosa, J. Tojo, J. Chem. Thermodyn. 33 (2001) 1383–1397.
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1383–1403.
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291 (2010) 13–17.
[11] X. Zhao, X. Xie, Y. Yan, Thermochim. Acta 516 (2011) 46–51.
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J.A.P. Coutinho, Chem. Eur. J. 18 (2012) 1831–1839.
[15] G. Ulloa, C. Coutens, M. Sánchez, J. Sineiro, A. Rodríguez, F.J. Deive, M.J. Núñez,
J. Chem. Thermodyn. 47 (2012) 62–67.
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[19] M.S. Álvarez, F. Moscoso, A. Rodríguez, M.A. Sanromán, F.J. Deive, Bioresour.
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JCT 14-412
ANNEX 13
ENVIRONMENTALLY BENIGN SEQUENTIAL EXTRACTION OF HEAVY METALS FROM MARINE
SEDIMENTS (INDUSTRIAL & ENGINEERING CHEMISTRY RESEARCH, 2014, 53: 86158620).
Article
pubs.acs.org/IECR
Environmentally Benign Sequential Extraction of Heavy Metals from
Marine Sediments
María S. Á lvarez, Esther Gutiérrez, Ana Rodríguez, M. Á ngeles Sanromán, and Francisco J. Deive*
Department of Chemical Engineering, University of Vigo, Vigo, Pontevedra 36310, Spain
S Supporting Information
*
ABSTRACT: An environmentally friendly heavy metals remediation process from polluted marine sediments is proposed. The
efficiency of three organic and inorganic salts (ammonium acetate, ammonium nitrate, and sodium potassium tartrate) to salt out
these pollutants was ascertained in sediment washing waters containing nonionic surfactants. The immiscibility regions were
correlated by means of three known models, and the experimental data were interpreted in the light of thermodynamic
parameters such as Gibbs free energy of hydration and molar entropy of hydration. The proposed process was applied to model
aqueous solutions containing two representative heavy metals (zinc and copper). The viability of the suggested strategy was
checked in real contaminated marine sediments by including a sequential treatment: marine sediment washing−contaminant
extraction, which led to total remediation values higher than 80% for copper and 90% for zinc.
1. INTRODUCTION
Heavy metals are in the limelight because they have been
recognized as carcinogenic, persistent, and bioaccumulative
contaminants.1 These pollutants are widely found in nature as a
result of anthropogenic activities, including industrial and
domestic wastewater. The high solubility in aqueous solutions
of these metals makes it possible for them to be found as watersoluble species, suspended forms, colloids, and sedimentary
phases. Coastal and marine sediments are considered one of the
ecological niches most probably affected by this kind of
contamination, since more than 99% of heavy metals entering
the aquatic ecosystems can be stored in sediments in various
forms.2,3 Nonetheless, the variation of the physicochemical
properties of water could revert the fixation of metals to a
solubilized form, thus being bioavailable again for living beings.
In this sense, dredging activities involve the generation of
great amounts of polluted marine sediments that should be
treated. The relevance of this statement is patent when
analyzing examples such as the number of remediation actions
in the USA (71 projects) focused on the treatment of more
than 4.5 million m3 of contaminated sediments.4 Bearing this in
mind, the search of efficient sediments remediation strategies
are a very active field of research. The remediation methods are
often classified into ex situ and in situ, depending on the place
where the treatment is carried out. Thus, amendment, sand cap,
and phytoremediation have already been recommended as in
situ alternatives, due to their low cost and more benignity to
natural hydrological conditions. On the other hand, washing,
electrokinetic remediation, immobilization, flotation, and ultrasonic-assisted extraction have been proposed as viable ex situ
remediation processes.5,6
Among the above-mentioned methods, in this work, we have
focused on sediment washing, since it is a commonly used
technique due to its inherent operational simplicity. This
strategy consists of transferring metal ions from dredged
samples to aqueous solutions. The efficiency of this process can
be improved by the addition of specific compounds such as
© 2014 American Chemical Society
acids, chelating agents, and surfactants, which have been proved
to further contaminant solubilization, dispersion, and desorption. Therefore, the use of nonionic surfactants (Triton X-100
and Tween 20) and KSCN as complexation agent in acid media
was considered in the present investigation.
This family of surfactants has been commonly used in
bioremediation processes applied to the removal of contaminants as widely disparate as polycyclic aromatic hydrocarbons
and dyes.7−9 This fact, together with their biodegradability, has
led us to bet in them for the present work. The adsorption/
release ratio between sediment and water can be strongly
altered by introducing a chelating agent such as KSCN. This
salt assists in the formation of copper and zinc complexes,
which are spontaneously released from the sediment, thus
helping to diminish the levels of pollutant charge.
Once the pollutant was dissolved in the aqueous solution, a
second step to concentrate the contaminant charge is desirable.
The presence of surfactants from washing and salts from a
marine environment in the obtained aqueous effluent made us
hypothesize that the addition of salting out agents to the
polluted aqueous solution could assist us in this purpose.
Aqueous biphasic system (ABS) is a competitive separation
technique based on the induction of phase segregation by the
addition of inorganic or organic salts to an aqueous solution of
a hydrophilic organic compound. The appeal of this separation
method lies in well-documented merits such as process
economy, short operation time, low energy demand, and easy
scale-up.7,10
Among the existing types of ABS, nonionic surfactant-based
ones are a promising alternative that remains almost unexplored. In this line, surfactant-based ABS entail several benefits
in relation to other commonly used alternatives, such as a lower
Received:
Revised:
Accepted:
Published:
8615
March 4, 2014
April 28, 2014
May 1, 2014
May 1, 2014
dx.doi.org/10.1021/ie500927q | Ind. Eng. Chem. Res. 2014, 53, 8615−8620
Industrial & Engineering Chemistry Research
Article
Figure 1. Experimental solubility curves and correlation data for (a) Triton X-100 and (b) Tween 20. (○) NH4CH3COO; (□) NH4NO3; (Δ)
NaKC4H4O6.
deionized water was added until a clear solution was attained.
These operations were repeated until completion of the
solubility curve. This methodology was carried out in a
jacketed glass vessel with magnetic stirring and connected to a
circulating bath, and the temperature was controlled by a F200
ASK digital thermometer (±0.01 K).
2.3. Experimental Determination of Tie-Lines and
Metal Partition. Analogously to the procedure described for
the solubility curves, the tie-line data (TL) were obtained by
adding a known amount of a given salt to the aqueous solutions
containing Triton X-100 or Tween 20, until the detection of
turbidity. The temperature was controlled at 298.15 K and
vigorously stirred prior to settling for 24 h in order to ensure
chemical and thermodynamic equilibria. Experimental TL data
were calculated by using the level arm rule. For studying heavy
metal partition, aqueous solutions of the metal ions were
included in the initial nonionic surfactant aqueous solutions
prior to the addition of the selected salting out agent.
2.4. Extraction of Metals from Dredged Marine
Sediments. The marine sediment samples were collected in
the Galician coast (NW Spain). The classification of the
sediments according to the Particle Size Analysis method
indicates that the dredged samples are silty clay. A complete
characterization of the samples has been recently reported by
our research group, and Zn and Cu were the two metal ions
clearly trespassing the CEDEX reccomendations for dredged
marine sediments for Spanish harbors.6
Metals were extracted from the marine sediments based on
the following procedure: 0.25 g of soil was added to Erlenmeyer
flasks together with 15 mL of aqueous solutions of the selected
nonionic surfactant at 30% concentration. Alternatively, 0.87 g
of KSCN was added as complexing agent when stated to the
above-mentioned mixture. 1 M HCl was added to adjust the
pH since this parameter is decisive to promote metal
solubilization. These mixtures were shaken at 200 rpm for 24
h at 298.15 and 343.15 K. Then, the mixture was centrifuged at
5000 rpm for 5 min, and the supernatant was kept for a second
centrifugation step at 5000 rpm and 5 min. Metals were
determined in this supernatant. This solution was then used for
ABS extraction. Milli-Q-Plus water leaching was performed as a
control. All experiments were run in triplicate.
2.5. Experimental Determination of Metal Ions. The
concentrations of copper and zinc in the top and bottom
phases of the ABS were determined by flame atomic absorption
spectroscopy (AAS) with an Agilent Technologies 200 series
AA. Copper and zinc were determined with an air-acetylene
interface tension, economical reasons (low cost of the reagents
and rapid phase segregation), greater immiscibility windows,
null flammability, and commercial availability of all components
at bulk quantities.11 In our group, we have recently
demonstrated the viability of implementing this kind of process
for the extraction of contaminants (dyes),7 as well as for the
separation of value-added compounds such as antioxidants.12 In
this particular case, this extraction process is applied to the
separation of metal thiocyanate complexes from acid aqueous
solutions in the presence of the selected surfactants Triton X100 and Tween 20. There are no references in the literature to
apply this hybrid strategy for remediating heavy metals-polluted
sediments.
Hence, in this work, ammonium nitrate, ammonium acetate,
and sodium potassium tartrate have been selected as salting out
agents to generate an immiscibility window in aqueous
solutions of the nonionic surfactants Triton X-100 and
Tween 20. The solubility data obtained, together with the tielines characterization, will be fitted to different equations in
order to suitably describe the phase behavior. This first step
constitutes the basis for the implementation of a two-stage
remediation process to remove metal ions (Cu2+ and Zn2+)
from marine sediments. Thus, a model aqueous solution
containing the metal ions will be used prior to carrying out the
extraction with real polluted sediments, and the efficiency of the
proposed strategy will be evaluated in terms of extraction
capacity.
2. EXPERIMENTAL SECTION
2.1. Chemicals. The nonionic surfactants Tween 20 and
Triton X-100 (Sigma-Aldrich, St. Louis, MO, US), the
inorganic and organic salts, ammonium nitrate (NH4NO3;
VWR Chemicals, Radnor, PA, US), ammonium acetate
(NH4CH3COO; Sigma-Aldrich, St. Louis, MO, US), sodium
potassium tartrate (NaKC4H4O6; Panreac, Barcelona, Spain),
the complexing agent potassium thiocyanate (KSCN; Fluka, St.
Gallen, Switzerland), and the metal ions of copper (CuSO4·
5H2O; Merck, Darmstadt, Germany) and zinc (ZnSO4·H2O;
VWR, Radnor, PA, US) were used as received without further
purification.
2.2. Determination of Solubility Curves. The solubility
curves determination was carried out by means of the cloud
point titration method at 298 K, as previously reported
elsewhere.13 In brief, drops of saturated salt solution were
added to aqueous solutions of the nonionic surfactants (Triton
X-100 or Tween 20) until the detection of turbidity, which
indicates that the biphasic region was reached. Afterward,
8616
dx.doi.org/10.1021/ie500927q | Ind. Eng. Chem. Res. 2014, 53, 8615−8620
Industrial & Engineering Chemistry Research
Article
the NaKC4H4O6 is by far the salt leading to the greatest
immiscibility region, no matter the nonionic surfactant
employed.
The interpretation of the influence of the amount of salt
necessary to trigger phase disengagement has been widely
assessed by the Hofmeister series, yet recent works have
focused on the molecular mechanisms behind these effects,
explaining them in terms of thermodynamic parameters such as
the Gibbs free energy of hydration (ΔhydG) and the molar
entropy of hydration (ΔhydS).14,15 The values of these
thermodynamic parameters for the different ions used in this
work are listed in Table 1. It is patently clear that the anion
burner at wavelengths of 324.8 and 213.9 nm and lamp currents
of 4 and 5 mA, respectively.
The determination of metal concentrations in dredged
sediments was performed according to EPA Methods 3010
and 3050. In brief, they were analyzed by inductively coupled
plasma optical emission spectrometry (Optima 4300DV
PerkinElmer). After setting analytical conditions and making
background corrections for wavelength spectra in accordance
with the standard solution profile, sample or test solutions were
introduced via the Cross-Flow nebulizer (Scott) inside the
plasma torch, equipped with an Echelle polychrometer. The
operating conditions for auxiliary gas, nebulizer gas, and cool
gas (Ar) were 0.2, 1.10, and 15 L/min, respectively. The
spectral lines for copper and zinc were 327.393 and 206.200
nm, respectively. Calibration was carried out by using a
multielement standard solution VI (Merck) by appropriate
dilution in 2% (v/v) HNO3.
Table 1. Molar Gibbs Energies of Hydration (ΔhydG) and
Molar Entropy of Hydration (ΔhydS)
ions
Na
K+
NH4+
CH3COO−
NO3−
C4H4O62−
3. RESULTS AND DISCUSSION
3.1. Selection of Phase Segregation Agent. The
solubility curves of the ternary systems containing the inorganic
and organic salts NH4NO3, NH4CH3COO, and NaKC4H4O6
and aqueous solutions of Triton X-100 or Tween 20 were first
determined at 298.15 K and are graphically plotted in Figure 1.
The experimental data are also shown in Tables S1 and S2,
Supporting Information. A complete characterization of the
experimental data requires one to find a suitable equation to
describe the phase segregation behavior. Different empirical
equations14 have already been reported to properly fit to ABS
data and have thus been selected for the present work:
w1 = a ln(w2 + b) + c
(1)
w1 = exp(d + ew2 0.5 + fw2 + gw2 2)
(2)
w1 = hexp(iw2 k − jw2 l)
(3)
a
ΔhydS/J·mol−1K−1
−365
−295a
−285a
−365a
−300a
−1102b
−130a
−93a
−131a
−189a
−95a
n.a.c
Ref 16. bRef 17. cn.a. (not available).
C4H4O62− is the one showing the lowest values of ΔhydG, which
correlates with solubility curves closer to the origin. This
behavior is coincident with what is expected from the analysis
of ions valence, since the tartrate binary valence leads to more
ion−water interactions than those provided by the monovalent
acetate and nitrate anions. Therefore, tartrate-based salt will be
selected for further implementation of the separation of copper
and zinc metal ions from dredged marine sediments.
A proper description of the separation process requires the
calculation of the tie-lines data, as a means to quantify the
amount of Triton X-100 or Tween 20 and NaKC4H4O6 in each
of the phases Then, the lever arm rule was applied to calculate
the tie-line data, together with eq 3, on the basis of the
procedure detailed elsewhere.11,18 In a visual inspection of the
results shown in Table 5, it seems clear that the addition of
more salt to the systems triggers the segregation of more
nonionic surfactant to the top phase. An empirical parameter
that can be used to demonstrate this fact is the tie-line length
(TLL), and these data are shown in Table 2.
where w1 and w2 are the mass compositions of nonionic
surfactant and salt, respectively, and a, b, c, d, e, f, g, h, i, j, k, and
l are the fitting parameters. The values of these parameters were
calculated by applying the SOLVER function in Microsoft
EXCEL, on the basis of the minimization of the standard
deviation. This was calculated as follows:
2 ⎞1/2
⎛ ∑nDAT (z − z
exp
adjust)
i
⎟
σ = ⎜⎜
⎟
nDAT
⎝
⎠
ΔhydG/kJ·mol−1
a
+
(4)
TLL = [(w1I − w1II)2 + (w2I − w2II)2 ]1/2
where the experimental and adjustable solubility data are
represented by zexp and zadjust, respectively, and nDAT is the
number of experimental data. The obtained fitting parameters
are shown in Tables S3, S4, and S5 in the Supporting
Information, together with the values of the standard
deviations. Bearing these data in mind, it is possible to
conclude that all the equations serve our goal to describe the
immiscibility behavior, although eq 3 is the one entailing lower
standard deviation values.
The analysis of the salting out potential of the selected salts
reveals that the phase segregation results from the competition
between Triton X-100 or Tween 20 and NH 4 NO 3 ,
NH4CH3COO, or NaKC4H4O6 for the water molecules. As a
consequence of this competition, the segregation of two phases
is yielded: a top phase enriched in nonionic surfactant and a
salt-rich bottom phase. From the observed trends, it is clear that
(5)
Table 2. Experimental Tie-Lines in Mass Composition for
{Nonionic Surfactant + NaKC4H4O6 + H2O} at 298.15 K
nonionic surfactant-rich
phase
100
wI1
26.47
39.81
48.45
43.88
50.45
56.16
8617
100
wI2
salt-rich phase
100 wII1
100 wII2
Triton X-100 + NaKC4H4O6 + H2O
7.36
0.80
14.28
5.06
0.10
16.41
2.86
0.01
18.47
Tween 20 + NaKC4H4O6 + H2O
5.16
1.51
18.61
3.24
0.49
20.61
1.72
0.19
22.02
TLL
26.59
41.30
50.90
44.44
52.90
59.54
dx.doi.org/10.1021/ie500927q | Ind. Eng. Chem. Res. 2014, 53, 8615−8620
Industrial & Engineering Chemistry Research
Article
Table 4. Extraction Capacity, E (%), of Metal Ions in the
Top Phase in the Absence and Presence of Complex
Extractant (KSCN)
In this equation, 1 and 2 refer to mass fraction of the
nonionic surfactant and the organic or inorganic salt,
respectively, while I and II indicate the top and bottom phases,
respectively. The TLL data analysis permits us to confirm that
greater concentrations of salt in the bottom phase involve
higher values of TLLs, since more salt molecules in the system
make it possible to obtain a top phase enriched in Triton X-100
or Tween 20.
Additionally, a two-parameter equation19 was employed to
correlate the experimental tie-line data and shed more light on
the capacity of each surfactant to be salted out.
⎛ wI ⎞
ln⎜ II2 ⎟
⎝ w2 ⎠
= β + k(w1II − w1I)
system
14.6 ± 0.8
62.8 ± 6.3
18.4 ± 7.9
66.2 ± 7.1
11.3 ± 1.0
80.9 ± 3.3
6.2 ± 0.7
86.1 ± 4.3
Zn2+
Triton X-100 + NaKC4H4O6 +
H2O
Tween 20 + NaKC4H4O6 + H2O
(6)
KSCN has been proposed, and the extraction values are also
shown in Table 4. The analysis of the data permits us to
conclude a quite different behavior, since both zinc and copper
are mostly segregated to the top phase, at levels higher than
80% and 62%, respectively.
The rationale underlying this scenario is explained in terms
of the complexation capacity of metals in the presence of
tartrate and thiocyanate, according to the following equilibrium,
where M is the heavy metal copper or zinc:
Table 3. Salting out Ability for {Nonionic Surfactant +
NaKC4H4O6 + H2O} at 298.15 K
M2 +(aq) + xC4 H4O6 2 −(aq) ↔ M(C4H4O6 )x(2x − 2) − (aq)
k
β
R2
0.0513
0.0924
0.7109
2.6715
0.95
0.98
(8)
M2 +(aq) + xSCN−(aq) ↔ M(SCN)x(x − 2) − (aq)
(9)
Then, the presence of the metal-ion complex in the upper
phase can be explained on the basis of the competition between
thiocyanate or tartrate anions for the metal cations and the
interaction of this complex with the selected nonionic
surfactant, which is the major component in this top layer.
On the one hand, taking into account the existing tartrate
interactions, it can be stated that the presence of tartrate-based
complex will be intimately influenced by the standard
thermodynamic constant of formation of the metal-tartrate
complex. Thus, the order of the formation constant is Cu2+ (log
K = 3) > Zn2+ (log K = 2.7) and reveals a higher affinity of
copper for the tartrate anion.20 This behavior points out the
higher extraction capacity of Zn, since metal extraction is
inversely proportional to the given formation constants.
On the other hand, it seems that thiocyanate ions coordinate
to copper and zinc with the N end to form tetrahedral
complexes, such as [Cu(NCS)4]2− and [Zn(NCS)4]2−. In this
sense, many authors21,22 have converged upon the idea that
these complexes are present exclusively in nonaqueous
solutions, which would justify its preferential partition to the
surfactant-rich phase where hydrophobic domains exist.
The final stage of this research consisted of coupling the
proposed ABS to a previous dredged sediments washing step.
The data obtained were also presented in terms of extraction
capacity E (%), as can be visualized in Table 5. The combined
heavy metals remediation strategy yielded total remediation
values about 80% or higher for both zinc and copper, as can be
inferred from the extraction data. It becomes patent that the use
of Tween 20 rather than Triton X-100 is always preferred. This
fact may be explained in terms of the different hydrophobicities
of both nonionic surfactants, as demonstrated by their
hydrophilic−lipophilic balance values (HLBTriton X‑100 = 13.4;
HLBTween 20 = 16.7).11 The data obtained demonstrate that
thiocyanate-based complexes show a preferential interaction for
the more hydrophilic Tween 20, in line with the data obtained
is the nonionic surfactant that can be better salted out to the
upper phase. Although the scientific rationale behind this
phenomenon has not been fully understood, it seems that
salting out electrolyte tends to be preferentially excluded from
the vicinity of the surfactant units. This thermodynamic
information, together with the visual observation of a faster
phase segregation, allows us to conclude a better behavior of
Tween 20 than that with Triton X100 from both a
thermodynamic and kinetic point of view.
3.2. Removal of Metals from Polluted Dredged
Marine Sediments. After having demonstrated the suitability
of NaKC4H4O6 as segregation agent in aqueous solutions of the
selected nonionic surfactants, the potential of this salt to extract
two metal ions (Cu2+ and Zn2+) from aqueous solutions was
ascertained. This step makes up a first approach to develop an
entire remediation process of metal-polluted sediments.
Therefore, a model solution containing surfactant, water, and
metal ions was employed to study the partition behavior after
addition of the tartrate-based salt. The remediation data were
analyzed in terms of extraction capacity, E (%), defined as
⎛ m surfactant ⎞
⎟⎟ ·100
E (%) = ⎜⎜ i
⎝ mi ⎠
E (%) (with
KSCN)
Cu2+
Triton X-100 + NaKC4H4O6 +
H2O
Tween 20 + NaKC4H4O6 + H2O
where the fitting parameter k is the salting out coefficient and β
is a constant related to the activity coefficient, respectively. The
empirical thermodynamic parameter k represents the specific
effects of salts on the free energy of transfer of 1 mol of
surfactant units from aqueous solution to a 1 m salt solution.
The values of the fitting parameters and correlation coefficients
are listed in Table 3. From the k values, it seems that Tween 20
Triton X-100 + NaKC4H4O6 + H2O
Tween 20 + NaKC4H4O6 + H2O
E (%) (without
KSCN)
(7)
misurfactant
where
and mi are the metal ions mass content in the
upper phase and the total metal ions mass content, respectively.
The results obtained are compiled in Table 4 and reveal that
heavy metal ions are both concentrated in the salt-rich phase at
concentrations higher than 85% for copper and 90% for zinc.
This may be due to the existence of specific interactions
between metal and salt ions, so the search of a suitable
complexation agent can be a tool to allow an effective
separation of the targeted contaminants. Thus, the use of
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Industrial & Engineering Chemistry Research
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Table 5. Extraction Capacity E (%) of Metals from Marine
Dredged Sediments after Sequential Treatment
system
Triton X-100 + NaKC4H4O6 +
H2O
Tween 20 + NaKC4H4O6 + H2O
Triton X-100 + NaKC4H4O6 +
H2O
Tween 20 + NaKC4H4O6 + H2O
temp
(K)
Zn2+
298.15
343.15
298.15
343.15
Cu2+
298.15
343.15
298.15
343.15
E (%)
washing
should be applied, and then, it could be reused again. It is worth
mentioning that this surfactant is completely biodegradable,
and this family has even been reported to act as a carbon
source.23
E (%) ATPS
72.3
78.0
85.6
89.4
±
±
±
±
0.0
2.7
2.7
8.1
88.8
89.3
89.4
89.3
±
±
±
±
1.1
1.8
0.7
1.4
77.0
84.1
98.4
98.4
±
±
±
±
0.1
0.1
0.2
0.1
85.7
84.9
85.3
88.8
±
±
±
±
0.8
2.4
4.6
3.5
4. CONCLUSIONS
In this work, we have demonstrated the suitability of a twostages remediation strategy for the removal of heavy metals
from marine dredged sediments. High levels of copper and zinc
extraction (about 70% and 90%, respectively) for a model
system containing KSCN, Tween 20, and NaKC4H4O6 were
demonstrated after a preliminary comparison of the salting out
potential of different salts and nonionic surfactants. All the
obtained solubility and tie-line data were adequately modeled
and made up the basis for the proposal of a remediation process
in real marine sediments. The analysis of the extraction
efficiency after a first sediment washing step and a second ABS
concentration stage allowed us to conclude the viability of the
integrated process, since remediation levels higher than 80% for
copper and 90% for zinc were yielded.
for the model systems containing the heavy metals (see Table
4).
Bearing in mind the promising remediation efficiency, a flow
sheet of the proposed process is shown in Figure 2. The
presented approach involves different advantages when
compared with the EPA Method 3010 and 3050 recommended
for heavy metal extraction. First of all, it is clear that the use of
room temperature does not involve any decline in the metal
ions remediation levels (Table 5), which is advantageous from
an economic standpoint. Additionally, this alternative avoids
the use of nitric acid in the washing, which is also beneficial in
terms of environmental and health risks.
In summary, in this paper, we have proposed the nonionic
surfactant Tween 20, the salting out compound sodium
potassium tartrate, and potassium thiocyanate as a complexing
agent in order to propose a viable metal remediation strategy
for marine sediments. This first contribution tackles just the
viability of this separation technique for metal removal, but
further study must be undertaken in order to search for an
effective second stage to recycle the selected components. The
removal of metals and thiocyanate is not complicated, since
their precipitation could be achieved by just modifying the pH
or adding compounds such as ferric sulfate. In relation to
sodium potassium tartrate, there are several strategies that
could be implemented, such as salt recovery by evaporation or
reverse osmosis, or even the effluent disposal in a sewage
treatment plant, since this salt is completely biodegradable.
Finally, regarding the nonionic surfactant, after having used it
for several cycles (sediments washing−ABS), the abovementioned treatment for thiocyanate and metals removals
■
ASSOCIATED CONTENT
* Supporting Information
S
Experimental solubility data for the ternary systems {nonionic
surfactant + salt + H2O} at 298.15 K; parameters of eqs 1, 2,
and 3 and standard deviation for {nonionic surfactant + salt +
H2O} at 298.15 K. This material is available free of charge via
the Internet at http://pubs.acs.org/.
■
AUTHOR INFORMATION
Corresponding Author
*Tel.: +34986818723. E-mail: [email protected].
Notes
The authors declare no competing financial interest.
■
ACKNOWLEDGMENTS
This work has been supported by the Spanish Ministry of
Economy and Competitiveness and FEDER funds (IPT310000-2010-17). F.J.D. thanks Xunta de Galicia for funding
through an Isidro Parga Pondal position. E.G. acknowledges
University of Vigo for a master grant.
■
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ANNEX 14
PROCESO INTEGRADO DE REMEDIACIÓN DE HIDROCARBUROS AROMÁTICOS POLICÍCLICOS
MEDIANTE COMBINACIÓN
DE
BIODEGRADACIÓN
Y
SISTEMAS ACUOSOS BIFÁSICOS
(SPANISH PATENT, APPLICATION NUMBER 201301068, 2013).
TRATADO DE COOPERACIÓN EN MATERIA DE PATENTES
Remitente: LA ADMINISTRACIÓN ENCARGADA
DE LA BÚSQUEDA INTERNACIONAL
PCT
Destinatario:
UNIVERSIDADE DE VIGO
CAMPUS UNIVERSITARIO, s/n
C. P. 36310 VIGO (PONTEVEDRA)
- ESPAÑA -
NOTIFICACIÓN DE LA TRANSMISIÓN DEL INFORME DE
BÚSQUEDA INTERNACIONAL Y DE LA OPINIÓN ESCRITA
DE LA ADMINISTRACIÓN ENCARGADA DE LA BÚSQUEDA
INTERNACIONAL, O DE LA DECLARACIÓN
(Regla 44.1 del PCT)
Fecha de expedición
(día/mes/año)
06 FEBRERO 2015
(06.02.2015)
Referencia del expediente del solicitante o del mandatario
PARA CONTINUAR LA TRAMITACION
Véanse los puntos 1 y 4
Solicitud internacional Nº
PCT/ES2014/000193
Fecha de presentación internacional (día/mes/año)
10 NOVIEMBRE 2014 (10.11.2014)
Solicitante
UNIVERSIDADE DE VIGO.
1.

Se notifica al solicitante que el informe de búsqueda internacional y la opinión escrita de la Administración encargada de la
búsqueda internacional se han elaborado y se transmiten adjuntos.
Presentación de modificaciones y de una declaración, según el Artículo 19:
El solicitante tiene derecho, si así lo desea, a modificar las reivindicaciones de la solicitud internacional (ver la Regla 46):
¿Cuándo? El plazo para la presentación de dichas modificaciones es, normalmente, de dos meses desde la fecha de la
transmisión del informe de búsqueda internacional.
¿Dónde?
Directamente ante la Oficina Internacional de la OMPI
34, Chemin des Colombettes
1211 Ginebra 20, Suiza
Fax Nº : (41-22)338.82.70
Para instrucciones más detalladas consultar la Guía del solicitante PCT, Fase Internacional, párrafos 9004-9011.
Se notifica al solicitante que no se elaborará el informe de búsqueda internacional y que la declaración a tal efecto en virtud del
Artículo 17.2)a), y la opinión escrita de la Administración encargada de la búsqueda internacional se adjuntan a esta
notificación.
Con relación a cualquier protesta que pudiera formularse, de conformidad con la Regla 40.2, respecto del pago de una o más
tasas adicionales, se notifica al solicitante que:
 la protesta, así como la decisión relativa a la misma, han sido remitidas a la Oficina Internacional conjuntamente con la
petición del solicitante de que los textos de la protesta y la decisión en cuestión sean notificados a las oficinas designadas.
2.

3.

4.
Recordatorios:
El solicitante puede presentar comentarios informales sobre la opinión escrita de la Administración de Búsqueda Internacional ante
la Oficina Internacional. La Oficina Internacional enviará una copia de los mismos a todas las Oficinas designadas salvo que se haya
emitido o se vaya a emitir un informe de examen preliminar internacional. Transcurridos 30 meses desde la fecha de prioridad, estos
comentarios se pondrán también a disposición del público.
La Oficina Internacional publicará la solicitud internacional lo antes posible después de haber transcurrido 18 meses desde la fecha
de prioridad. Si el solicitante desea evitar o diferir la publicación, deberá hacer llegar a la Oficina Internacional una declaración de
retirada de la solicitud internacional o de la reivindicación de prioridad antes de la finalización de los preparativos técnicos de la
publicación internacional (Reglas 90 bis.1 y 90 bis.3).
En el plazo de 19 meses a contar desde la fecha de prioridad, pero únicamente en lo relativo a algunas oficinas designadas, el
solicitante deberá presentar una petición de examen preliminar internacional si desea diferir la entrada en la fase nacional hasta un
plazo de 30 meses a contar desde la fecha de prioridad (en algunas Oficinas, incluso más tarde); en caso contrario, el solicitante
deberá cumplir en un plazo de 20 meses a contar desde la fecha de prioridad con los actos prescritos para la entrada en la fase
nacional ante esas oficinas designadas.
En lo que respecta a otras oficinas designadas, el plazo de 30 meses (o más) se aplicará igualmente aunque no se haya presentado
una petición de examen preliminar internacional en el plazo de 19 meses.

no ha sido adoptada aún decisión alguna sobre la protesta; el solicitante será notificado en cuanto se adopte.
Para los detalles sobre los plazos límite aplicables, Oficina por Oficina, véase www.wipo.int/pct/en/texts/time_limits.html y la Guía
del solicitante PCT, Capítulos Nacionales.
Nombre y dirección postal de la Administración encargada de la búsqueda Funcionario autorizado
internacional
Miguel Angel Casado Santiago
OFICINA ESPAÑOLA DE PATENTES Y MARCAS
Paseo de la Castellana
Nº de fax: 91 349 53 04
Nº de teléfono: 91 349 32 92
Formulario PCT/ISA/220 (Julio 2010)
TRATADO DE COOPERACIÓN EN MATERIA DE PATENTES
PCT
INFORME DE BÚSQUEDA INTERNACIONAL
(Artículo 18 y Reglas 43 y 44 del PCT)
Referencia del expediente del
solicitante o del mandatario
Solicitud internacional Nº
ver Formulario PCT/ISA/220 y, en su caso,
el punto 5 de esta hoja.
PARA CONTINUAR
LA TRAMITACIÓN
Fecha de presentación internacional (día/mes/año)
10 NOVIEMBRE 2014 (10.11.2014)
PCT/ES2014/000193
Fecha de prioridad (la más antigua)
(día/mes/año)
14 NOVIEMBRE 2013 (14.11.2013)
Solicitante
UNIVERSIDADE DE VIGO.
El presente informe de búsqueda internacional, elaborado por esta Administración encargada de la búsqueda internacional, se transmite al
solicitante, conforme al Artículo 18. Se remite una copia del mismo a la Oficina Internacional.
Este informe de búsqueda internacional comprende un total de 5 hojas.
 Se adjunta una copia de cada uno de los documentos del estado de la técnica citados en el informe.
1. Base del informe
a. En lo que se refiere al idioma, la búsqueda internacional se ha realizado sobre la base de :
 la solicitud en el idioma en el que se presentó
 una traducción de la solicitud al
, que es el idioma de la traducción proporcionada a los fines de la
búsqueda internacional (Reglas 12.3.a) y 23.1.b))
b.  Este informe de búsqueda internacional se ha realizado teniendo en cuenta la rectificación de un error evidente autorizado por
o notificado a esta Administración según la Regla 91 (Regla 43.6bis.a)).
c.
 En lo que se refiere a las secuencias de nucleótidos y/o de aminoácidos divulgadas en la solicitud internacional,
2.
véase Recuadro I.
 Se estima que algunas reivindicaciones no pueden ser objeto de búsqueda (ver Recuadro II).
3.
 Falta unidad de invención (ver Recuadro III).
4. Con respecto al título,
 el texto se aprueba según fue remitido por el solicitante.
 el texto ha sido establecido por esta Administración con la siguiente redacción:
5. Con respecto al resumen,
 el texto se aprueba según fue remitido por el solicitante.
 el texto (reproducido en el Recuadro IV) ha sido establecido por esta Administración de conformidad con la Regla 38.2.
El solicitante puede presentar observaciones a esta Administración en el plazo de un mes a contar desde la fecha de expedición
del presente informe de búsqueda internacional.
6. Con respecto a los dibujos,
a. la figura de los dibujos a publicar junto con el resumen es la Figura Nº
b.




1
propuesta por el solicitante.
propuesta por esta Administración, por no haber propuesto el solicitante ninguna figura.
propuesta por esta Administración, por caracterizar mejor, esta figura, la invención.
no debe publicarse ninguna figura.
Formulario PCT/ISA/210 (primera hoja) (Julio 2009)
INFORME DE BÚSQUEDA INTERNACIONAL
Solicitud internacional nº
PCT/ES2014/000193
A. CLASIFICACIÓN DEL OBJETO DE LA SOLICITUD
Ver Hoja Adicional
De acuerdo con la Clasificación Internacional de Patentes (CIP) o según la clasificación nacional y CIP.
B. SECTORES COMPRENDIDOS POR LA BÚSQUEDA
Documentación mínima buscada (sistema de clasificación seguido de los símbolos de clasificación)
C02F
Otra documentación consultada, además de la documentación mínima, en la medida en que tales documentos formen parte de los sectores
comprendidos por la búsqueda
Bases de datos electrónicas consultadas durante la búsqueda internacional (nombre de la base de datos y, si es posible, términos de
búsqueda utilizados)
INVENES,EPODOC,WPI,TXTE,BIOSIS EMBASE,NPL,XPESP,XPESP2,PUBMED,GOOGLESCHOLAR
C. DOCUMENTOS CONSIDERADOS RELEVANTES
Categoría*
Documentos citados, con indicación, si procede, de las partes relevantes
Relevante para las
reivindicaciones nº
A
ÁLVAREZ, M.S. et al. “Novel physico-biological treatment for
the remediation of textile dyes-containing industrial effluents”.
Bioresource Technology 2013, Volumen 146, páginas 689-695.
[Disponible en línea el 06.08.2013]. Ver página 689, resumen;
página 694, conclusión; página 690, apartados 2.2, 2.3, 2.5; página 691,
apartado 2.7; página 692, apartado 3.2; páginas 693-694, apartado 3.3.
1-11
A
WO 2011/119112 A1 (BIOMAX TECHNOLOGIES PTE LTD)
29.09.2011, página 2, línea 31-página 3, línea 5; página 4, líneas 1-5;
página 20, línea 28-página 21, línea 16.
1-11
A
DURAN, C. et al. “Cloud-Point Extraction of Rhodamine 6 by
Using Triton X-100 as the Non-Ionic Surfactant”. Journal of AOAC
International 2011, Volumen 94, Número 1, páginas 286-292.
Ver página 127, resumen.
1-11
 En la continuación del recuadro C se relacionan otros documentos  Los documentos de familias de patentes se indican en el
*
Categorías especiales de documentos citados:
"T"
"A" documento que define el estado general de la técnica no
considerado como particularmente relevante.
"E" solicitud de patente o patente anterior pero publicada en la
fecha de presentación internacional o en fecha posterior.
"L" documento que puede plantear dudas sobre una reivindicación "X"
de prioridad o que se cita para determinar la fecha de
publicación de otra cita o por una razón especial (como la
indicada).
"O" documento que se refiere a una divulgación oral, a una "Y"
utilización, a una exposición o a cualquier otro medio.
"P" documento publicado antes de la fecha de presentación
internacional pero con posterioridad a la fecha de prioridad
reivindicada.
"&"
Fecha en que se ha concluido efectivamente la búsqueda internacional.
05/02/2015
Nombre y dirección postal de la Administración encargada de la
búsqueda internacional
OFICINA ESPAÑOLA DE PATENTES Y MARCAS
Paseo de la Castellana, 75 - 28071 Madrid (España)
Nº de fax: 91 349 53 04
Formulario PCT/ISA/210 (segunda hoja) (Julio 2009)
anexo
documento ulterior publicado con posterioridad a la fecha de
presentación internacional o de prioridad que no pertenece al
estado de la técnica pertinente pero que se cita por permitir
la comprensión del principio o teoría que constituye la base
de la invención.
documento particularmente relevante; la invención
reivindicada no puede considerarse nueva o que implique
una actividad inventiva por referencia al documento
aisladamente considerado.
documento particularmente relevante; la invención
reivindicada no puede considerarse que implique una
actividad inventiva cuando el documento se asocia a otro u
otros documentos de la misma naturaleza, cuya combinación
resulta evidente para un experto en la materia.
documento que forma parte de la misma familia de patentes.
Fecha de expedición del informe de búsqueda internacional.
06 de febrero de 2015 (06/02/2015)
Funcionario autorizado
G. Esteban García
Nº de teléfono 91 3495425
INFORME DE BÚSQUEDA INTERNACIONAL
Solicitud internacional nº
PCT/ES2014/000193
C (Continuación).
Categoría *
DOCUMENTOS CONSIDERADOS RELEVANTES
Documentos citados, con indicación, si procede, de las partes relevantes
Relevante para las
reivindicaciones nº
A
WILLAUER, H.D. et al. “Investigation of aqueous biphasic systems
for the separation of lignins from cellulose in the paper pulping process”.
Journal of Chromatography B 2000, Volumen 743, páginas 127-135.
Ver página 127, resumen.
1-11
A
WO 94/12619 A1 (NOVO NORDISK A/S) 09.06.1994,
página 9, líneas 8-9; página 10, líneas 14-35; reivindicación 19.
1-11
A
MOSCOSO, F. et al. “Technoeconomic assessment of phenanthrene
degradation by Pseudomonas stutzeri CECT 930 in a batch bioreactor”.
Bioresource Technology 2012, Volumen 104, páginas 81-89.
[Disponible en línea el 21.10.2011]. Ver página 81, resumen;
página 82, apartados 2.1, 2.2; página 88, apartado 4.
1-11
A
MOSCOSO, F. et al. “Efficient PAHs biodegradation by a bacterial
consortium at flask and bioreactor scale”. Bioresource Technology 2012,
Volumen 119, páginas 270-276. [Disponible en línea el 26.05.2012].
Ver página 270, resumen; página 271, apartados 2.1-2.2;
página 275, apartado 4.
1-11
Formulario PCT/ISA/210 (continuación de la segunda hoja) (Julio 2009)
INFORME DE BÚSQUEDA INTERNACIONAL
Informaciones relativas a los miembros de familias de patentes
Solicitud internacional nº
PCT/ES2014/000193
Documento de patente citado
en el informe de búsqueda
Fecha de
Publicación
Miembro(s) de la
familia de patentes
Fecha de
Publicación
WO 2011/119112 A1
29.09.2011
-------------------------------------------------------WO 94/12619 A1
----------------------09.06.1994
--------------------------------------------------------
-----------------------
US2014318201 A1
NZ602555 A
JP2013527104 A
MX2012010879 A
KR20130055564 A
US2013091912 A1
EA201270731 A1
EA201270731 A8
CN102985392 A
CN102985392B B
SG184157 A1
EP2550244 A1
CA2793923 A1
AU2011230001 A1
GB2478929 A
GB2478929 B
-----------------------FI952646 A
US5700769 A
JPH08503370 A
EP0672125 A1
CA2150564 A1
BR9307574 A
------------------------
30.10.2014
26.09.2014
27.06.2013
07.02.2013
28.05.2013
18.04.2013
29.03.2013
30.09.2014
20.03.2013
01.10.2014
30.10.2012
30.01.2013
29.09.2011
18.10.2012
28.09.2011
14.08.2013
--------------31.05.1995
23.12.1997
16.04.1996
20.09.1995
09.06.1994
15.06.1999
---------------
PCT/ISA/210 (Anexo – familias de patentes) (Julio 2009)
INFORME DE BÚSQUEDA INTERNACIONAL
Solicitud internacional nº
PCT/ES2014/000193
CLASIFICACIONES DE INVENCIÓN
C02F9/14 (2006.01)
C02F3/02 (2006.01)
C02F3/34 (2006.01)
C02F1/54 (2006.01)
C02F101/30 (2006.01)
Formulario PCT/ISA/210 (hoja adicional) (Julio 2009)
International application No.
INTERNATIONAL SEARCH REPORT
PCT/ES2014/000193
A. CLASSIFICATION OF SUBJECT MATTER
See extra sheet
According to International Patent Classification (IPC) or to both national classification and IPC
B. FIELDS SEARCHED
Minimum documentation searched (classification system followed by classification symbols)
C02F
Documentation searched other than minimum documentation to the extent that such documents are included in the fields searched
Electronic data base consulted during the international search (name of data base and, where practicable, search terms used)
INVENES,EPODOC,WPI,TXTE,BIOSIS,EMBASE,NPL,XPESP,XPESP2,PUBMED,GOOGLE SCHOLAR
C. DOCUMENTS CONSIDERED TO BE RELEVANT
Category*
Citation of document, with indication, where appropriate, of the relevant passages
Relevant to claim No.
A
ÁLVAREZ, M.S. et al. “Novel physico-biological treatment for
the remediation of textile dyes-containing industrial effluents”.
Bioresource Tecnhnology 2013, Volume 146, pages 689-695.
[Available online 06.08.2013]. See page 689, abstract;
page 694, conclusion; page 690, parts 2.2, 2.3, 2.5; page 691,
part 2.7; page 692, part 3.2; pages 693-694, part 3.3.
1-11
A
WO 2011/119112 A1 (BIOMAX TECHNOLOGIES PTE LTD)
29.09.2011, page 2, line 31-page 3, line 5; page 4, lines 1-5;
page 20, line 28-page 21, line 16.
1-11
A
DURAN, C. et al. “Cloud-Point Extraction of Rhodamine 6 by
Using Triton X-100 as the Non-Ionic Surfactant”. Journal of AOAC
International 2011, Volume 94, Number 1, pages 286-292.
See page 127, abstract.
1-11
 Further documents are listed in the continuation of Box C.
 See patent family annex.
*
Special categories of cited documents:
"T"
"A" document defining the general state of the art which is not
considered to be of particular relevance.
"E" earlier document but published on or after the international
filing date
"L" document which may throw doubts on priority claim(s) or "X"
which is cited to establish the publication date of another
citation or other special reason (as specified)
"O" document referring to an oral disclosure use, exhibition, or "Y"
other means.
"P" document published prior to the international filing date but
later than the priority date claimed
"&"
Date of the actual completion of the international search
later document published after the international filing date or
priority date and not in conflict with the application but cited
to understand the principle or theory underlying the
invention
document of particular relevance; the claimed invention
cannot be considered novel or cannot be considered to
involve an inventive step when the document is taken alone
document of particular relevance; the claimed invention
cannot be considered to involve an inventive step when the
document is combined with one or more other documents ,
such combination being obvious to a person skilled in the art
document member of the same patent family
Date of mailing of the international search report
05/02/2015
Name and mailing address of the ISA/
OFICINA ESPAÑOLA DE PATENTES Y MARCAS
Paseo de la Castellana, 75 - 28071 Madrid (España)
Facsimile No.: 91 349 53 04
Form PCT/ISA/210 (second sheet) (July 2009)
(06/02/2015)
Authorized officer
G. Esteban García
Telephone No. 91 3495425
INTERNATIONAL SEARCH REPORT
International application No.
PCT/ES2014/000193
C (continuation).
Category *
DOCUMENTS CONSIDERED TO BE RELEVANT
Citation of documents, with indication, where appropriate, of the relevant passages
Relevant to claim No.
A
WILLAUER, H.D. et al. “Investigation of aqueous biphasic systems
for the separation of lignins from cellulose in the paper pulping process”.
Journal of Chromatography B 2000, Volume 743, pages 127-135.
See page 127, abstract.
1-11
A
WO 94/12619 A1 (NOVO NORDISK A/S) 09.06.1994,
page 9, lines 8-9; page 10, lines 14-35; claim 19.
1-11
A
MOSCOSO, F. et al. “Technoeconomic assessment of phenanthrene
degradation by Pseudomonas stutzeri CECT 930 in a batch bioreactor”.
Bioresource Technology 2012, Volume 104, pages 81-89.
[Available online 21.10.2011]. See page 81, abstract;
page 82, parts 2.1, 2.2; page 88, part 4.
1-11
A
MOSCOSO, F. et al. “Efficient PAHs biodegradation by a bacterial
consortium at flask and bioreactor scale”. Bioresource Technology 2012,
Volumen119, pages 270-276. [Available online 26.05.2012].
See page 270, abstract; page 271, parts 2.1-2.2; page 275, part 4.
1-11
Form PCT/ISA/210 (continuation of second sheet) (July 2009)
INTERNATIONAL SEARCH REPORT
International application No.
PCT/ES2014/000193
Information on patent family members
Patent document cited
in the search report
Publication
date
Patent family
member(s)
Publication
date
WO 2011/119112 A1
29.09.2011
-------------------------------------------------------WO 94/12619 A1
----------------------09.06.1994
--------------------------------------------------------
-----------------------
US2014318201 A1
NZ602555 A
JP2013527104 A
MX2012010879 A
KR20130055564 A
US2013091912 A1
EA201270731 A1
EA201270731 A8
CN102985392 A
CN102985392B B
SG184157 A1
EP2550244 A1
CA2793923 A1
AU2011230001 A1
GB2478929 A
GB2478929 B
-----------------------FI952646 A
US5700769 A
JPH08503370 A
EP0672125 A1
CA2150564 A1
BR9307574 A
------------------------
30.10.2014
26.09.2014
27.06.2013
07.02.2013
28.05.2013
18.04.2013
29.03.2013
30.09.2014
20.03.2013
01.10.2014
30.10.2012
30.01.2013
29.09.2011
18.10.2012
28.09.2011
14.08.2013
--------------31.05.1995
23.12.1997
16.04.1996
20.09.1995
09.06.1994
15.06.1999
---------------
Form PCT/ISA/210 (patent family annex) (July 2009)
INTERNATIONAL SEARCH REPORT
International application No.
PCT/ES2014/000193
CLASSIFICATION OF SUBJECT MATTER
C02F9/14 (2006.01)
C02F3/02 (2006.01)
C02F3/34 (2006.01)
C02F1/54 (2006.01)
C02F101/30 (2006.01)
Form PCT/ISA/210 (extra sheet) (July 2009)
TRATADO DE COOPERACIÓN EN MATERIA DE PATENTES
Remitente: LA ADMINISTRACIÓN ENCARGADA
DE LA BÚSQUEDA INTERNACIONAL
PCT
Destinatario:
UNIVERSIDADE DE VIGO
CAMPUS UNIVERSITARIO, s/n
C. P. 36310 VIGO (PONTEVEDRA)
- ESPAÑA -
OPINIÓN ESCRITA DE LA ADMINISTRACIÓN
ENCARGADA DE LA BÚSQUEDA INTERNACIONAL
(Regla 43bis.1 del PCT)
Fecha de expedición
(día/mes/año)
06 FEBRERO 2015
(06.02.2015)
Referencia del expediente del solicitante o del mandatario
Solicitud internacional Nº
PCT/ES2014/000193
PARA CONTINUAR LA TRAMITACIÓN
Véase el punto 2
Fecha de prioridad (día/mes/año)
Fecha de presentación internacional
(día/mes/año)
10 NOVIEMBRE 2014 (10.11.2014)
14 NOVIEMBRE 2013
(14.11.2013)
Clasificación Internacional de Patentes (CIP) o a la vez clasificación nacional e CIP
VER HOJA ADICIONAL
Solicitante
UNIVERSIDADE DE VIGO.
1.
2.
La presente opinión contiene indicaciones relativas a los puntos siguientes:



Recuadro I
Base de la opinión
Recuadro II
Prioridad
Recuadro III


Recuadro IV
No formulación de opinión sobre la novedad, la actividad inventiva y la aplicación
industrial
Falta de unidad de invención



Recuadro VI
Declaración motivada según la Regla 43bis.1.a)i) sobre la novedad, la actividad
inventiva y la aplicación industrial; citas y explicaciones en apoyo de esta declaración
Ciertos documentos citados
Recuadro VII
Defectos en la solicitud internacional
Recuadro VIII
Observaciones relativas a la solicitud internacional
Recuadro V
CONTINUACIÓN DE LA TRAMITACIÓN
Si se hace una petición de examen preliminar internacional, esta opinión se considerará como una opinión escrita
de la Administración encargada del examen preliminar internacional ("IPEA") salvo en aquellos casos en los que el
solicitante elija una Administración distinta a ésta y, la IPEA elegida haya notificado a la Oficina Internacional
según lo previsto en la Regla 66.1 bis(b) que las opiniones escritas de esta Administración encargada de la
búsqueda internacional no serán consideradas como tales.
Si esta opinión es, como se indica más arriba, considerada como una opinión escrita de la IPEA, se invita al
solicitante a que presente ante la IPEA una respuesta por escrito junto con modificaciones, en su caso, antes de la
expiración del plazo de 3 meses a contar desde la fecha de envío del formulario PCT/ISA/220 o antes de la
expiración del plazo de 22 meses a contar desde la fecha de prioridad, aplicándose el plazo que expire más tarde.
Para otras opciones, consultar el formulario PCT/ISA/220.
Nombre y dirección postal de la Administración
encargada de la búsqueda internacional
OFICINA ESPAÑOLA DE PATENTES Y MARCAS
Paseo de la Castellana
Fecha en que se ha
efectivamente esta opinión
concluido Funcionario autorizado
G. Esteban García
5 febrero 2015 (05.02.2015)
Nº de fax: 91 349 53 04
Formulario PCT/ISA/237 (Primera página)(Julio 2011)
Nº de teléfono: 91 349 54 25
Solicitud internacional Nº
OPINIÓN ESCRITA DE LA ADMINISTRACIÓN
ENCARGADA DE LA BÚSQUEDA INTERNACIONAL
Recuadro I.
PCT/ES2014/000193
Base de la opinión
1. Por lo que respecta al idioma esta opinión se ha establecido sobre la base de:

la solicitud internacional en el idioma en el cual se depositó

una traducción de la solicitud original al , que es el idioma de una traducción proporcionada a los fines de la
búsqueda internacional (según las Reglas 12.3.a) y 23.1.b)).
2.
Esta opinión se ha establecido teniendo en cuenta la rectificación de un error evidente autorizado por o
notificado a esta Administración según la Regla 91 (Regla 43bis.1 a)).
3. En lo que se refiere a las secuencias de nucleótidos y/o de aminoácidos divulgadas en la solicitud internacional y
necesarias para la invención reivindicada, esta opinión se ha establecido sobre la base de una lista de secuencias
presentada o entregada:
a. Medios


en papel
en formato electrónico
b. Cuando



4.

en la solicitud internacional tal y como se presentó
presentado junto con la solicitud internacional en formato electrónico
presentado posteriormente a esta Administración a los fines de la búsqueda
Además, en caso de que se haya presentado más de una versión o copia de una lista de secuencias y/o tabla
relacionada con ella, se ha entregado la declaración requerida de que la información contenida en las copias
subsiguientes o adicionales es idéntica a la de la solicitud tal y como se presentó o no va más allá de lo presentado
inicialmente.
5. Comentarios adicionales:
Formulario PCT/ISA/237 (Recuadro I)(Julio 2011)
Solicitud internacional Nº
OPINIÓN ESCRITA DE LA ADMINISTRACIÓN
ENCARGADA DE LA BÚSQUEDA INTERNACIONAL
Recuadro V.
PCT/ES2014/000193
Declaración motivada según la Regla 43bis.1.a)i) sobre la novedad, la actividad inventiva y la
aplicación industrial; citas y explicaciones en apoyo de esta declaración
1. Declaración
Novedad
Reivindicaciones
Reivindicaciones
1-11
Sí
NO
Actividad inventiva
Reivindicaciones
Reivindicaciones
1-11
Sí
NO
Aplicación industrial
Reivindicaciones
Reivindicaciones
1-11
Sí
NO
2. Citas y explicaciones
Doc.
Número Publicación o Identificación
Fecha Pub.
D01
ÁLVAREZ, M.S. et al. Bioresource Technology 2013,
Vol. 146, pp. 689-695
WO 2011/119112 A1 (BIOMAX TECHNOLOGIES PTE LTD)
DURAN, C. et al. Journal of AOAC International 2011, Vol. 94,
Nº1, pp. 286-292.
WILLAUER, R.D. et al. Journal of Chromatography B 2000, Vol.
743, pp. 127-135.
06.08.2013
D02
D03
D04
29.09.2011
2011
2000
El objeto de la invención es un procedimiento en varias etapas para la eliminación de
compuestos orgánicos presentes en aguas residuales que comprende el tratamiento de la corriente
acuosa en un reactor biológico, la separación de la materia biológica y la extracción de los
compuestos orgánicos no metabolizados; y el uso del procedimiento anterior en aguas residuales
de origen urbano o industrial, y provenientes del tratamiento de suelos contaminados.
El documento D01, que se considera el estado de la técnica más cercano a la invención, divulga
una estrategia de remediación para la eliminación de pigmentos de una corriente de aguas
contaminadas procedente de la industria textil que consiste en un proceso biológico y físico
secuencial (ver página 689, resumen; página 694, conclusión). Para ello se estudiaron diversos
sistemas acuosos bifásicos que contenían un surfactante no iónico, como puede ser Tween 20 ó
Tween 80, y una sal orgánica potásica (citrato, oxalato y tartrato), que actúa como agente de
“salting out”, y su aplicación para la extracción de pigmentos orgánicos sintéticos (ver página
691, apartado 2.7; página 692, apartado 3.2). Así, el procedimiento de remediación divulgado en
este documento comienza con una primera etapa, en la que el efluente que contiene Reactive
Black 5 (Reactivo negro 5) y Acid Black 48 (Ácido negro 48), pigmentos de tipo diazo y
antraquinona, respectivamente, se somete a degradación biológica por tratamiento con
Anoxybacillus flavithermus, cultivado en un medio que contiene tripticasa, levadura, cloruro
sódico y agar (ver página 690, apartados 2.2 y 2.3). Una vez separadas las células mediante
centrifugación (ver página 690, apartado 2.5), se utilizó un sistema bifásico (Tween 20+citrato
potásico) para extraer la carga contaminante no degradada (ver páginas 693-694, apartado 3.3).
Continúa en página siguiente…
Formulario PCT/ISA/237 (Recuadro V)(Julio 2011)
Solicitud internacional Nº
OPINIÓN ESCRITA DE LA ADMINISTRACIÓN
ENCARGADA DE LA BÚSQUEDA INTERNACIONAL
Continuación Recuadro V.
Continuación
PCT/ES2014/000193
Declaración motivada según la Regla 43bis.1.a)i) sobre la novedad, la actividad
inventiva y la aplicación industrial; citas y explicaciones en apoyo de esta declaración.
La diferencia existente entre el procedimiento descrito en el documento D01 y el de la invención
es que, en este último, el medio de cultivo de los microorganismos comprende sales y un
surfactante no iónico.
El documento D02 divulga un procedimiento para el tratamiento de aguas residuales, que
comprende una etapa de contacto entre los residuos orgánicos y al menos un microorganismo, que
puede ser Bacillus sp. o Pseudomonas sp. (ver página 2, línea 31-página 3, línea 5).
Adicionalmente, se pueden añadir al residuo orgánico determinados aditivos, como surfactantes
(sorbitán, polisorbatos, etc.) o nutrientes, como sales inorgánicas (sulfato magnésico, fosfato
sódico o potásico, cloruro sódico o cálcico y nitrato amónico), con el fin de favorecer la
conversión (ver página 4, líneas 1-5; página 20, línea 28-página 21, línea 16).
El documento D03 divulga la aplicación de la técnica de extracción “cloud point” (punto de
turbidez) utilizando el surfactante no iónico Triton X-100 para eliminar un pigmento altamente
tóxico, como es la rodamina 6G de agua y de aguas residuales (ver página 286, resumen). El
documento divulga un estudio sobre los efectos de diferentes parámetros analíticos en la
eficiencia de la extracción, como son el pH, la concentración de Triton X-100 y de sales, la
temperatura de equilibrio y el tiempo de incubación.
El documento D04 divulga la aplicación de un sistema bifásico acuoso de extracción al proceso de
fabricación de papel, lo que incluye un estudio sobre la distribución de las diversas fracciones de
lignina y celulósicas en dicho sistema, así como el efecto de la temperatura en la composición del
sistema y la partición de solutos (ver página 127, resumen). Los sistemas bifásicos estudiados se
prepararon a partir de soluciones de polietilénglicol (PEG)-2000 y concentraciones crecientes de
sales, como son carbonato potásico, sulfato amónico e hidróxido sódico.
Los documentos citados muestran sólo el estado de la técnica del campo al que pertenece la
invención. Ninguno de ellos, tomado solo o en combinación con los otros, divulga ni contiene
sugerencia alguna que pudiera dirigir al experto en la materia hacia la invención recogida en la
reivindicación independiente 1, que se refiere a un procedimiento en varias etapas para la
eliminación de compuestos orgánicos presentes en aguas residuales que comprende la
utilización de un surfactante no iónico y sales en la etapa de tratamiento biológico y en la
extracción de los compuestos orgánicos no metabolizados; y, por lo tanto, tampoco hacia el uso
del procedimiento anterior en aguas residuales de origen urbano o industrial, y provenientes del
tratamiento de suelos contaminados (reivindicaciones independientes 10 y 11).
Por lo tanto, se considera que el objeto de las reivindicaciones 1-11 reúne los requisitos de
novedad, actividad inventiva y aplicación industrial recogidos en los Artículos 33(2), (3) y (4)
PCT.
Formulario PCT/ISA/237 (Recuadro V continuación )(Julio 2011)
Solicitud internacional Nº
OPINIÓN ESCRITA DE LA ADMINISTRACIÓN
ENCARGADA DE LA BÚSQUEDA INTERNACIONAL
CLASIFICACIÓN OBJETO DE LA SOLICITUD
C02F9/14 (2006.01)
C02F3/02 (2006.01)
C02F3/34 (2006.01)
C02F1/54 (2006.01)
C02F101/30 (2006.01)
Formulario PCT/ISA/237 (Hoja Adicional)(Julio 2011)
PCT/ES2014/000193
Nota Informativa
Para recuperar los documentos citados en el informe, deberá introducir los siguientes datos en la
página web indicada más abajo:
Página web: https://tramites.oepm.es/pater/citados/
Número de expediente: PCT_ES2014_000193
Contraseña: 3ec056eb
Dichos documentos estarán disponibles para su descarga al menos un año.
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Si tiene problemas para
[email protected]
la
descarga
de
los
documentos,
por
favor
contacte
con
Nota: Los documentos de literatura no patente citados de carácter técnico o científico pueden
estar sujetos a protección por derechos de autor y/o cualquier otra protección de obras escritas
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1109P3 ( 02.12 )
1
de 1
ANNEX 15
FIGURES AND TABLES.
Annex
1.2
Pseudomonas stutzeri
Adapted Pseudomonas stutzeri
0.9
0.9
0.6
0.6
0.3
0.3
0.0
Absorbance
Absorbance
1.2
0.0
0
2
4
6
8 warneri 0
Staphylococcus
2
4
6
8
Shewanella
oneidensis
0.9
0.9
0.6
0.6
0.3
0.3
0.0
0.0
0
2
4
6
8
Consortium
C26b 0
2
4
Days
6
8
10
X Data
0.9
0.6
0.3
0.0
0
2
4
Days
6
8
10
Figure A.1. Microbial growth at MIC concentration of C2PyC2SO4 in RM (∆) and MM (○) of Ps. stutzeri,
adapted Ps. stutzeri, St. warneri, S. oneidensis and Consortium C26b
1
Annex
1.2
1.2
Absorbance
0.9
0.9
0.6
0.6
0.3
0.3
0.0
Absorbance
Anoxybacillus flavithermus
Thermus thermophilus
0.0
0
2
4
6
8
0
Phanerochaete
chrysosporium
2
6Trametes 8versicolor
4
0.9
0.9
0.6
0.6
0.3
0.3
0.0
0.0
0
2
4
6
8
Halobacterium
salinarum
0
2
4
Days
6
8
10
0.9
0.6
0.3
0.0
0
2
4
Days
6
8
10
Figure A.2. Microbial growth at MIC concentration of C2PyC2SO4 in RM of T. thermophilus, A.
flavithermus, P. chrysosporium, Tr. versicolor and H. salinarum
2
Annex
Adapted Pseudomonas stutzeri
Pseudomonas stutzeri
1.2
0.9
0.9
0.6
0.6
0.3
0.3
0.0
0
2
4
Staphylococcus
6
8 warneri 0
2
Shewanella
oneidensis
6
8
4
0.0
0.9
0.9
0.6
0.6
0.3
0.3
0.0
Absorbance
Absorbance
1.2
0.0
0
2
4
6
Consortium
C26b0
8
2
4
6
8
10
Days
X Data
0.9
0.6
0.3
0.0
0
2
4
6
8
10
Days
Figure A.3. Microbial growth at MIC concentration of C2C1imC1SO4 in RM (∆) and MM (○) of Ps. stutzeri,
adapted Ps. stutzeri, St. warneri, S. oneidensis and Consortium C26b
3
Annex
Thermus thermophilus
Anoxybacillus flavithermus
1.2
0.9
0.9
0.6
0.6
0.3
0.3
0.0
Absorbance
Absorbance
1.2
0.0
0
2
4
6
8
0
Phanerochaete
chrysosporium
2
6Trametes 8versicolor
4
0.9
0.9
0.6
0.6
0.3
0.3
0.0
0.0
0
2
4
6
8
Halobacterium
salinarum
2
4
6
8
10
Days
0.9
0.6
0.3
0.0
0
2
4
Days
6
8
10
Figure A.4. Microbial growth at MIC concentration of C2C1imC1SO4 in RM of T. thermophilus, A.
flavithermus, P.. chrysosporium, Tr. versicolor and H. salinarum
4
Annex
1.2
1.2
Adapted Pseudomonas stutzeri
0.9
0.9
0.6
0.6
0.3
0.3
0.0
Absorbance
Absorbance
Pseudomonas stutzeri
0.0
0
2
Staphylococcus
4
warneri
6
8
0
2
Shewanella
4
oneidensis
6
8
0.9
0.9
0.6
0.6
0.3
0.3
0.0
0.0
0Consortium
2
C26b
4
6
8
0
2
4
Days
6
8
10
X Data
0.9
0.6
0.3
0.0
0
2
4
6
8
10
Figure A.5. Microbial growth at MIC concentration of C2C1imC2SO4 in RM (∆) and MM (○) of Ps. stutzeri,
adapted Ps. stutzeri, St. warneri, S oneidensis and Consortium C26b
5
Annex
1.2
1.2
Anoxybacillus flavithermus
0.9
0.9
0.6
0.6
0.3
0.3
0.0
Absorbance
Absorbance
Thermus thermophilus
0.0
0Phanerochaete
2
4
6
chrysosporium
8
0Trametes 2versicolor 4
6
8
0.9
0.9
0.6
0.6
0.3
0.3
0.0
0.0
0Halobacterium
2
4
salinarum
6
8
0
2
4
Days
6
8
10
0.9
0.6
0.3
0.0
0
2
4
Days
6
8
10
Figure A.6. Microbial growth at MIC concentration of C2C1imC2SO4 in RM of T. thermophilus, A.
flavithermus, P.. chrysosporium, Tr. versicolor and H. salinarum
6
Annex
0.8
0.8
Absorbance
0.6
0.6
0.4
0.4
0.2
0.2
0.0
Absorbance
Adapted Pseudomonas stutzeri
Pseudomonas stutzeri
0.0
0
2
4 Staphylococcus
6
warneri
0
2
4
Shewanella6 oneidensis
0.6
0.6
0.4
0.4
0.2
0.2
0.0
0.0
0
2
4
6
Consortium
C26b
2
4
Days
6
8
X Data
0.6
0.4
0.2
0.0
0
2
4
Days
6
8
Figure A.7. Microbial growth at MIC concentration of P4441C1SO4 in RM (∆) and MM (○) of Ps. stutzeri,
adapted Ps. stutzeri, St. warneri, S. oneidensis and Consortium C26b
7
Annex
0.6
0.6
Anoxybacillus flavithermus
0.4
0.4
0.2
0.2
0.0
Absorbance
Absorbance
Thermus thermophilus
0.0
0
2
4
6
80
Phanerochaete
chrycosporium
2
4
6 versicolor 8
Trametes
0.4
0.4
0.2
0.2
0.0
0.0
0
2
4
Days
6
0
2
4
Days
6
8
Figure A.8. Microbial growth at MIC concentration of P4441C1SO4 in RM of T. thermophilus, A.
flavithermus, P.. chrysosporium and Tr. versicolor
8
Annex
Table A.1. Binodal data for {Triton-X 100 (1) + C2C1imC2SO4 (2) + + H2O (3)} at several temperatures.
298.15 K
313.15 K
323.15 K
333.15 K
100 w1
100 w2
100 w1
100 w2
100 w1
100 w2
100 w1
100 w2
61.66
33.49
76.72
18.96
80.50
14.98
85.16
9.45
59.12
32.11
73.00
18.04
76.17
14.17
81.22
9.02
56.65
37.67
72.63
24.05
72.65
24.25
81.84
14.61
53.54
35.60
67.15
22.24
65.38
21.82
75.66
13.50
52.47
42.80
62.41
33.51
58.19
38.97
73.11
24.20
49.22
38.52
55.68
29.89
48.99
32.81
64.35
21.30
47.18
47.18
58.29
38.98
52.79
44.09
63.39
33.96
43.21
43.21
50.44
33.73
43.77
36.56
52.01
27.86
37.66
56.28
51.87
42.45
48.15
48.42
58.28
39.24
33.89
50.64
46.00
37.64
39.27
39.49
46.85
31.54
33.12
61.29
47.09
47.65
38.39
57.14
52.61
44.52
28.96
53.59
40.53
41.01
30.43
45.29
41.28
34.93
24.13
69.81
38.01
57.36
33.57
63.00
48.28
48.74
20.71
60.00
31.34
47.28
26.05
48.83
36.71
37.06
18.65
73.91
30.98
64.38
28.91
66.83
38.04
58.63
16.18
64.13
25.17
52.31
22.00
50.86
27.89
42.99
9.09
82.14
28.48
66.52
23.57
71.58
33.86
62.60
7.84
70.82
22.87
53.41
17.75
53.90
24.16
44.67
4.19
87.13
23.26
71.46
9.42
83.18
28.98
67.52
3.65
75.86
18.63
57.24
7.59
67.03
20.26
47.20
9.66
85.41
4.51
87.09
23.92
72.05
7.17
63.40
3.49
67.47
16.91
50.94
4.67
86.52
14.93
79.69
3.78
69.98
11.03
58.89
9.12
81.87
7.00
62.86
4.24
82.69
3.87
75.45
Standard uncertainties are ur(w) = ± 0.02, u(T) = ± 0.01 K; u(P) = ± 2 kPa.
9
Annex
Table A.2. Binodal data for {Triton-X 102 (1) + C2C1imC2SO4 (2) + + H2O (3)} at several temperatures.
298.15 K
313.15 K
323.15 K
333.15 K
100 w1
100 w2
100 w1
100 w2
100 w1
100 w2
100 w1
100 w2
56.30
37.78
71.75
24.03
79.75
16.02
80.95
14.93
54.32
36.45
67.79
22.70
76.96
15.46
76.95
14.19
52.02
42.49
63.13
33.56
72.46
24.61
73.50
23.99
49.03
40.05
57.13
30.37
66.33
22.53
65.61
21.42
47.35
47.07
58.02
38.87
56.79
40.17
62.50
34.78
44.52
44.26
52.02
34.85
49.07
34.71
53.47
29.76
37.48
56.59
52.42
43.14
52.57
43.10
58.51
39.10
34.36
51.87
46.86
38.57
45.60
37.60
49.18
32.86
32.71
60.39
49.36
47.26
49.03
48.29
53.65
43.94
29.59
54.63
43.18
41.34
41.38
40.76
44.31
36.30
23.05
70.07
38.39
57.41
38.22
57.73
48.81
48.69
20.75
63.08
32.97
49.31
31.97
48.29
40.03
39.94
9.01
82.59
33.42
62.12
33.68
62.73
38.56
58.15
8.05
73.79
28.42
52.82
27.48
51.20
31.19
47.03
4.62
85.77
28.76
66.58
29.67
66.23
33.42
62.38
4.22
78.19
24.31
56.29
24.63
53.31
28.94
53.75
23.63
71.12
24.15
71.56
28.97
67.40
19.94
60.03
19.45
57.64
22.55
52.45
9.37
84.27
9.24
84.61
24.01
71.68
7.73
69.51
7.70
70.56
19.37
57.83
4.56
86.76
4.44
86.94
14.14
80.64
3.93
74.67
3.80
74.34
11.26
64.21
9.56
84.69
7.71
68.26
4.79
86.86
4.09
74.09
Standard uncertainties are ur(w) = ± 0.02, u(T) = ± 0.01 K; u(P) = ± 2 kPa.
10
Annex
Table A.3. Experimental tie–lines in mass percentage for {Surfactant (1) + C2C1imC2SO4 (2) + H2O (3)} at
several temperatures.
Surfactant-rich phase
I
100 w1
I
100 w2
Ionic liquid-rich phase
II
100 w1
Feed
II
100 w 2
100w1
100w2
Triton X-100 (1) + C2C1imC2SO4 (2) + H2O (3) T = 298.15 K
58.19
33.06
1.87
79.35
25.74
59.43
59.79
35.01
3.18
84.98
27.59
63.66
55.86
34.14
3.43
75.30
25.36
58.16
59.50
33.64
2.34
84.53
27.22
62.56
Triton X-102 (1) + C2C1imC2SO4 (2) + H2O (3) T = 298.15 K
54.74
40.37
3.44
85.97
27.55
64.22
53.83
39.24
2.66
84.11
27.18
62.92
51.97
38.14
2.18
82.34
26.17
61.27
Triton X-100 (1) + C2C1imC2SO4 (2) + H2O (3) T = 313.15 K
75.86
20.25
3.64
84.39
22.34
68.05
72.89
20.05
3.76
82.48
21.766
66.05
69.90
21.12
2.22
79.64
21.36
63.72
65.80
23.30
2.18
76.41
19.99
61.87
59.66
27.11
1.90
73.89
19.89
59.66
Triton X-102 (1) + C2C1imC2SO4 (2) + H2O (3) T = 313.15 K
69.82
26.19
3.18
86.28
27.59
64.36
69.54
25.10
2.38
83.79
26.71
62.64
65.65
25.47
2.05
81.95
26.20
61.15
62.55
27.45
1.93
79.17
25.36
59.42
58.66
29.93
1.99
78.00
24.94
58.09
Triton X-100 (1) + C2C1imC2SO4 (2) + H2O (3) T = 323.15 K
79.29
16.81
4.15
83.94
27.21
63.64
78.63
15.29
2.59
81.64
26.38
61.22
76.79
15.16
2.24
77.82
25.45
58.96
74.92
15.81
2.56
73.08
23.93
56.28
Triton X-102 (1) + C2C1imC2SO4 (2) + H2O (3) T = 323.15 K
79.55
17.49
2.79
85.19
27.32
63.59
78.01
16.55
1.87
82.57
26.57
61.82
75.98
16.63
2.21
79.52
26.62
59.14
70.22
20.18
3.20
75.96
25.00
58.29
Triton X-100 (1) + C2C1imC2SO4 (2) + H2O (3) T = 333.15 K
83.48
11.69
2.49
86.32
27.54
63.71
82.96
10.73
3.26
81.34
26.09
61.00
78.36
10.38
2.15
76.67
24.74
57.87
11
Annex
73.66
15.68
1.93
74.21
23.68
56.49
69.02
22.12
2.99
69.20
23.08
53.73
Triton X-102 (1) + C2C1imC2SO4 (2) + H2O (3) T = 333.15 K
12
81.14
21.56
4.27
85.89
28.36
64.25
78.70
16.31
3.74
83.53
27.44
62.47
76.23
16.87
3.29
80.28
27.22
60.51
73.98
17.15
2.86
79.00
26.17
59.09
Annex
Table A.4. Binodal data for {Triton X-100 (1) + N1112OHCl (2) +H2O (3)} at several temperatures.
T = 298.15 K
T = 313.15 K
T = 333.15 K
T = 323.15 K
100 w2
100 w1
100 w2
100 w1
100 w2
100 w1
100 w2
100 w1
77.41
1.42
75.14
0.19
76.27
1.01
76.09
1.06
74.51
0.32
74.89
0.90
71.84
8.52
70.08
9.58
71.34
8.31
71.60
7.11
70.02
0.51
68.01
0.57
69.71
0.28
69.37
0.68
64.88
16.58
64.89
16.36
65.61
16.76
64.89
16.47
64.47
0.75
64.38
0.61
64.44
0.31
64.07
0.68
59.32
0.65
59.79
23.84
59.21
0.61
59.38
0.49
59.30
25.87
59.42
0.71
58.71
25.22
57.42
25.48
54.12
0.44
54.17
1.03
54.12
0.39
54.02
0.39
52.34
34.60
52.32
33.62
51.39
34.53
51.54
33.83
49.88
0.57
49.49
0.62
49.55
0.31
49.18
0.59
44.47
0.65
45.31
41.61
45.29
0.12
43.72
42.68
44.24
43.45
44.34
0.51
43.78
44.03
43.53
0.48
39.01
0.49
39.74
0.31
38.74
0.21
39.89
0.44
35.84
53.76
35.55
53.21
36.27
53.82
36.27
51.58
34.83
0.24
35.01
0.50
34.71
0.22
34.98
0.54
29.03
0.65
29.52
0.51
29.83
0.23
29.59
0.58
26.53
64.67
26.85
62.63
28.36
0.52
24.21
65.37
19.51
0.55
19.47
0.61
27.74
64.22
20.02
0.38
18.06
74.81
18.09
73.60
26.53
3.09
19.72
0.24
14.71
0.54
10.07
84.78
25.95
6.63
18.77
72.58
12.84
0.17
9.46
0.41
25.87
11.11
18.75
1.86
12.12
29.55
7.68
31.23
24.75
16.63
18.47
4.69
11.95
7.90
7.20
0.10
23.19
23.32
18.45
8.19
11.73
1.39
6.68
56.28
13
Annex
21.16
31.40
18.16
11.92
11.60
17.40
5.97
13.92
19.33
74.23
17.80
17.37
11.45
2.93
4.97
0.68
18.43
42.68
16.15
22.96
11.27
11.06
4.95
7.41
14.04
53.91
13.92
37.57
10.82
4.72
4.92
3.16
9.67
86.67
12.23
47.31
10.52
43.59
4.61
1.16
7.64
68.50
10.63
83.46
9.38
85.88
4.52
1.80
1.06
97.33
7.65
60.02
6.61
60.55
4.35
0.58
0.97
88.71
1.07
95.25
0.82
95.29
4.13
3.79
0.95
85.15
0.74
85.92
0.88
96.51
0.78
85.47
Standard uncertainties are ur(w) = ± 0.02, u(T) = ± 0.01 K; u(P) = ± 2 kPa.
14
Annex
Table A.5. Binodal data for {Triton X-102 (1) + N1112OHCl (2) +H2O (3)} at several temperatures.
T = 298.15 K
T = 313.15 K
T = 333.15 K
T = 323.15 K
100 w2
100 w1
100 w2
100 w1
100 w2
100 w1
100 w2
100 w1
75.22
0.98
74.97
0.70
75.85
0.97
75.89
0.83
74.14
0.68
74.84
0.49
70.12
7.72
68.14
0.44
70.46
7.90
71.11
4.74
70.01
0.52
67.94
9.77
69.31
0.67
69.54
0.51
64.44
16.94
65.14
15.00
64.73
15.99
64.11
0.64
64.33
0.89
64.51
0.48
64.11
0.64
63.13
16.09
59.59
0.38
59.81
0.32
59.04
0.78
59.21
0.66
58.65
25.04
58.08
24.77
58.55
24.88
57.31
24.75
54.22
0.34
54.48
0.72
53.87
0.64
54.11
0.30
50.63
33.76
52.17
32.67
51.34
34.52
53.35
30.05
49.88
0.57
49.66
0.45
49.24
0.62
49.05
0.72
44.51
0.61
44.55
41.74
45.01
0.40
43.56
0.45
43.16
43.40
44.48
0.37
43.87
43.86
41.51
44.37
39.01
0.49
39.54
0.52
38.21
0.73
40.01
0.32
34.42
0.65
37.13
51.64
37.48
0.49
36.06
51.58
34.41
55.21
35.03
0.48
36.72
4.12
35.22
0.30
29.35
0.33
29.28
0.75
35.26
8.71
30.83
0.29
27.46
64.29
27.65
62.82
35.18
55.36
29.89
0.28
26.12
0.34
19.70
0.22
32.75
13.92
28.84
7.35
23.98
2.64
19.49
0.59
30.11
20.25
28.18
1.88
23.01
6.05
19.18
75.37
28.71
62.48
27.18
11.73
22.11
9.44
18.31
2.63
27.11
27.11
26.64
63.16
21.48
14.32
18.29
4.21
22.97
36.14
26.22
14.77
19.62
19.73
16.34
6.97
15
Annex
19.64
42.73
22.95
24.53
17.81
75.82
15.89
9.95
18.86
74.80
20.59
29.46
17.59
28.23
14.86
13.92
14.06
55.76
18.58
73.78
15.69
36.73
14.42
20.05
9.39
86.92
16.89
40.03
11.22
47.76
13.08
29.71
7.51
69.49
12.92
51.30
10.29
84.53
10.62
41.75
0.96
96.66
9.25
85.76
7.37
60.51
8.67
86.65
0.88
88.45
6.93
64.23
1.05
95.53
5.97
59.69
1.25
95.78
0.94
85.08
1.01
96.76
1.12
85.60
0.88
83.88
Standard uncertainties are ur(w) = ± 0.02, u(T) = ± 0.01 K; u(P) = ± 2 kPa.
16
Annex
Table A.6. Binodal data for {Tween 80 (1) + N1112OHCl (2) +H2O (3)} at several temperatures.
298.15 K
313.15 K
333.15 K
323.15 K
100 w2
100 w1
100 w2
100 w1
100 w2
100 w1
100 w2
100 w1
74.47
0.35
75.00
0.33
70.19
0.34
68.21
0.37
72.27
1.11
71.36
1.30
70.18
0.83
69.53
1.23
71.66
8.16
69.66
0.39
67.97
7.87
67.84
7.81
69.65
0.33
69.50
8.14
64.86
0.36
64.60
0.39
65.82
16.18
64.35
0.40
63.08
15.19
63.23
15.42
64.39
0.36
64.21
15.96
59.59
0.38
59.72
0.41
59.48
0.34
59.47
0.40
58.51
23.00
55.34
25.10
59.46
25.66
57.40
24.83
54.22
0.34
54.75
0.45
54.17
0.34
54.05
0.36
51.00
33.96
50.62
33.00
51.80
34.88
50.58
34.24
50.08
0.37
49.64
0.47
49.49
0.37
49.39
0.38
44.73
0.39
44.44
0.41
45.01
0.40
43.64
0.37
42.92
43.60
40.98
44.67
44.16
43.85
43.25
44.08
39.09
0.41
39.67
0.39
40.28
0.62
39.96
0.37
34.71
54.64
35.14
0.37
38.55
0.39
36.26
53.52
34.67
0.40
33.57
53.99
36.81
4.19
35.13
0.39
29.31
0.37
29.65
0.38
36.23
54.29
31.11
0.57
27.81
63.74
27.51
63.28
35.16
8.64
29.83
0.35
24.38
0.29
19.71
0.37
32.73
14.12
28.10
6.98
22.21
5.35
18.86
0.33
30.90
20.81
27.93
3.27
22.07
2.56
18.74
74.17
29.07
67.24
27.82
65.26
21.74
8.55
17.68
2.03
28.53
28.33
26.92
11.64
20.22
13.46
16.86
4.11
24.63
36.91
25.52
17.28
19.58
19.89
16.36
7.42
20.40
47.19
23.55
24.00
18.28
28.78
15.89
17.32
17
Annex
19.19
78.82
22.29
32.91
18.27
74.36
15.60
10.17
14.10
57.92
19.90
74.46
15.85
36.32
14.46
33.26
9.94
88.96
17.68
41.47
12.58
51.21
14.20
22.83
8.09
72.39
14.04
52.55
10.96
84.59
11.86
46.93
0.99
98.77
9.72
88.07
8.36
64.48
8.68
86.88
0.95
95.23
7.68
69.60
0.69
98.58
6.32
63.27
0.92
98.18
0.66
93.87
1.02
97.80
0.88
93.96
0.96
92.04
Standard uncertainties are ur(w) = ± 0.02, u(T) = ± 0.01 K; u(P) = ± 2 kPa.
18
Annex
Table A.7. Binodal data for {Tween 20 (1) + N1112OHCl (2) +H2O (3)} at several temperatures.
298.15 K
313.15 K
333.15 K
323.15 K
100 w2
100 w1
100 w2
100 w1
100 w2
100 w1
100 w2
100 w1
76.46
0.90
76.61
1.29
75.08
0.34
74.50
0.32
74.50
0.32
75.01
0.32
73.75
0.85
74.29
1.00
71.57
7.81
70.19
7.87
70.20
0.33
69.67
0.31
69.67
0.31
69.72
0.33
69.07
7.72
68.60
7.52
64.59
16.28
66.39
16.09
64.88
0.34
64.46
0.29
64.46
0.29
64.42
0.33
63.82
15.39
61.12
17.22
59.52
0.30
59.52
0.35
59.60
0.37
59.52
0.30
54.22
0.29
58.95
25.27
56.60
25.07
56.51
24.69
54.11
30.23
54.00
0.41
54.18
0.38
54.22
0.29
50.35
35.19
52.01
34.56
50.28
32.74
49.55
32.76
49.55
0.31
49.42
0.35
50.05
0.40
49.55
0.31
45.09
0.32
44.47
43.55
44.82
0.30
45.09
0.32
44.85
0.53
43.65
0.36
42.87
42.39
42.05
43.62
43.68
43.53
39.99
0.34
39.15
0.35
35.83
52.43
43.35
4.73
37.62
0.63
35.24
52.52
27.36
3.00
40.11
10.11
36.64
4.11
34.71
0.36
25.90
7.30
35.75
53.65
36.06
54.81
32.47
3.63
25.35
11.07
35.48
19.82
35.39
8.57
32.29
0.37
25.34
0.34
33.71
23.56
32.72
14.03
31.69
7.64
24.12
15.95
29.33
29.23
29.45
19.57
29.32
0.36
23.91
67.34
27.45
64.18
27.39
64.07
28.47
60.80
21.23
22.03
25.08
37.64
26.73
26.17
28.33
12.54
19.59
28.67
20.17
47.16
23.20
35.27
27.14
17.67
18.04
73.99
18.62
75.21
19.07
44.61
24.35
24.07
14.92
42.03
19
Annex
14.62
59.07
18.52
74.90
21.54
32.10
12.46
51.11
9.62
87.82
13.64
55.16
18.81
72.64
9.67
84.96
7.92
72.27
9.57
86.23
18.35
39.18
7.42
65.15
1.03
97.47
7.66
68.98
12.86
49.65
1.07
97.85
1.00
94.21
1.16
97.09
9.71
83.33
1.00
91.74
1.10
92.28
7.84
67.28
1.00
97.46
0.94
91.58
Standard uncertainties are ur(w) = ± 0.02, u(T) = ± 0.01 K; u(P) = ± 2 kPa.
20
Annex
Table A.8. Experimental tie–lines in mass percentage for Surfactant (1) + N 1112OHCl (2) + H2O (3) at
several temperatures
Surfactant-rich phase
I
100 w1
Ionic liquid-rich phase
I
100 w2
II
100 w1
II
100 w 2
TLL
S
Triton X-100 (1) + N1112OHCl (2) + H2O (3)
T = 298.15 K
93.32
1.06
0.16
66.87
114.1
-1.4156
78.25
4.96
0.22
54.94
92.7
-1.5612
53.91
14.04
0.29
45.59
62.2
-1.6995
T = 313.15 K
90.23
1.22
0.19
66.85
111.4
-1.3719
68.71
5.91
0.22
51.89
82.5
-1.4896
37.57
13.92
0.31
36.54
43.6
-1.6472
T = 323.15 K
85.92
0.74
0.15
68.10
109.1
-1.2733
71.03
4.32
0.21
55.62
87.4
-1.3805
60.55
6.61
0.27
43.30
70.6
-1.6430
43.59
10.52
0.30
35.40
49.9
-1.7400
29.55
12.12
0.32
23.06
31.2
-2.6718
T = 333.15 K
87.39
0.53
0.14
67.55
110.0
-1.3019
79.90
2.59
0.18
58.34
97.3
-1.4300
65.51
5.18
0.24
46.03
67.0
-1.5978
58.96
3.67
0.28
35.27
66.6
-1.8570
42.42
7.50
0.33
23.71
45.1
-2.5965
34.07
4.63
0.42
14.29
35.0
-3.4834
Triton X-102 (1) + N1112OHCl (2) + H2O (3)
T = 298.15 K
93.69
0.72
0.16
65.06
113.5
-1.4537
88.45
0.88
0.27
55.81
103.9
-1.6053
69.49
7.51
0.30
44.77
78.6
-1.8570
T = 313.15 K
93.69
0.72
0.15
67.60
115.0
-1.3986
85.60
1.12
0.27
57.23
102.1
-1.5208
64.23
6.93
0.30
43.23
73.5
-1.7612
T = 323.15 K
92.89
0.77
0.16
67.44
114.2
-1.3909
85.08
0.94
0.19
58.01
102.3
-1.4875
70.35
4.49
0.24
46.03
81.5
-1.6878
21
Annex
47.76
11.23
0.28
33.44
52.4
-2.1378
T = 333.15 K
89.15
0.85
0.18
66.82
110.8
-1.3486
78.75
1.15
0.22
57.55
96.7
-1.3924
59.69
5.97
0.26
45.62
71.4
-1.4989
41.75
10.62
0.31
35.56
48.4
-1.6616
29.71
13.08
0.37
25.94
32.0
-2.2815
Standard uncertainties are ur(w) = ± 0.02, u(T) = ± 0.01 K; u(P) = ± 2 kPa.
22
Annex
Table A.9. Experimental tie–lines in mass percentage for Surfactant (1) + N 1112OHCl (2) + H2O (3) at
several temperatures.
Surfactant-rich phase
I
100 w1
Ionic liquid-rich phase
I
100 w2
II
100 w1
II
100 w 2
TLL
S
Tween 80 (1) + N1112OHCl (2) + H2O (3)
T = 298.15 K
95.23
0.95
0.18
68.61
116.7
-1.4048
85.55
4.45
0.26
60.45
102.0
-1.5230
72.35
8.09
0.35
49.43
83.0
-1.7417
T = 313.15 K
93.96
0.88
0.16
65.35
113.8
-1.4549
74.02
8.17
0.28
55.85
87.8
-1.5466
52.55
14.04
0.39
45.16
60.7
-1.6761
T = 323.15 K
93.87
0.66
0.15
66.33
114.4
-1.4271
71.02
8.99
0.23
53.28
83.5
-1.5983
51.21
12.58
0.42
41.22
58.3
-1.7734
T = 333.15 K
92.04
0.96
0.20
64.33
111.6
-1.4493
67.22
8.11
0.25
50.62
79.3
-1.5754
46.93
11.86
0.35
35.84
52.4
-1.9425
Tween 20 (1) + N1112OHCl (2) + H2O (3)
T = 298.15 K
94.21
0.99
0.11
67.73
115.4
-1.4099
78.00
7.00
0.31
61.11
94.7
-1.4358
59.07
14.62
0.34
54.21
70.8
-1.4835
T = 313.15 K
92.28
1.10
0.22
68.49
114.1
-1.3661
68.98
7.66
0.25
59.51
86.1
-1.3256
44.61
19.07
0.38
50.95
54.5
-1.3874
T = 323.15 K
91.58
0.94
0.28
63.89
113.4
-1.3614
67.28
7.84
0.34
56.93
82.3
-1.4037
49.65
12.89
0.39
46.46
58.0
-1.6238
T = 333.15 K
91.79
1.00
0.12
63.20
110.8
-1.4738
65.15
7.41
0.25
52.45
79.0
-1.4409
42.03
14.92
0.40
42.70
50.0
-1.4986
Standard uncertainties are ur(w) = ± 0.02, u(T) = ± 0.01 K; u(P) = ± 2 kPa.
23
Annex
Table A.10. Binodal data for {Triton X-100 (1) + salt (2) + H2O (3)} two-phase systems at T=298.15 K.
K3PO4
K2HPO4
K2SO3
K2CO3
(NH4)2HPO4
(NH4)2SO4
100 w2
100 w1
100 w2
100 w1
100 w2
100 w1
100 w2
100 w1
100 w2
100 w1
100 w2 100 w1
0.17
56.73
0.81
60.62
4.00
42.40
2.22
49.43
1.26
52.69
0.33
65.53
0.21
56.33
1.26
55.57
4.16
39.87
2.67
46.22
1.60
49.87
2.17
53.11
1.53
50.06
2.10
50.16
4.26
40.83
3.12
41.08
2.06
46.11
3.01
47.29
2.24
46.35
2.26
48.70
4.36
37.16
3.33
39.23
3.20
40.11
3.68
41.47
2.50
44.87
2.44
48.02
4.70
35.89
3.46
39.76
3.26
36.13
4.15
39.31
2.92
41.89
2.47
47.21
4.95
35.47
3.55
37.82
3.29
37.36
4.17
38.47
3.46
38.71
2.91
40.71
5.31
33.84
4.03
34.49
3.88
33.13
4.69
35.28
3.71
37.61
3.04
44.07
5.57
32.58
4.68
29.48
4.16
31.14
5.00
32.35
4.25
33.37
3.50
38.05
5.77
30.96
5.22
24.41
4.16
31.14
5.42
28.88
4.50
32.11
3.81
36.58
5.96
28.61
5.71
20.54
4.41
29.21
5.79
26.76
5.31
27.18
4.60
34.03
6.51
26.72
5.97
17.31
4.89
25.06
6.16
24.05
5.65
24.11
5.05
30.45
6.79
23.97
6.19
14.82
5.34
23.50
6.72
19.29
6.04
20.97
5.47
27.55
7.54
20.11
6.50
12.79
5.77
18.93
7.00
16.59
6.38
18.89
5.82
25.16
7.87
18.59
6.74
10.69
5.95
17.55
7.48
14.02
6.72
16.62
6.13
23.10
8.09
16.37
6.97
6.67
6.22
15.68
7.76
12.16
7.01
14.02
6.30
21.17
8.25
17.34
6.98
8.70
7.18
12.32
8.00
10.16
7.28
11.60
6.62
19.63
8.37
14.56
7.17
5.82
7.47
10.39
8.18
8.47
7.82
8.80
6.89
17.77
8.40
13.44
7.25
5.00
7.69
8.62
8.69
5.11
8.23
6.34
7.17
16.11
8.73
11.39
7.40
4.20
7.95
6.68
8.89
3.71
8.54
4.48
7.44
14.45
8.83
9.90
7.87
2.74
8.06
5.12
13.28
0.06
8.69
3.52
7.69
13.05
9.01
8.66
11.19
0.06
11.35
0.40
15.19
0.00
8.87
2.38
7.78
11.23
9.22
7.42
13.30
0.00
14.64
0.00
16.42
0.00
9.02
1.67
8.22
9.92
9.37
5.89
15.80
0.00
15.98
0.00
11.23
0.40
8.45
8.50
9.59
4.09
24
Annex
13.38
0.02
8.75
6.99
9.79
3.28
15.14
0.00
9.10
5.66
9.99
2.35
9.35
4.87
12.03
1.19
9.44
4.19
12.65
0.68
9.53
3.30
13.44
0.30
9.66
2.56
9.88
1.86
13.15
0.19
15.07
0.01
17.30
0.00
Standard uncertainties are u(w) = ±0.0002, u(T) = ±0.01 K; u(P) = ±0.03 kPa
25
Annex
Table A.11. Binodal data for {Tween 20 (1) + salt (2) + H2O (3)} two-phase systems at T=298.15 K.
K3PO4
K2HPO4
K2SO3
K2CO3
(NH4)2HPO4
100 w2
100 w1
100 w2
100 w1
100 w2
100 w1
100 w2
100 w1
1.51
55.48
1.38
55.32
2.50
55.18
1.35
52.50
1.13
47.75
0.44
59.78
2.22
48.67
2.26
49.71
3.65
48.66
2.28
49.72
2.21
42.89
1.01
56.10
2.67
46.40
2.45
49.09
5.03
41.25
2.50
47.07
2.37
41.86
2.26
50.68
2.84
45.87
3.12
45.44
5.50
38.52
3.50
43.67
3.20
39.56
2.50
53.00
3.05
44.63
3.18
45.15
5.93
35.74
4.08
39.22
3.64
37.19
2.97
49.21
3.45
41.44
3.24
45.00
6.27
34.40
5.03
35.87
3.91
36.73
3.88
44.53
4.42
36.22
3.85
40.13
6.43
30.92
5.70
30.04
4.45
33.69
4.53
40.08
4.50
36.33
4.45
37.63
6.99
28.21
6.09
26.53
4.79
31.59
5.28
37.64
5.19
31.86
5.01
35.19
7.49
25.07
6.80
22.54
5.39
29.05
5.78
35.47
5.86
28.07
5.45
32.44
8.10
22.84
7.70
16.87
6.08
26.24
6.16
33.30
6.66
24.28
5.74
30.31
8.61
20.43
8.21
13.06
6.40
24.41
6.55
31.74
7.48
19.93
6.34
28.01
9.03
18.05
8.52
10.71
6.89
22.23
6.56
31.64
8.12
16.23
6.94
25.28
9.47
15.97
8.61
8.05
7.53
19.48
7.17
28.91
8.27
14.84
7.58
22.39
10.02
14.27
8.74
8.74
8.02
16.86
7.72
25.97
8.36
14.79
8.00
20.08
10.51
12.29
8.93
5.92
8.87
13.20
8.24
23.33
8.80
12.62
8.38
17.74
10.67
10.69
9.00
5.14
9.36
11.66
8.63
21.36
9.13
10.74
8.86
15.59
10.92
9.22
9.08
4.12
9.69
10.29
9.05
19.29
9.26
9.50
9.03
14.37
11.08
7.88
9.34
3.50
10.12
8.19
9.54
16.61
9.61
8.15
9.31
12.73
11.31
6.32
9.39
2.34
10.49
6.40
9.81
14.84
9.88
5.91
9.60
9.62
11.53
5.14
12.88
0.18
10.87
4.09
10.20
13.24
10.07
4.19
9.68
11.23
11.70
4.14
15.46
0.00
13.61
1.11
10.64
11.47
10.19
3.11
10.03
7.82
13.62
2.23
17.45
0.00
16.08
0.13
10.88
9.29
12.61
1.34
10.50
6.25
15.54
0.58
19.21
0.00
11.30
7.91
14.19
0.35
10.80
4.03
18.27
0.05
11.55
6.47
26
100 w2
(NH4)2SO4
100 w1
100 w2 100 w1
Annex
15.62
0.32
11.00
2.90
12.21
4.85
11.12
2.04
12.70
3.43
11.40
1.56
15.56
0.72
13.37
1.18
17.58
0.13
15.05
0.32
19.69
0.01
16.55
0.06
Standard uncertainties are u(w) = ±0.0002, u(T) = ±0.01 K; u(P) = ±0.03 kPa
27
Annex
Table A.12. Experimental tie–lines in mass percentage for {Surfactant (1) + salt (2) + H 2O (3)} at T =
298.15 K.
Surfactant-rich phase
I
100 w1
Inorganic salt-rich phase
I
100 w2
II
100 w1
II
100 w 2
TLL
S
Triton X-100 (1) + K3PO4 (2) + H2O (3)
44.88
2.51
0.40
11.23
45.32
-5.10
50.15
1.47
0.02
13.38
51.53
-4.21
56.34
0.21
0.00
15.15
58.28
-3.77
Triton X-100 (1) + K2HPO4 (2) + H2O (3)
47.21
2.48
0.19
13.15
48.22
-4.40
55.57
1.26
0.00
15.07
57.26
-4.02
60.62
0.81
0.01
17.30
62.80
-3.68
Triton X-100 (1) + K2SO3 (2) + H2O (3)
33.84
5.31
1.20
12.03
33.33
-4.86
40.83
4.26
0.68
12.65
41.02
-4.79
49.00
2.80
0.30
13.44
49.85
-4.57
Triton X-100 (1) + K2CO3 (2) +H2O (3)
39.76
3.46
0.06
11.19
40.45
-5.14
46.18
2.63
0.00
13.30
47.39
-4.32
50.22
2.10
0.00
15.80
52.06
-3.67
Triton X-100 (1) + (NH4)2HPO4 (2) + H2O (3)
33.13
3.88
0.40
11.35
33.57
-4.38
37.36
3.29
0.00
14.64
39.04
-3.29
49.87
1.60
0.00
15.98
51.91
-3.47
Triton X-100 (1) + (NH4)2SO4 (2) + H2O (3)
47.29
3.01
0.00
15.19
48.83
-3.88
39.31
4.15
0.06
13.28
40.30
-4.30
53.11
2.17
0.00
16.42
54.99
-4.07
Tween 20 (1) + K3PO4 (2) + H2O (3)
44.63
3.05
0.35
14.19
45.66
-3.98
48.67
2.22
0.32
15.62
50.17
-3.61
36.33
4.50
1.34
12.61
35.92
-4.32
Tween 20 (1) + K2HPO4 (2) + H2O (3)
35.19
5.01
1.18
13.37
35.03
-4.07
49.71
2.26
0.32
15.05
51.03
-3.86
45.44
3.12
0.06
16.55
47.35
-3.36
Tween 20 (1) + K2SO3 (2) + H2O (3)
28
35.74
5.93
2.23
13.62
34.38
-4.36
55.18
2.50
0.05
18.27
57.35
-3.50
Annex
48.66
3.65
0.58
15.54
49.53
-4.04
Tween 20 (1) + K2CO3 (2) + H2O (3)
35.87
5.03
0.18
12.88
36.54
-4.55
43.67
3.50
0.00
15.46
45.28
-3.65
49.72
2.28
0.00
17.45
51.98
-3.28
Tween 20 (1) + (NH4)2HPO4 (2) + H2O (3)
37.19
3.64
1.11
13.61
37.43
-3.62
42.89
2.21
0.13
16.08
44.95
-3.08
47.75
1.13
0.00
19.21
51.06
-2.64
Tween 20 (1) + (NH4)2SO4 (2) + H2O (3)
50.68
2.26
0.72
15.56
51.70
-3.76
56.10
1.01
0.13
17.58
58.37
-3.38
59.78
0.44
0.01
19.69
62.79
-3.11
29
Annex
Table A.13. Binodal data for {Triton X-100 (1) + salt (2) + H2O (3)} two-phase systems at T=298.15 K.
K3C6H5O7
K2C4H4O6
K2C2O4
(NaK)C4H4O6
(NH4)2C4H4O6
100 w2
100 w1
100 w2
100 w1
100 w2
100 w1
100 w2
100 w1
100 w2
100 w1
3.29
52.67
4.28
49.83
3.79
45.14
2.60
50.12
2.80
63.00
4.03
49.49
4.90
44.66
4.30
41.80
4.42
41.28
4.09
53.09
4.90
44.26
5.41
42.97
5.52
34.91
5.36
37.74
5.60
43.26
5.30
42.02
6.13
38.32
6.52
28.35
6.24
33.39
6.13
42.47
6.62
36.56
6.94
33.89
7.12
22.98
6.84
28.66
6.43
39.48
7.22
33.17
7.60
30.86
7.44
20.87
7.42
25.99
6.86
36.81
7.82
29.29
8.36
26.05
7.67
19.12
7.94
24.04
7.11
34.54
8.25
26.70
8.65
24.87
8.05
16.38
8.51
21.33
7.33
33.00
8.57
25.02
9.63
21.44
8.20
14.09
8.79
18.89
7.52
31.89
9.25
21.57
10.06
19.07
8.66
12.46
9.18
16.47
7.85
30.29
9.49
19.78
10.17
17.21
8.87
10.49
9.43
14.54
8.03
29.10
9.94
17.47
10.53
15.27
9.12
8.91
9.75
12.67
8.25
27.28
10.24
15.42
10.95
14.11
9.34
7.41
10.10
10.47
8.57
25.39
10.51
13.76
11.22
12.53
9.47
6.05
10.52
8.49
8.85
22.33
10.84
12.14
11.54
10.59
9.64
4.82
10.88
5.82
8.91
20.79
11.11
10.33
11.77
9.05
9.73
3.88
11.31
2.59
9.40
18.75
11.48
8.79
11.77
7.57
9.76
2.75
9.59
16.02
11.60
7.30
11.98
6.40
3.31
50.09
9.80
13.58
12.08
5.82
12.15
5.49
1.95
54.89
10.07
11.50
12.31
4.12
12.43
3.79
10.64
8.50
12.36
3.27
12.67
2.28
10.76
6.05
10.89
4.62
14.58
0.58
17.43
0.02
19.14
0.00
30
Annex
Table A.14. Binodal data for {Tween 20 (1) + salt (2) + H2O (3)} two-phase systems at T=298.15 K.
K3C6H5O7
K2C4H4O6
K2C2O4
(NaK)C4H4O6
(NH4)2C4H4O6
100 w2
100 w1
100 w2
100 w1
100 w2
100 w1
100 w2
100 w1
100 w2
100 w1
4.20
50.35
4.85
46.34
4.32
47.60
4.03
48.47
3.18
55.40
5.32
45.66
6.15
42.39
5.03
43.11
4.62
45.92
6.00
49.10
6.42
42.08
7.31
37.68
5.61
39.78
5.35
42.66
6.57
45.61
7.05
38.93
8.19
33.91
6.28
36.53
5.88
40.62
7.25
42.64
7.89
35.09
9.04
30.45
7.16
33.62
7.02
37.69
7.67
42.02
8.80
32.10
9.61
28.25
7.87
29.82
7.15
35.16
8.14
40.40
9.48
29.34
10.63
26.21
8.46
27.37
8.06
32.68
9.44
35.54
10.31
26.83
11.40
23.41
8.71
24.70
8.76
29.56
9.91
33.97
11.39
23.52
12.30
20.09
9.19
22.84
9.14
27.98
10.29
32.49
12.07
21.08
13.47
17.56
9.77
20.78
9.76
26.02
10.69
30.12
12.64
18.36
13.95
15.54
10.19
19.06
10.22
24.35
11.60
27.93
13.24
16.97
14.64
13.03
10.68
17.50
10.63
21.99
12.13
26.26
13.65
15.53
15.30
11.08
11.02
15.62
11.14
20.19
12.58
24.68
14.03
13.35
15.72
9.31
11.40
13.95
11.76
17.91
12.90
23.09
14.65
11.79
16.55
6.91
11.76
12.10
12.46
15.25
13.39
20.98
14.84
10.16
17.15
4.95
12.08
10.57
13.27
13.29
13.85
19.25
15.28
8.91
17.56
3.08
12.26
9.04
13.91
10.53
14.33
17.27
15.54
7.52
12.62
7.77
14.34
8.72
15.05
14.93
16.05
5.48
12.90
6.50
14.81
6.69
15.53
13.66
16.57
3.47
13.09
5.27
15.21
5.25
16.11
11.80
16.93
1.91
13.18
4.09
15.88
2.86
16.52
10.49
13.85
2.27
16.96
8.47
17.34
6.86
17.88
5.54
31
Annex
Table A.15. Experimental tie–lines in mass percentage for {Surfactant (1) + salt (2) + H 2O (3)} at T =
298.15 K.
Surfactant-rich phase
I
100 w1
Organic salt-rich phase
I
100 w2
II
100 w1
II
100 w 2
TLL
S
Triton X-100 (1) + K3C6H5O7 (2) + H2O (3)
32.31
7.34
0.86
15.30
32.45
-3.95
40.16
6.01
0.09
17.76
41.76
-3.41
47.54
4.44
0.01
19.92
49.99
-3.07
Triton X-100 (1) + K2C4H4O6 (2) + H2O (3)
30.02
7.81
0.74
15.90
30.37
-3.62
40.06
6.05
0.08
18.31
41.82
-3.26
49.83
4.28
0.01
20.34
52.35
-3.10
Triton X-100 (1) + K2C2O4 (2) + H2O (3)
28.34
6.52
0.08
12.75
23.81
-3.51
37.59
5.20
0.13
14.06
38.49
-4.23
44.30
4.22
0.03
14.79
45.51
-4.19
Triton X-100 (1) + NaKC4H4O6 (2) +H2O (3)
26.47
7.36
0.80
14.28
26.59
-3.71
39.81
5.06
0.10
16.41
41.30
-3.50
48.45
2.86
0.01
18.47
50.90
-3.10
Triton X-100 (1) + (NH4)2C4H4O6 (2) + H2O (3)
47.29
3.01
0.00
15.19
48.83
-3.88
39.31
4.15
0.06
13.28
40.30
-4.30
53.11
2.17
0.00
16.42
54.99
-4.07
Tween 20 (1) + K3C6H5O7 (2) + H2O (3)
43.65
5.94
1.57
19.80
44.30
-3.04
53.14
3.04
0.36
22.51
56.26
-2.71
56.81
2.01
0.07
24.97
61.21
-2.47
Tween 20 (1) + K2C4H4O6 (2) + H2O (3)
43.64
5.58
1.31
21.31
45.16
-2.69
49.74
3.81
0.35
23.91
53.33
-2.46
54.50
2.57
0.09
26.09
59.27
-2.31
Tween 20 (1) + K2C2O4 (2) + H2O (3)
42.99
5.11
1.84
15.62
42.47
-3.91
48.32
3.67
0.50
17.56
49.79
-3.44
53.34
2.11
0.15
19.07
55.83
-3.13
Tween 20 (1) + NaKC4H4O6 (2) + H2O (3)
32
43.88
5.16
1.51
18.61
44.44
-3.15
50.45
3.24
0.49
20.62
52.84
-2.87
Annex
56.17
1.72
0.19
22.02
59.54
-2.76
Tween 20 (1) + (NH4)2C4H4O6 (2) + H2O (3)
42.02
7.67
3.13
20.30
40.89
-3.08
45.61
6.57
0.73
23.57
47.99
-2.64
55.40
3.18
0.16
26.22
59.85
-2.40
33
ABBREVIATIONS
Abbreviations
Dyes
AB
AB 48
Ablu25
AnB
AO52
AR 88
ARS
AS-GR
ATB-2G
BB3
BBG
BBR
BF
BG
BR46
BY2
CBB
CR
CRB
CV
DB71
DB9
DO3
DR19
DR81
DV-K2RL
EB
FA
GY
IC
LG
MB
MG
Mg I
MO
MV
NB-HE2R
NB-RX
NcsBG
O II
RB
RB (4,160,171)
RB160
RB5
RBB
RBblu-R
RB-KNB
Rbla
Rbla B
Rblu
Rblu RR
RGFL
RGY-RNL
Rho B
RO II
RO16
RR
RR (11,120,141,198,239)
RRB
RRB
Rred
Azure B
Acid Black 48
Acid blue 25
Aniline Blue
Acid Orange 52
Acid Red 88
Alizarin Red S
Acid Scarlet GR
Acid Turquioise Blue 2G
Basic Blue 3
Brilliant Blue G
Brilliant Blue R
Basic Fuchsin
Brilliant Green
Basic Red 46
Basic Yellow 2
Cibacron Black B
Congo Red
Cibacron Red B
Crystal Violet
Direct Blue 71
Disperse Black 9
Disperse Orange 3
Disperse Red 19
Direct Red 81
Drimaren Violet K2RL
Evans Blue
Fuchsin Acid
Golden Yellow
Indigo Carmine
Lissamine Green
Methylene Blue
Malachite Green
Magneson I
Methyl Orange
Methyl Violet
Navy Blue HE2R
Navy Blue RX
Nava cron super Black G
Orange II
Reactive Black
Reactive Black (4,160,171)
Reactive Black 160
Reactive Black 5
Remazol Brilliant Blue
Remazol Brilliant blue R
Reactive Black KNB
Remazol black
Remazol black B
Remazol blue
Remazol blue RR
Rubine GFL
Remazol Golden Yellow RNL
Rhodamine B
Reactive Orange II
Reactive Orange 16
Reactive Red
Reactive Red (11,120,141,198,239)
Red RB
Red RB
Remazol red
RR-K2BP
RV
RV-K3R
RY
RY (15,84)
SRR
TB
TRww-3BS
Reactive Red K-2BP
Remazol Violet
Reactive Violet K3R
Remazol Yellow
Reactive Yellow (15,84)
Scarlet RR
Trypan blue
Terasil Red ww 3BS
Polycyclic Aromatic Hydrocarbons
AC
CAN
AN
BaA
BaP
BbF
BeP
BkF
CHR
FA
FLU
IP
NA
PHE
PYR
Acenaphthene
Acenaphthene
Anthracene
Benzo(a)anthracene
Benzo(a)Pyrene
Benzo(b)fluoranthrene
Benzo(e)pyrene
Benzo(k)fluoranthrene
Chrysene
Fluoranthrene
Fluorene
Indeno(1,2,3-cd)Pyrene
Naphthalene
Phenanthrene
Pyrene
Emerging Pollutants
AMP
AMX
ATN
BZF
CF
CLX
Dcf
Ibp
MET
NPX
OF
OLA
OTC
PCT
TYL
VIG
Ampicillin
Amoxicillin
Atenolol
Bezafibrate
Ciprofloxacin
Cloxacillin
Diclofenac
Ibuprofen
Metronidazole
Naproxen
Ofloxacin
Olaquindox
Oxitetracycline
Paracetamol
Tylosin
Sildenafil citrate (viagra)
Polymers and Surfactants
(12-3-12)
AS
C10E4
C11EO2
C11pPHCNa
C12-18EO5
C12C6C12(Me)
C12CnC12(Et)
C12S
CnTAB
DBAB
DPDS
DTAB
PEG
PPG
1,3-propanedyil bis(dodecyl dimethylammonium bromide)
Sodium dodecylsulfonate
n-decil tetra(ethylene oxide)
Polyoxyethylene fatty alcohol
o,Ó-bis(sodium 2-lauricate)-p-benzenediol
Polyoxyethylene fatty alcohol
Hexanediyl---bis-dodecyldimethylammonium bromide
Alkanediyl---bis-dodecyldiethylammonium bromide
Sodium dodecylsulfate
Alkyltrimethylammonium bromide
n-dodecyltributylammonium bromide
Alkyl diphenyloxide disulfonate
Dodecyltrimethylammonium bromide
Polyethylene glycol
Polypropylene glycol
SL
Tritón X
Tween
Sodium laurate
Polyoxyethylene t-octylphenol
Polyethoxylated sorbitan
Ionic Liquids
AC1imCl
BzC1imC1SO4
BzC1imCl
C1PyC1SO4
C2C1imC2SO4
C2C1imCnSO3
C2C1imCnSO4
C4C1imBF4
C4C1imBr
C4C1imC1SO4
C4C1imCl
C6C1imBF4
C6imCl
C8imC1imCl
CmC1imBr
CnC1imC1SO4
CnC1imCH3COOH
CnC1imCl
N1112OHCl
OHC2C1imCl
P4444Br
P444C1SO4
Pi444Tos
1-allyl-3- methylimidazolium chloride
1-benzyl-3-methylimidazolium methylsulfate
1-benzyl-3-methylimidazolium chloride
Methylpyridinium methylsulfate
1-ethyl-3-methylimidazolium ethylsulfate
1-ethyl-3-methylimidazolium alkylsulfonate
1-ethyl-3-methylimidazolium alkylsulfate
1-butyl-3-methylimidazolium tetrafluoroborate
1-butyl-3-methylimidazolium bromide
1-butyl-3-methylimidazolium methylsulfate
1-butyl-3-methylimidazolium chloride
1-hexyl-3-methylimidazolium tetrafluoroborate
1-hexyl-3-methylimidazolium chloride
3-methyl-1-octylimidazolium chloride
1-alkyl-3-methylimidazolium bromide
1-alkyl-3-methylimidazolium methylsulfate
1-alkyl-3-methylimidazolium acetate
1-alkyl-3-methylimidazolium chloride
Cholinium chloride
1-hydroxyethyl-3-methylimidazolium chloride
tetrabutylphosphonium bromide
tributyl(methyl)-phosphonium methylsulfate
triisobutyl(methyl)phosphonium tosylate