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Modelling and prediction of the thermophysical properties of aqueous mixtures of Choline Geranate and Geranic acid (CAGE) using SAFT-g Mie

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Title: Modelling and prediction of the thermophysical properties of aqueous mixtures of Choline Geranate and Geranic acid (CAGE) using SAFT-g Mie
Authors: Di Lecce, S
Galindo, A
Khalit, SH
Adjiman, C
Jackson, G
Lazarou, G
McQueen, L
Item Type: Journal Article
Abstract: Deep eutectic solvents and room temperature ionic liquids are increasingly recognised as appro-priate materials for use as active pharmaceutical ingredients and formulation additives. Aque-ous mixtures of choline and geranate (CAGE), in particular, have been shown to offer promisingbiomedical properties but the understanding of the thermophysical behaviour of these mixturesremains limited. Here, we develop interaction potentials for use in the SAFT–γgroup–contributionapproach, to study the thermodynamic properties and phase behaviour of aqueous mixtures ofcholine geranate and geranic acid. The determination of the interaction parameters betweenchemical functional groups is carried out in a successive fashion, characterising each group basedon those previously developed. The parameters of the groups relevant to geranic acid are esti-mated using experimental phase–equilibrium data such as vapour pressure and saturated–liquiddensity of simple pure components (n–alkenes, branched alkenes and carboxylic acids) and thephase equilibrium data of mixtures (aqueous solutions of branched alkenes and of carboxylicacids). Geranate is represented by further incorporating the anionic carboxylate group, COO−,which is characterised using aqueous solution data of sodium carboxylate salts, assuming fulldissociation of the salt in water. Choline is described by incorporating the cationic quaternaryammonium group, N+, using data on choline choride solutions. The osmotic pressure of aque-ous mixtures of CAGE at several concentrations is predicted and compared to experimental dataobtained as part of our work to assess the accuracy of the modelling platform. The SAFT–γMieapproach is shown to be predictive, providing a good description of the measured data for a widerange of mixtures and properties. Furthermore, the new group interaction parameters neededto represent CAGE extend the set of functional groups of the modelling group–contribution ap-proach, and can be used in a transferable way to predict the properties of systems beyond thosestudied in the current work.
Issue Date: 21-Nov-2019
Date of Acceptance: 25-Oct-2019
URI: http://hdl.handle.net/10044/1/74844
DOI: 10.1039/c9ra07057e
ISSN: 2046-2069
Publisher: Royal Society of Chemistry
Start Page: 38017
End Page: 38031
Journal / Book Title: RSC Advances: an international journal to further the chemical sciences
Volume: 9
Issue: 65
Copyright Statement: © The Royal Society of Chemistry 2019. This article is licensed under a Creative Commons Attribution-NonCommercial 3.0 Unported Licence.
Sponsor/Funder: Engineering & Physical Science Research Council (EPSRC)
GlaxoSmithKline Services Unlimited
Royal Academy Of Engineering
Engineering & Physical Science Research Council (EPSRC)
Petronas Research Sdn. Bhd.
Engineering & Physical Science Research Council (E
Funder's Grant Number: EP/J014958/1
Mark Buswell
Keywords: 03 Chemical Sciences
Publication Status: Published
Online Publication Date: 2019-11-21
Appears in Collections:Faculty of Engineering
Chemical Engineering
Faculty of Natural Sciences