Modelling and prediction of the thermophysical properties of aqueous mixtures of Choline Geranate and Geranic acid (CAGE) using SAFT-g Mie
File(s)
Published version
Author(s)
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.
Date Issued
2019-11-21
Date Acceptance
2019-10-25
Citation
RSC Advances: an international journal to further the chemical sciences, 2019, 9 (65), pp.38017-38031
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
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
Grant Number
EP/J014958/1
Mark Buswell
RCSRF1819\7\33
EP/E016340/1
PET/ICL/2014/3
151299
Subjects
03 Chemical Sciences
Publication Status
Published
Date Publish Online
2019-11-21