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首页> 外文期刊>RSC Advances >Modelling and prediction of the thermophysical properties of aqueous mixtures of choline geranate and geranic acid (CAGE) using SAFT-γ Mie
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Modelling and prediction of the thermophysical properties of aqueous mixtures of choline geranate and geranic acid (CAGE) using SAFT-γ Mie

机译：使用SAFT-γMie建模和预测胆碱香叶酸和香叶酸（CAGE）的水性混合物的热物理性质

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Deep-eutectic solvents and room temperature ionic liquids are increasingly recognised as appropriate materials for use as active pharmaceutical ingredients and formulation additives. Aqueous mixtures of choline and geranate (CAGE), in particular, have been shown to offer promising biomedical properties but understanding the thermophysical behaviour of these mixtures remains limited. Here, we develop interaction potentials for use in the SAFT-γ Mie group-contribution approach, to study the thermodynamic properties and phase behaviour of aqueous mixtures of choline geranate and geranic acid. The determination of the interaction parameters between chemical functional groups is carried out in a sequential fashion, characterising each group based on those previously developed. The parameters of the groups relevant to geranic acid are estimated using experimental fluid phase-equilibrium data such as vapour pressure and saturated-liquid density of simple pure components ( n -alkenes, branched alkenes and carboxylic acids) and the phase equilibrium data of mixtures (aqueous solutions of branched alkenes and of carboxylic acids). Geranate is represented by further incorporating the anionic carboxylate group, COO ~(?) , which is characterised using aqueous solution data of sodium carboxylate salts, assuming full dissociation of the salt in water. Choline is described by incorporating the cationic quaternary ammonium group, N ~(+) , using data for choline chloride solutions. The osmotic pressure of aqueous mixtures of CAGE at several concentrations is predicted and compared to experimental data obtained as part of our work to assess the accuracy of the modelling platform. The SAFT-γ Mie approach is shown to be predictive, providing a good description of the measured data for a wide range of mixtures and properties. Furthermore, the new group-interaction parameters needed to represent CAGE extend the set of functional groups of the group-contribution approach, and can be used in a transferable way to predict the properties of systems beyond those studied in the current work.
机译：深共熔溶剂和室温离子液体越来越被认为是用作活性药物成分和制剂添加剂的合适材料。胆碱和香叶酸盐的水性混合物（CAGE）已显示出特别的生物医学特性，但对这些混合物的热物理行为的了解仍然有限。在这里，我们开发了SAFT-γMie基团贡献方法中使用的相互作用势，以研究胆碱香叶酸和香叶酸的水性混合物的热力学性质和相行为。以连续的方式确定化学官能团之间的相互作用参数，并基于先前开发的特征对每个官能团进行表征。使用实验性的流体相平衡数据，例如简单纯净组分（正烯烃，支链烯烃和羧酸）的蒸气压和饱和液体密度，以及混合物的相平衡数据，估算与香叶酸有关的基团的参数（支链烯烃和羧酸的水溶液）。香叶酸盐的特征在于进一步掺入阴离子羧酸盐基团COO-（α），假定羧酸盐在水中完全解离，则使用羧酸钠盐的水溶液数据对其进行表征。使用氯化胆碱溶液的数据通过结合阳离子季铵基团N〜（+）来描述胆碱。可以预测几种浓度的CAGE水性混合物的渗透压，并将其与作为我们评估模型平台准确性的工作所获得的实验数据进行比较。 SAFT-γMie方法显示出可预测性，可以很好地描述各种混合物和性能的测量数据。此外，表示CAGE所需的新的组交互参数扩展了组贡献方法的功能组，并且可以以可转移的方式用于预测系统的性能，这些性能超出了当前工作中研究的系统。

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