Exploring the Influence of Ionic Liquid Anion Structure on Gas-Ionic Liquid Partition Coefficients of Organic Solutes Using Machine Learning
Uko Maran
Ionic liquids (IL) form a class of chemical compounds consisting of ions, they are considered organic salts characterized by their low melting point. These unique substances have remarkable properties, including exceptionally low vapor pressure, high polarity, and excellent thermal stability. These properties have led to extensive research on their usefulness as green solvents, electrolytes, and in various applications in chemical synthesis, catalysis, electrochemistry, extraction, energy storage, and separation chemistry, where various mixing and distribution interactions in ILs play an important role. The recent article presents an in-depth investigation into the influence of anionic structures of ILs on gas-ionic liquid partition coefficients (log K) of organic solutes in three ILs. While the primary objective was to examine whether there is a relationship between the molecular structure of the IL anion component and log K, additionally it was looked at whether the molecular descriptors of the anion in the relationships encode possible molecular interactions during the miscibility and partitioning in the IL. The research involves the compilation of data series of experimental log K values, where the cation component is constant. Such representative data series were obtained for three solutes─benzene, cyclohexane, and methanol─in three ILs with a uniform cationic component of methylimidazoliums. Using multiple linear regression models enhanced with machine learning techniques, the relationship between anionic structures and log K values was successfully quantified and modeled. Systematically selected molecular descriptors describing the anion structure show that in the case of methanol log K is strongly dependent on hydrogen bonds and Coulomb-dipolar interactions with the anion component, while in the case of benzene and cyclohexane the dispersion forces of the anion component are dominant. The outlier analysis and data interpretation highlight the need for extensive experimental data. The results confirm the initial hypothesis and provide valuable information on the role of the structure of the anionic component in determining the partitioning behavior of organic solutes. This knowledge is important for the design and optimization of ILs for specific applications, particularly as solvents in various industrial processes. The research also provides useful information about molecular interactions taking place in the interfaces of IL and organic additives in complex liquid media such as multicomponent electrolyte solutions, for example in energy storage applications.
Article: https://doi.org/10.1021/acs.langmuir.4c02628
FAIR data and models: http://dx.doi.org/10.15152/QDB.264
