When considering the usage of ionic liquids (ILs) for reactions and separations involving non-polar or weak-polar hydrocarbons, the knowledge of the mutual solubility behaviors of ILs and hydrocarbons is of the utmost importance. In this work, taking four typical C6-hydrocarbons namely benzene, cyclohexene, cyclohexane, and hexane as representatives, the mutual solubility of ILs and non-polar or weak-polar hydrocarbons are systematically studied based on the COSMO-RS model. The reliability of COSMO-RS for these systems is first evaluated by comparing experimental and predicted hydrocarbon-in-IL activity coefficient at infinite dilution and binary/ternary liquid-liquid equilibria of related systems. Then, the mutual solubility of the four hydrocarbons and 13,650 ILs (composed by 210 cations and 65 anions) are predicted. The effect of different IL structural characteristics including alkyl chain length, cation family/symmetry/functional group, and anion on the IL-hydrocarbon mutual solubility behaviors are further analyzed by the analyses of interaction energy and screen charge distribution. The mutual solubility databases and the structural effects identified thereon could provide useful guidance for IL selection in related applications.

This work presents the development of molecular-based mathematical model for the prediction of CO₂ solubility in deep eutectic solvents (DESs). First, a comprehensive database containing 1011 CO₂ solubility data in various DESs at different temperatures and pressures is established, and the COSMO-RS-derived descriptors of involved hydrogen bond acceptors and hydrogen bond donors of DESs are calculated. Afterwards, the efficiency of the input variables, i.e., temperature, pressure, COSMO-RS-derived descriptors of HBA and HBD as well as their molar ratio, is explored by a qualitative analysis of CO₂ solubility in DESs using a simple multiple linear regression model. A machine learning method namely random forest is then employed to develop more accurate nonlinear quantitative structure-property relationship (QSPR) model. Combining the QSPR validation and comparisons with literature-reported models (i.e., COSMO-RS model, traditional thermodynamic models and equations of state methods), the developed QSPR model with COSMO-RS-derived parameters as molecular descriptors is suggested to be able to give reliable predictions of CO₂ solubility in DESs and could be used as a useful tool in selecting DESs for CO₂ capture processes.