Global warming caused primarily by excessive emissions of CO₂ has attracted worldwide attention. Herein, three 2-hydroxypyridium ionic liquids (ILs) based task-specific deep eutectic solvents (DESs) were synthesized to absorb CO₂ and physical properties including density, viscosity, and melting points were measured to explore the effect on CO₂ absorption. The CO₂ absorption capacities of the ILs-based task-specific DESs were investigated at different pressures and temperatures, which showed that the maximum absorption capacity of the DES was up to 1.48 mol_CO2·mol_DES⁻¹ or 0.233 g_CO2·g_DES⁻¹ at the atmospheric pressure and 25 ℃. The plausible absorption mechanism was also proposed by a combination of 1:1 and 2:1 stoichiometric reactions of CO₂ and the IL-based task-specific DES via multiple-site absorption, which was confirmed by ¹³C and ¹H nuclear magnetic resonance (NMR), Fourier transform infrared (FT-IR) spectroscopy, quantum chemical calculation, and reaction equilibrium thermodynamic modeling. The thermodynamic properties, including absorption Gibbs free energy, absorption enthalpy, and absorption entropy were rationally deduced and explained. Furthermore, the excellent CO₂ absorption capacity and regenerability of multiple-site task-specific DES make it a new environmentally eco-friendly choice for highly efficient CO₂ absorption and subsequent CO₂ transformation.

Ionic liquid (IL) can be inserted into metal organic framework (MOF) to form IL@MOF composite with enhanced properties. In this work, hypothetical IL@MOFs were computationally constructed and screened by integrating molecular simulation and convolutional neural network (CNN) for CO₂ capture. First, the IL [BMIM][DCA] with a large CO₂ solubility was inserted into 1631 pre-selected Computational-Ready Experimental (CoRE) MOFs to create hypothetical IL@MOFs. Then, given the temperature and pressure of adsorption and desorption, the CO₂/N₂ selectivity and CO₂ working capacity of 700 representative IL@MOFs were assessed via molecular simulations. Based on the results, two CNN models were trained and used to predict the performance of other IL@MOFs, which reduces the computational costs effectively. By combining the simulation results and CNN model predictions, 22 IL@MOFs with top-ranked performance were identified. Three distinct ones IL@HABDAS, IL@GUBKUL, and IL@MARJAQ were chosen for explicit analysis. It was found that a desired balance between CO₂/N₂ selectivity and CO₂ working capacity can be obtained by inserting the optimal number of IL molecules. This helps guide a novel design of IL@MOF composites with advanced performance on carbon capture.

For discovering uncharted chemical space of ionic liquids (ILs) for CO₂ dissolution, a reliable generative framework combining re-balanced variational autoencoder (VAE), artificial neural network (ANN), and particle swarm optimization (PSO) is developed based on a comprehensive experimental solubility database from literature. The re-balanced VAE transforms the chemical space of ILs into continuous latent space, which is demonstrated by t-distributed stochastic neighbor embedding (t-SNE) visualization and sampled ions of the latent space. ANN is connected with the re-balanced VAE to predict the CO₂ solubility and the resultant VAE-ANN model achieves a low mean absolute error (MAE) of 0.022 on the test set. Lastly, the PSO algorithm is employed to search the latent space for optimal IL structures with the highest predicted solubility. A total of 5120 ILs are generated and optimized through 10 parallel runs of PSO. Their CO₂ solubilities are predicted and compared to those of the 3735 ILs combined with the already-known cations and anions in the CO₂ solubility database under 298.15 K and 100 kPa. The results demonstrate a notably larger distribution of higher CO₂ solubility in optimized ILs after PSO, which effectively points out the significance and directions for exploring the wide IL chemical space.

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.