Enhancing Gas Solubility in Nanopores: A Combined Study Using Classical Density Functional Theory and Machine Learning.

Geometrical confinement has a large impact on gas solubilities in nanoscale pores. This phenomenon is closely associated with heterogeneous catalysis, shale gas extraction, phase separation, etc. Whereas several experimental and theoretical studies have been conducted that provide meaningful insights into the over-solubility and under-solubility of different gases in confined solvents, the microscopic mechanism for regulating the gas solubility remains unclear. Here, we report a hybrid theoretical study for unraveling the regulation mechanism by combining classical density functional theory (CDFT) with machine learning (ML). Specifically, CDFT is employed to predict the solubility of argon in various solvents confined in nanopores of different types and pore widths, and these case studies then supply a valid training set to ML for further investigation. Finally, the dominant parameters that affect the gas solubility are identified, and a criterion is obtained to determine whether a confined gas-solvent system is enhance-beneficial or reduce-beneficial. Our findings provide theoretical guidance for predicting and regulating gas solubilities in nanopores. In addition, the hybrid method proposed in this work sets up a feasible platform for investigating complex interfacial systems with multiple controlling parameters.