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

Area of Science:

• Physical Chemistry
• Materials Science
• Computational Chemistry

Background:

• Geometrical confinement significantly alters gas solubility in nanoscale pores, impacting fields like heterogeneous catalysis and shale gas extraction.
• While experimental and theoretical studies offer insights into gas solubility variations, the underlying microscopic mechanisms remain elusive.

Purpose of the Study:

• To elucidate the microscopic mechanism regulating gas solubility in nanopores.
• To develop a predictive criterion for gas-solvent systems in confined environments.

Main Methods:

• A hybrid theoretical approach combining classical density functional theory (CDFT) and machine learning (ML).
• CDFT was used to predict argon solubility in various solvents within different nanopore types and widths.
• ML models were trained on CDFT data to identify dominant parameters influencing gas solubility.

Main Results:

• Identified dominant parameters that regulate gas solubility in confined systems.
• Developed a criterion to determine if confinement enhances or reduces gas solubility (enhance-beneficial vs. reduce-beneficial).

Conclusions:

• The hybrid CDFT-ML method provides a feasible platform for investigating complex interfacial systems.
• Findings offer theoretical guidance for predicting and controlling gas solubilities in nanopores, crucial for diverse applications.