文献类型：期刊文献

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

作者：Qiao, Chongzhi[1,2];Yu, Xiaochen[1,2];Song, Xianyu[1,2];Zhao, Teng[1,2];Xu, Xiaofei[1,2];Zhao, Shuangliang[1,2,3,4];Gubbins, Keith E.[5]

机构：[1]East China Univ Sci & Technol, State Key Lab Chem Engn, Shanghai 200237, Peoples R China;[2]East China Univ Sci & Technol, Sch Chem Engn, Shanghai 200237, Peoples R China;[3]Guangxi Univ, Guangxi Key Lab Petrochem Resource Proc & Proc In, Nanning 530004, Peoples R China;[4]Guangxi Univ, Sch Chem & Chem Engn, Nanning 530004, Peoples R China;[5]North Carolina State Univ, Dept Chem & Biomol Engn, Raleigh, NC 27695 USA

年份：2020

卷号：36

期号：29

起止页码：8527

外文期刊名：LANGMUIR

收录：;EI(收录号：20203509107527);WOS:【SCI-EXPANDED(收录号:WOS:000557757700021)】；

基金：This work is supported by the National Natural Science Foundation of China (Nos. 91934302 and 21878078) and by the Shanghai Science and Technology Innovation Action Plan (18160743700) and by the 111 Project of China (grant no. B08021). K.E.G. thanks the U.S. National Science Foundation for support of this work under grant no. CBET-1603851.

语种：英文

外文关键词：Gases - Phase separation - Machine learning - Solvents - Catalysis - Nanopores - Solubility

摘要：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.