Affiliation:
1. Guangzhou Key Laboratory for New Energy and Green Catalysis, School of Chemistry and Chemical Engineering Guangzhou University Guangzhou China
2. State Key Laboratory of Organic‐Inorganic Composites Beijing University of Chemical Technology Beijing China
Abstract
AbstractMost traditional D2/H2 separation techniques are energy‐intensive with low efficiency. Metal–organic frameworks (MOFs) provide a promising solution for D2/H2 separation due to their excellent chemical and structural characteristics. Here, machine learning‐assisted high‐throughput computational screening was employed to identify the high‐performance MOFs for the dynamic D2/H2 separation. Extensive data analysis reveals that there were two adsorption behaviors in the optimal MOFs, independent adsorption and competitive adsorption, and the independent adsorption was favorable for the preferential adsorption of D2. To quantify these two adsorption behaviors, we introduced and defined overlap degree (OD) and independence degree (ID), and developed a software for the rapid assessment of OD/ID. After batch simulation of the breakthrough curves of 2000 optimal MOFs, ~80% MOFs exhibited independent occupancy, confirming its contribution to good dynamic separation capabilities. This work provides a new idea for designing MOFs with independent adsorption behavior to improve the dynamic separation performance of hydrogen isotopes.
Funder
China Postdoctoral Science Foundation
Guangdong Provincial Pearl River Talents Program
Guangzhou Municipal Science and Technology Project
National Natural Science Foundation of China
Natural Science Foundation of Guangdong Province
Subject
General Chemical Engineering,Environmental Engineering,Biotechnology
Cited by
3 articles.
订阅此论文施引文献
订阅此论文施引文献,注册后可以免费订阅5篇论文的施引文献,订阅后可以查看论文全部施引文献