Combining Interpretable Machine Learning and Molecular Simulation to Advance the Discovery of COF-Based Membranes for Acid Gas Separation-Reference-Cited by-同舟云学术

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Combining Interpretable Machine Learning and Molecular Simulation to Advance the Discovery of COF-Based Membranes for Acid Gas Separation

Published:2024-04-24 Issue:18 Volume:63 Page:8369-8382
ISSN:0888-5885
Container-title:Industrial & Engineering Chemistry Research
language:en
Short-container-title:Ind. Eng. Chem. Res.

Author:

Xin Bingru¹,Feng Minggao²,Cheng Min³,Dai Zhongde⁴^ORCID,Ye Shiyang¹,Zhou Li¹^ORCID,Dai Yiyang¹^ORCID,Ji Xu¹^ORCID

Affiliation:

1. School of Chemical Engineering, Sichuan University, Chengdu 610065, China

2. Department of Chemistry and Materials Innovation Factory, University of Liverpool, Liverpool L7 3NY, U.K.

3. BYD Company Ltd., No 5. Xiusha Road, Kenzie Town, Pingshan district, Shenzhen, Guangdong 518122, China

4. School of Carbon Neutrality Future Technology, Sichuan University, Chengdu 610065, China

Funder

National Key Research and Development Program of China

National Natural Science Foundation of China

Publisher

American Chemical Society (ACS)

Link

https://pubs.acs.org/doi/pdf/10.1021/acs.iecr.4c00855

Reference68 articles.

1. Natural gas origin, composition, and processing: A review

2. Hydrogen sulfide removal from natural gas using membrane technology: a review

3. Hydrogen sulfide capture and removal technologies: A comprehensive review of recent developments and emerging trends

4. Active chemisorption sites in functionalized ionic liquids for carbon capture

5. Assessment of Acid Gas Adsorption Selectivities in MIL-125-NH₂

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