Combining large-scale screening and machine learning to predict the metal-organic frameworks for organosulfurs removal from high-sour natural gas-Reference-Cited by-同舟云学术

Combining large-scale screening and machine learning to predict the metal-organic frameworks for organosulfurs removal from high-sour natural gas

Published:2019-09 Issue:9 Volume:7 Page:091101
ISSN:2166-532X
Container-title:APL Materials
language:en
Short-container-title:APL Materials

Author:

Liang Hong¹,Yang Wenyuan¹^ORCID,Peng Feng¹²,Liu Zili¹,Liu Jie³,Qiao Zhiwei¹²^ORCID

Affiliation:

1. Guangzhou Key Laboratory for New Energy and Green Catalysis, School of Chemistry and Chemical Engineering, Guangzhou University, Guangzhou 510006, People’s Republic of China

2. School of Chemistry and Chemical Engineering, South China University of Technology, Guangzhou 510640, People’s Republic of China

3. Key Laboratory for Green Chemical Process of Ministry of Education, School of Chemical Engineering and Pharmacy, Wuhan Institute of Technology, Wuhan 430073, People’s Republic of China

Funder

National Natural Science Foundation of China

Publisher

AIP Publishing

Subject

General Engineering,General Materials Science

Link

http://aip.scitation.org/doi/pdf/10.1063/1.5100765

Reference45 articles.

1. Ion-Exchanged Zeolites Y for Selective Adsorption of Methyl Mercaptan from Natural Gas: Experimental Performance Evaluation and Computational Mechanism Explorations

2. Evaluating topologically diverse metal–organic frameworks for cryo-adsorbed hydrogen storage

3. Reaction Kinetics of Carbonyl Sulfide (COS) with Diethanolamine in Methanolic Solutions

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