Machine learning assisted high-throughput screening of transition metal single atom based superb hydrogen evolution electrocatalysts-Reference-Cited by-同舟云学术

Machine learning assisted high-throughput screening of transition metal single atom based superb hydrogen evolution electrocatalysts

Published:2022 Issue:12 Volume:10 Page:6679-6689
ISSN:2050-7488
Container-title:Journal of Materials Chemistry A
language:en
Short-container-title:J. Mater. Chem. A

Author:

Umer Muhammad¹,Umer Sohaib¹,Zafari Mohammad¹,Ha Miran¹^ORCID,Anand Rohit¹,Hajibabaei Amir¹,Abbas Ather²,Lee Geunsik¹^ORCID,Kim Kwang S.¹^ORCID

Affiliation:

1. Center for Superfunctional Materials, Department of Chemistry, Ulsan National Institute of Science and Technology (UNIST), 50 UNIST-gil, Ulsan 44919, Republic of Korea

2. Urban and Environmental Engineering, Ulsan National Institute of Science and Technology (UNIST), 50 UNIST-gil, Ulsan 44919, Republic of Korea

Abstract

Carbon-based transition metal (TM) single-atom catalysts (SACs) have shown great potential toward electrochemical water splitting and H2 production.

Funder

National Research Foundation of Korea

Korea Institute of Science and Technology Information

Ulsan National Institute of Science and Technology

Publisher

Royal Society of Chemistry (RSC)

Subject

General Materials Science,Renewable Energy, Sustainability and the Environment,General Chemistry

Link

http://pubs.rsc.org/en/content/articlepdf/2022/TA/D1TA09878K

Reference86 articles.

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2. Economics of converting renewable power to hydrogen

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4. Sustainable Hydrogen Production

5. Multi-heteroatom-doped carbon from waste-yeast biomass for sustained water splitting

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