First principles and machine learning based superior catalytic activities and selectivities for N2 reduction in MBenes, defective 2D materials and 2D π-conjugated polymer-supported single atom catalysts-Reference-Cited by-同舟云学术

First principles and machine learning based superior catalytic activities and selectivities for N2 reduction in MBenes, defective 2D materials and 2D π-conjugated polymer-supported single atom catalysts

Published:2021 Issue:14 Volume:9 Page:9203-9213
ISSN:2050-7488
Container-title:Journal of Materials Chemistry A
language:en
Short-container-title:J. Mater. Chem. A

Author:

Zafari Mohammad¹²³⁴⁵,Nissimagoudar Arun S.¹²³⁴⁵^ORCID,Umer Muhammad¹²³⁴⁵,Lee Geunsik¹²³⁴⁵^ORCID,Kim Kwang S.¹²³⁴⁵^ORCID

Affiliation:

1. Center for Superfunctional Materials

2. Department of Chemistry

3. Ulsan National Institute of Science and Technology (UNIST)

4. Ulsan 44919

5. Korea

Abstract

The catalytic activity and selectivity can be improved for nitrogen fixation by using hollow sites and vacancy defects in 2D materials, while a new machine learning descriptor accelerates screening of efficient electrocatalysts.

Funder

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/2021/TA/D1TA00751C

Reference72 articles.

1. Ammonia for hydrogen storage: challenges and opportunities

2. Catalysts for nitrogen reduction to ammonia