There are no files associated with this item.
Full metadata record
DC Field | Value | Language |
---|---|---|
dc.citation.endPage | 9213 | - |
dc.citation.number | 14 | - |
dc.citation.startPage | 9203 | - |
dc.citation.title | JOURNAL OF MATERIALS CHEMISTRY A | - |
dc.citation.volume | 9 | - |
dc.contributor.author | Zafari, Mohammad | - |
dc.contributor.author | Nissimagoudar, Arun S. | - |
dc.contributor.author | Umer, Muhammad | - |
dc.contributor.author | Lee, Geunsik | - |
dc.contributor.author | Kim, Kwang S. | - |
dc.date.accessioned | 2023-12-21T16:07:25Z | - |
dc.date.available | 2023-12-21T16:07:25Z | - |
dc.date.created | 2021-04-27 | - |
dc.date.issued | 2021-04 | - |
dc.description.abstract | Production of ammonia through an electrochemical process suffers from two challenging issues, namely low catalytic activity and low Faraday efficiency. Here, we boost the N-2 reduction to NH3, while inhibiting the hydrogen evolution reaction (HER) using novel two-dimensional (2D) transition metal borides (MBenes), defect-engineered 2D-materials, and 2D pi-conjugated polymer (2DCP)-supported single-atom catalysts (SACs). Density functional theory (DFT) calculations show that nitrogen molecules can be captured in the hollow sites of MBenes, with significant increases in the adsorption strength and N N bond length. Also, defective 2D-materials formed by the vacancy sites of Te, Se and S expose N-2 molecules to a specific environment adjacent to three transition metals, which drastically improves the catalytic activity and selectivity (by dramatic increase in the N N bond length up to 1.38 angstrom). We report a new mechanism for the nitrogen reduction reaction (NRR) as a combination of dissociative and associative mechanisms. A machine-learning (ML) based fast-screening strategy to predict efficient NRR electrocatalysts is described. Overall, TaB, NbTe2, NbB, HfTe2, MoB, MnB, HfSe2, TaSe2 and Nb@SAC exhibit impressive selectivities over HER with overpotentials of 0.44 V, 0.40 V, 0.24 V, 0.60 V, 0.17 V, 0.17 V, 0.64 V, 0.37 V and 0.58 V, respectively. This study opens a new doorway to overcome previous drawbacks of 2D-materials for NRR. | - |
dc.identifier.bibliographicCitation | JOURNAL OF MATERIALS CHEMISTRY A, v.9, no.14, pp.9203 - 9213 | - |
dc.identifier.doi | 10.1039/d1ta00751c | - |
dc.identifier.issn | 2050-7488 | - |
dc.identifier.scopusid | 2-s2.0-85104096024 | - |
dc.identifier.uri | https://scholarworks.unist.ac.kr/handle/201301/52784 | - |
dc.identifier.url | https://pubs.rsc.org/en/content/articlelanding/2021/TA/D1TA00751C#!divAbstract | - |
dc.identifier.wosid | 000637068600001 | - |
dc.language | 영어 | - |
dc.publisher | ROYAL SOC CHEMISTRY | - |
dc.title | First principles and machine learning based superior catalytic activities and selectivities for N-2 reduction in MBenes, defective 2D materials and 2D pi-conjugated polymer-supported single atom catalysts | - |
dc.type | Article | - |
dc.description.isOpenAccess | FALSE | - |
dc.relation.journalWebOfScienceCategory | Chemistry, Physical; Energy & Fuels; Materials Science, Multidisciplinary | - |
dc.relation.journalResearchArea | Chemistry; Energy & Fuels; Materials Science | - |
dc.type.docType | Article | - |
dc.description.journalRegisteredClass | scie | - |
dc.description.journalRegisteredClass | scopus | - |
Items in Repository are protected by copyright, with all rights reserved, unless otherwise indicated.
Tel : 052-217-1404 / Email : scholarworks@unist.ac.kr
Copyright (c) 2023 by UNIST LIBRARY. All rights reserved.
ScholarWorks@UNIST was established as an OAK Project for the National Library of Korea.