Machine-Learning-Driven High-Throughput Screening of Transition-Metal Atom Intercalated g-C3N4/MX2 (M = Mo, W; X = S, Se, Te) Heterostructures for the Hydrogen Evolution Reaction-Reference-Cited by-同舟云学术

Machine-Learning-Driven High-Throughput Screening of Transition-Metal Atom Intercalated g-C₃N₄/MX₂ (M = Mo, W; X = S, Se, Te) Heterostructures for the Hydrogen Evolution Reaction

Published:2024-03-04 Issue:10 Volume:16 Page:12437-12445
ISSN:1944-8244
Container-title:ACS Applied Materials & Interfaces
language:en
Short-container-title:ACS Appl. Mater. Interfaces

Author:

Jyothirmai M. V.¹^ORCID,Dantuluri Roshini¹^ORCID,Sinha Priyanka¹,Abraham B. Moses²^ORCID,Singh Jayant K.¹³^ORCID

Affiliation:

1. Department of Chemical Engineering, Indian Institute of Technology Kanpur, Kanpur 208016, India

2. Departament de Ciència de Materials i Química Física, Institut de Química Teòrica i Computacional (IQTCUB), Universitat de Barcelona, c/Martí i Franquès 1-11, Barcelona 08028, Spain

3. Prescience Insilico Private Limited, Bangalore 560049, India

Funder

Ministerio de Ciencia e Innovaci?n

Department of Science and Technology, Ministry of Science and Technology, India

Publisher

American Chemical Society (ACS)

Link

https://pubs.acs.org/doi/pdf/10.1021/acsami.3c17389

Reference48 articles.

1. Effect of terminations on the hydrogen evolution reaction mechanism on Ti₃C₂ MXene

2. Ternary Cu(OH)₂/P(g-C₃N₄)/MoS₂ Nanostructures for Photocatalytic Hydrogen Production

3. Thermodynamics and Kinetics of Molecular Hydrogen Adsorption and Dissociation on MXenes: Relevance to Heterogeneously Catalyzed Hydrogenation Reactions