Identifying efficient and inexpensive hydrodesulfurization catalysts through Machine Learning-Assisted analysis of Metal–Sulfur bonds in transition metal sulfides-Reference-Cited by-同舟云学术

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Identifying efficient and inexpensive hydrodesulfurization catalysts through Machine Learning-Assisted analysis of Metal–Sulfur bonds in transition metal sulfides

Published:2024-10 Issue: Volume:298 Page:120337
ISSN:0009-2509
Container-title:Chemical Engineering Science
language:en
Short-container-title:Chemical Engineering Science

Author:

Ding Yu,Shang Hui^ORCID,Yang Changze,Zhao Liang^ORCID,Duan Aijun

Publisher

Elsevier BV

Reference68 articles.

1. Machine Learning Enabled Computational Screening of Inorganic Solid Electrolytes for Suppression of Dendrite Formation in Lithium Metal Anodes;Ahmad;ACS Cent. Sci.,2018

2. Searching for the rate determining step of the H2S reaction on Fe (110) surface;Akande;Applied Surface Science,2020

3. Thermodynamic limit for synthesis of metastable inorganic materials;Aykol;Science Advances,2018

4. Alkyldibenzothiophenes hydrodesulfurization-promoter effect, reactivity, and reaction mechanism;Bataille;Journal of Catalysis,2000

5. Ligand effects in heterogeneous catalysis and electrochemistry;Bligaard;Electrochimica Acta,2007

Cited by 1 articles. 订阅此论文施引文献订阅此论文施引文献，注册后可以免费订阅5篇论文的施引文献，订阅后可以查看论文全部施引文献

1. Modulating the Reaction Pathway of Ni₂P/Al₂O₃ by Introducing Different Noble Metals for Hydrodesulfurization of Diesel;Inorganic Chemistry;2024-08-28

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