Machine Learning-Assisted Screening of Cu-Based Trimetallic Catalysts for Electrochemical Conversion of CO<sub>2</sub> to CO-Reference-Cited by-同舟云学术

Machine Learning-Assisted Screening of Cu-Based Trimetallic Catalysts for Electrochemical Conversion of CO₂ to CO

Published:2023-09-25 Issue:3 Volume:38 Page:2074-2083
ISSN:0887-0624
Container-title:Energy & Fuels
language:en
Short-container-title:Energy Fuels

Author:

Xiong Bo¹,Liu Jing¹^ORCID,Yang Yingju¹,Liu Wei¹,Chen Man¹,Bai Hongcun²

Affiliation:

1. State Key Laboratory of Coal Combustion, School of Energy and Power Engineering, Huazhong University of Science and Technology, Wuhan 430074, Huibei, People’s Republic of China

2. State Key Laboratory of High-efficiency Utilization of Coal and Green Chemical Engineering, School of Chemistry and Chemical Engineering, Ningxia University, Yinchuan 750021, Ningxia, People’s Republic of China

Funder

State Key Laboratory of High-efficiency Utilization of Coal and Green Chemical Engineering, Ningxia University

Natural Science Foundation of Hubei Province

Fundamental Research Funds for the Central Universities

Publisher

American Chemical Society (ACS)

Subject

Energy Engineering and Power Technology,Fuel Technology,General Chemical Engineering

Link

https://pubs.acs.org/doi/pdf/10.1021/acs.energyfuels.3c02359

Reference41 articles.

1. Catalysis for Selected C1 Chemistry

2. CO2 reduction routes to value-added oxygenates: a review

3. Solar driven CO₂reduction: from materials to devices

4. A review of major non-power-related carbon dioxide stream compositions

5. Advances and Challenges for the Electrochemical Reduction of CO 2 to CO: From Fundamentals to Industrialization

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