Research Progress in High-Throughput Screening of CO 2 Reduction Catalysts

Qinglin Wu, Meidie Pan, Shikai Zhang, Dongpeng Sun, Yang Yang, Dong Chen (), David A. Weitz and Xiang Gao ()
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Qinglin Wu: College of Energy Engineering and State Key Laboratory of Clean Energy Utilization, Zhejiang University, Hangzhou 310003, China
Meidie Pan: Department of Medical Oncology, The First Affiliated Hospital, School of Medicine, Zhejiang University, Hangzhou 310003, China
Shikai Zhang: College of Energy Engineering and State Key Laboratory of Clean Energy Utilization, Zhejiang University, Hangzhou 310003, China
Dongpeng Sun: College of Energy Engineering and State Key Laboratory of Clean Energy Utilization, Zhejiang University, Hangzhou 310003, China
Yang Yang: College of Energy Engineering and State Key Laboratory of Clean Energy Utilization, Zhejiang University, Hangzhou 310003, China
Dong Chen: College of Energy Engineering and State Key Laboratory of Clean Energy Utilization, Zhejiang University, Hangzhou 310003, China
David A. Weitz: John A. Paulson School of Engineering and Applied Sciences, Harvard University, Cambridge, MA 02138, USA
Xiang Gao: College of Energy Engineering and State Key Laboratory of Clean Energy Utilization, Zhejiang University, Hangzhou 310003, China

Energies, 2022, vol. 15, issue 18, 1-18

Abstract: The conversion and utilization of carbon dioxide (CO 2 ) have dual significance for reducing carbon emissions and solving energy demand. Catalytic reduction of CO 2 is a promising way to convert and utilize CO 2 . However, high-performance catalysts with excellent catalytic activity, selectivity and stability are currently lacking. High-throughput methods offer an effective way to screen high-performance CO 2 reduction catalysts. Here, recent advances in high-throughput screening of electrocatalysts for CO 2 reduction are reviewed. First, the mechanism of CO 2 reduction reaction by electrocatalysis and potential catalyst candidates are introduced. Second, high-throughput computational methods developed to accelerate catalyst screening are presented, such as density functional theory and machine learning. Then, high-throughput experimental methods are outlined, including experimental design, high-throughput synthesis, in situ characterization and high-throughput testing. Finally, future directions of high-throughput screening of CO 2 reduction electrocatalysts are outlooked. This review will be a valuable reference for future research on high-throughput screening of CO 2 electrocatalysts.

Keywords: CO 2 reduction; electrocatalyst; high-throughput computing; machine learning; high-throughput screening; in situ characterization (search for similar items in EconPapers)
JEL-codes: Q Q0 Q4 Q40 Q41 Q42 Q43 Q47 Q48 Q49 (search for similar items in EconPapers)
Date: 2022
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (1)

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