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

Author

Listed:
  • 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
    Department of Medical Oncology, The First Affiliated Hospital, School of Medicine, Zhejiang University, Hangzhou 310003, China
    Zhejiang Key Laboratory of Smart Biomaterials, College of Chemical and Biological Engineering, Zhejiang University, Hangzhou 310027, 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)

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.

Suggested Citation

  • Qinglin Wu & Meidie Pan & Shikai Zhang & Dongpeng Sun & Yang Yang & Dong Chen & David A. Weitz & Xiang Gao, 2022. "Research Progress in High-Throughput Screening of CO 2 Reduction Catalysts," Energies, MDPI, vol. 15(18), pages 1-18, September.
  • Handle: RePEc:gam:jeners:v:15:y:2022:i:18:p:6666-:d:913110
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    References listed on IDEAS

    as
    1. Jeremy L. Hitt & Yuguang C. Li & Songsheng Tao & Zhifei Yan & Yue Gao & Simon J. L. Billinge & Thomas E. Mallouk, 2021. "A high throughput optical method for studying compositional effects in electrocatalysts for CO2 reduction," Nature Communications, Nature, vol. 12(1), pages 1-10, December.
    2. Volker Liermann & Sangmeng Li, 2021. "Methods of Machine Learning," Springer Books, in: Volker Liermann & Claus Stegmann (ed.), The Digital Journey of Banking and Insurance, Volume III, pages 225-238, Springer.
    3. Li Zeng & Jun Shi & Hanxin Chen & Chong Lin, 2021. "Ag Nanowires/C as a Selective and Efficient Catalyst for CO 2 Electroreduction," Energies, MDPI, vol. 14(10), pages 1-10, May.
    4. Gianluca Zanellato & Pier Giorgio Schiavi & Robertino Zanoni & Antonio Rubino & Pietro Altimari & Francesca Pagnanelli, 2021. "Electrodeposited Copper Nanocatalysts for CO 2 Electroreduction: Effect of Electrodeposition Conditions on Catalysts’ Morphology and Selectivity," Energies, MDPI, vol. 14(16), pages 1-15, August.
    5. Soohyun Kim & Yunxia Yang & Renata Lippi & Hokyung Choi & Sangdo Kim & Donghyuk Chun & Hyuk Im & Sihyun Lee & Jiho Yoo, 2021. "Low-Rank Coal Supported Ni Catalysts for CO 2 Methanation," Energies, MDPI, vol. 14(8), pages 1-13, April.
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    Cited by:

    1. Wei, Yimeng & Zhuang, Zitong & Shi, Jinwen & Jin, Hui, 2024. "Thermochemical conversion of guaiacol with supercritical CO2: Experimental insights," Energy, Elsevier, vol. 299(C).

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