4.8 Article

Dynamic restructuring induced Cu nanoparticles with ideal nanostructure for selective multi-carbon compounds production via carbon dioxide electroreduction

Journal

JOURNAL OF CATALYSIS
Volume 383, Issue -, Pages 42-50

Publisher

ACADEMIC PRESS INC ELSEVIER SCIENCE
DOI: 10.1016/j.jcat.2020.01.002

Keywords

CO2 reduction; Electrocatalysis; Copper; Dynamic restructuring; Nanostructure

Funding

  1. National Natural Science Foundation of China [21806111, 61771318, 21675080]
  2. China Postdoctoral Science Foundation [2018M643166]
  3. Natural Science Foundation of Jiangsu Province [BK20170073]

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Production of multi-carbon (C2+) products via electrochemical CO2 reduction reaction (CO2RR) has aroused intensive interest in this decade. However, it is still a great challenge in large-scale synthesis of electrocatalysts for C2+ compounds production with high selectivity and rate in neutral electrolyte. In this study, a robust and scalable aqueous synthesis method was proposed to prepare CuO nanosheets, which was directly used as CO2RR electrocatalyst. The catalyst underwent dynamic restructuring during the pre-reduction stage, which significantly changed the morphology, crystalline structure and electrochemical properties of the catalyst. As a result, the pristine CuO nanosheets gradually evolved to corallike Cu nanoparticle ensembles (CL-Cu NEs). In 0.1 M KHCO3 at -1.1 V vs. RHE, the CL-Cu NEs exhibited a faradaic efficiency and partial current density of C2+ products of 72.1% and 25.2 mA cm(-2) respectively and no degradation was observed in 15 h running. In contrast to many Cu-based catalysts showing considerable C2+ products selectivity, the (sub)surface oxygen and Cu' species were unlikely to play a significant role in this study. The strong performance of CL-Cu NEs was ascribed to the synergism between the coral-like morphology and abundant Cu(1 0 0)/Cu(1 1 1) interfaces. This methodology may provide a cost-effective approach for large-scale preparation of CO2RR electrocatalysts with ideal nanostructure. (C) 2020 Elsevier Inc. All rights reserved.

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