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The effects of currents and potentials on the selectivities of copper toward carbon dioxide electroreduction

Open access
Ren, Dan, Fong, Jinhuan, Yeo, Boon Siang·2018·Nature Communications

Abstract

Copper electrodes have been shown to be selective toward the electroreduction of carbon dioxide to ethylene, carbon monoxide, or formate. However, the underlying causes of their activities, which have been attributed to a rise in local pH near the surface of the electrode, presence of atomic-scale defects, and/or residual oxygen atoms in the catalysts, etc., have not been generally agreed on. Here, we perform a study of carbon dioxide reduction on four copper catalysts from −0.45 to −1.30 V vs. reversible hydrogen electrode. The selectivities exhibited by 20 previously reported copper catalysts are also analyzed. We demonstrate that the selectivity of carbon dioxide reduction is greatly affected by the applied potentials and currents, regardless of the starting condition of copper catalysts. This study shows that optimization of the current densities at the appropriate potential windows is critical for designing highly selective copper catalysts.

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Metadata

Authors
Ren, D., Fong, J., & Yeo, B. S.
Venue
Nature Communications
Published
2018-03-02
Volume
9
Issue
1
Pages
925-925
ISSN
2041-1723 (ISSN-L)
OpenAlex
W2790092629
Cited by
316
References
54
FWCI
8.03
Type
journal article
Language
en
Open access
gold

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CoChat. (2026). Citation check: The effects of currents and potentials on the selectivities of copper toward carbon dioxide electroreduction. https://app.cochat.ai/papers/10.1038/s41467-018-03286-w
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Ren, D., Fong, J., & Yeo, B. S. (2018). The effects of currents and potentials on the selectivities of copper toward carbon dioxide electroreduction. Nature Communications. https://doi.org/10.1038/s41467-018-03286-w

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Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).

Semantic Scholar’s data is described in: Kinney, R., et al. (2023). The Semantic Scholar Open Data Platform. arXiv:2301.10140.