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Cited by 352

The DOI matches its Crossref record; 3 of the 3 references it cites were found in a registry or an index.

Suppression of Hydrogen Evolution in Acidic Electrolytes by Electrochemical CO2 Reduction

Open access
Bondue, Christoph J., Graf, Matthias, Goyal, Akansha, Koper, Marc T. M.·2021·Journal of the American Chemical Society

Abstract

Abstract In this article we investigate the electrochemical reduction of CO2 at gold electrodes under mildly acidic conditions. Differential electrochemical mass spectroscopy (DEMS) is used to quantify the amounts of formed hydrogen and carbon monoxide as well as the consumed amount of CO2. We investigate how the Faradaic efficiency of CO formation is affected by the CO2 partial pressure (0.1–0.5 bar) and the proton concentration (1–0.25 mM). Increasing the former enhances the rate of CO2 reduction and suppresses hydrogen evolution from proton reduction, leading to Faradaic efficiencies close to 100%. Hydrogen evolution is suppressed by CO2 reduction as all protons at the electrode surfaces are used to support the formation of water (CO2 + 2H+ + 2e– → CO + H2O). Under conditions of slow mass transport, this leaves no protons to support hydrogen evolution. On the basis of our results, we derive a general design principle for acid CO2 electrolyzers to suppress hydrogen evolution from proton reduction: the rate of CO/OH– formation must be high enough to match/compensate the mass transfer of protons to the electrode surface.

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Crossref · 10.1021/jacs.0c10397

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Metadata

Authors
Bondue, C. J., Graf, M., Goyal, A., & Koper, M. T. M.
Venue
Journal of the American Chemical Society
Published
2020-12-24
Volume
143
Issue
1
Pages
279-285
ISSN
  • 0002-7863 (ISSN-L)
  • 1520-5126
OpenAlex
W3116535123
Cited by
352
References
3
FWCI
6.65
Type
journal article
Language
en
Open access
hybrid

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Bibliographic data: Semantic Scholar, OpenAlex, Crossref.

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.