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Cited by 447

The DOI matches its Crossref record; 10 of the 10 references it cites were found in a registry or an index.

Grain-Boundary-Rich Copper for Efficient Solar-Driven Electrochemical CO2 Reduction to Ethylene and Ethanol

Chen, Zhiqiang, Wang, Tuo, Liu, Bin, Cheng, Dongfang, Hu, Congling, Zhang, Gong, Zhu, Wenjin, Wang, Huaiyuan, Zhao, Zhi-Jian, Gong, Jinlong·2020·Journal of the American Chemical Society

Abstract

The grain boundary in copper-based electrocatalysts has been demonstrated to improve the selectivity of solar-driven electrochemical CO 2 reduction toward multicarbon products. However, the approach to form grain boundaries in copper is still limited. This paper describes a controllable grain growth of copper electrodeposition via poly(vinylpyrrolidone) used as an additive. A grain-boundary-rich metallic copper could be obtained to convert CO 2 into ethylene and ethanol with a high selectivity of 70% over a wide potential range. In situ attenuated total reflection surface-enhanced infrared absorption spectroscopy unveils that the existence of grain boundaries enhances the adsorption of the key intermediate (*CO) on the copper surface to boost the further CO 2 reduction. When coupling with a commercially available Si solar cell, the device achieves a remarkable solar-to-C2-products conversion efficiency of 3.88% at a large current density of 52 mA·cm –2 . This low-cost and efficient device is promising for large-scale application of solar-driven CO 2 reduction.

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Crossref · 10.1021/jacs.0c00971

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Metadata

Authors
Chen, Z., Wang, T., Liu, B., Cheng, D., Hu, C., Zhang, G., Zhu, W., Wang, H., Zhao, Z.-J., & Gong, J.
Venue
Journal of the American Chemical Society
Published
2020-03-27
Volume
142
Issue
15
Pages
6878-6883
ISSN
  • 0002-7863 (ISSN-L)
  • 1520-5126
OpenAlex
W3014036038
Cited by
447
References
10
FWCI
12.29
Type
journal article
Language
en
Open access
closed

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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.