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Cited by 645

The DOI matches its Crossref record; 18 of the 18 references it cites were found in a registry or an index.

Copper nanoparticle ensembles for selective electroreduction of CO 2 to C 2 –C 3 products

Open access
Kim, Dohyung, Kley, Christopher S., Li, Yifan, Yang, Peidong·2017·Proceedings of the National Academy of Sciences

Abstract

Significance: Electrochemical conversion of CO 2 to carbon-based products, which can be used directly as fuels or indirectly as fuel precursors, is suggested as one of the promising solutions for sustainability. Not only does this process allow using renewables such as solar electricity as energy input, but CO 2 emitted from the consumption process can be recycled back into fuels. The success of this technology depends on the value added to the product that forms from CO 2 , and therefore it is important to facilitate multicarbon product generation. This work presents a copper-based catalyst, formed in situ from an ensemble of nanoparticles, that is able to selectively generate C 2 –C 3 products at low overpotentials with good stability, where their efficient formation has been difficult to achieve.

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Crossref · 10.1073/pnas.1711493114

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Metadata

Authors
Kim, D., Kley, C. S., Li, Y., & Yang, P.
Venue
Proceedings of the National Academy of Sciences
Published
2017-09-18
Volume
114
Issue
40
Pages
10560-10565
ISSN
  • 0027-8424 (ISSN-L)
  • 1091-6490
OpenAlex
W2755271205
Cited by
645
References
18
FWCI
14.94
Type
journal article
Language
en
Open access
bronze

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CoChat. (2026). Citation check: Copper nanoparticle ensembles for selective electroreduction of CO 2 to C 2 –C 3 products. https://app.cochat.ai/papers/10.1073/pnas.1711493114
The paper
Kim, D., Kley, C. S., Li, Y., & Yang, P. (2017). Copper nanoparticle ensembles for selective electroreduction of CO 2 to C 2 –C 3 products. Proceedings of the National Academy of Sciences. https://doi.org/10.1073/pnas.1711493114

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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.