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Cited by 680

The DOI matches its Crossref record; 14 of the 14 references it cites were found in a registry or an index.

Electrochemical Activation of CO2 through Atomic Ordering Transformations of AuCu Nanoparticles

Open access
Kim, Dohyung, Xie, Chenlu, Becknell, Nigel, Yu, Yi, Karamad, Mohammadreza, Chan, Karen, Crumlin, Ethan J., Nørskov, Jens K., Yang, Peidong·2017·Journal of the American Chemical Society

Abstract

Precise control of elemental configurations within multimetallic nanoparticles (NPs) could enable access to functional nanomaterials with significant performance benefits. This can be achieved down to the atomic level by the disorder-to-order transformation of individual NPs. Here, by systematically controlling the ordering degree, we show that the atomic ordering transformation, applied to AuCu NPs, activates them to perform as selective electrocatalysts for CO 2 reduction. In contrast to the disordered alloy NP, which is catalytically active for hydrogen evolution, ordered AuCu NPs selectively converted CO 2 to CO at faradaic efficiency reaching 80%. CO formation could be achieved with a reduction in overpotential of ∼200 mV, and catalytic turnover was enhanced by 3.2-fold. In comparison to those obtained with a pure gold catalyst, mass activities could be improved as well. Atomic-level structural investigations revealed three atomic gold layers over the intermetallic core to be sufficient for enhanced catalytic behavior, which is further supported by DFT analysis.

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Crossref · 10.1021/jacs.7b03516

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Metadata

Authors
Kim, D., Xie, C., Becknell, N., Yu, Y., Karamad, M., Chan, K., Crumlin, E. J., Nørskov, J. K., & Yang, P.
Venue
Journal of the American Chemical Society
Published
2017-06-07
Volume
139
Issue
24
Pages
8329-8336
ISSN
  • 0002-7863 (ISSN-L)
  • 1520-5126
OpenAlex
W2619141030
Cited by
680
References
14
FWCI
18.50
Type
journal article
Language
en
Open access
green

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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).

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