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Tailoring Copper Nanocrystals towards C2 Products in Electrochemical CO2 Reduction

Open access
Loiudice, Anna, Lobaccaro, Peter, Kamali, Esmail A., Thao, Timothy, Huang, Brandon H., Ager, Joel W., Buonsanti, Raffaella·2016·Angewandte Chemie International Edition

Abstract

Favoring the CO 2 reduction reaction (CO2RR) over the hydrogen evolution reaction and controlling the selectivity towards multicarbon products are currently major scientific challenges in sustainable energy research. It is known that the morphology of the catalyst can modulate catalytic activity and selectivity, yet this remains a relatively underexplored area in electrochemical CO 2 reduction. Here, we exploit the material tunability afforded by colloidal chemistry to establish unambiguous structure/property relations between Cu nanocrystals and their behavior as electrocatalysts for CO 2 reduction. Our study reveals a non‐monotonic size‐dependence of the selectivity in cube‐shaped copper nanocrystals. Among 24 nm, 44 nm and 63 nm cubes tested, the cubes with 44 nm edge length exhibited the highest selectivity towards CO2RR (80 %) and faradaic efficiency for ethylene (41 %). Statistical analysis of the surface atom density suggests the key role played by edge sites in CO2RR.

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Metadata

Authors
Loiudice, A., Lobaccaro, P., Kamali, E. A., Thao, T., Huang, B. H., Ager, J. W., & Buonsanti, R.
Venue
Angewandte Chemie International Edition
Publisher
Wiley
Published
2016-04-05
Volume
55
Issue
19
Pages
5789-5792
ISSN
  • 1433-7851 (ISSN-L)
  • 1521-3773
OpenAlex
W2336845479
Cited by
896
References
7
FWCI
17.88
Type
journal article
Language
en
Open access
green

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CoChat. (2026). Citation check: Tailoring Copper Nanocrystals towards C2 Products in Electrochemical CO2 Reduction. https://app.cochat.ai/papers/10.1002/anie.201601582
The paper
Loiudice, A., Lobaccaro, P., Kamali, E. A., Thao, T., Huang, B. H., Ager, J. W., & Buonsanti, R. (2016). Tailoring Copper Nanocrystals towards C2 Products in Electrochemical CO2 Reduction. Angewandte Chemie International Edition. https://doi.org/10.1002/anie.201601582

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