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Cited by 270

The DOI matches its Crossref record; 7 of the 7 references it cites were found in a registry or an index.

Kirkendall Effect and Lattice Contraction in Nanocatalysts: A New Strategy to Enhance Sustainable Activity

Wang, Jia X., Ma, Chao, Choi, YongMan, Su, Dong, Zhu, Yimei, Liu, Ping, Si, Rui, Vukmirovic, Miomir B., Zhang, Yu, Adzic, Radoslav R.·2011·Journal of the American Chemical Society

Abstract

Core-shell nanoparticles increasingly are found to be effective in enhancing catalytic performance through the favorable influence of the core materials on the active components at the surface. Yet, sustaining high activities under operating conditions often has proven challenging. Here we explain how differences in the components' diffusivity affect the formation and stability of the core-shell and hollow nanostructures, which we ascribe to the Kirkendall effect. Using Ni nanoparticles as the templates, we fabricated compact and smooth Pt hollow nanocrystals that exhibit a sustained enhancement in Pt mass activity for oxygen reduction in acid fuel cells. This is achieved by the hollow-induced lattice contraction, high surface area per mass, and oxidation-resistant surface morphology--a new route for enhancing both the catalysts' activity and durability. The results indicate challenges and opportunities brought by the nanoscale Kirkendall effect for designing, at the atomic level, nanostructures with a wide range of novel properties.

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Crossref · 10.1021/ja204518x

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Metadata

Authors
Wang, J. X., Ma, C., Choi, Y., Su, D., Zhu, Y., Liu, P., Si, R., Vukmirovic, M. B., Zhang, Y., & Adzic, R. R.
Venue
Journal of the American Chemical Society
Published
2011-08-10
Volume
133
Issue
34
Pages
13551-13557
ISSN
  • 0002-7863 (ISSN-L)
  • 1520-5126
OpenAlex
W1990348970
Cited by
270
References
7
FWCI
12.22
Type
journal article
Language
en
Open access
closed

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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.