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Titel:

Oxygen Reduction Activities of Strained Platinum Core-Shell Electrocatalysts Predicted by Machine Learning

Dokumenttyp:
Zeitschriftenaufsatz
Autor(en):
Rueck, M.; Garlyyev, B.; Mayr, F.; Bandarenka, A.S.; Gagliardi, A.
Abstract:
Core-shell nanocatalyst activities are chiefly controlled by bimetallic material composition, shell thickness, and nanoparticle size. We present a machine learning framework predicting strain with site-specific precision to rationalize how strain on Pt core-shell nanocatalysts can enhance oxygen reduction activities. Large compressive strain on Pt@Cu and Pt@Ni induces optimal mass activities at 1.9 nm nanoparticle size. It is predicted that bimetallic Pt@Au and Pt@Ag have best mass activities at...     »
Zeitschriftentitel:
J. Phys. Chem. Lett. 11, XXX, 1773-1780 2020-02
Jahr:
2020
Jahr / Monat:
2020-02
Quartal:
1. Quartal
Monat:
Feb
Seitenangaben Beitrag:
1-25
Sprache:
en
Volltext / DOI:
doi:10.1021/acs.jpclett.0c00214
WWW:
https://pubs.acs.org/doi/abs/10.1021/acs.jpclett.0c00214
Verlag / Institution:
American Chemical Society ACS
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