Journal
JOURNAL OF PHYSICAL CHEMISTRY LETTERS
Volume 11, Issue 5, Pages 1773-1780Publisher
AMER CHEMICAL SOC
DOI: 10.1021/acs.jpclett.0c00214
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Funding
- German Research Foundation (DFG) [355784621]
- Germany's Excellence Cluster e-conversion - DFG
- Technical University of Munich, International Graduate School of Science and Engineering project [11.01]
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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 the best mass activities at 2.8 nm, where active sites are exposed to weak compressive strain. We demonstrate that optimal strain depends on the nanoparticle size; for instance, strengthening compressive strain on 1.92 nm sized Pt@Cu and Pt@Ni, or weakening compressive strain on 2.83 nm sized Pt@Ag and Pt@Au, can lead to further enhanced mass activities.
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