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

Marlon Rück, Batyr Garlyyev, Felix Mayr, Aliaksandr S. Bandarenka, Alessio Gagliardi

Research output: Contribution to journalArticlepeer-review

38 Scopus citations

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 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.

Original languageEnglish
Pages (from-to)1773-1780
Number of pages8
JournalJournal of Physical Chemistry Letters
Volume11
Issue number5
DOIs
StatePublished - 5 Mar 2020

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