Rück Marlon, Garlyyev Batyr, Mayr Felix, Bandarenka Aliaksandr S, Gagliardi Alessio
Department of Electrical and Computer Engineering, Technical University of Munich, 80333 München, Germany.
Department of Physics, Technical University of Munich, 85748 Garching, Germany.
J Phys Chem Lett. 2020 Mar 5;11(5):1773-1780. doi: 10.1021/acs.jpclett.0c00214. Epub 2020 Feb 19.
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.
