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

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.

Optimizing the Size of Platinum Nanoparticles for Enhanced Mass Activity in the Electrochemical Oxygen Reduction Reaction.

High oxygen reduction (ORR) activity has been for many years considered as the key to many energy applications. Herein, by combining theory and experiment we prepare Pt nanoparticles with optimal size for the efficient ORR in proton-exchange-membrane fuel cells. Optimal nanoparticle sizes are predicted near 1, 2, and 3 nm by computational screening. To corroborate our computational results, we have addressed the challenge of approximately 1 nm sized Pt nanoparticle synthesis with a metal-organic framework (MOF) template approach. The electrocatalyst was characterized by HR-TEM, XPS, and its ORR activity was measured using a rotating disk electrode setup. The observed mass activities (0.87±0.14 A mgPt -1 ) are close to the computational prediction (0.99 A mgPt -1 ). We report the highest to date mass activity among pure Pt catalysts for the ORR within similar size range. The specific and mass activities are twice as high as the Tanaka commercial Pt/C catalysis.

Fast identification of optimal pure platinum nanoparticle shapes and sizes for efficient oxygen electroreduction.

Recent advances in experimental synthesis of nanostructures have shown that the interplay between nanoparticle shapes and sizes is crucial to achieve catalysts with high mass activity toward oxygen electroreduction. This is particularly important for proton-exchange membrane fuel cells (PEMFCs), in which expensive and scarce Pt electrocatalysts are used. In this work, we propose a theoretical approach for oxygen electroreduction on PEMFCs to identify not only the size of optimal nanoparticles, but also their shapes. Remarkably, high mass activities up to 4.28 A mgPt -1 are predicted for rod-like nanostructures. Furthermore, we examine nanostructure size effects to guide chemical routes for experimental synthesis of the identified electrocatalysts. Our fast theoretical evaluation of thousands of different nanostructures aids in the search for active catalysts, as substantially enhanced mass activities over commercial Pt/C are predicted for pure Pt electrocatalysts, thus unveiling great potential to reduce the Pt loading in PEMFCs.

Oxygen Reduction Reaction: Rapid Prediction of Mass Activity of Nanostructured Platinum Electrocatalysts.

Tailored Pt nanoparticle catalysts are promising candidates to accelerate the oxygen reduction reaction (ORR) in fuel cells. However, the search for active nanoparticle catalysts is hindered by the laborious effort of experimental synthesis and measurements. On the other hand, density functional theory-based approaches are still time-consuming and often not efficient. In this study, we introduce a computational model which enables rapid catalytic activity calculation of unstrained pure Pt nanoparticle electrocatalysts. Regarding particle size effects on Pt nanoparticles, experimental catalytic mass activities from previous studies are accurately reproduced by our computational model. Moreover, beyond available experiments, our computational model identifies potential enhancement in mass activity up to 190% over the experimentally detected maximum. Importantly, the rapid activity calculation enabled by our computational model may pave the way for extensive nanoparticle screening to expedite the search for improved electrocatalysts.