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

TL;DR

This paper introduces a computational model that rapidly predicts the catalytic activity of unstrained platinum nanoparticle electrocatalysts, significantly reducing computation time compared to traditional DFT methods and aiding catalyst discovery.

Contribution

The study presents a novel computational approach that bypasses DFT calculations during runtime, enabling fast and accurate predictions of nanoparticle catalytic activity based on extensive experimental data.

Findings

Accurately reproduces experimental mass activities of Pt nanoparticles.

Predicts potential activity enhancements up to 190%.

Facilitates extensive nanoparticle screening for improved catalysts.

Abstract

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 laborious effort of experimental synthesis and measurements. On the other hand, DFT-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. The generic setup of the computational model is based on DFT results and experimental data obtained worldwide over the past ca 20 years; whereas, importantly, the computational model dispenses with DFT calculations during runtime. This realizes feasible and sharply reduced computation effort in comparison to theoretical approaches where DFT calculations must be performed for each nanoparticle individually. Regarding particle size effects on Pt nanoparticles, experimental catalytic mass activities from previous studies are accurately reproduced by our computational model. Shedding light on the parameter space of particle size effects, this study enables predictions 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.