Reducing Ensemble Averaging for Mechanistic Understanding of Electrocatalysis in Energy Conversion Reactions

Abstract

The rational design of high-performance electrocatalytic systems for green and sustainable synthetic and energy-conversion applications requires a deeper understanding than what traditional analytical methods and experimental designs can provide. Ensemble-averaged responses produced by these approaches reveal little about the heterogeneities of molecules, reactions, active sites, crystalline domains, and other features important in understanding reaction mechanisms in electrochemical systems. Measurements with high spatial and temporal resolution effectively reduce the ensemble of entities being sampled, enabling studies of small-scale features and species therein, fast changes in the catalyst's surface, and transient intermediates that would otherwise be averaged over. In this Perspective, we describe recent advancements in fundamental understanding of electrocatalytic processes enabled by approaches that reduce ensemble averaging with high spatial or temporal resolution measurements.