Single-Particle Electrochemistry for Probing Catalyst Turnover Number

The long-term stability of electrocatalysts is increasingly recognized as a key parameter for catalyst design. However, it is still less emphasized in fundamental electrocatalysis studies compared to applied research on fuel cells and batteries. A valuable metric for measuring catalyst durability is turnover number, which indicates the maximum product yield from a catalytic center. Traditionally, turnover number for a specific catalyst is assessed on a macroscopic scale, providing a single value with error margins under specific reaction conditions. We have developed a new method to quantitatively assess both the number of active sites on individual platinum nanoparticles and their overall catalytic charge during electrochemical hydrogen evolution at high current densities (>0.4 A/cm²). This approach enables the direct measurement of the turnover number of individual monodispersed nanoparticles, revealing a wide statistical distribution spanning more than an order of magnitude. We further demonstrate that the heterogeneity is influenced by nanoparticle size and applied potential. These findings establish this method as a powerful accelerated testing platform for assessing macroscopic catalyst performance, which is strongly influenced by underlying nanoscale heterogeneity. To further investigate the origins of catalyst deactivation, we employed electrochemical correlative microscopy, which combines scanning electrochemical cell microscopy with transmission electron microscopy. We determined that deactivation of individual Pt nanoparticles seems to involve surface-blocking effects (such as nanobubble formation or subsurface hydrogen incorporation), consistent across nanoimpact, microelectrode, and scanning electrochemical cell microscopy measurements. Together, single-particle turnover number quantification, electrochemical correlative microscopy imaging, and multiscale measurements create a unified framework for linking nanoscale electrocatalytic behavior to macroscopic performance. This work advances the mechanistic understanding of catalyst degradation. Understanding these processes from the bottom up is crucial for predictive catalyst design and the early detection of catalyst degradation. Building on this platform, we plan on investigating a deliberately poisoned system using industrially relevant inhibitors, addressing a critical challenge for fuel-cell technologies.