.png){kind=icon}
.png){kind=icon}
Return
Operando Cluster Catalysis via Coupled Surface–Subsurface Dynamics
.png){kind=icon}
PRE
AI
DOI：10.1021/jacs.5c15920.png)
.png){kind=icon}
Original request for help.png){kind=icon}
Share.png){kind=icon}
Save
Abstract
En 中文
Catalytic surfaces and subsurfaces undergo continuous restructuring under reaction conditions, yet how coupled surface–subsurface dynamics governs the emergence and performance of active sites remains unresolved. Here, we introduce a machine-learning-accelerated multiscale framework that integrates grand-canonical Monte Carlo sampling, neural-network molecular dynamics, and first-principles microkinetics to resolve operando catalyst restructuring at the atomic scale. Using Pd-catalyzed acetylene hydrogenation as a prototypical system, we show that adsorbed hydrocarbons weaken Pd–Pd bonds, whereas subsurface carbon anchors low-coordination atoms, together promoting the operando formation of Pd1 single atoms and Pd2, Pd3, Pd6, and Pd10 clusters. A population-weighted activity analysis identifies Pd10 as the dominant active ensemble, achieving an ∼36,000-fold rate enhancement and >99% ethylene selectivity over clean and hydrocarbon-covered Pd surfaces. A structure–activity landscape based on cluster height further quantifies this relationship. Extending this approach to Ag, Cu, Au, Ni, Rh, and Pt reveals that operando cluster formation requires moderate hydrocarbon coadsorption and subsurface carbon. This transferable approach reveals how coupled surface–subsurface dynamics govern the emergence and performance of active sites, offering broad applicability to other reactions in complex environments.
Journal
IF:
15.6
Papers:
20.0W
Citations:
60.2W