Dynamic Polaronic Control of Metal Cluster Adaptability on Reducible Oxides
Metal–oxide interactions are ubiquitous in many technological applications and involve a complex interplay between the oxide support and the metal nanoparticle. Particularly, it has been proposed that in strong metal–support interaction, the defect chemistry affects the metal cluster morphology. Her...
| Autores: | , , , |
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| Tipo de recurso: | artículo |
| Fecha de publicación: | 2026 |
| País: | España |
| Institución: | Varias* (Consorci de Biblioteques Universitáries de Catalunya, Centre de Serveis Científics i Acadèmics de Catalunya) |
| Repositorio: | Recercat. Dipósit de la Recerca de Catalunya |
| OAI Identifier: | oai:recercat.cat:2072/489211 |
| Acceso en línea: | http://hdl.handle.net/2072/489211 https://doi.org/10.1021/jacs.5c13140 |
| Access Level: | acceso abierto |
| Palabra clave: | Química 54 |
| Sumario: | Metal–oxide interactions are ubiquitous in many technological applications and involve a complex interplay between the oxide support and the metal nanoparticle. Particularly, it has been proposed that in strong metal–support interaction, the defect chemistry affects the metal cluster morphology. Here we develop a physics-guided machine learning framework to decode these interactions using Pt7 and Pt13 representative of planar and tridimensional clusters, analyzing the impact of across oxygen vacancy concentrations of CeO2–x = 0–12.5% (528 configurations). Our models (R2 > 0.97) reveal that polaron swarms, rather than defect concentrations, predominantly control cluster shape and charge through size-dependent pathways. The framework yields quantitative design principles for defect-driven catalyst optimization and provides a general methodology for systematic mechanisms of metal–support interactions across diverse catalyst systems |
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