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An Element-Based Generalized Coordination Number for Predicting the Oxygen Binding Energy on Pt(3)M (M = Co, Ni, or Cu) Alloy Nanoparticles
[Image: see text] We studied the binding energies of O species on face-centered-cubic Pt(3)M nanoparticles (NPs) with a Pt-skin layer using density functional theory calculations, where M is Co, Ni, or Cu. It is desirable to express the property by structural parameters rather than by calculated ele...
Autores principales: | , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
American Chemical Society
2021
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Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7860238/ https://www.ncbi.nlm.nih.gov/pubmed/33553938 http://dx.doi.org/10.1021/acsomega.0c05649 |
Sumario: | [Image: see text] We studied the binding energies of O species on face-centered-cubic Pt(3)M nanoparticles (NPs) with a Pt-skin layer using density functional theory calculations, where M is Co, Ni, or Cu. It is desirable to express the property by structural parameters rather than by calculated electronic structures such as the d-band center. A generalized coordination number (GCN) is an effective descriptor to predict atomic or molecular adsorption energy on Pt-NPs. The GCN was extended to the prediction of highly active sites for oxygen reduction reaction. However, it failed to explain the O binding energies on Pt-skin Pt(150)M(51)-NPs. In this study, we introduced an element-based GCN, denoted as GCN(A–B), and considered it as a descriptor for supervised learning. The obtained regression coefficients of GCN(Pt–Pt) were smaller than those of the other GCN(A–B). With increasing M atoms in the subsurface layer, GCN(Pt–M), GCN(M–Pt), and GCN(M–M) increased. These factors could reproduce the calculated result that the O binding energies of the Pt-skin Pt(150)M(51)-NPs were less negative than those of the Pt(201)-NPs. Thus, GCN(A–B) explains the ligand effect of the O binding energy on the Pt-skin Pt(150)M(51)-NPs. |
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