A Combined First-Principles and Data-Driven Computational Framework to Analyze the Surface Structure, Composition, and Stability of Binary Alloy Catalysts

Pt-based bimetallic alloys are considerably more active than Pt in the oxygen reduction reaction (ORR). This increased activity has been attributed to the weakening of the adsorption of ORR intermediates due to the presence of Pt “skins.” Density functional theory (DFT) calculations have, in turn, pointed to the importance of surface segregation energies and Pt leaching on the formation and stability of the skins on close-packed surfaces of Pt alloys. The generalizability of these insights across different chemical environments, surface compositions, and facets, however, remains a subject of active research and is the focus of this work. We present a generalized computational framework combining DFT calculations and data-driven methods to predict the stability of different Pt3X (X = Ni, Co, Fe, and Cu) alloy facets under vacuum conditions and in the presence of an electrochemical environment, wherein we analyze the combined effect of segregation, intrasurface phase separation, leaching, and surface oxidation as a function of electrode potential. The analysis reveals that a subtle interplay of these factors influences Pt skin formation and stability, with Pt segregation being a strong function of the surface structure and continuous base metal dissolution being thermodynamically, although not always kinetically, favored at ORR-relevant voltages.

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Publication Details

Journal
The Journal of Physical Chemistry C
Published
2024-09-26
DOI
https://doi.org/10.1021/acs.jpcc.4c04150
Citations
2
Primary Topic
Machine Learning in Materials Science
Type
article
Field-Weighted Citation Impact
0.23

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article

A Combined First-Principles and Data-Driven Computational Framework to Analyze the Surface Structure, Composition, and Stability of Binary Alloy Catalysts

Gaurav Deshmukh, Jeffrey Greeley, Pushkar Ghanekar
2 citations
The Journal of Physical Chemistry C
Machine Learning in Materials Science
0.23
article

A Combined First-Principles and Data-Driven Computational Framework to Analyze the Surface Structure, Composition, and Stability of Binary Alloy Catalysts

Gaurav Deshmukh, Jeffrey Greeley, Pushkar Ghanekar
article en
2 citations

Abstract

Pt-based bimetallic alloys are considerably more active than Pt in the oxygen reduction reaction (ORR). This increased activity has been attributed to the weakening of the adsorption of ORR intermediates due to the presence of Pt “skins.” Density functional theory (DFT) calculations have, in turn, pointed to the importance of surface segregation energies and Pt leaching on the formation and stability of the skins on close-packed surfaces of Pt alloys. The generalizability of these insights across different chemical environments, surface compositions, and facets, however, remains a subject of active research and is the focus of this work. We present a generalized computational framework combining DFT calculations and data-driven methods to predict the stability of different Pt3X (X = Ni, Co, Fe, and Cu) alloy facets under vacuum conditions and in the presence of an electrochemical environment, wherein we analyze the combined effect of segregation, intrasurface phase separation, leaching, and surface oxidation as a function of electrode potential. The analysis reveals that a subtle interplay of these factors influences Pt skin formation and stability, with Pt segregation being a strong function of the surface structure and continuous base metal dissolution being thermodynamically, although not always kinetically, favored at ORR-relevant voltages.

The Journal of Physical Chemistry CVol. 128(40)
Purdue University West Lafayette (US)
Basic Energy Sciences
Openalex Percentile: Top 51%
Machine Learning in Materials Science
0.23
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