Unknown

Dataset Information

Exploring a bimetallic catalyst family for hydrogen oxidation with insights into superior activity and durability.


ABSTRACT: Anion exchange membrane fuel cells are limited by the slow kinetics of the alkaline hydrogen oxidation reaction (HOR). Aided by density functional theory combined with fine-tuned machine learning interatomic potential, we establish a family of bimetallic catalysts with controlled surface atomic arrangements to identify the optimal catalysts for HOR. Our theoretical analysis successfully predicts the HOR activity rankings of these catalysts, consistent with the experimental results. RuIr exhibits the highest activity, followed by PtRu, AuIr, PtRh, PtIr, PtAu, RhIr, RuRh, AuRu, and AuRh. These trends correlate with the electron-accepting tendencies and the adsorption strengths of H2 and OH* on the catalysts. Among all candidates, RuIr emerges as the most active and durable bimetal

SUBMITTER: Kao IT 

PROVIDER: S-EPMC12647760 | biostudies-literature | 2025 Nov

REPOSITORIES: biostudies-literature

altmetric image

Publications

Sorry, this publication's infomation has not been loaded in the Indexer, please go directly to PUBMED or Altmetric.

Similar Datasets