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Oxygen Vacancy-Mediated Synthesis of Inter-Atomically Ordered Ultrafine Pt-Alloy Nanoparticles for Enhanced Fuel Cell Performance

Fantao Kong, Yifan Huang, Yu Xu, Min Li, Kunming Song, Qiuyun Guo, Xiangzhi Cui, Jianlin Shi

2024Journal of the American Chemical Society76 citationsDOI

Abstract

Pt-based intermetallics are expected to be the highly active catalysts for oxygen reduction reaction (ORR) in proton-exchange membrane fuel cells but still face great challenges in controllable synthesis of interatomically ordered and ultrafine intermetallic nanoparticles. Here, we propose an oxygen vacancy-mediated atomic diffusion strategy by mechanical alloying to reduce the energy barrier of the transition from interatomic disordering to ordering, and to resist interparticulate sintering via strong M–O–C bonding. This synthesis results in a nanosized core/shell structure featuring an interatomically ordered PtM core and a Pt shell of two to three atomic layers in thickness and can be extended to the multicomponent PtM (M = Co, FeCo, FeCoNi, FeCoNiGa) systems. The electron enrichment in the Pt outer shell induced by the compressive strain leads to the enhanced antibonding orbital occupation below the Fermi level and accelerated OH* desorption kinetics. The optimized PtCo–O/C-6 catalyst presents excellent ORR activity (mass activity = 1.28 A mg Pt –1 at 0.9 V iR -free, peak power densities = 2.38/1.25 W cm –2 in H 2 –O 2 /–air) and durability (∼1% activity loss in over 50 h in air condition) in fuel cells at a total Pt loading of 0.1 mg Pt cm –2 . Furthermore, we establish a systematic correlation to elucidate the formation mechanisms of highly ordered intermetallic catalysts underlying oxygen vacancies. This study provides a general approach for the large-scale production of highly ordered and nanosized Pt-dispersed intermetallic catalysts.

Topics & Concepts

ChemistryAlloyNanoparticleOxygenVacancy defectFuel cellsChemical engineeringNanotechnologyCrystallographyOrganic chemistryMaterials scienceEngineeringElectrocatalysts for Energy ConversionFuel Cells and Related MaterialsMachine Learning in Materials Science
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