Hao Li, Muhammad Ajmal, Xinquan Wu, Shishi Zhang, Xiaokang Liu, Zhen-Feng Huang, Ruijie Gao, Lun Pan, Xiangwen Zhang, Ji-Jun Zou
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引用次数: 0
Abstract
Pt nanoparticles dispersed on carbon supports (Pt/C) are the benchmark oxygen reduction reaction (ORR) catalysts in proton exchange membrane fuel cells (PEMFCs). However, their widespread application is hindered by severe stability degradation under high potentials and acidic environments, primarily due to carbon support corrosion. To address this challenge, a multiscale template-assisted method is proposed, combined with ethylene glycol reduction, to fabricate Pt nanoparticles supported onto multiscale porous conductive antimony tin oxides (Pt/PT-SSO). Both theoretical and experimental approaches have shown that the strong interaction between Pt and support markedly accelerates electron transfer and optimizes the adsorption strength of key intermediates on the Pt surface. Furthermore, the unique multiscale porous structure of support not only provides an ideal platform for the uniform dispersion of Pt nanoparticles but also greatly enhances confinement effect, effectively preventing Pt aggregation. As a result, the Pt/PT-SSO exhibits superior ORR activity and durability compared to commercial Pt/C catalysts. Specifically, its mass activity at 0.9 V (vs RHE) reaches 0.617 A mgPt⁻¹, which is twice that of Pt/C, while maintaining outstanding stability over 50 h. Notably, PEMFCs utilizing Pt/PT-SSO achieve a high power density of 1.173 W cm⁻2 and retain 94.9% after 30,000 cycles of accelerated durability testing.
Small MethodsMaterials Science-General Materials Science
CiteScore
17.40
自引率
1.60%
发文量
347
期刊介绍:
Small Methods is a multidisciplinary journal that publishes groundbreaking research on methods relevant to nano- and microscale research. It welcomes contributions from the fields of materials science, biomedical science, chemistry, and physics, showcasing the latest advancements in experimental techniques.
With a notable 2022 Impact Factor of 12.4 (Journal Citation Reports, Clarivate Analytics, 2023), Small Methods is recognized for its significant impact on the scientific community.
The online ISSN for Small Methods is 2366-9608.