Machine-learning-accelerated design of high-performance platinum intermetallic nanoparticle fuel cell catalysts.

ABSTRACT: Carbon supported PtCo intermetallic alloys are known to be one of the most promising candidates as low-platinum oxygen reduction reaction electrocatalysts for proton-exchange-membrane fuel cells. Nevertheless, the intrinsic trade-off between particle size and ordering degree of PtCo makes it challenging to simultaneously achieve a high specific activity and a large active surface area. Here, by machine-learning-accelerated screenings from the immense configuration space, we are able to statistically quantify the impact of chemical ordering on thermodynamic stability. We find that introducing of Cu/Ni into PtCo can provide additional stabilization energy by inducing Co-Cu/Ni disorder, thus facilitating the ordering process and achieveing an improved tradeoff between specific activity and ac

SUBMITTER: Yin P 

PROVIDER: S-EPMC10776629 | biostudies-literature | 2024 Jan

REPOSITORIES: biostudies-literature