Publications by authors named "Yinliang Cao"
Article Synopsis
- Proton exchange membrane fuel cells face challenges due to high costs of Pt-based catalysts, limiting their large-scale use.
- Machine learning (ML) is introduced to optimize the utilization of platinum (Pt) in membrane electrode assemblies (MEAs) by training nine algorithms on experimental datasets to predict Pt performance.
- The study successfully identifies optimal synthesis conditions, achieving significant improvements in Pt utilization and power density, thus promoting the economical use of hydrogen energy through MEA optimization.
Novel hierarchical lamellar porous carbon (HLPC) with high BET specific surface area of 2730 m(2) g(-1) and doped by nitrogen atoms has been synthesized from the fish scale without any post-synthesis treatment, and applied to support the platinum (Pt) nanoparticle (NP) catalysts (Pt/HLPC). The Pt NPs could be highly dispersed on the porous surface of HLPC with a narrow size distribution centered at ca. 2.
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