We proudly announce our 19th #OpenAccessPublication within project PULSELiON! Another great achievement by the team – and still more to come soon
The article “Machine learning-driven optimization of gas diffusion layer microstructure and properties for high-performance PEM fuel cells” has been published in Journal of Power Sources (Vol. 625, 2025).
The study introduces a novel machine learning framework that predicts and optimizes gas diffusion layer (GDL) properties for Proton Exchange Membrane Fuel Cells (PEMFCs). With R² scores above 90% and a drastic reduction in computational cost (hours → seconds), this work paves the way for more efficient, durable, and sustainable fuel cells. A comparable framework is applied in PULSELiON for our SSB.
Congratulations to the authors from CNRS, LRCS and others: Rashen Lou Omongos, Diego E. Galvez-Aranda, Franco M. Zanotto, András Vernes, and Alejandro A. Franco.