Modelling of Electrochemical Energy and Conversion Devices

Pablo A. García-Salaberri (PAGS) holds around 15 years of teaching and research experience on Fluid Mechanics and Electrochemical Energy Conversion and Storage Devices, fields in which he was initiated at the end of his B.S. in Industrial Engineering (2009) at Universidad Carlos III de Madrid (UC3M). In 2015, PAGS concluded his Doctoral Thesis, supervised by Dr. Marcos Vera, which dealt with the multiphysics modeling of direct methanol fuel cells (DMFC) and the numerical characterization of diffusive properties of carbon-paper gas diffusion layers. During this period, PAGS performed a research stay at McGill University with Dr. Jeff T. Gostick. Subsequently, PAGS worked as Postdoctoral Researcher in the German Aerospace Center (DLR) and UC3M (6 months in each center), where he developed comprehensive models of proton exchange membrane fuel cells (PEMFC). Between 2017-2019, PAGS worked as Assistant Professor at UC3M, and in 2019 he performed a Fulbright stay at Lawrence Berkeley National Lab together with Dr. Adam Z. Weber. Back in Spain, PAGS started a new position at UC3M as Visiting Professor, where he taught several Bachelor and Master Degree courses on Fluid Mechanics, and he continued his research activity on PEMFCs and other electrochemical devices, such as redox flow batteries, lithium-ion batteries and electrolyzers. In 2024, PAGS started his new stage at King Juan Carlos University (URJC), where he teaches courses related with Fluid Mechanics and Chemical Engineering, and he works actively in the design, modeling and characterization of electrochemical reactive processes and transport in porous media. In total, PAGS has published 53 scientific articles, 6 book chapters, more than 50 congress contributions, participated in 7 research projects as Principal Investigator, and contributed to 2 technological projects with companies as member of the Research Team. PAGS also supervises one PhD thesis focused on the development of a multiscale model for PEMFC performance diagnostics using machine learning.