Descriptif des activités de recherche
PhD in machine learning applied to material discovery for IoT sensor batteries supervised by Prof. A. Franco (LRCS, Amiens), Prof. R. Palacin (ICMAB, Barcelona), Prof. C. Grey (University of Cambridge).Parcours
Double degree MSc (Università degli Studi di Padova, Justus Liebig Universität Giessen; Prof. Durante, Prof. Janek)
Compétences
Material characterisation: XPS, XRD, SEM, EDS.
Electrochemistry: Cell galvanostatic cycling, PEIS, CV.
Inorganic materials synthesis: Solid state synthesis.
Organic synthesis: (On-surface) Synthesis of (cyclo)para-phenylenes.
Organic characterisation: NMR, FT-IR, GC, MS, UV-Vis.
Programming: Python applied to machine learning models.
Electrochemistry: Cell galvanostatic cycling, PEIS, CV.
Inorganic materials synthesis: Solid state synthesis.
Organic synthesis: (On-surface) Synthesis of (cyclo)para-phenylenes.
Organic characterisation: NMR, FT-IR, GC, MS, UV-Vis.
Programming: Python applied to machine learning models.
Projets en cours
PhD funded by ALISTORE until November 2024.
VOLTA: a tool for battery screening bridging the gap between virtual electrode materials and practical applications
Antonio Carnevali, M. Rosa Palacin, Clare P. Grey and Alejandro A. Franco
Energy Storage Materials, 2024
