Main Research Activities
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).Academic Training
Double degree MSc (Università degli Studi di Padova, Justus Liebig Universität Giessen; Prof. Durante, Prof. Janek)
Professional skills
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.
Running Projects
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
