Publication: Assessing Mesoscale Heterogeneities in Hard Carbon Electrodes Through Deep Learning-Assisted FIB-SEM Characterization, Manufacturing and Electrochemical Modeling

With sodium-ion batteries (SIBs) entering the energy market, optimizing their performance has become a key research focus. At the electrode level, the parameters selected during their manufacturing process influence their electrodes microstructure and, consequently, the electrochemical performance. Here, we address different manufacturing conditions of hard carbon (HC) negative electrodes by varying the solid content in the slurry (35 and 40 % SC) and the calendering degree applied to the electrodes (uncalendered and 30 % calendered). The 3D microstructure of each sample is acquired using a focused ion beam (FIB) and scanning electron microscopy (SEM) technique, from which the real shape of HC particles is extracted and used to generate input microstructures for a discrete element method (DEM) calendering model designed to address the mechanical electrode behavior and its effects on current collector deformation. Also, the 3D electrode microstructures are used in a finite element method (FEM) model to obtain the electrochemical performance for C-rates ranging from C/50 to C/2, and to compare these results with experimental ones. Furthermore, the DEM predicted microstructures are also injected into the FEM model to validate the electrochemical performance and compare it with the performance of microstructures reconstructed with FIB-SEM. We demonstrate a novel approach combining experimentation and advanced mesoscopic modeling, to address how manufacturing parameters influence the performance of HC electrodes, providing an important guideline for optimizing their production for SIB applications.

Abstract from the publication. Read the full article here.