Bridging the Gap: Electrode Microstructure and Interphase Characterization by Combining ToF‐SIMS and Machine Learning

Abstract

This article presents a new analytical methodology to analyze large (hundreds of µm) battery electrode microstructures by mapping the spatial distribution of the main phases (e.g., active material and carbon‐binder domain) and degradation products (solid‐ or cathode‐electrolyte interphase) formed during cycling. The methodology can be used for a better understanding of the relationships between electrode architecture and degradation, paving the way toward the analysis of interphases spatial distribution and their correlations to the electrode formulation, microstructure, and cycling conditions. This work is based on time‐of‐flight secondary ion mass spectrometry (ToF‐SIMS), and focuses on analyzing large 2D electrode cross‐sections at both the microstructure and single particle/agglomerate level. It also shows that this analysis can be expanded to 3D electrode microstructures when combining ToF‐SIMS and devoted machine learning procedures, which can be of particular interest to the 3D electrochemical modeling community.

Secondary Ion Mass Spectral Imaging of Metals and Alloys

“…Lombardo et al applied ML to Li-ion battery electrode microstructures by mapping both main phases and degradation products [195]. This method identified and characterized single particles through a watershed-based slicing algorithm, which segments objects in images apart [196].…”