Machine Learning-Assisted Battery Electrode Microstructure Characterization through Secondary-Ions Imaging
Authors: Lombardo Teo , Kern Christine , Rohnke Marcus , Sann Joachim , Janek Jürgen 
Published: September 2023

Electrodes are a key component of modern Li-ion and solid-state batteries, and their microstructure is recognized to affect their cycle life, safety, energy and power densities, underlying the importance of microstructure characterization.1–4

Among the different techniques used to this scope, Secondary Electrons Microscopy (SEM) and Focused-Ion Beam SEM (FIB-SEM) are widely used for acquiring 2D and 3D information, respectively.5,6 Despite the advantages of these techniques, one main limitation is that chemically different species having similar atomic components or weights are often not distinguishable, not allowing the characterization of, for instance, the solid electrolyte interfaces (SEI), i.e., degradation products formed at the active material surfaces and playing a key role in the electrode performance and lifetime.
Time-of-Flight Secondary Ion Mass Spectrometry (ToF-SIMS) is a surface technique based on the analysis of the secondary ions generated through the impact between an accelerated primary ion and the sample (Figure 1A). ToF-SIMS allows obtaining chemically-specific information and acquiring images of sizes in the order of hundreds of um, with a resolution down to 150 nm.7,8 However, the long acquisition time for imaging limits its applications to 2D microstructural characterization.
In this work, we combined a devoted experimental set-up to image a statistically representative section of the electrode (its cross-section) through ToF-SIMS, and a machine learning procedure9 based on generative adversarial networks to reconstruct a realistic 3D electrode microstructure using, as input, the 2D ToF-SIMS image (Figure 1B). This approach showed to be applicable not only to characterize the spatial distribution of the electrode main components, but also to map the spatial distribution of degradation products forming the SEI, opening the door to more in-depth analyses of the relationships between electrode microstructure, spatial distribution of the degradation products, and electrochemical performance. This approach could be of use for both experimental and computational analyses, the latter through 3D electrochemical models using as input the so-obtained 3D electrode microstructure.

References

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Figure 1