Authors: Teo Lombardo | Christine Kern | Marcus Rohnke | Joachim Sann | Jürgen Janek
Source: ECS Meeting Abstracts (article, 2023-08-28)
Field: Materials Science — Surfaces, Coatings and Films

Machine Learning-Assisted Battery Electrode Microstructure Characterization through Secondary-Ions Imaging 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 procedure 9 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 (1) Xu, H.; Zhu, J.; Finegan, D. P.; Zhao, H.; Lu, X.; Li, W.; Hoffman, N.; Bertei, A.; Shearing, P.; Bazant, M. Z. Guiding the Design of Heterogeneous Electrode Microstructures for Li‐Ion Batteries: Microscopic Imaging, Predictive Modeling, and Machine Learning. Adv Energy Mater 2021 , 2003908 , 2003908. https://doi.org/10.1002/aenm.202003908. (2) Lu, X.; Bertei, A.; Finegan, D. P.; Tan, C.; Daemi, S. R.; Weaving, J. S.; Regan, K. B. O.; Heenan, T. M. M.; Hinds, G.; Kendrick, E.; Brett, D. J. L.; Shearing, P. R. 3D Microstructure Design of Lithium-Ion Battery Electrodes Assisted by X-Ray Nano-Computed Tomography and Modelling. Nat Commun 2020 , 11 (2079), 1–13. https://doi.org/10.1038/s41467-020-15811-x. (3) Chouchane, M.; Franco, A. A. Deconvoluting the Impacts of the Active Material Skeleton and the Inactive Phase Morphology on the Performance of Lithium Ion Battery Electrodes. Energy Storage Mater 2022 , 47 , 649–655. https://doi.org/10.1016/j.ensm.2022.02.016. (4) Bielefeld, A.; Weber, D. A.; Janek, J. Microstructural Modeling of Composite Cathodes for All-Solid-State Batteries. Journal of Physical Chemistry C 2019 , 123 (3), 1626–1634. https://doi.org/10.1021/acs.jpcc.8b11043. (5) Joos, J.; Buchele, A.; Schmidt, A.; Weber, A.; Ivers-Tiffée, E. Virtual Electrode Design for Lithium-Ion Battery Cathodes. Energy Technology 2021 , 2000891 . https://doi.org/10.1002/ente.202000891. (6) Zielke, L.; Hutzenlaub, T.; Wheeler, D. R.; Chao, C. W.; Manke, I.; Hilger, A.; Paust, N.; Zengerle, R.; Thiele, S. Three-Phase Multiscale Modeling of a LiCoO2 Cathode: Combining the Advantages of FIB-SEM Imaging and X-Ray Tomography. Adv Energy Mater 2015 , 5 (5). https://doi.org/10.1002/aenm.201401612. (7) Walther, F.; Strauss, F.; Wu, X.; Mogwitz, B.; Hertle, J.; Sann, J.; Rohnke, M.; Brezesinski, T.; Janek, J. The Working Principle of a Li2CO3/LiNbO3Coating on NCM for Thiophosphate-Based All-Solid-State Batteries. Chemistry of Materials 2021 , 33 (6), 2110–2125. https://doi.org/10.1021/acs.chemmater.0c04660. (8) Walther, F.; Koerver, R.; Fuchs, T.; Ohno, S.; Sann, J.; Rohnke, M.; Zeier, W. G.; Janek, J. Visualization of the Interfacial Decomposition of Composite Cathodes in Argyrodite-Based All-Solid-State Batteries Using Time-of-Flight Secondary-Ion Mass Spectrometry. Chemistry of Materials 2019 . https://doi.org/10.1021/acs.chemmater.9b00770. (9) Kench, S.; Cooper, S. J. Generating Three-Dimensional Structures from a Two-Dimensional Slice with Generative Adversarial Network-Based Dimensionality Expansion. Nat Mach Intell 2021 , 3 (4), 299–305. https://doi.org/10.1038/s42256-021-00322-1. Figure 1 Characterization (materials science) ; Microstructure ; Focused ion beam ; Materials science ; Secondary ion mass spectrometry ; Electrode ; Ion ; Battery (electricity) ; Ion beam ; Nanotechnology ; Computer science ; Composite material ; Power (physics) ; Chemistry ; Physics Electron and X-Ray Spectroscopy Techniques ; Integrated Circuits and Semiconductor Failure Analysis ; Ion-surface interactions and analysis