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Classification of FIB/SEM-tomography images for highly porous multiphase materials using random forest classifiers

Osenberg, Markus ; Hilger, André; Neumann, Matthias 1; Wagner, Amalia 2; Bohn, Nicole 2; Binder, Joachim R. 1,2; Schmidt, Volker; Banhart, John; Manke, Ingo
1 Post Lithium Storage (POLiS), Karlsruher Institut für Technologie (KIT)
2 Institut für Angewandte Materialien – Energiespeichersysteme (IAM-ESS), Karlsruher Institut für Technologie (KIT)


FIB/SEM tomography represents an indispensable tool for the characterization of three-dimensional nanostructures in battery research and many other fields. However, contrast and 3D classification/reconstruction problems occur in many cases, which strongly limits the applicability of the technique especially on porous materials, like those used for electrode materials in batteries or fuel cells. Distinguishing the different components like active Li storage particles and carbon/binder materials is difficult and often prevents a reliable quantitative analysis of image data, or may even lead to wrong conclusions about structure-property relationships. In this contribution, we present a novel approach for data classification in three-dimensional image data obtained by FIB/SEM tomography and its applications to NMC battery electrode materials. We use two different image signals, namely the signal of the angled SE2 chamber detector and the Inlens detector signal, combine both signals and train a random forest, i.e. a particular machine learning algorithm. We demonstrate that this approach can overcome current limitations of existing techniques suitable for multi-phase measurements and that it allows for quantitative data reconstruction even where current state-of the art techniques fail, or demand for large training sets. ... mehr

Volltext §
DOI: 10.5445/IR/1000157865
Veröffentlicht am 14.04.2023
Cover der Publikation
Zugehörige Institution(en) am KIT Institut für Angewandte Materialien – Energiespeichersysteme (IAM-ESS)
Post Lithium Storage (POLiS)
Publikationstyp Forschungsbericht/Preprint
Publikationsjahr 2022
Sprache Englisch
Identifikator KITopen-ID: 1000157865
Verlag arxiv
Umfang 16 S.
Schlagwörter Materials Science (cond-mat.mtrl-sci); Machine Learning (cs.LG)
Nachgewiesen in arXiv
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