Conference on Artificial Intelligence in Materials Science and Engineering - AI MSE 2023
Poster
Machine learning for post-processing of tomography in battery electrodes as a feature of the DataCharge.IO platform
DP

Dominik Perius (M.Sc.)

Leibniz-Institut für Neue Materialien gGmbH

Perius, D. (Speaker)¹; Beran, L.²; Heim, Y.³; Kraus, T.²; Nebel, V.³; Werth, D.³
¹INM Leibniz Institute for New Materials, Saarbrücken; ²INM-Leibniz-Institute for New Materials gGmbH, Saarbrücken; ³August-Wilhelm Scheer Institute for digital products and processes gGmbH, Saarbrücken

Investigating the Carbon Binder Domain (CBD) requires adequate resolution in 3D. Of all tomographic procedures (destructive and non-destructive), not many tomography techniques inherit the necessary resolution to visualize the fractal nature of the CBD (see Figure 1). The special requirement for destructive tomography is the infiltration of the pore-space with a polymer. The challenge lies in choosing a polymer which gives adequate contrast in the images to differentiate the CBD from the other phases in the material.

Abstract

Abstract

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Poster

Poster

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