Demonstrating Faster Multi-Label Grey-Level Analysis for Crack Detection in Ex Situ and Operando Micro-CT Images of NMC Electrode

Abstract

During battery operation, cracking occurs in Nickel Manganese Cobalt (NMC) oxide secondary particles. Cracked particles appear darker in micro-computed tomography (micro-CT) images due to the partial volume effect, where voxels containing both void and solid yield intermediate grey-levels. This work presents an automated method for tracking grey-level changes caused by this effect in large, statistically meaningful micro-CT datasets containing over 10 000 individual particles. It extends earlier work using the GREAT algorithm to analyze NMC particles in tomography images. The new GREAT2 algorithm increases processing speed, from around 1,400 particles per day with GREAT to over 10 000 particles in under a minute. Furthermore, this work introduces methods for automated tracking of grey-level intensity changes in individual particles through different states of charge in an operando experiment. This capability enables temporal analysis of particle degradation mechanisms. Additional data processing methods are presented that extract useful insights. Through this work we show that the large sample sizes, enabled by this method and GREAT2, allow for statistically robust analysis of particle populations. These advances significantly accelerate the tomographic study of cracking in battery electrodes. The GREAT2 algorithm and associated workflows have been made available as the GRAPES Python toolkit.