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展示用于NMC电极异位和原位微观CT图像中裂纹检测的更快多标签灰度分析

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

作者信息

Jones Matthew P, Parks Huw C W, Llewellyn Alice V, Reid Hamish T, Tan Chun, Wade Aaron, Heenan Thomas M M, Iacoviello Francesco, Marathe Shashidhara, Shearing Paul R, Jervis Rhodri

机构信息

Electrochemical Innovation Laboratory, Department of Chemical Engineering, University College London, London, WC1E 6BT, UK.

Advanced Propulsion Lab, Marshgate, University College London, London, E20 2AE, UK.

出版信息

Small Methods. 2025 Aug;9(8):e2500082. doi: 10.1002/smtd.202500082. Epub 2025 Jun 23.

DOI:10.1002/smtd.202500082
PMID:40545856
原文链接:https://pmc.ncbi.nlm.nih.gov/articles/PMC12391646/
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.

摘要

在电池运行过程中，镍锰钴（NMC）氧化物二次颗粒会出现开裂。由于部分体积效应，在微观计算机断层扫描（micro-CT）图像中，开裂颗粒看起来颜色更深，其中包含空隙和固体的体素会产生中间灰度级。这项工作提出了一种自动化方法，用于跟踪在包含超过10000个单个颗粒的大型、具有统计学意义的micro-CT数据集中，由这种效应引起的灰度变化。它扩展了早期使用GREAT算法分析断层图像中NMC颗粒的工作。新的GREAT2算法提高了处理速度，从使用GREAT时每天约1400个颗粒提高到不到一分钟内超过10000个颗粒。此外，这项工作还介绍了在原位实验中通过不同充电状态自动跟踪单个颗粒灰度强度变化的方法。这种能力能够对颗粒降解机制进行时间分析。还提出了提取有用见解的额外数据处理方法。通过这项工作，我们表明，这种方法和GREAT2实现的大样本量允许对颗粒群体进行统计学上稳健的分析。这些进展显著加速了电池电极开裂的断层扫描研究。GREAT2算法和相关工作流程已作为GRAPES Python工具包提供。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/f41d/12391646/9f9117532ad3/SMTD-9-2500082-g007.jpg
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https://cdn.ncbi.nlm.nih.gov/pmc/blobs/f41d/12391646/e097dd742573/SMTD-9-2500082-g013.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/f41d/12391646/bfb7742b03b8/SMTD-9-2500082-g011.jpg
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