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

IF 10.7 2区 材料科学 Q1 CHEMISTRY, PHYSICAL
Matthew P Jones, Huw C W Parks, Alice V Llewellyn, Hamish T Reid, Chun Tan, Aaron Wade, Thomas M M Heenan, Francesco Iacoviello, Shashidhara Marathe, Paul R Shearing, Rhodri Jervis
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引用次数: 0

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.

在NMC电极非原位和操作微ct图像中更快的多标签灰度分析检测裂纹。
在电池运行过程中,镍锰钴(NMC)氧化物二次颗粒会发生开裂。由于部分体积效应,裂纹颗粒在微计算机断层扫描(micro-CT)图像中显得更暗,其中包含空隙和固体的体素产生中等灰度水平。这项工作提出了一种自动化的方法,用于跟踪由这种影响引起的灰度级变化,这些变化在包含超过10,000个单个颗粒的大型、统计上有意义的微ct数据集中。它扩展了使用GREAT算法分析断层扫描图像中的NMC粒子的早期工作。新的GREAT2算法提高了处理速度,从每天大约1400个粒子到一分钟内超过10000个粒子。此外,本工作还介绍了在operando实验中通过不同电荷状态自动跟踪单个粒子灰度强度变化的方法。这种能力使粒子降解机制的时间分析成为可能。提出了其他数据处理方法来提取有用的见解。通过这项工作,我们表明,通过这种方法和GREAT2实现的大样本量允许对粒子种群进行统计上的稳健分析。这些进展显著地促进了电池电极裂纹的层析成像研究。GREAT2算法和相关工作流已经作为GRAPES Python工具包提供。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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来源期刊
Small Methods
Small Methods Materials Science-General Materials Science
CiteScore
17.40
自引率
1.60%
发文量
347
期刊介绍: Small Methods is a multidisciplinary journal that publishes groundbreaking research on methods relevant to nano- and microscale research. It welcomes contributions from the fields of materials science, biomedical science, chemistry, and physics, showcasing the latest advancements in experimental techniques. With a notable 2022 Impact Factor of 12.4 (Journal Citation Reports, Clarivate Analytics, 2023), Small Methods is recognized for its significant impact on the scientific community. The online ISSN for Small Methods is 2366-9608.
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