Daemi Sohrab R, Tan Chun, Tranter Thomas G, Heenan Thomas M M, Wade Aaron, Salinas-Farran Luis, Llewellyn Alice V, Lu Xuekun, Matruglio Alessia, Brett Daniel J L, Jervis Rhodri, Shearing Paul R
Electrochemical Innovation Lab, Department of Chemical Engineering, University College London, London, WC1E 7JE, UK.
The Faraday Institution, Quad One, Harwell Science and Innovation Campus, Didcot, OX11 0RA, UK.
Small Methods. 2022 Oct;6(10):e2200887. doi: 10.1002/smtd.202200887. Epub 2022 Sep 11.
X-ray computed tomography (X-ray CT) is a non-destructive characterization technique that in recent years has been adopted to study the microstructure of battery electrodes. However, the often manual and laborious data analysis process hinders the extraction of useful metrics that can ultimately inform the mechanisms behind cycle life degradation. This work presents a novel approach that combines two convolutional neural networks to first locate and segment each particle in a nano-CT LiNiMnCoO (NMC) electrode dataset, and successively classifies each particle according to the presence of flaws or cracks within its internal structure. Metrics extracted from the computer vision segmentation are validated with respect to traditional threshold-based segmentation, confirming that flawed particles are correctly identified as single entities. Successively, slices from each particle are analyzed by a pre-trained classifier to detect the presence of flaws or cracks. The models are used to quantify microstructural evolution in uncycled and cycled NMC811 electrodes, as well as the number of flawed particles in a NMC622 electrode. As a proof-of-concept, a 3-phase segmentation is also presented, whereby each individual flaw is segmented as a separate pixel label. It is anticipated that this analysis pipeline will be widely used in the field of battery research and beyond.
X射线计算机断层扫描（X射线CT）是一种无损表征技术，近年来已被用于研究电池电极的微观结构。然而，通常手动且费力的数据分析过程阻碍了有用指标的提取，而这些指标最终可以揭示循环寿命退化背后的机制。这项工作提出了一种新颖的方法，该方法结合了两个卷积神经网络，首先在纳米CT锂镍锰钴氧化物（NMC）电极数据集中定位和分割每个颗粒，然后根据其内部结构中是否存在缺陷或裂纹对每个颗粒进行分类。从计算机视觉分割中提取的指标相对于传统的基于阈值的分割进行了验证，证实有缺陷的颗粒被正确识别为单个实体。随后，由预训练的分类器对每个颗粒的切片进行分析，以检测缺陷或裂纹的存在。这些模型用于量化未循环和循环的NMC811电极中的微观结构演变，以及NMC622电极中有缺陷颗粒的数量。作为概念验证，还提出了一种三相分割方法，其中每个单独的缺陷被分割为一个单独的像素标签。预计该分析流程将在电池研究及其他领域得到广泛应用。
