Three-dimensional structural measurement and material identification of an all-solid-state lithium-ion battery by X-Ray nanotomography and deep learning

IF 5.4 Q2 CHEMISTRY, PHYSICAL
M. Kodama , A. Ohashi , H. Adachi , T. Miyuki , A. Takeuchi , M. Yasutake , K. Uesugi , T. Kaburagi , S. Hirai
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引用次数: 13

Abstract

Three-dimensional measuring method of the material distribution of an all-solid-state lithium-ion battery (ASSLiB) cathode, by synchrotron radiation high-resolution X-ray computational tomography (nanotomography, nano-CT) and deep learning is proposed in this study. The cathode of the ASSLiB comprised materials with high X-ray absorption coefficients, such as LiCoO2 and LiNi0.5Co0.2Mn0.3O2. Such high absorption coefficients imparted difficulties in obtaining a high-resolution, high-contrast image and in identifying materials with conventional CT value method. The method proposed in this study was effective in acquiring a high-resolution image with fewer artifacts and measured the heavy materials at a high signal-to-noise ratio. We used deep learning with a customized U-net, enabling high accuracy and ultra-high-speed material identification. Using this method, constituent materials were successfully identified in three dimensions. This material identification technique showed great potential for application to other techniques such as focused ion beam–scanning electron microscopy.

Abstract Image

基于x射线纳米层析成像和深度学习的全固态锂离子电池三维结构测量和材料识别
本研究提出了采用同步辐射高分辨率x射线计算机断层扫描(纳米断层扫描,纳米ct)和深度学习技术对全固态锂离子电池(ASSLiB)阴极材料分布进行三维测量的方法。ASSLiB的阴极由LiCoO2和LiNi0.5Co0.2Mn0.3O2等具有高x射线吸收系数的材料组成。如此高的吸收系数给获得高分辨率、高对比度图像以及用常规CT值方法识别材料带来了困难。该方法能有效地获取高分辨率图像,伪影较少,并能以高信噪比测量重材料。我们将深度学习与定制的U-net相结合,实现了高精度、超高速的材料识别。利用该方法,成功地在三维空间上识别了成分。这种材料识别技术在聚焦离子束扫描电子显微镜等其他技术中显示出巨大的应用潜力。
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
CiteScore
9.10
自引率
0.00%
发文量
18
审稿时长
64 days
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