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Mapping the architecture of single lithium ion electrode particles in 3D, using electron backscatter diffraction and machine learning segmentation
Journal of Power Sources ( IF 9.2 ) Pub Date : 2020-11-16 , DOI: 10.1016/j.jpowsour.2020.229148
Orkun Furat , Donal P. Finegan , David Diercks , Francois Usseglio-Viretta , Kandler Smith , Volker Schmidt

Accurately quantifying the architecture of lithium ion electrode particles in 3D is critical to understanding sub-particle lithium transport, rate limitations, and degradation mechanisms within lithium ion batteries. Most commercial positive electrode materials consist of polycrystalline particles, where intra-particle grains have a range of morphologies and orientations. Here, focused ion beam slicing in sequence with electron backscatter diffraction is used to accurately quantify intra-particle grain morphologies in 3D. The intra-particle grains are identified using convolution neural network segmentation and distinctly labeled. Efficient morphological characterization of the grain architectures is achieved. Bivariate probability density maps are developed to show correlative relationships between morphological grain descriptors. The implication of morphological features on cell performance, as well as the extension of this dataset to guide artificial generation of realistic particle architectures for 3D multi-physics models, is discussed.



中文翻译:

使用电子背散射衍射和机器学习分段以3D方式绘制单个锂离子电极颗粒的结构图

准确量化3D形式的锂离子电极颗粒的结构对于了解亚颗粒锂的运输,速率限制和锂离子电池内的降解机理至关重要。大多数市售正极材料由多晶颗粒组成,其中颗粒内的晶粒具有一系列形态和方向。在这里,通过电子反向散射衍射按顺序聚焦离子束切片,可以精确地量化3D中的颗粒内颗粒形态。使用卷积神经网络分割识别颗粒内的颗粒,并进行明显标记。实现了晶粒结构的有效形态表征。开发了双变量概率密度图以显示形态晶粒描述符之间的相关关系。

更新日期:2020-11-17
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