Mapping the architecture of single lithium ion electrode particles in 3D, using electron backscatter diffraction and machine learning segmentation
Mapping the architecture of single lithium ion electrode particles in 3D, using electron backscatter diffraction and machine learning segmentation
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DOI：
10.1016/j.jpowsour.2020.229148
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发表时间：
2021-01
影响因子：
9.2
通讯作者：
O. Furat;D. Finegan;D. Diercks;F. Usseglio-Viretta;K. Smith;V. Schmidt
中科院分区：
文献类型：
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作者：
O. Furat;D. Finegan;D. Diercks;F. Usseglio-Viretta;K. Smith;V. Schmidt
准确量化3D中锂离子电极颗粒的结构对于理解锂离子电池内的亚颗粒锂传输、速率限制和降解机制至关重要。大多数商业正极材料由多晶颗粒组成，其中颗粒内晶粒具有一系列形态和取向。在这里，聚焦离子束切片与电子背散射衍射顺序被用来准确地量化3D中的颗粒内晶粒形态。利用卷积神经网络分割识别颗粒内的颗粒，并对其进行清晰的标记。实现了颗粒结构的有效形态表征。二元概率密度图的开发，以显示形态晶粒描述符之间的相关关系。形态特征对细胞性能的影响，以及该数据集的扩展，以指导人工生成三维多物理场模型的真实粒子架构，进行了讨论。
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.