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High-throughput 3D reconstruction of stochastic heterogeneous microstructures in energy storage materials (储能材料中随机异质微结构的高通量三维重建)
发布时间:2019-03-05

High-throughput 3D reconstruction of stochastic heterogeneous microstructures in energy storage materials (储能材料中随机异质微结构的高通量三维重建)
Yanxiang Zhang, Mufu Yan, Yanhong Wan, Zhenjun Jiao, Yu Chen, Fanglin Chen, Changrong Xia & Meng Ni 
npj Computational Materials 5:11 (2019)
doi:s41524-019-0149-4
Published online:31 January 2019
Abstract| Full Text | PDF OPEN

摘要:随机异质微结构广泛应用于结构和功能材料,在决定其性能方面发挥着至关重要的作用。X射线断层摄影和聚焦离子束连续切片是3D微结构重建的常用方法,但不仅技术要求高而且分辨率有限。本研究开发了一种基于距离相关函数(DCFs)的高通量多阶段3D重建方法,仅用单个具有代表性的大尺寸2D显微照片即可重建材料的三维结构,并适用于各向异/同性材料,且通过固体氧化物燃料电池电极的X射线微断层扫描数据证实了结果。该方法为能量转换与存储材料中的随机异质微结构3D重建提供了一种经济、易用、高通量技术,可拓展应用于其他材料   

Abstract:Stochastic heterogeneous microstructures are widely applied in structural and functional materials, playing a crucial role in determining their performance. X-ray tomography and focused ion beam serial sectioning are frequently used methods to reconstruct three-dimensional (3D) microstructures, yet are demanding techniques and are resolution-limited. Here, a high-throughput multi-stage 3D reconstruction method via distance correlation functions is developed using a single representatively large-sized 2D micrograph for stochastic microstructures, and verified by X-ray micro-tomography datasets of isotropic and anisotropic solid oxide fuel cell electrodes. This method provides an economic, easy-to-use and high-throughput approach for reconstructing stochastic heterogeneous microstructures for energy conversion and storage devices, and can readily be extended to other materials. 

Editorial Summary

Stochastic Heterogeneous Microstructures: High-throughput 3D Reconstruction储能材料随机异质微结构:高通量三维重建 

该研究仅以2D显微照片即可重建代表性大尺寸3D微结构。随机异质微结构广泛应用于结构和功能材料,调控了材料的性能。为高效重建随机异质微结构,被人们寄予厚望的距离相关函数,却难以收敛到真实值,因而能否准确捕获多尺度微观结构特征,成了微结构重建的关键问题。来自哈尔滨工业大学材料科学与工程学院金属精密热加工国家级重点实验室的张雁祥副教授和闫牧夫教授等,使用单个代表性大尺寸2D显微照片开发了基于距离相关函数的高通量多阶段3D重建方法,并使用固体氧化物燃料电池电极的X射线微断层扫描数据集证实了结果。他们的方法也适用于各种各向异/同性微结构,并可进一步扩展用以研究材料加工、微结构和性能之间的关系

Representative large-size 3D microstructures was reconstructed using only 2D micrographs. Stochastic heterogeneous microstructures are widely used in structural and functional materials to regulate the properties of materials. To efficiently reconstruct stochastic heterostructures, high-order distance correlation functions (DCFs) have been used, but it difficult to converge to real values. Therefore, it is a big problem to fulfill the reconstruction. A team led by Yanxiang Zhang and Mufu Yan from National Key Laboratory for Precision Hot Processing of Metals, Harbin Institute of Technology, developed a high-throughput multi-stage 3D reconstruction method via distance correlation functions using a single representatively large-sized 2D micrograph for stochastic microstructures, and verified by X-ray micro-tomography data sets of isotropic and anisotropic solid oxide fuel cell electrodes. Their method can be further extended to reveal relations among material processing, microstructure, and performance.

 
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