High-throughput 3D reconstruction of stochastic heterogeneous microstructures in energy storage materials

Zhang, Yanxiang; Yan, Mufu; Wan, Yanhong; Jiao, Zhenjun; Chen, Yu; Chen, Fanglin; Xia, Changrong; Ni, Meng

doi:10.1038/s41524-019-0149-4

Title: High-throughput 3D reconstruction of stochastic heterogeneous microstructures in energy storage materials

Journal Article · 31 January 2019 · npj Computational Materials

DOI: https://doi.org/10.1038/s41524-019-0149-4 · OSTI ID:1619631

Zhang, Yanxiang; Yan, Mufu; Wan, Yanhong; Jiao, Zhenjun; Chen, Yu; Chen, Fanglin; Xia, Changrong; Ni, Meng

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.

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Research Organization:: Univ. of South Carolina, Columbia, SC (United States)

Sponsoring Organization:: USDOE; USDOE Office of Fossil Energy (FE)

Grant/Contract Number:: FE0031670

OSTI ID:: 1619631

Journal Information:: npj Computational Materials, Journal Name: npj Computational Materials Journal Issue: 1 Vol. 5; ISSN 2057-3960

Publisher:: Nature Publishing GroupCopyright Statement

Country of Publication:: United Kingdom

Language:: English

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