Please use this identifier to cite or link to this item: http://hdl.handle.net/10397/80644
Title: High-throughput 3D reconstruction of stochastic heterogeneous microstructures in energy storage materials
Authors: Zhang, Y
Yan, M
Wan, Y
Jiao, Z
Chen, Y
Chen, F
Xia, C
Ni, M 
Issue Date: 2019
Publisher: Nature Publishing Group
Source: NPJ computational materials, 2019, v. 5, no. 1, 11 How to cite?
Journal: NPJ computational materials 
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.
URI: http://hdl.handle.net/10397/80644
EISSN: 2057-3960
DOI: 10.1038/s41524-019-0149-4
Rights: © The Author(s) 2019. This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. The images or other third party material in this article are included in the article’s Creative Commons license, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons license and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this license, visit http://creativecommons.org/licenses/by/4.0/.
The following publication Zhang, Y., Yan, M., Wan, Y., Jiao, Z., Chen, Y., Chen, F., ... & Ni, M. (2019). High-throughput 3D reconstruction of stochastic heterogeneous microstructures in energy storage materials. npj Computational Materials, 5(1), 11 is available at https://doi.org/10.1038/s41524-019-0149-4
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