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Language: English

Conflict of Interest: In relation to this article, we declare that there is no conflict of interest.

Publication history: Received March 10, 2023
Revised June 23, 2023
Accepted June 29, 2023

Acknowledgements: This work was supported by the National Research Foundation of Korea (NRF) grant funded by the Korean Government (MSIT) (No. NRF-2018R1A5A1024127, NRF-2023R1A2C2004002, and NRF-2021M3H4A6A01041234), and Soonchunhyang University Research Fund (No. 20221186).

This is an Open-Access article distributed under the terms of the Creative Commons Attribution Non-Commercial License (http://creativecommons.org/licenses/bync/3.0) which permits unrestricted non-commercial use, distribution, and reproduction in any medium, provided the original work is properly cited.

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Reconstruction of particle size distribution from cross-sections

Jihoon Oh^1† Dongjae Kim^{2 3†} Seunggeon Lee¹ Jaewook Nam^{1 4†}

¹School of Chemical and Biological Engineering, Seoul National University, 1 Gwanak-ro, Gwanak-gu, Seoul 08826, Korea ²Department of Chemical Engineering, Soonchunhyang University, 22 Soonchunhyang-ro, Shinchang-myeon, Asan-si, Chungcheongnam-do 31538, Kore ³Department of Electronic Materials, Devices, and Equipment Engineering, Soonchunhyang University, 22 Soonchunhyang-ro, Shinchang-myeon, Asan-si, Chungcheongnam-do 31538, Korea ⁴Institute of Chemical Process, Seoul National University, 1 Gwanak-ro, Gwanak-gu, Seoul 08826, Korea

jaewooknam@snu.ac.kr

Korean Journal of Chemical Engineering, December 2023, 40(12), 3079-3086(8), 10.1007/s11814-023-1521-0

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Abstract

Controlling the microstructure enables higher energy density and lower energy consumption of a battery. Although particle size distribution is an important property of microstructures, its study is hindered by limited analytical tools. In this study, we precisely estimate the 3-dimensional (3D) spherical size distribution from a 2-dimensional circular size distribution. Here, we introduce the least absolute shrinkage and selection operator (LASSO) regularization method to handle the existing issues in 3D reconstruction efficiently. Using a virtual structure from various predefined distributions, we demonstrate that the LASSO regression outperforms other regularization methods in predicting the original distribution. Finally, we suggest an effective number of cross sections, that is, the minimum required number of cross sections, for 3D reconstruction consisting of spherical particles.

Keywords

LASSO Regression Stereology 3D Reconstruction Wicksell’s Corpuscle Problem Regularization The Number of Cross-sectio

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