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- Conflict of Interest
- In relation to this article, we declare that there is no conflict of interest.
- Publication history
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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).
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Reconstruction of particle size distribution from cross-sections
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
References
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24. M. Konert and J. Vandenberghe, Sedimentology, 44, 523 (1997).
25. W. Pabst and T. Uhlířová, Ceram. Silik, 61, 147 (2017).
26. K. C. G. Chan and J. Qin, Biometrika, 103, 273 (2016).
27. J. Wilson, J. Stat. Comput. Simul., 31, 195 (1989).
28. S. Champier and L. Grammont, Inverse Probl., 18, 79 (2002).
29. R. Tibshirani, J. R. Stat. Soc., B: Stat. Methodol., 58, 267 (1996).
30. D. L. Sahagian and A. A. Proussevitch, J. Volcanol. Geotherm. Res.,84, 173 (1998).
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38. D. Depriester and R. Kubler, J. Struct. Geol., 151, 104418 (2021)
