Using a Neural Network to Study the Effect of the Means of Synthesizing Exfoliated Graphite on Its Macropore Structure

A. V. Krautsou; Кравцов А. В.; O. N. Shornikova; Шорникова О. Н.; V. V. Avdeev; Авдеев В. В.

doi:10.31857/S0044453723060110

Using a Neural Network to Study the Effect of the Means of Synthesizing Exfoliated Graphite on Its Macropore Structure

Authors: Krautsou A.V.¹, Shornikova O.N.¹, Avdeev V.V.¹
Affiliations:
1. Faculty of Chemistry, Moscow State University
Issue: Vol 97, No 6 (2023)
Pages: 821-826
Section: СТРОЕНИЕ ВЕЩЕСТВА И КВАНТОВАЯ ХИМИЯ
Submitted: 27.02.2025
Published: 01.06.2023
URL: https://ruspoj.com/0044-4537/article/view/668727
DOI: https://doi.org/10.31857/S0044453723060110
EDN: https://elibrary.ru/JIBFGL
ID: 668727

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Abstract

Graphite intercalated compounds (GICs) with different stage numbers are prepared chemically from highly oriented pyrolytic graphite (HOPG), natural flaked graphite (FG) and nitric acid. Exfoliated graphite samples (EG-T) are synthesized from GICs via water treatment followed by thermal shock. The aim of this work is to investigate the dependence of the inner EG-T pore structure on the extent of oxidation and type of graphite by processing scanning electron microscopy (SEM) micrographs of EG-T cross sections. A procedure is developed on the basis of a deep convolutional neural network that speeds up image processing with no appreciable loss of accuracy. A strong correlation is found between EG-T pore structure parameters, the depth of oxidation, and the type of graphite.

Keywords

thermally expanded graphite, porous structure, segmentation, neural networks

References

Chung D.D.L. // J. Mater. Sci. 2016. V. 51. P. 554. https://doi.org/10.1007/s10853-015-9284-6
Nayak S.K., Mohanty S., Nayak S.K. // High Perform. Polym. 2019. V. 32. P. 506. https://doi.org/10.1177/0954008319884616
Sorokina N.E., Redchitz A.V., Ionov S.G. et al. // J. Phys. Chem. Solids. 2006. V. 67. P. 1202. https://doi.org/10.1016/j.jpcs.2006.01.048
Inagaki M., Kang F., Toyoda M. et al. // Advanced Materials Science and Engineering of Carbon, Butterworth-Heinemann. 2014. P. 313. https://doi.org/10.1016/B978-0-12-407789-8.00014-4
Wang Z., Han E., Ke W. // Corros. Sci. 2007. V. 49. P. 2237. https://doi.org/10.1016/j.corsci.2006.10.024
Inagaki M., Suwa T. // Carbon. 2001. V. 39. P. 915. https://doi.org/10.1016/S0008-6223(00)00199-8
Inagaki M., Tashiro R., Washino Y. et al. // J. Phys. Chem. Solids. 2004. V. 65. P. 133. https://doi.org/10.1016/j.jpcs.2003.10.007
Inagaki M., Saji N., Zheng Y.-P. et al. // TANSO. 2004. V. 2004. P. 258. https://doi.org/10.7209/tanso.2004.258
Bellens S., Vandewalle P., Dewulf W. // Procedia CIRP. 2020. V. 96. P. 336. https://doi.org/10.1016/j.procir.2021.01.157
Varfolomeev I., Yakimchuk I., Safonov I. // Computers. 2019. V. 8. № 4. P. 72. https://doi.org/10.3390/computers8040072
Li X., Lai T., Wang S. et al. // 2019 IEEE Intl. Conf. Parallel Distrib. Process. with Appl. Big Data Cloud Comput. Sustain. Comput. Commun. Soc. Comput. Networking. 2019. P. 1500. https://doi.org/10.1109/ISPA-BDCloud-SustainCom-SocialCom48970.2019.00217
Kang F., Zheng Y.-P., Wang H.-N. et al. // Carbon. 2002. V. 40. № 9. P. 1575. https://doi.org/10.1016/S0008-6223(02)00023-4