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

作者: Krautsou A.V.¹, Shornikova O.N.¹, Avdeev V.V.¹
隶属关系:
1. Faculty of Chemistry, Moscow State University
期: 卷 97, 编号 6 (2023)
页面: 821-826
栏目: СТРОЕНИЕ ВЕЩЕСТВА И КВАНТОВАЯ ХИМИЯ
##submission.dateSubmitted##: 27.02.2025
##submission.datePublished##: 01.06.2023
URL: https://freezetech.ru/0044-4537/article/view/668727
DOI: https://doi.org/10.31857/S0044453723060110
EDN: https://elibrary.ru/JIBFGL
ID: 668727

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详细

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.

关键词

thermally expanded graphite, porous structure, segmentation, neural networks

参考

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