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

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