Issue 28, 2024

Determining the orderliness of carbon materials with nanoparticle imaging and explainable machine learning

Abstract

Carbon materials have paramount importance in various fields of materials science, from electronic devices to industrial catalysts. The properties of these materials are strongly related to the distribution of defects—irregularities in electron density on their surfaces. Different materials have various distributions and quantities of these defects, which can be imaged using a procedure that involves depositing palladium nanoparticles. The resulting scanning electron microscopy (SEM) images can be characterized by a key descriptor—the ordering of nanoparticle positions. This work presents a highly interpretable machine learning approach for distinguishing between materials with ordered and disordered arrangements of defects marked by nanoparticle attachment. The influence of the degree of ordering was experimentally evaluated on the example of catalysis via chemical reactions involving carbon–carbon bond formation. This represents an important step toward automated analysis of SEM images in materials science.

Graphical abstract: Determining the orderliness of carbon materials with nanoparticle imaging and explainable machine learning

Supplementary files

Article information

Article type
Paper
Submitted
05 mar 2024
Accepted
20 iyn 2024
First published
04 iyl 2024

Nanoscale, 2024,16, 13663-13676

Determining the orderliness of carbon materials with nanoparticle imaging and explainable machine learning

M. Yu. Kurbakov, V. V. Sulimova, A. V. Kopylov, O. S. Seredin, D. A. Boiko, A. S. Galushko, V. A. Cherepanova and V. P. Ananikov, Nanoscale, 2024, 16, 13663 DOI: 10.1039/D4NR00952E

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