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

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

Journal

Nanoscale
ISSN: 2040-3372 , E-ISSN: 2040-3364

Output data

Year: 2024, Volume: 16, Number: 28, Pages: 13663-13676 Pages count : 14 DOI: 10.1039/d4nr00952e

Authors

Kurbakov Mikhail Yu. ¹ , Sulimova Valentina V. ¹ , Kopylov Andrei V. ¹ , Seredin Oleg S. ¹ , Boiko Daniil A. ² , Galushko Alexey S. ² , Cherepanova Vera A. ² , Ananikov Valentine P. ²

Affiliations

1	Tula State University, Lenina Ave. 92, 300012 Tula, Russia
2	Zelinsky Institute of Organic Chemistry, Russian Academy of Sciences, Leninsky Prospekt 47, Moscow, 119991, Russia

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.

Kurbakov M.Y. , Sulimova V.V. , Kopylov A.V. , Seredin O.S. , Boiko D.A. , Galushko A.S. , Cherepanova V.A. , Ananikov V.P.
Determining the orderliness of carbon materials with nanoparticle imaging and explainable machine learning
Nanoscale. 2024. V.16. N28. P.13663-13676. DOI: 10.1039/d4nr00952e Scopus OpenAlex

Scopus:	2-s2.0-85198176569
OpenAlex:	W4400319555

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