Morphological analysis of Pd/C nanoparticles using SEM imaging and advanced deep learning.

Autor:	Thuan ND; School of Electrical and Electronic Engineering, Hanoi University of Science and Technology Hanoi Vietnam thuan.nguyenduc1@hust.edu.vn hong.hoangsy@hust.edu.vn., Cuong HM; School of Electrical and Electronic Engineering, Hanoi University of Science and Technology Hanoi Vietnam thuan.nguyenduc1@hust.edu.vn hong.hoangsy@hust.edu.vn., Nam NH; School of Electrical and Electronic Engineering, Hanoi University of Science and Technology Hanoi Vietnam thuan.nguyenduc1@hust.edu.vn hong.hoangsy@hust.edu.vn., Lan Huong NT; School of Electrical and Electronic Engineering, Hanoi University of Science and Technology Hanoi Vietnam thuan.nguyenduc1@hust.edu.vn hong.hoangsy@hust.edu.vn., Hong HS; School of Electrical and Electronic Engineering, Hanoi University of Science and Technology Hanoi Vietnam thuan.nguyenduc1@hust.edu.vn hong.hoangsy@hust.edu.vn.
Jazyk:	angličtina
Zdroj:	RSC advances [RSC Adv] 2024 Nov 05; Vol. 14 (47), pp. 35172-35183. Date of Electronic Publication: 2024 Nov 05 (Print Publication: 2024).
DOI:	10.1039/d4ra06113f
Abstrakt:	In this study, we present a comprehensive approach for the morphological analysis of palladium on carbon (Pd/C) nanoparticles utilizing scanning electron microscopy (SEM) imaging and advanced deep learning techniques. A deep learning detection model based on an attention mechanism was implemented to accurately identify and delineate small nanoparticles within unlabeled SEM images. Following detection, a graph-based network was employed to analyze the structural characteristics of the nanoparticles, while density-based spatial clustering of applications with noise was utilized to cluster the detected nanoparticles, identifying meaningful patterns and distributions. Our results demonstrate the efficacy of the proposed model in detecting nanoparticles with high precision and reliability. Furthermore, the clustering analysis reveals significant insights into the morphological distribution and structural organization of Pd/C nanoparticles, contributing to the understanding of their properties and potential applications. Competing Interests: There are no conflicts of interest to declare. (This journal is © The Royal Society of Chemistry.)
Databáze:	MEDLINE
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