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. 2024 Nov 5;14(47):35172-35183.
doi: 10.1039/d4ra06113f. eCollection 2024 Oct 29.

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

Nguyen Duc Thuan  1 , Hoang Manh Cuong  1 , Nguyen Hoang Nam  1 , Nguyen Thi Lan Huong  1 , Hoang Si Hong  1
Affiliations

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

Nguyen Duc Thuan et al. RSC Adv. .

Abstract

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.

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Conflict of interest statement

There are no conflicts of interest to declare.

Figures

Fig. 1
Fig. 1. The structure of employed deep learning-based detector model.
Fig. 2
Fig. 2. Illustration of palladium deposition process on carbon surface. (a) A screw cap tube with Pd2dba3·CHCl3 (5 mg), graphite powder (100 mg), and CHCl3 (5 mL). (b) Stirring at 50 °C for 1 hour. (c) Filtration to separate the transparent solution from the carbon material. (d) The material is washed with acetone.
Fig. 3
Fig. 3. Examples of acquired SEM images.
Fig. 4
Fig. 4. (a) Original SEM image. (b) Nanoparticles detected by blob detection algorithm. (c) Nanoparticles detected by proposed deep learning model.
Fig. 5
Fig. 5. (a) Original SEM image and detected nanoparticles. (b) Shape statistics of detected nanoparticles. (c) Enlarged examples of detected nanoparticles.
Fig. 6
Fig. 6. Visualization of structured nanoparticles based on GB network construction.
Fig. 7
Fig. 7. Visualization of nanoparticle clusters based on DBSCAN clustering.
See this image and copyright information in PMC

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