ArticleRSC advances2024
Morphological analysis of Pd/C nanoparticles using SEM imaging and advanced deep learning.
Article in RSC advances, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 papers.
What it found
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
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Who cites it
4 citing papers in PubMed.
- Enhanced Activity of Leather Materials Coated with Silver, Copper and Graphene Oxides-Decorated TiOMaterials (Basel, Switzerland) · 2026Article
- Sustainable infrared-driven deposition of palladium nanoparticles on viscose for multi-functional textile engineering.BMC chemistry · 2025Article
- Fractal theory-based analysis of strength degradation in steel slag powder-stabilized soil under chloride ion environment.Scientific reports · 2025Article
- GSH/pH-Responsive Chitosan-PLA Hybrid Nanosystems for Targeted Ledipasvir Delivery to HepG2 Cells: Controlled Release, Improved Selectivity, DNA Interaction, Electrochemical and Stopped-Flow Kinetics Analyses.International journal of molecular sciences · 2025Article
Corrections and comments
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Authors and funding
5 authors.
Funding
No grant is acknowledged in the PubMed record.
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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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.