Thuan Nguyen Duc, Cuong Hoang Manh, Nam Nguyen Hoang, Lan Huong Nguyen Thi, Hong Hoang Si
School of Electrical and Electronic Engineering, Hanoi University of Science and Technology Hanoi Vietnam
RSC Adv. 2024 Nov 5;14(47):35172-35183. doi: 10.1039/d4ra06113f. eCollection 2024 Oct 29.
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.
在本研究中，我们提出了一种综合方法，利用扫描电子显微镜（SEM）成像和先进的深度学习技术对碳载钯（Pd/C）纳米颗粒进行形态分析。实施了一种基于注意力机制的深度学习检测模型，以准确识别和描绘未标记SEM图像中的小纳米颗粒。检测后，采用基于图的网络分析纳米颗粒的结构特征，同时利用带噪声应用的基于密度的空间聚类对检测到的纳米颗粒进行聚类，识别有意义的模式和分布。我们的结果证明了所提出模型在高精度和可靠地检测纳米颗粒方面的有效性。此外，聚类分析揭示了关于Pd/C纳米颗粒形态分布和结构组织的重要见解，有助于理解它们的性质和潜在应用。
