A complex network approach for nanoparticle agglomeration analysis in nanoscale images

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• 作者：Bruno Brandoli Machado ; Leonardo Felipe Scabini…
• 关键词：Nanoparticle cluster ; Agglomeration analysis ; Complex networks ; Computer simulations
• 刊名：Journal of Nanoparticle Research
• 出版年：2017
• 出版时间：February 2017
• 年：2017
• 卷：19
• 期：2
• 全文大小：
• 刊物类别：Chemistry and Materials Science
• 刊物主题：Nanotechnology; Inorganic Chemistry; Characterization and Evaluation of Materials; Physical Chemistry; Optics, Lasers, Photonics, Optical Devices;
• 出版者：Springer Netherlands
• ISSN：1572-896X
• 卷排序：19

文摘

Complex networks have been widely used in science and technology because of their ability to represent several systems. One of these systems is found in Biochemistry, in which the synthesis of new nanoparticles is a hot topic. However, the interpretation of experimental results in the search of new nanoparticles poses several challenges. This is due to the characteristics of nanoparticle images and due to their multiple intricate properties; one property of recurrent interest is the agglomeration of particles. Addressing this issue, this paper introduces an approach that uses complex networks to detect and describe nanoparticle agglomerates so to foster easier and more insightful analyses. In this approach, each detected particle in an image corresponds to a vertice and the distances between the particles define a criterion for creating edges. Edges are created if the distance is smaller than a radius of interest. Once this network is set, we calculate several discrete measures able to reveal the most outstanding agglomerates in a nanoparticle image. Experimental results using images of scanning tunneling microscopy (STM) of gold nanoparticles demonstrated the effectiveness of the proposed approach over several samples, as reflected by the separability between particles in three usual settings. The results also demonstrated efficacy for both convex and non-convex agglomerates.