Literature DB >> 32463671

Quantitative Analysis of Nanorod Aggregation and Morphology from Scanning Electron Micrographs Using SEMseg.

Rashad Baiyasi1, Miranda J Gallagher2, Lauren A McCarthy2, Emily K Searles2, Qingfeng Zhang2,3, Stephan Link1,2,3, Christy F Landes1,2,3,4.   

Abstract

General methods to achieve better physical insight about nanoparticle aggregation and assembly are needed because of the potential role of aggregation in a wide range of materials, environmental, and biological outcomes. Scanning electron microscopy (SEM) is fast and affordable compared to transmission electron microscopy, but SEM micrographs lack contrast and resolution due to lower beam energy, topographic contrast, edge effects, and charging. We present a new segmentation algorithm called SEMseg that is robust to the challenges inherent in SEM micrograph analysis and demonstrate its utility for analyzing gold (Au) nanorod aggregates. SEMseg not only supports nanoparticle size analysis for dispersed nanoparticles, but also discriminates between nanoparticles within an aggregate. We compare our algorithm to those incorporated into the commonly used software ImageJ and demonstrate improved segmentation of aggregate structures. New physical insight about aggregation is demonstrated by the introduction of an order parameter describing side-by-side structure in nanoparticle aggregates. We also present the segmentation and fitting algorithms included in SEMseg within a user-friendly graphical user interface. The resulting code is provided with an open-source interface to provide quantitative image processing tools for researchers to characterize both dispersed nanoparticles and nanoparticle assemblies in SEM micrographs with high throughput.

Entities:  

Year:  2020        PMID: 32463671     DOI: 10.1021/acs.jpca.0c03190

Source DB:  PubMed          Journal:  J Phys Chem A        ISSN: 1089-5639            Impact factor:   2.781


  1 in total

1.  Deep Learning Based Instance Segmentation of Titanium Dioxide Particles in the Form of Agglomerates in Scanning Electron Microscopy.

Authors:  Paul Monchot; Loïc Coquelin; Khaled Guerroudj; Nicolas Feltin; Alexandra Delvallée; Loïc Crouzier; Nicolas Fischer
Journal:  Nanomaterials (Basel)       Date:  2021-04-09       Impact factor: 5.076

  1 in total

北京卡尤迪生物科技股份有限公司 © 2022-2023.