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Quantitative Analysis of Nanorod Aggregation and Morphology from Scanning Electron Micrographs Using SEMseg.
Baiyasi, Rashad; Gallagher, Miranda J; McCarthy, Lauren A; Searles, Emily K; Zhang, Qingfeng; Link, Stephan; Landes, Christy F.
Afiliação
  • Baiyasi R; Department of Electrical and Computer Engineering, Rice University, MS 366, Houston, Texas 77005, United States.
  • Gallagher MJ; Department of Chemistry, Rice University, MS 60, Houston, Texas 77005, United States.
  • McCarthy LA; Department of Chemistry, Rice University, MS 60, Houston, Texas 77005, United States.
  • Searles EK; Department of Chemistry, Rice University, MS 60, Houston, Texas 77005, United States.
  • Zhang Q; Department of Chemistry, Rice University, MS 60, Houston, Texas 77005, United States.
  • Link S; Smalley-Curl Institute, Rice University, Houston, Texas 77005, United States.
  • Landes CF; Department of Electrical and Computer Engineering, Rice University, MS 366, Houston, Texas 77005, United States.
J Phys Chem A ; 124(25): 5262-5270, 2020 Jun 25.
Article em En | MEDLINE | ID: mdl-32463671
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.

Texto completo: 1 Base de dados: MEDLINE Idioma: En Ano de publicação: 2020 Tipo de documento: Article

Texto completo: 1 Base de dados: MEDLINE Idioma: En Ano de publicação: 2020 Tipo de documento: Article