Literature DB >> 27119552

Automated quantification of one-dimensional nanostructure alignment on surfaces.

Jianjin Dong1, Irene A Goldthorpe, Nasser Mohieddin Abukhdeir.   

Abstract

A method for automated quantification of the alignment of one-dimensional (1D) nanostructures from microscopy imaging is presented. Nanostructure alignment metrics are formulated and shown to be able to rigorously quantify the orientational order of nanostructures within a two-dimensional domain (surface). A complementary image processing method is also presented which enables robust processing of microscopy images where overlapping nanostructures might be present. Scanning electron microscopy (SEM) images of nanowire-covered surfaces are analyzed using the presented methods and it is shown that past single parameter alignment metrics are insufficient for highly aligned domains. Through the use of multiple parameter alignment metrics, automated quantitative analysis of SEM images is shown to be possible and the alignment characteristics of different samples are able to be quantitatively compared using a similarity metric. The results of this work provide researchers in nanoscience and nanotechnology with a rigorous method for the determination of structure/property relationships, where alignment of 1D nanostructures is significant.

Year:  2016        PMID: 27119552     DOI: 10.1088/0957-4484/27/23/235701

Source DB:  PubMed          Journal:  Nanotechnology        ISSN: 0957-4484            Impact factor:   3.874


  1 in total

1.  Neural Network for Nanoscience Scanning Electron Microscope Image Recognition.

Authors:  Mohammad Hadi Modarres; Rossella Aversa; Stefano Cozzini; Regina Ciancio; Angelo Leto; Giuseppe Piero Brandino
Journal:  Sci Rep       Date:  2017-10-16       Impact factor: 4.379

  1 in total

北京卡尤迪生物科技股份有限公司 © 2022-2023.