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Detecting Carbon Nanotube Orientation with Topological Analysis of Scanning Electron Micrographs

Authors :
Liyu Dong
Haibin Hang
Jin Gyu Park
Washington Mio
Richard Liang
Source :
Nanomaterials, Vol 12, Iss 8, p 1251 (2022)
Publication Year :
2022
Publisher :
MDPI AG, 2022.

Abstract

As the aerospace industry is increasingly demanding stronger, lightweight materials, ultra-strong carbon nanotube (CNT) composites with highly aligned CNT network structures could be the answer. In this work, a novel methodology applying topological data analysis (TDA) to scanning electron microscope (SEM) images was developed to detect CNT orientation. The CNT bundle extensions in certain directions were summarized algebraically and expressed as visible barcodes. The barcodes were then calculated and converted into the total spread function, V(X, θ), from which the alignment fraction and the preferred direction could be determined. For validation purposes, the random CNT sheets were mechanically stretched at various strain ratios ranging from 0 to 40%, and quantitative TDA was conducted based on the SEM images taken at random positions. The results showed high consistency (R2 = 0.972) compared to Herman’s orientation factors derived from polarized Raman spectroscopy and wide-angle X-ray scattering analysis. Additionally, the TDA method presented great robustness with varying SEM acceleration voltages and magnifications, which might alter the scope of alignment detection. With potential applications in nanofiber systems, this study offers a rapid and simple way to quantify CNT alignment, which plays a crucial role in transferring the CNT properties into engineering products.

Details

Language :
English
ISSN :
20794991
Volume :
12
Issue :
8
Database :
Directory of Open Access Journals
Journal :
Nanomaterials
Publication Type :
Academic Journal
Accession number :
edsdoj.75a198961a3d41cba51f9f09988a4760
Document Type :
article
Full Text :
https://doi.org/10.3390/nano12081251