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As the aerospace industry becomes increasingly demanding for strongerlightweight materials, the ultra-strong carbon nanotube (CNT) composites withhighly aligned CNT network structures could be the answer. In this work, anovel methodology applying topological data analysis (TDA) to the scanningelectron microscope (SEM) images was developed to detect CNT orientation. TheCNT bundle extensions in certain directions were summarized algebraically andexpressed as visible barcodes. The barcodes were then calculated and convertedinto the total spread function $V(X,\theta)$, from which the alignment fractionand the preferred direction could be determined. For validation purposes, therandom CNT sheets were mechanically stretched at various strain ratios rangingfrom $0-40\%$, and quantitative TDA analysis was conducted based on the SEMimages taken at random positions. The results showed high consistency($R^2=0.975$) compared to the Herman's orientation factors derived from thepolarized Raman spectroscopy and wide-angle X-ray scattering analysis.Additionally, the TDA method presented great robustness with varying SEMacceleration voltages and magnifications, which might alter the scope inalignment detection. With potential applications in nanofiber systems, thisstudy offers a rapid and simple way to quantify CNT alignment, which plays acrucial role in transferring the CNT properties into engineering products.

Detecting Carbon Nanotube Orientation with Topological Data Analysis of Scanning Electron Micrographs