Facile alignment estimation in carbon nanotube films using image processing

• Published in: Signal processing Vol. 202; p. 108751
• Main Authors: Imtiaz, Tamjid, Doumani, Jacques, Tay, Fuyang, Komatsu, Natsumi, Marcon, Stephen, Nakamura, Motonori, Ghosh, Saunab, Baydin, Andrey, Kono, Junichiro, Zubair, Ahmed
• Format: Journal Article
• Language: English
• Published: Elsevier B.V 01.01.2023
• Subjects: 2D-FFT; CNT alignment estimation; Image processing; SEM imaging; Total variation decomposition; CNT alignment estimation; SEM imaging; Image processing; Total variation decomposition; 2D-FFT
• ISSN: 0165-1684; 1872-7557
• DOI: 10.1016/j.sigpro.2022.108751

•We propose a novel image processing framework based on a total variation-based image decomposition algorithm to estimate the CNT orientation distribution.•We demonstrate CNT spatial orientation in films using scanning electron micrographs.•Local and global nanotube orientation can be acquired at a fast rate with simplicity.•We employ a Fourier subtraction technique to discard unwanted alignment information arising from holes, cracks, and contaminants. [Display omitted] Whether a macroscopic assembly of carbon nanotubes can exhibit the one-dimensional properties expected from individual nanotubes critically depends on how well the nanotubes are aligned inside the assembly. Therefore, a simple and accurate method for assessing the degree of alignment is desired for the rapid characterization of carbon nanotube films and fibers. Here, we present an end-to-end solution for determining the global and local spatial orientation of carbon nanotubes in films within a short amount of time using a fast, precise, and economical approach based on an image processing method applied to scanning electron microscopy images. We first use Laplacian edge enhancement filtering for improving the appearance of edge regions, which is followed by image partitioning into multiple blocks to capture the nanoscale orientation characteristics and total variation-based image decomposition of these image blocks. We then perform a 2D-fast Fourier transform on the image decomposed textural components of these edge-enhanced image blocks to determine the orientation distribution, which is utilized to estimate the 2D nematic order parameter. To show the effectiveness of our method, we corroborated our results against results obtained with other state-of-the-art image processing and experimental techniques.