Unpaired Learning-Enabled Nanotube Identification from AFM Images.

ABSTRACT: Nanotubes, particularly single-walled carbon nanotubes (SWCNTs), represent an important class of materials with valuable electrical, mechanical, and thermal properties. However, accurate characterization of nanotube network morphologies remains a significant challenge. We present a deep learning-based approach for extracting nanotube morphologies from atomic force microscopy (AFM) images utilizing an image-to-image (I2I) translation framework based on cycleGAN with a specialized loss function that learns to transform AFM images containing nanotubes to images of pure substrates. By subtracting these translated substrate images from the original AFM images, we effectively isolated nanotube morphologies even on substrates with roughness exceeding the nanotube diameter. We validate our approac

SUBMITTER: Na S 

PROVIDER: S-EPMC12948285 | biostudies-literature | 2026 Feb

REPOSITORIES: biostudies-literature