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A transfer learning approach for improved classification of carbon nanomaterials from TEM images

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Abstract

The extensive use of carbon nanomaterials such as carbon nanotubes/nanofibers (CNTs/CNFs) in industrial settings has raised concerns over the potential health risks associated with occupational exposure to these materials. These exposures are commonly in the form of CNT/CNF-containing aerosols, resulting in a need for a reliable structure classification protocol to perform meaningful exposure assessments. However, airborne carbonaceous nanomaterials are very likely to form mixtures of individual nano-sized particles and micron-sized agglomerates with complex structures and irregular shapes, making structure identification and classification extremely difficult. While manual classification from transmission electron microscopy (TEM) images is widely used, it is time-consuming due to the lack of automation tools for structure identification. In the present study, we applied a convolutional neural network (CNN) based machine learning and computer vision method to recognize and classify airborne CNT/CNF particles from TEM images. We introduced a transfer learning approach to represent images by hypercolumn vectors, which were clustered via K-means and processed into a Vector of Locally Aggregated Descriptors (VLAD) representation to train a softmax classifier with the gradient boosting algorithm. This method achieved 90.9% accuracy on the classification of a 4-class dataset and 84.5% accuracy on a more complex 8-class dataset. The developed model established a framework to automatically detect and classify complex carbon nanostructures with potential applications that extend to the automated structural classification for other nanomaterials.

Graphical abstract: A transfer learning approach for improved classification of carbon nanomaterials from TEM images

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Supplementary files

Article information


Submitted
01 Aug 2020
Accepted
12 Oct 2020
First published
14 Oct 2020

This article is Open Access

Nanoscale Adv., 2020, Advance Article
Article type
Paper

A transfer learning approach for improved classification of carbon nanomaterials from TEM images

Q. Luo, E. A. Holm and C. Wang, Nanoscale Adv., 2020, Advance Article , DOI: 10.1039/D0NA00634C

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