A transfer learning approach for improved classification of carbon nanomaterials from TEM images. Issue 1 (19th October 2020)

• Record Type: Journal Article
• Authors: Luo, Qixiang; Holm, Elizabeth A.; Wang, Chen
• Is Part Of: Nanoscale advances. Volume 3:Issue 1(2021)
• Page Start: 206
• Page End: 213
• Publication Date: 2020-10-19
• Subjects: 620.5
• DOI: 10.1039/d0na00634c
• Languages: English
• ISSNs: 2516-0230

Abstract

Abstract : A machine learning framework was developed to classify complex carbon nanostructures from TEM images. 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.