A Transfer Learning Approach for Improved Classification of Carbon Nanomaterials from TEM Images

Details

• Personal Author:
  Holm EA ; Luo Q ; Wang C
• Description:
  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. [Description provided by NIOSH]
• Subjects:
  Aerosols Air Pollutants, Occupational Occupational Health Particulate Matter Safety
• Keywords:
  Airborne Particulates Carbon Nanotubes CNT Electron Microscopy Health Effects Models Nanofibers Nanomaterials Nanoparticles Nanostructures Nanotechnology

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• ISSN:
  2516-0230
• Document Type:
  Text
• Genre:
  Journal Article
• Place as Subject:
  Ohio ; OSHA Region 3 ; OSHA Region 5 ; Pennsylvania
• CIO:
  National Institute for Occupational Safety and Health (NIOSH)
• Division:
  HELD - Health Effects Laboratory Division
• Topic:
  Workplace Safety & Health
• Location:
  North America
• Pages in Document:
  206-213
• Volume:
  3
• Issue:
  1
• NIOSHTIC Number:
  nn:20061855
• Citation:
  Nanoscale Adv 2021 Jan; 3(1):206-213
• Contact Point Address:
  C. Wang, Health Effects Lab Division, National Institute for Occupational Safety and Health, Centers for Disease Control and Prevention, Cincinnati, OH
• Email:
  xli7@cdc.gov
• CAS Registry Number:
  Carbon nanotubes (CAS RN 308068-56-6)
• Federal Fiscal Year:
  2021
• NORA Priority Area:
  Manufacturing
• Peer Reviewed:
  True
• Source Full Name:
  Nanoscale Advances
• Collection(s):
  National Institute for Occupational Safety and Health
• Main Document Checksum:
  urn:sha-512:88732301af49cacac29b16a99581555ea9a09ec8bc1c49449f56e0ae918b936550caf2640e2ef39206818d5f33ad622cefac17ba6c4419649c2523035b202d33
• Download URL:
  https://stacks.cdc.gov/view/cdc/225216/cdc_225216_DS1.pdf
• File Type:
  [PDF - 1.10 MB ]