ArticleNanoscale advances2021
A transfer learning approach for improved classification of carbon nanomaterials from TEM images.
Article in Nanoscale advances, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 6 papers.
What it found
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Who cites it
6 citing papers in PubMed.
- An Image-Based Transfer Learning Approach for Using3D printing and additive manufacturing · 2025Article
- Machine Learning as a "Catalyst" for Advancements in Carbon Nanotube Research.Nanomaterials (Basel, Switzerland) · 2024Review
- Expanding the Horizons of Machine Learning in Nanomaterials to Chiral Nanostructures.Advanced materials (Deerfield Beach, Fla.) · 2024Review
- Machine learning-augmented surface-enhanced spectroscopy toward next-generation molecular diagnostics.Nanoscale advances · 2023Review
- Deep learning approach for chemistry and processing history prediction from materials microstructure.Scientific reports · 2022Article
- Occupational Exposures to Engineered Nanomaterials: a Review of Workplace Exposure Assessment Methods.Current environmental health reports · 2021Review
Corrections and comments
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Authors and funding
3 authors.
Funding
No grant is acknowledged in the PubMed record.
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
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Registered trials
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