Intelligent nanoscope for rapid nanomaterial identification and classification

Author:

Jin Geonsoo1ORCID,Hong Seongwoo2ORCID,Rich Joseph3ORCID,Xia Jianping1ORCID,Kim Kyeri3ORCID,You Lingchong345,Zhao Chenglong67ORCID,Huang Tony Jun1ORCID

Affiliation:

1. Thomas Lord Department of Mechanical Engineering and Materials Science, Duke University, Durham, NC 27708, USA

2. Office of Biomedical Graduate Education, Duke University School of Medicine, Durham, NC 27710, USA

3. Department of Biomedical Engineering, Duke University, Durham, NC 27708, USA

4. Center for Genomic and Computational Biology, Duke University, Durham, NC 27708, USA

5. Department of Molecular Genetics and Microbiology, Duke University School of Medicine, Durham, NC 27708, USA

6. Department of Physics, University of Dayton, 300 College Park, Dayton, Ohio 45469, USA

7. Department of Electro-Optics and Photonics, University of Dayton, 300 College Park, Dayton, Ohio 45469, USA

Abstract

Microspheres array based intelligent nanoscope processed data collection for deep learning training. The trained convolutional neural network model classified the different sizes of nanoparticle samples.

Funder

National Science Foundation

National Institutes of Health

Publisher

Royal Society of Chemistry (RSC)

Subject

Biomedical Engineering,General Chemistry,Biochemistry,Bioengineering

Reference75 articles.

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3. Deep learning for biological image classification

4. L.Perez and J.Wang , 2017 , arXiv preprint arXiv:1712.04621

5. A.Mikołajczyk and M.Grochowski , 2018 international interdisciplinary PhD workshop (IIPhDW) , 2018

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