Chirality Analysis of Complex Microparticles using Deep Learning on Realistic Sets of Microscopy Images-Reference-Cited by-同舟云学术

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Chirality Analysis of Complex Microparticles using Deep Learning on Realistic Sets of Microscopy Images

Published:2023-04-14 Issue:8 Volume:17 Page:7431-7442
ISSN:1936-0851
Container-title:ACS Nano
language:en
Short-container-title:ACS Nano

Author:

Visheratina Anastasia¹^ORCID,Visheratin Alexander²,Kumar Prashant¹^ORCID,Veksler Michael¹,Kotov Nicholas A.¹³^ORCID

Affiliation:

1. Department of Chemical Engineering and Biointerfaces Institute, University of Michigan, Ann Arbor, Michigan 48109, United States

2. Beehive AI, San Francisco, California 94080, United States

3. Department of Aeronautics, Faculty of Engineering, Imperial College London, South Kensington Campus London, SW7 2AZ, United Kingdom

Funder

Division of Chemistry

Office of Naval Research

Publisher

American Chemical Society (ACS)

Subject

General Physics and Astronomy,General Engineering,General Materials Science

Link

https://pubs.acs.org/doi/pdf/10.1021/acsnano.2c12056

Reference79 articles.

1. Emergence of complexity in hierarchically organized chiral particles

2. Calcium carbonate nucleation driven by ion binding in a biomimetic matrix revealed by in situ electron microscopy

3. Micelle-directed chiral seeded growth on anisotropic gold nanocrystals

4. Amino-acid- and peptide-directed synthesis of chiral plasmonic gold nanoparticles

5. Helical nanostructures based on DNA self-assembly

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1. Machine learning for analyses and automation of structural characterization of polymer materials;Progress in Polymer Science;2024-06

2. Expanding the Horizons of Machine Learning in Nanomaterials to Chiral Nanostructures;Advanced Materials;2024-02-03

3. Chiral nanomaterials in tissue engineering;Nanoscale;2024

4. Geometric Control and Optical Properties of Intrinsically Chiral Plasmonic Nanomaterials;Advanced Materials;2023-11-29

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