A deep learning-based framework for automatic analysis of the nanoparticle morphology in SEM/TEM images-Reference-Cited by-同舟云学术

A deep learning-based framework for automatic analysis of the nanoparticle morphology in SEM/TEM images

Published:2022 Issue:30 Volume:14 Page:10761-10772
ISSN:2040-3364
Container-title:Nanoscale
language:en
Short-container-title:Nanoscale

Author:

Sun Zhijian¹²³^ORCID,Shi Jia¹²^ORCID,Wang Jian¹²³,Jiang Mingqi¹²³,Wang Zhuo¹²,Bai Xiaoping¹²,Wang Xiaoxiong¹²³

Affiliation:

1. Shenyang Institute of Automation, Chinese Academy of Science, China

2. Institutes for Robotics and Intelligent Manufacturing, Chinese Academy of Sciences, China

3. University of Chinese Academy of Sciences, China

Abstract

A novel and smart three-stage framework having a powerful and light-weight NSNet to conduct high-throughput online real-time analysis of the nanoparticle morphology in complex SEM/TEM images.

Publisher

Royal Society of Chemistry (RSC)

Subject

General Materials Science

Link

http://pubs.rsc.org/en/content/articlepdf/2022/NR/D2NR01029A

Reference19 articles.

1. Morphology control, spectrum modification and extended optical applications of rare earth ion doped phosphors

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3. Progress in morphology control from fullerene to nonfullerene acceptors for scalable high-performance organic photovoltaics

4. Microfluidic strategy for rapid and high-quality control of crystal morphology of explosives

5. Solution-processed organic semiconductor crystals for field-effect transistors: from crystallization mechanism towards morphology control

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