Revealing microscopic dynamics: in situ liquid-phase TEM for live observations of soft materials and quantitative analysis via deep learning

Author:

Sun Yangyang1,Zhang Xingyu1ORCID,Huang Rui2,Yang Dahai2,Kim Juyeong3ORCID,Chen Junhao2,Ang Edison Huixiang4ORCID,Li Mufan5,Li Lin6,Song Xiaohui2ORCID

Affiliation:

1. School of Mathematics, Statistics and Mechanics, Beijing University of Technology, Beijing 100124, China

2. School of Materials Science and Engineering, Hefei University of Technology, Anhui Province, 230009, China

3. Department of Chemistry and Research Institute of Natural Sciences, Gyeongsang National University, Jinju 52828, South Korea

4. Natural Sciences and Science Education, National Institute of Education, Nanyang Technological University, Singapore 637616, Singapore

5. Institute of Physical Chemistry, the College of Chemistry and Molecular Engineering, Pecking University, Beijing, 100871, China

6. Beijing Shunce Technology Co., Ltd, Beijing, 102629, China

Abstract

By leveraging liquid-phase TEM and deep learning, this work explores quantitative aspects of soft matter dynamics-encompassing phenomena such as surfactant molecule nucleation, polymer self-assembly, and Au nanoparticle ripening in solution.

Funder

Hefei University

National Research Foundation of Korea

Korea Basic Science Institute

Publisher

Royal Society of Chemistry (RSC)

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