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Early Detection of Nucleation Events From Solution in LC-TEM by Machine Learning

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Title: Early Detection of Nucleation Events From Solution in LC-TEM by Machine Learning
Authors: Katsuno, Hiroyasu Browse this author →KAKEN DB
Kimura, Yuki Browse this author →KAKEN DB
Yamazaki, Tomoya Browse this author
Takigawa, Ichigaku Browse this author
Keywords: transmission electron microscopy
object detection
machine learning
Issue Date: 24-Jan-2022
Publisher: Frontiers Media
Journal Title: Frontiers in Chemistry
Volume: 10
Start Page: 818230
Publisher DOI: 10.3389/fchem.2022.818230
Abstract: To support the detection, recording, and analysis of nucleation events during in situ observations, we developed an early detection system for nucleation events observed using a liquid-cell transmission electron microscope. Detectability was achieved using the machine learning equivalent of detection by humans watching a video numerous times. The detection system was applied to the nucleation of sodium chloride crystals from a saturated acetone solution of sodium chlorate. Nanoparticles with a radius of more greater than 150 nm were detected in a viewing area of 12 mu m x 12 mu m by the detection system. The analysis of the change in the size of the growing particles as a function of time suggested that the crystal phase of the particles with a radius smaller than 400 nm differed from that of the crystals larger than 400 nm. Moreover, the use of machine learning enabled the detection of numerous nanometer sized nuclei. The nucleation rate estimated from the machine-learning-based detection was of the same order as that estimated from the detection using manual procedures.
Type: article
Appears in Collections:低温科学研究所 (Institute of Low Temperature Science) > 雑誌発表論文等 (Peer-reviewed Journal Articles, etc)

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