2024-03-28T22:09:54Zhttps://eprints.lib.hokudai.ac.jp/dspace-oai/requestoai:eprints.lib.hokudai.ac.jp:2115/843192022-11-17T02:08:08Zhdl_2115_20056hdl_2115_147Early Detection of Nucleation Events From Solution in LC-TEM by Machine Learning1000070377927Katsuno, Hiroyasu1000050449542Kimura, YukiYamazaki, TomoyaTakigawa, Ichigakumetadata only accessCreative Commons Attribution 4.0 Internationaltransmission electron microscopyobject detectionnucleationmachine learningYOLOv5420To 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.Frontiers Media2022-01-24engjournal articleNAhttp://hdl.handle.net/2115/84319https://doi.org/10.3389/fchem.2022.8182302296-2646Frontiers in Chemistry10818230