Japan Geoscience Union Meeting 2022

Presentation information

[J] Oral

M (Multidisciplinary and Interdisciplinary) » M-IS Intersection

[M-IS12] Interface- and nano-phenomena on crystal growth and dissolution

Sun. May 22, 2022 1:45 PM - 3:15 PM 104 (International Conference Hall, Makuhari Messe)

convener:Yuki Kimura(Institute of Low Temperature Science, Hokkaido University), convener:Hitoshi Miura(Graduate School of Science, Department of Information and Basic Science, Nagoya City University), Hisao Satoh(Low-Level Radioactive Waste Disposal Center, Japan Nuclear Fuel Limited), convener:Katsuo Tsukamoto(Tohoku University), Chairperson:Hitoshi Miura(Graduate School of Science, Department of Information and Basic Science, Nagoya City University), Yuki Kimura(Institute of Low Temperature Science, Hokkaido University)

2:30 PM - 2:45 PM

[MIS12-03] Detection of nucleation and measurement of growth rate in LC-TEM using machine learning

*Hiroyasu Katsuno1, Yuki Kimura1, Tomoya Yamazaki1, Ichigaku Takigawa2,3 (1.Institute of Low Temperature Science Hokkaido University, 2.RIKEN, Center for Advanced Intelligence Project, 3.Institute for Chemical Reaction Design and Discovery)

Keywords:LC-TEM, Liquid growth, Machine learning, Particle detection , growth rate

The nucleation of crystals is the first stage of crystallization and the origin of all materials. There are numerous studies of the crystallization of materials. The general understanding of crystallization is summarized in the classical nucleation theory, which is an extension of a macroscopic picture to a microscopic picture. The validity of the nucleation theory on an atomic scale has been studied in vaper growth. Recently, nonclassical nucleation phenomena have been reported in solution growth. Although the development of LC-TEM has enabled us in-situ observation in solution, it is difficult to capture the stochastic nucleation phenomena. To support the detection, recording, and analysis of nucleation events during in situ observations, we developed an early detection system by machine learning 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. Our developed detection system was applied to the nucleation of sodium chloride crystals from a saturated acetone solution of sodium chlorate by electron-beam radiolysis. Nanoparticles with a radius of greater than 150 nm were detected in a viewing area of 12 um x 12 um by the detection system. 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. 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.