[21p-D901-1~19] 12.6 Nanobiotechnology

6:00 PM - 6:15 PM JST(9:00 AM - 9:15 AM UTC)Presentation by Applicant for JSAP Young Scientists Presentation Award

[21p-D901-16] Deep Learning Analysis of Scattered Light Intensity for Shape Classification of Nanoparticles Measured by Nano Tracking Analysis

〇Keisuke Yamamoto¹, Hiromi Kuramochi^1,2, Hiroaki Fukuda¹, Yasushi Shibuta¹, Takanori Ichiki^1,2 (1.Tokyo Univ.okyo, 2.iCONM)

Keywords:

Nanoparticle,Nano-Metrology,Machine Learning

In order to efficiently characterize heterogeneous nanoparticle populations in liquids, it is necessary to achieve single-particle measurement and multiparametric analysis while identifying individual particles. In reality, however, Nano Tracking Analysis (NTA), a widely used single-particle measurement technique, is limited in its ability to characterize nanoparticles in liquids. We have succeeded in detecting the effects of shape anisotropy by using deep learning to analyze Brownian motion trajectory data of nanoparticles in liquids in order to obtain a broader range of physical property information. In this study, we investigated the possibility of shape classification using luminance information obtained from NTA images in order to better understand the physical properties of nanoparticles in liquids.