期刊
ACS NANO
卷 14, 期 12, 页码 17125-17133出版社
AMER CHEMICAL SOC
DOI: 10.1021/acsnano.0c06809
关键词
transmission electron microscope (TEM); image analysis; machine learning; morphological properties; statistics; big data
类别
资金
- National Research Foundation (NRF) of Korea - Ministry of Education [NRF-2019R1A6A1A03033215, NRF-2019R1F1A1061418]
- Ministry of Science and ICT [NRF-2020R1A2C2006100]
- NRF of Korea - Ministry of Science and ICT [NRF-2018M3C1B7021997]
- Korea Basic Science Institute (National Research Facilities and Equipment Center) - Ministry of Education [2019R1A6C1010031, 2020R1A6C103B101]
- Basic Science Research Program - Ministry of Education [NRF-2020R1A6A3A13076992]
- National Research Foundation of Korea [2020R1A6C103B101, 22A20154613485, 4199990514093, 2019R1A6C1010031] Funding Source: Korea Institute of Science & Technology Information (KISTI), National Science & Technology Information Service (NTIS)
Although transmission electron microscopy (TEM) may be one of the most efficient techniques available for studying the morphological characteristics of nanoparticles, analyzing them quantitatively in a statistical manner is exceedingly difficult. Herein, we report a method for mass-throughput analysis of the morphologies of nanoparticles by applying a genetic algorithm to an image analysis technique. The proposed method enables the analysis of over 150,000 nanoparticles with a high precision of 99.75% and a low false discovery rate of 0.25%. Furthermore, we clustered nanoparticles with similar morphological shapes into several groups for diverse statistical analyses. We determined that at least 1,500 nanoparticles are necessary to represent the total population of nanoparticles at a 95% credible interval. In addition, the number of TEM measurements and the average number of nanoparticles in each TEM image should be considered to ensure a satisfactory representation of nanoparticles using TEM images. Moreover, the statistical distribution of polydisperse nanoparticles plays a key role in accurately estimating their optical properties. We expect this method to become a powerful tool and aid in expanding nanoparticle-related research into the statistical domain for use in big data analysis.
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