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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