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.

中文翻译：

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通过基于机器学习的电子显微镜图像分析对纳米颗粒形态进行统计表征

尽管透射电子显微镜（TEM）可能是研究纳米颗粒形态特征的最有效技术之一，但以统计方式定量分析它们却非常困难。本文中，我们报告了一种通过将遗传算法应用于图像分析技术来对纳米颗粒的形态进行质量通量分析的方法。所提出的方法能够分析150,000多个纳米颗粒，其精确度高达99.75％，错误发现率低至0.25％。此外，我们将具有相似形态形状的纳米颗粒聚集成几组，以进行各种统计分析。我们确定至少有1,500个纳米粒子代表95％可信区间的纳米粒子总数。此外，应考虑TEM测量次数和每个TEM图像中纳米颗粒的平均数量，以确保使用TEM图像能够令人满意地表示纳米颗粒。此外，多分散纳米颗粒的统计分布在准确估计其光学性质方面起着关键作用。我们希望该方法将成为强大的工具，并有助于将与纳米粒子相关的研究扩展到统计领域，以用于大数据分析。