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.

Although carbon nanotubes (CNTs) are remarkable materials with many exceptional characteristics, their poor chemical functionality limits their potential applications. Herein, a strategy is proposed for functionalizing CNTs, which can be achieved with any functional group (FG) without degrading their intrinsic structure by using a deoxyribonucleic acid (DNA)‐binding peptide (DBP) anchor. By employing a DBP tagged with a certain FG, such as thiol, biotin, and carboxyl acid, it is possible to introduce any FG with a controlled density on DNA‐wrapped CNTs. Additionally, different types of FGs can be introduced on CNTs simultaneously, using DBPs tagged with different FGs. This method can be used to prepare CNT nanocomposites containing different types of nanoparticles (NPs), such as Au NPs, magnetic NPs, and quantum dots. The CNT nanocomposites decorated with these NPs can be used as reusable catalase‐like nanocomposites with exceptional catalytic activities, owing to the synergistic effects of all the components. Additionally, the unique DBP–DNA interaction allows the on‐demand detachment of the NPs attached to the CNT surface; further, it facilitates a CNT chirality‐specific NP attachment and separation using the sequence‐specific programmable characteristics of oligonucleotides. The proposed method provides a novel chemistry platform for constructing new functional CNTs suitable for diverse applications.

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