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Identifying the grain distribution and grain boundaries of nanoparticles is important for predicting their properties. Experimental methods for identifying the crystallographic distribution, such as precession electron diffraction, are limited by their probe size. In this study, we developed an unsupervised learning method by applying a Gabor filter to HAADF-STEM images at the atomic level for image segmentation and automatic counting of grains in polycrystalline nanoparticles. The methodology comprises a Gabor filter for feature extraction, non-negative matrix factorization for dimension reduction, and K-means clustering. We set the threshold distance and angle between the clusters required for the number of clusters to converge so as to automatically determine the optimal number of grains. This approach can shed new light on the nature of polycrystalline nanoparticles and their structure–property relationships.

2D crystals are attractive due to their unique atomic, electronic structures, and physiochemical properties, which strongly rely on the synthesis conditions. The atomic structure and presence of defects in the crystal lattice, such as vacancies, dopants, grain boundaries, and edge terminations, significantly influence the properties of 2D materials. Due to its high spatial resolution, aberration‐corrected scanning transmission electron microscopy (AC‐STEM) has become a powerful tool to provide atomic‐scale insights into the crystal structure, defects, heterointerfaces, ferroelectricity, and in situ observations of 2D materials. This review will cover the status of atomic‐scale studies on various 2D materials, including graphene, boron nitride, transition metal dichaogenides, MXenes, and phosphorene using AC‐STEM. The future perspective of AC‐STEM for new findings in 2D materials using machine learning is further discussed.

Although various fabrication methods for metal–oxide nanostructures have been well developed for enlarged surface area, numerous efforts to further enhance the effective surface area for their chemical sensor applications are still being studied. Herein, a high‐power laser is irradiated on the existing metal–oxide nanostructures to expose the hidden inner surface of the nanostructures for full participation in the surface gas‐sensing reactions, resulting in extraordinary gas‐sensing performance. In addition, noble metal catalyst decoration at both the inner and outer surfaces of the nanostructures records extremely high gas response and selectivity to volatile organic compounds. The numerical simulation and experimental verification of the effects of high‐power laser irradiation for morphological evolution of the metal–oxide nanostructures can provide a new perspective toward the time‐efficient development of nanostructure‐based electronic devices.