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Bismuth oxide is an important bismuth compound having applications in electronics, photo-catalysis and medicine. At the nanoscale, bismuth oxide experiences a variety of new physico-chemical properties because of its increased surface to volume ratio leading to potentially new applications. In this manuscript, we report for the very first time the synthesis of bismuth oxide (Bi 2 O 3 ) nano-flakes by pulsed laser ablation in liquids without any external assistance (no acoustic, electric field, or magnetic field). The synthesis was performed by irradiating, pure bismuth needles immerged in de-ionized water, at very high fluence ∼160 J cm −2 in order to be highly selective and only promote the growth of two-dimensional structures. The x - and y -dimensions of the flakes were around 1 μm in size while their thickness was 47.0 ± 12.7 nm as confirmed by AFM analysis. The flakes were confirmed to be α- and γ-Bi 2 O 3 by SAED and Raman spectroscopy. By using this mixture of flakes, we demonstrated that the nanostructures can be used as antimicrobial agents, achieving a complete inhibition of Gram positive (MSRA) and Gram negative bacteria (MDR-EC) at low concentration, ∼50 ppm. 

Electron backscattering diffraction provides the analysis of crystalline phases at large scales (microns) while precession electron diffraction may be used to get 4D‐STEM data to elucidate structure at nanometric resolution. Both are limited by the probe size and also exhibit some difficulties for the generation of large datasets, given the inherent complexity of image acquisition. The latter appoints the application of advanced machine learning techniques, such as deep learning adapted for several tasks, including pattern matching, image segmentation, etc. This research aims to show how Gabor filters provide an appropriate feature extraction technique for electron microscopy images that could prevent the need of large volumes of data to train deep learning models. The work presented herein combines an algorithm based on Gabor filters for feature extraction and an unsupervised learning method to perform particle segmentation of polyhedral metallic nanoparticles and crystal orientation mapping at atomic scale. Experimental results have shown that Gabor filters are convenient for electron microscopy images analysis, that even a nonsupervised learning algorithm can provide remarkable results in crystal segmentation of individual nanoparticles. This approach enables its application to dynamic analysis of particle transformation recorded with aberration‐corrected microscopy, offering new possibilities of analysis at nanometric scale.