LAUSR

Since the advent of aberration-correction around two decades ago, scanning transmission electron microscopy (STEM) has been providing structural and compositional materials information that was previously unattainable. However, for many applications the quality of the STEM data is limited by environmental and experimental factors, such as microscope instabilities and signal to noise ratio, rather than sophisticated instrument capabilities, such as aberration correction. New data science techniques offer the possibility to overcome some environmental and experimental limitations and produce data that contain higher spatial precision and signal to noise ratio, making it richer in materials information. We acquire high-precision STEM data by using non-rigid registration (NRR) and averaging of a high angle annular dark field (HAADF) STEM image series [1,2]. NRR corrects STEM image distortions and enables image averaging and SNR enhancement without the negative effects of the image distortions. The usefulness of this techniques has been demonstrated in a number of applications, such as achieving subpm precision in locating atom positions in single crystals [2], measuring pm-scale atomic column displacements at nanocatalyst surfaces [2,3], enhancing 3D atomic structural information in STEM data of nanoparticles (NPs) [2,4], measuring point defect structures, and improving atomic-scale composition information [5].