RESUMEN
A method for mapping elastic strains by TEM in plastically deformed materials is presented. A characteristic feature of plastically deformed materials, which cannot be handled by standard strain measurement method, is the presence of orientation gradients. To circumvent this issue, we couple orientation and strain maps obtained from scanning precession electron diffraction datasets. More specifically, orientation gradients are taken into account by 1) identifying the diffraction spot positions in a reference pattern, 2) measuring the disorientation between the diffraction patterns in the map and the reference pattern, 3) rotating the coordinate system following the measured disorientation at each position in the map, 4) calculating strains in the rotated coordinate system. At present, only azimuthal rotations of the crystal are handled. The method is illustrated on a Cr2AlC monocrystal micropilar deformed in near simple flexion during a nanomechanical test. After plastic deformation, the sample contains dislocations arranged in pile-ups and walls. The strain-field around each dislocation is consistent with theory, and a clear difference is observed between the strain fields around pile-ups and walls. It is further remarked that strain maps allow for the orientation of the Burgers vector to be identified. Since the loading undergone by the sample is known, this also allows for the position of the dislocation sources to be estimated. Perspectives for the study of deformed materials are finally discussed.
RESUMEN
The technique known as 4D-STEM has recently emerged as a powerful tool for the local characterization of crystalline structures in materials, such as cathode materials for Li-ion batteries or perovskite materials for photovoltaics. However, the use of new detectors optimized for electron diffraction patterns and other advanced techniques requires constant adaptation of methodologies to address the challenges associated with crystalline materials. In this study, we present a novel image-processing method to improve pattern matching in the determination of crystalline orientations and phases. Our approach uses sub-pixel adaptive image processing to register and reconstruct electron diffraction signals in large 4D-STEM datasets. By using adaptive prominence and linear filters, we can improve the quality of the diffraction pattern registration. The resulting data compression rate of 103 is well-suited for the era of big data and provides a significant enhancement in the performance of the entire ACOM data processing method. Our approach is evaluated using dedicated metrics, which demonstrate a high improvement in phase recognition. Several features are extracted from the registered data to map properties such as the spot count, and various virtual dark fields, which are used to enhance the handling of the results maps. Our results demonstrate that this data preparation method not only enhances the quality of the resulting image but also boosts the confidence level in the analysis of the outcomes related to determining crystal orientation and phase. Additionally, it mitigates the impact of user bias that may occur during the application of the method through the manipulation of parameters.