ABSTRACT
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.
ABSTRACT
Interface-influenced crystallization is crucial to understanding the nucleation- and growth-dominated crystallization mechanisms in phase-change materials (PCMs), but little is known. Here, we find that composition vacancy can reduce the interface energy by decreasing the coordinate number (CN) at the interface. Compared to growth-dominated GeTe, nucleation-dominated Ge2Sb2Te5 (GST) exhibits composition vacancies in the (111) interface to saturate or stabilize the Te-terminated plane. Together, the experimental and computational results provide evidence that GST prefers (111) with reduced CN. Furthermore, the (8 - n) bonding rule, rather than CN6, in the nuclei of both GeTe and GST results in lower interface energy, allowing crystallization to be observed at the simulation time in general PCMs. In comparison to GeTe, the reduced CN in the GST nuclei further decreases the interface energy, promoting faster nucleation. Our findings provide an approach to designing ultrafast phase-change memory through vacancy-stabilized interfaces.
ABSTRACT
Understanding the defect structure is fundamental to correlating the structure and properties of materials. However, little is known about the defects of soft matter at the nanoscale beyond their external morphology. We report here on the molecular-level structural details of kink defects of cellulose nanocrystals (CNCs) based on a combination of experimental and theoretical methods. Low-dose scanning nanobeam electron diffraction analysis allowed for correlation of the local crystallographic information and nanoscale morphology and revealed that the structural anisotropy governed the kink formation of CNCs. We identified two bending modes along different crystallographic directions with distinct disordered structures at kink points. The drying strongly affected the external morphology of the kinks, resulting in underestimating the kink population in the standard dry observation conditions. These detailed defect analyses improve our understanding of the structural heterogeneity of nanocelluloses and contribute to the future exploitation of soft matter defects.
