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The effect of gnomonic distortion on orientation indexing of electron backscatter diffraction patterns is explored through simulation of electron diffraction patterns for sample-to-detector geometries associated with transmission Kikuchi diffraction (TKD) and electron backscatter diffraction (EBSD). Simulated data were analysed by computing a similarity index for both Hough transformed data and simulated patterns to determine the sensitivity of each method for detecting subtle differences in the effect of gnomonic distortions on electron diffraction patterns. These results indicate that the increased gnomonic distortions in electron diffraction patterns for a TKD geometry enhance the sensitivity for detecting subtle differences in interband angles. Additionally, the utilisation of a Hough transform-based indexing approach further enhances the sensitivity.
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A centrosymmetric stress cannot induce a polar response in centric materials; piezoelectricity is, for example, possible only in non-centrosymmetric structures. An exception is metamaterials with shape asymmetry, which may be polarized by stress even when the material is centric. In this case the mechanism is flexoelectricity, which relates polarization to a strain gradient. The flexoelectric response scales inversely with size, thus a large effect is expected in nanoscale materials. Recent experiments in polycrystalline, centrosymmetric perovskites (for example, (Ba, Sr)TiO3) have indicated values of flexoelectric coefficients that are orders of magnitude higher than theoretically predicted, promising practical applications based on bulk materials. We show that materials with unexpectedly large flexoelectric response exhibit breaking of the macroscopic centric symmetry through inhomogeneity induced by the high-temperature processing. The emerging electro-mechanical coupling is significant and may help to resolve the controversy surrounding the large apparent flexoelectric coefficients in this class of materials.
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Here, we report reproducible and accurate measurement of crystallographic parameters using scanning transmission electron microscopy. This is made possible by removing drift and residual scan distortion. We demonstrate real-space lattice parameter measurements with <0.1% error for complex-layered chalcogenides Bi2Te3, Bi2Se3, and a Bi2Te2.7Se0.3 nanostructured alloy. Pairing the technique with atomic resolution spectroscopy, we connect local structure with chemistry and bonding. Combining these results with density functional theory, we show that the incorporation of Se into Bi2Te3 causes charge redistribution that anomalously increases the van der Waals gap between building blocks of the layered structure. The results show that atomic resolution imaging with electrons can accurately and robustly quantify crystallography at the nanoscale.
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Laser powder bed fusion (LPBF) enables the fabrication of intricate, geometrically complex structures with a sufficiently fine surface finish for many engineering applications with a diversity of available feedstock metals. However, the production rate of LPBF systems is not well suited for mass production in comparison to traditional manufacturing methods. LPBF systems measure their deposition rates in 100's of grams per hour, while other processes measure in kilograms per hour or even in the case of processes such as forming, stamping, and casting, 100's of kilograms per hour. To be widely adopted in industry for mass production, LPBF requires a new scalable architecture that enables many orders of magnitude improvement in deposition rate, while maintaining the geometry freedom of additive manufacturing. This article explores concepts that could achieve as much as four orders of magnitude increase in the production rate through the application of (1) rotary table kinematic arrangements; (2) a dramatic number of simultaneously operating lasers; (3) reductions of laser optic size; (4) improved scanning techniques; and (5) an optimization of toroidal build plate size. To theoretically demonstrate the possibilities of production improvements, a productivity analysis is proposed for synchronous reluctance motors with relevance to the electric vehicle industry, given the recent increase in the diversity of printable soft magnetic alloys. The analysis provides insights into the impact of the architecture and process parameters necessary to optimize rotary powder bed fusion for mass production.
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Probing the stress state using a high density of measurement points is time intensive and presents a limitation for what is experimentally feasible. Alternatively, individual strain fields used for determining stresses can be reconstructed from a subset of points using a Gaussian process regression (GPR). Results presented in this paper evidence that determining stresses from reconstructed strain fields is a viable approach for reducing the number of measurements needed to fully sample a component's stress state. The approach was demonstrated by reconstructing the stress fields in wire-arc additively manufactured walls fabricated using either a mild steel or low-temperature transition feedstock. Effects of errors in individual GP reconstructed strain maps and how these errors propagate to the final stress maps were assessed. Implications of the initial sampling approach and how localized strains affect convergence are explored to give guidance on how best to implement a dynamic sampling experiment.
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Crystallographic textures are pervasive in ferroelectrics and underpin the functional properties of devices utilizing these materials because many macroscopic properties (e.g., piezoelectricity) require a non-random distribution of dipoles. Inducing a preferred grain texture has become a viable route to improve these functional properties. X-ray and neutron diffraction have become valuable tools to probe crystallographic textures. This paper presents an overview of qualitative and quantitative methods for assessing crystallographic textures in electroceramics (domain and grain textures) and discusses their strengths and weaknesses.
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Motivated by the problem of detecting changes in two-dimensional X-ray diffraction data, we propose a Bayesian spatial model for sparse signal detection in image data. Our model places considerable mass near zero and has heavy tails to reflect the prior belief that the image signal is zero for most pixels and large for an important subset. We show that the spatial prior places mass on nearby locations simultaneously being zero, and also allows for nearby locations to simultaneously be large signals. The form of the prior also facilitates efficient computing for large images. We conduct a simulation study to evaluate the properties of the proposed prior and show that it outperforms other spatial models. We apply our method in the analysis of X-ray diffraction data from a two-dimensional area detector to detect changes in the pattern when the material is exposed to an electric field.
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A novel technique, which directly and nondestructively maps polar domains using electron backscatter diffraction (EBSD) is described and demonstrated. Through dynamical diffraction simulations and quantitative comparison to experimental EBSD patterns, the absolute orientation of a non-centrosymmetric crystal can be determined. With this information, the polar domains of a material can be mapped. The technique is demonstrated by mapping the non-ferroelastic, or 180°, ferroelectric domains in periodically poled LiNbO3 single crystals. Further, the authors demonstrate the possibility of mapping polarity using this technique in other polar materials system.
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A Bayesian inference method for refining crystallographic structures is presented. The distribution of model parameters is stochastically sampled using Markov chain Monte Carlo. Posterior probability distributions are constructed for all model parameters to properly quantify uncertainty by appropriately modeling the heteroskedasticity and correlation of the error structure. The proposed method is demonstrated by analyzing a National Institute of Standards and Technology silicon standard reference material. The results obtained by Bayesian inference are compared with those determined by Rietveld refinement. Posterior probability distributions of model parameters provide both estimates and uncertainties. The new method better estimates the true uncertainties in the model as compared to the Rietveld method.