Journal of Applied Crystallography最新文献

Steels used in the automotive industry have seen significant improvements within the past few decades, as economic and environmental efficiency play major and increasing roles in current individual transportation. In particular, it has been possible to combine high formability with high strength, which contributes to passenger safety, vehicle performance and efficiency. Deep-drawing steels with higher manganese content play a special role in the class of automotive body sheet materials, in which the final mechanical properties are achieved, for example, through twinning-induced plasticity and/or transformation-induced plasticity effects. In this study, the deformation behavior of steels X40MnCrAlV-19-2.5-2, HCT690T and S355MC was investigated using laboratory X-ray diffraction analyses. The investigations cover the evolution of crystallographic texture and material anisotropy resulting from uniaxial deformation, as well as their influence on diffraction data, especially in the context of stress analyses. In contrast to the situation in the initial as-rolled state, the induced deformations up to the limit of uniform elongation lead to the formation of strong textures and a considerable impairment of the diffraction data due to material anisotropy. However, the formed crystallographic textures do not suffice to describe this impairment. Rather, this effect is mainly attributable to intergranular strains, which are caused by different degrees of deformation of individual crystallites within the elasto-plastic regime. These phenomena need to be considered, and this is demonstrated using the application example of a welded metal sheet. Correction approaches are proposed and their application is illustrated, with a focus on how readily the residual stresses can be evaluated.

The small-angle scattering form factors of two classes of composite particles with contrasting internal architectures have been studied: one consisting of inclusions of smaller spheres embedded within a larger sphere, and the other comprising a solid sphere with randomly distributed spherical voids. These systems serve as material- and void-based analogues, providing a model framework for examining how internal material distribution in porous particles influences scattering signatures. Monte Carlo simulations were used to generate scattering curves across a range of volume fractions and polydispersities, which were then employed to benchmark analytically derived form factor expressions. Steric repulsion between, respectively, spheres and voids was taken as hard-sphere interactions. The results reveal that internal structural asymmetries, especially in spatial correlations and contrast topology, significantly affect scattering patterns, despite the particles having similar overall structures and volume fractions. In particular, spheres-of-spheres structures exhibit features in the scattering signal from internal modulations, while void-based particles display smoother shell-like scattering features. The analytical models show excellent agreement with the simulated data, capturing both the global shape and fine structural characteristics. These findings demonstrate that relatively simple analytical approaches, validated against numerical simulations, can reliably describe complex heterogeneous particles. This methodology provides a robust basis for interpreting scattering data from porous and composite materials across a wide range of applications.

Crystallographic structure identification is crucial for understanding material properties; however, current methodologies often depend on labor-intensive and time-consuming analyses of 2D X-ray diffraction (XRD) patterns. To address these limitations, this study employs synthetic 2D XRD patterns combined with deep learning (DL) techniques to enable automated and high-throughput classification of the seven crystal systems and 230 space groups. We introduce the novel Auto Diffraction Pipeline, designed to generate synthetic 2D XRD spot patterns from crystallographic information files under diverse conditions, including varying zone axes, atomic substitution, atomic depletion and mechanical loading. These conditions enhance the realism of synthetic data, mitigating the scarcity of experimental datasets and enabling the creation of large representative training sets. Convolutional neural networks were trained and validated on these synthetic datasets to classify crystallographic structures across multiple scenarios. Our results demonstrate that integrating synthetic 2D XRD patterns with DL facilitates rapid, accurate and automated crystallographic classification, promoting the wider adoption of data-driven approaches in materials science.

[This corrects the article DOI: 10.1107/S1600576725002365.].

Grazing-incidence small-angle X-ray scattering (GISAXS) is a technique of choice for providing information about the morphology of nano- and micro-structures at surfaces and interfaces, also in real time. The geometry of the sample, in particular its curvature, has an impact on the observed X-ray scattering signal. There are a multitude of systems with sophisticated geometries (including curvature), ranging from electronic devices on flexible substrates to biological membranes, for which GISAXS could provide valuable information. Therefore, in this work the effect of the sample geometry on the GISAXS signal is addressed. More specifically the influence of the substrate curvature and extent along the X-ray beam is considered. The analytical expressions accounting for the effects of those two geometrical parameters are provided, and the way to include them in the analysis of GISAXS patterns is described. The calculations reveal that no corrections are needed for small samples (length over distance to the detector ratio smaller than 1%) and radius of curvature |R| > 50 m. These results allow for a combination of GISAXS with substrate curvature measurements. The latter technique is a non-destructive in situ and real-time method providing information about the intrinsic stress in a thin film during its growth. Morphological information from GISAXS is supposed to complement this stress information. Herein this methodology is applied to the growth of Ag thin films deposited by magnetron sputtering with N₂ plasma additive. The analysis of the GISAXS pattern obtained from the sample, which bends during the deposition, provided morphological parameters of the growing film. This methodology can be useful for understanding of the mechanisms at the nanoscale leading to the observed stress state. The ability to perform GISAXS on curved substrates enables its application to more complex systems.

Mechanisms of the lattice strain relaxation in molybdenum thin films that were grown heteroepitaxially on (001)- and (011)-oriented MgO wafers using magnetron sputtering were studied using a combination of X-ray and electron diffraction and transmission electron microscopy. For the Mo film grown on (001)-oriented MgO, the X-ray diffraction pole figure measurements revealed (001)_Mo ∥ (001)_MgO & [110]_Mo ∥ [100]_MgO as the main orientation relationship. On the (011)-oriented MgO, the Mo film grew with the orientations (112)_Mo ∥ (011)_MgO & ±[110]_Mo ∥ [100]_MgO. In all cases, the stress generated by the lattice misfit exceeded the elastic deformation limit of Mo, which activated the lattice strain relaxation mechanisms, mainly the formation of dislocations and slip and twinning on the lattice planes {112}. The dominant relaxation mechanism depends on the mutual orientation between the film and the substrate, which defines the direction of the deformation force in the film. In the (001)-oriented film, the lattice strain produced by the lattice misfit was reduced by twinning and dislocations. In the film having the (112) orientation, the main relaxation mechanism was the formation of dislocations. In both cases, the deformation energy was additionally reduced by the small lateral size of the Mo crystallites.

Recent advances in Bragg coherent diffraction imaging (BCDI) experimental techniques permit routine measurement of multiple Bragg peaks from a single crystalline grain. The resulting images contain the full lattice distortion vector field which can be differentiated to provide lattice strain and rotation. With the advent of fourth-generation synchrotron light sources, such multi-peak datasets are produced at high rates, facilitating the need for rapid phase retrieval of the multiple peaks and subsequent image analysis. Here we describe and demonstrate a new implementation of a coupled phase retrieval technique for multi-peak BCDI which simultaneously treats each Bragg peak of the dataset and produces a three-dimensional image of the crystal's morphology and lattice distortion field. In addition, this method uses the redundant information contained in the various Bragg diffraction patterns to detect and suppress spurious signal appearing on the detector in a subset of the measurements. Compared with manual data editing, adaptive coupling produces a more consistent phase profile in reciprocal space and sharper surfaces in direct space, with no significant difference in computational cost. These improvements reduce the need for manual preprocessing and enable robust high-throughput analysis of multi-peak BCDI data, supporting near-real-time strain microscopy at modern synchrotron facilities.

Understanding the interactions between microstructure, strain, phase and material behavior is crucial in scientific fields such as energy storage, carbon sequestration and biomedical engineering. However, quantifying these correlations is challenging, as it requires the use of multiple instruments and techniques, often separated by space and time. The Dual Imaging and Diffraction (DIAD) beamline at Diamond Light Source is designed to address this challenge. DIAD allows its users to visualize internal structures (in two and three dimensions), identify compositional/phase changes and measure strain. It enables in situ and operando experiments that require spatially correlated information. DIAD provides two independent beams combined at one sample position, allowing 'quasi-simultaneous' X-ray computed tomography and X-ray powder diffraction. A unique functionality of the DIAD configuration is the ability to perform 'image-guided diffraction', where the micrometre-sized diffraction beam is scanned over the complete area of the imaging field of view without moving the specimen. This moving-beam diffraction geometry enables the study of fast-evolving and motion-susceptible processes and samples. Here, we discuss the novel moving-beam diffraction geometry, presenting the latest findings on the reliability of both the geometry calibration and the data-reduction routines used. We provide a comprehensive quantitative assessment of the moving-beam diffraction geometry implemented at the DIAD beamline, which will serve as a reference for beamline users. Our measurements confirm that diffraction is most sensitive to the moving-beam geometry for the conventional transmission geometry of the detector. The observed data confirm that the motion of the Kirkpatrick-Baez mirror coupled with a fixed-aperture slit results in a rigid translation of the beam probe, without affecting the angle of the incident-beam path to the sample. Our measurements demonstrate that a nearest-neighbor calibration can achieve the same accuracy as a self-calibrated geometry when the distance between the calibrated and probed sample regions is smaller than or equal to the beam spot size. The absolute error of the moving-beam diffraction geometry at DIAD with typical calibration setup remains below 0.01%, which is the accuracy we observe for the beamline with stable beam operation.

We installed a 30 mm-bore 3.2 m-long magnetic lens in the small-angle neutron scattering diffractometer (SANS-J) at the Japan Research Reactor 3 to focus the neutron beam near the sample position for measurements at Q > 0.1 nm^-1. The focused beam at the sample position was over three times more intense than the standard non-focused beam, while the Q resolution was improved. The positive spin component of the neutron beam was focused near the sample, whereas the negative spin component was defocused and blocked by the slit just in front of the sample, producing a polarized beam. The 8 mm-diameter focusing polarized beam achieved a polarization of 0.95-0.96 and an intensity up to 22 times greater than that of a non-focusing polarized beam with the same collimation length. Using this beam with a remanent supermirror-coated spin analyser, we demonstrate that polarization analysis measurements of hydrogen-containing samples can be completed within tens of minutes.

Advances in X-ray and neutron sources, as well as in area-detector technologies, enable the recording of several terabytes of raw two-dimensional detector data in a single experiment. While several efficient integration and conversion tools are available for data collected in transmission geometry, analogous solutions for grazing-incidence diffraction (including grazing-incidence X-ray diffraction and grazing-incidence wide-angle X-ray scattering) experiments have not yet achieved the same level of efficiency. The development of new data analysis tools, including machine-learning-based software for X-ray data, necessitates the establishment of a standardized format for the converted data. To address these challenges, we have developed a new Python library, pygid, which is designed to facilitate fast data processing while providing compatibility with various raw data formats, a standardized data storage format and an intuitive interface for straightforward use. pygid supports three types of coordinate systems and both transmission and grazing-incidence geometries. It is capable of handling large datasets, performing one-dimensional line cuts and simulating expected Bragg peak positions for given structures. The package facilitates sample and experimental metadata curation in accordance with the FAIR principles. As an integral part of the broader mlgid pipeline, pygid serves as the initial step linking raw scattering patterns with machine learning tools for data analysis. The pygid package is accessible at https://github.com/mlgid-project.