Journal of Applied Crystallography最新文献

Despite the growing use of small- and ultra-small-angle scattering (SAS/USAS) across various fields, data processing remains challenging due to the complexity of SAS analysis and the limited accessibility of existing analysis software. These issues are addressed with PRINSAS 2.0, a portable Python-based tool with an intuitive graphical user interface. It enables efficient fitting of the polydisperse spherical pore model to SAS data and is specifically designed for porous materials often encountered in geoscience. This paper outlines the scientific and mathematical foundations of the software, along with its numerical implementation, to provide users with theoretical context and to support future development. The software was tested and validated using data from a range of geological and engineered porous samples measured at various neutron scattering facilities, ensuring broad compatibility. Additional validation using synthetic data sets, along with comparisons with existing pore size distribution fitting tools, confirmed its robustness in recovering predefined pore size distributions. PRINSAS 2.0 offers wide accessibility while ensuring that the fit results adhere closely to the underlying theoretical model, making it a practical tool for non-specialist users of SAS techniques. It also integrates seamlessly with larger Python-based SAS analysis frameworks, while remaining fully functional as a standalone application.

Layered crystal structures, such as the Ruddlesden-Popper and Aurivillius families of layered perovskites, have long been studied for their diverse range of functionalities. The Aurivillius family has been extensively studied for its ferroelectric properties and potential applications in various fields, including multiferroic memories. A new analytical model is presented here that explains how out-of-phase boundaries (OPBs) in epitaxial thin films of layered materials affect X-ray diffraction (XRD) peak profiles. This model predicts which diffraction peaks will split and the degree of splitting in terms of simple physical parameters that describe the nanostructure of the OPBs, specifically the structural displacement perpendicular to the layers when moving across the OPB, the angle made by the OPB at the thin-film-substrate interface, and the OPB periodicity and its statistical distribution. The model was applied to epitaxial thin films of two Aurivillius oxides, SrBi₂(Ta,Nb)O₉ (SBTN) and Bi₄Ti₃O₁₂ (BiT), and its predictions were compared with experimental XRD data for these materials. The results showed good agreement between the predicted and observed peak splitting as a function of OPB periodicity for SBTN and for an XRD profile taken from a BiT thin film containing a well characterized distribution of OPBs. These results have proven the model's validity and accuracy. The model provides a new framework for analysing and characterizing this class of defect structures in layered systems containing OPBs.

The increase in the weighted agreement factor due to systematic errors in single-crystal X-ray and neutron diffraction experiments can be quantified precisely, provided the estimated standard uncertainties of the observed intensities, s.u.(I _obs), are sufficiently accurate. The increase in the weighted agreement factor quantifies the 'costs' of the systematic errors. This is achieved by comparison with the lowest possible weighted agreement factor for the specific data set. Application to 314 published data sets from inorganic, metal-organic and organic compounds shows that systematic errors increase the weighted agreement factor by a surprisingly large factor of g = 3.31 (or more) in 50% of the small-molecule data sets from the sample. Examples of twinning, disorder, neglect of bonding densities and low-energy contamination are taken from the literature and examined with respect to the increase in the weighted agreement factor, which is typically less than three. The large value g = 3.31 for the supposedly simple case of rather small molecules, as opposed to macromolecules, is interpreted as a warning sign that there are not only the expected remaining systematic errors, like not-modelled disorder, unrecognized twinning or neglect of bonding electrons or similar errors, but additionally a common systematic error of insufficiently accurate s.u.(I _obs). Inadequate s.u.(I _obs) may not just compromise the model parameters and model parameter errors; they are also a threat to the whole data quality evaluation procedure that relies crucially on adequate s.u.(I _obs).

Illite, a widespread clay mineral, plays a pivotal role in geological processes, notably as an indicator in diagenetic and hydro-thermal alteration environments, and possesses significant industrial relevance in applications including ceramics, construction and catalysis. However, challenges including its nanoscale crystallinity, structural disorder and frequent interstratification with other clay minerals have hindered detailed structural characterization using conventional X-ray diffraction (XRD) techniques. This study employs integrated synchrotron XRD and pair distribution function (PDF) analysis to elucidate the crystal structure of the 1M illite polytype, yielding the first determination of its anisotropic atomic displacement parameters (U _aniso). These U _aniso parameters provide critical insights into atomic dynamics and static disorder within the structure, enabling a more refined understanding of structure-property relationships. This integrated approach, combining synchrotron XRD, Rietveld refinement and PDF analysis, yields a comprehensive structural characterization, capturing both average crystallographic and local atomic arrangements. Considering illite's widespread geological occurrence and industrial importance, this high-precision structural dataset, especially the determined U _aniso values, provides a crucial benchmark for future modeling and simulation efforts targeting accurate prediction of its physicochemical behavior.

This study introduces an enhanced numerical technique tailored specifically for refining 1D small-angle scattering (SAS) intensity profiles affected by smearing. Our primary objective is to address the resolution blurring commonly encountered in SAS data, particularly in systems with clearly defined correlation peaks whose spread aligns with the width of the resolution function at corresponding Q positions. Unlike previous approaches that expanded the SAS intensity using central moment expansion, the new method focuses on expanding the resolution function itself, thus eliminating artificial oscillations observed in smeared spectra due to limitations inherent in our earlier algorithm. This method is straightforward to implement, computationally efficient and consistently performs well in numerical benchmarking. To illustrate its effectiveness, we present a case study of a lamellar phase characterized by distinct peaks in its small-angle neutron scattering intensities.

Dark-field (DF) imaging is a recent X-ray imaging modality which is promising because it gives access to information not resolved in conventional transmission X-ray imaging. The DF technique was first introduced as a loss of visibility of the grating interferometry modulations. DF signal is now measured with all the different X-ray phase contrast setups such as beam tracking or modulation-based imaging. Using a dedicated setup [Magnin et al. (2023). Opt. Lett. 48, 5839-5842], we present in the present article combined measurements of small-angle X-ray scattering and DF signal on the same material. We confirm that DF imaging is sensitive to multiple refraction from a sample, as can be found in the literature on lung imaging, but we show that the DF signal is also sensitive to scattering events. Finally, we measure a porous membrane that creates both types of signal (scattering and refraction), showing that, contrary to existing models, it is difficult to be quantitative about DF.

The mechanical behavior of piezoelectric semiconductor ZnO nanowires was studied in three-point bending configuration using the custom-built atomic force microscope SFINX coupled with in situ Laue microdiffraction. Besides bending, torsion of the nanowires was shown during mechanical loading. A fracture strength of up to 3 GPa was demonstrated, which is about one order of magnitude higher than that for bulk ZnO. In the case of a piezoelectric material like ZnO, this fracture strength represents the maximum elastic strain that could eventually be converted into electrical energy by the piezoelectric effect. The significantly increased fracture strength found for nanowires compared with bulk ZnO thus offers increased energy-harvesting potential from material flexing. While bulk ZnO is a brittle material, plasticity with the storage of dislocations in the basal plane was shown in the three-point bent ZnO nanowires.

The equations of the direct derivation method (DDM) and the unit-cell scattering power method are reviewed in this report. Their relationships and connections to the conventional Rietveld quantitative phase analysis (QPA) are revealed, leading to the development of the C_k -corrected DDM and the molecular scattering power (MSP) method. Both methods can be seamlessly integrated into the conventional Rietveld QPA routine as hybrid QPA, i.e. they enable fitting phases of partially or no known crystal structure simultaneously with conventional crystal structure modelling of other known crystalline phases. The accuracies of these hybrid QPA methods are evaluated using a calculated X-ray diffraction pattern for a mixture, the IUCr round robin CPD-1 dataset and synthetic mixtures of disordered source clay minerals (kaolinite KGa-2, chlorite CCa-2) with corundum, using both Launch Mode and Graphical User Interface (GUI) Mode of the TOPAS software. Although the accuracies of these hybrid QPA methods are slightly lower than that of conventional Rietveld QPA, their absolute deviations from weighed percentages are scarcely larger than 3 wt%. Compared with the original DDM, the C_k correction enhances QPA accuracy, particularly for mixtures containing phases of large differences in average atomic number. An advantage over the original unit-cell scattering power method is that the proposed MSP method eliminates the need to know the lattice parameters, unit-cell volume or number of molecules in the unit cell.

The processing and analysis of X-ray diffraction (XRD) data at synchrotrons is often left to the user groups, which limits the user base to groups with a background in analyzing XRD data. Pydidas is a new Python package for processing XRD data. It provides an easy and intuitive interface and versatile processing options with the aim of being accessible to non-experts in XRD analysis. A graphical user interface (GUI) allows users to perform the full pipeline of data browsing, experiment calibration, workflow setup, processing and visualization in a single tool. In addition, pydidas' logic is decoupled from the GUI and it can be fully used from within scripts or embedded into other processing pipelines. The pydidas processing pipeline is assembled from individual plugins which perform specific processing steps. This modular design allows for very versatile pipelines covering a wide range of applications. To improve the usability even further, custom plugins can be integrated in the pydidas workflow to allow specialized processing steps.

We report a comparison of modulation of intensity with zero effort (MIEZE), a neutron spin-echo technique, and neutron time-of-flight (ToF) spectroscopy, a conventional neutron scattering method. The evaluation of the respective recorded signals, which can be described by the intermediate scattering function I(Q, τ) (MIEZE) and the dynamic structure factor S(Q, E) (ToF), involves a Fourier transformation that requires detailed knowledge of the detector efficiency, instrumental resolution, signal background and range of validity of the spin-echo approximation. It is demonstrated that data obtained from pure water align well within the framework presented here, thereby extending the applicability of the MIEZE technique beyond the spin-echo approximation and emphasizing the complementarity of the two methods. Computational methods, such as molecular dynamics simulations, are highlighted as essential for enhancing the understanding of complex systems. Together, MIEZE and ToF provide a powerful framework for investigating dynamic processes across different time and energy domains, with particular attention required to ensure identical sample geometries for meaningful comparisons.