Journal of Applied Crystallography最新文献

Recent advancements in cold neutron instrumentation, designed to achieve the energy resolution necessary for studying strongly correlated materials, have driven the need for sophisticated modeling of neutron spectroscopy data from highly neutron-absorbing materials. These absorption effects are often highly dependent on both angular orientation and wavelength. To address this, the finite-volume algorithm for absorption correction developed by Wuensch & Prewitt [Z. Kristallogr. (1965), 122, 24–59] is examined in this paper in the context of cold neutron spectroscopy. This algorithm is based on the numerical integration of the transmission function, where three-dimensional quadratic surfaces define the sample boundaries. The algorithm can also determine the mean path length required for second-extinction calculations. We apply this method to neutron inelastic scattering measurements of an irregularly shaped CeRhIn₅ single crystal using the Multi-Axis Crystal Spectrometer at NIST. The algorithm has been expanded to correct for the absorption of multiple coaligned samples. We show that this procedure can account for the angle-dependent absorption, and the technique can be used to correct the data and plan experiments.

In situ X-ray scattering experiments, to study structure–function relationships in materials, have traditionally relied on bright synchrotron X-rays to resolve fast dynamic phenomena and efficiently probe structure as a function of environmental variables. However, recent technological advances have expanded the utility of laboratory-based diffraction instruments. Here we demonstrate how a modern laboratory-based X-ray diffraction instrument, equipped with a photon-counting area detector (EIGER2) and microfocus Mo X-ray source (Incoatec IµS), can effectively complement synchrotrons, bridging the gap between the time resolution of synchrotron-based experiments and what can be achieved in house. Specifically, the ability to acquire quantitative powder diffraction data within 2–3 min enables time-resolved studies of dynamic processes and efficient parametric studies on timescales suitable for solid-state transformations. The transmission measurement geometry using an area detector parallels that used at synchrotrons, allowing complex experiments and new sample environment developments to be prototyped in house before being transferred to synchrotron beamlines for powder diffraction and/or pair distribution function analysis.

A combination of the modified Warren–Averbach (mWA) and modified Williamson–Hall (mWH) methods was applied to characterize the local dislocation structure at the micrometre scale of a laser-shock-peened Ni specimen. Peak profiles obtained by energy scanning of Laue microdiffraction peaks were analyzed in terms of dislocation density, stored energy and interaction between dislocations. The applied methods, exploiting the asymptotic form of the Fourier transform of the peak (mWA method) and the long-range screening described by the full width at half-maximum (mWH), are complementary and offer for the first time the possibility of checking the adequacy of an assumed dislocation model. The combined method is applicable to a dilute dislocation structure, when the mWH plot should be linear. The results for the dislocation density are in reasonable agreement with previous literature data obtained by transmission electron microscopy.

We use machine learning (ML) enhanced computational reverse engineering analysis of scattering experiments (CREASE) to interpret small-angle X-ray scattering (SAXS) data obtained from a system of nanoparticles without a priori knowledge of their exact shapes (e.g. spheres or ellipsoids), sizes (0.5–50 nm) and distributions. The SAXS measurements yielded three categories of scattering profiles exhibiting `strong', `weak' and `no' features. Diminishing features (e.g. broadening or disappearing peaks) in scattering profiles have always been attributed to the presence of significant dispersity in the system. Such featureless SAXS data are not suitable for traditional analysis using analytical models. If one were to fit a relevant analytical model (e.g. the lmfit analytical model for polydisperse spheres) to these `weak' and `no' SAXS profiles from our nanoparticle systems, one would obtain non-unique interpretations of the data. Relying on electron microscopy to identify the distributions of nanoparticle shapes and sizes is also unfeasible, especially in high-throughput synthesis and characterization loops. In such situations, to identify the distributions of particle sizes and shapes that could be present in the sample, one must rely on methods like ML-CREASE to interpret the data quickly and output all relevant interpretations about the structure present in the system. The ML-CREASE optimization loop takes the experimental scattering profile as input and outputs multiple candidate solutions whose computed scattering profiles match the SAXS profile input. The ML-CREASE method outputs distributions of relevant structural features, such as the volume fraction of the nanoparticles in the system and the mean and standard deviation of the particle size and aspect ratio, assuming a type of distribution (e.g. normal, log-normal) for size and aspect ratio. We find that, for the SAXS profiles analyzed here, accounting for the shape dispersity along with size dispersity of the nanoparticles using ML-CREASE improved the match between the computed scattering profiles and input experimental profiles.

Small-angle X-ray scattering (SAXS) analysis of semi-crystalline polymers remains a labour-intensive process requiring expert interpretation of correlation functions. To address this challenge, we present CorFuncSAXSNet: a deep neural network framework designed to directly predict nanostructural parameters – including lamellar crystalline thickness (dc) and amorphous layer thickness (da) – from 1D raw SAXS curves. Building upon SAXS datasets collected at the Shanghai Synchrotron Radiation Facility's BL19U2 beamline, we developed three neural architectures: a convolutional neural network, a residual network and a q-space attention network. Data augmentation strategies, including Gaussian noise injection and q-shift interpolation, improved model robustness against experimental uncertainties. Cross-validation results demonstrate that all networks achieve mean absolute errors of 0.109–0.112 nm for dc and 0.459–0.499 nm for da. Though amorphous layer predictions at large values exhibit higher errors due to dataset skewness (83.3% of data clustered at 4.5 < dc < 6.5 nm, 5.0 < da < 20.0 nm), our framework enables rapid parameter extraction (<1 s per curve), reducing reliance on manual graphical methods. CorFuncSAXSNet bridges the gap between AI and synchrotron-based structural analysis, establishing a foundation for real-time smart beamline architectures.