Detailed total scattering analysis of disorder in ZIF-8
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Metz, Peter C.↗
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Parameter estimation for X-ray scattering analysis with Hamiltonian Markov Chain Monte Carlo
Bayesian-inference-based approaches, in particular the random-walk Markov Chain Monte Carlo (MCMC) method, have received much attention recently for X-ray scattering analysis. Hamiltonian MCMC, a state-of-the-art development in the field of MCMC, has become popular in recent years. It utilizes Hamiltonian dynamics for indirect but much more efficient drawings of the model parameters. We described the principle of the Hamiltonian MCMC for inversion problems in X-ray scattering analysis by estimating high-dimensional models for several motivating scenarios in small-angle X-ray scattering, reflectivity, and X-ray fluorescence holography. Hamiltonian MCMC with appropriate preconditioning can deliver superior performance over the random-walk MCMC, and thus can be used as an efficient tool for the statistical analysis of the parameter distributions, as well as model predictions and confidence analysis.
73 NUCLEAR PHYSICS AND RADIATION PHYSICS↗
FLASSH 1.0: Full Law Analysis Scattering System Hub
Nuclear technologies, such as thermal reactors and critical systems, are driven by reaction rates which are directly dependent on thermal neutrons. The ability to calculate reaction rates is directly linked to the capability to predict and understand the cross sections. For atoms bound within a material structure, the available modes of energy exchange are captured in the Thermal Scattering Law (TSL, i.e. S(α,β)). The TSL is fundamentally a material property and can be defined explicitly to account for the excitation and deexcitation of the lattice energy states. This work describes the development of the Full Law Analysis Scattering System Hub (FLASSH) which calculates the TSL and cross sections. FLASSH is a modern code which has been designed to produce accurate TSL evaluations, remove historical approximations, and improve formulations while making advance physics accessible and simple to use.
73 NUCLEAR PHYSICS AND RADIATION PHYSICS↗
Structural Changes during the Growth of Atomically Precise Metal Oxido Nanoclusters from Combined Pair Distribution Function and Small-Angle X-ray Scattering Analysis
The combination of in situ pair distribution function (PDF) analysis and small-angle X-ray scattering (SAXS) enables analysis of the formation mechanism of metal oxido nanoclusters and cluster–solvent interactions as they take place. Herein, we demonstrate the method for the formation of clusters with a [Bi 38 O 45 ] core. Upon dissolution of crystalline [Bi 6 O 5 (OH) 3 (NO 3 ) 5 ]∙3 H 2 O in DMSO, an intermediate rapidly forms, which slowly grows to stable [Bi 38 O 45 ] clusters. To identify the intermediate, we developed an automated modeling method, where smaller [Bi x O y ] structures based on the [Bi 38 O 45 ] framework are tested against the data. [Bi 22 O 26 ] was identified as the main intermediate species, illustrating how combined PDF and SAXS analysis is a powerful tool to gain insight into nucleation on an atomic scale. PDF also provides information on the interaction between nanoclusters and solvent, which is shown to depend on the nature of the ligands on the cluster surface.
37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
Uncertainty-Aware Machine Learning for Small-Angle X-ray Scattering Analysis in Autonomous Experimentation
Small-angle X-ray scattering (SAXS) is a powerful high-throughput characterization tool for probing nanoscale structure in native sample environments, providing real-time morphological information such as nanoparticle size and shape during synthesis. However, automated SAXS data analysis for extracting meaningful structural parameters is non-trivial and remains a bottleneck in closed-loop experimentation towards autonomous materials discovery, which demands fast, reliable, and uncertainty-aware data analysis. Here, we develop a machine-learning approach for automated SAXS analysis tailored to closed-loop nanoparticle synthesis. A Random Forest (RF) regression model is trained on 100,000 synthetic SAXS curves generated from polydisperse spherical nanoparticles with realistic background contributions. Using normalized one-dimensional SAXS intensity profiles as input, the RF model directly predicts nanoparticle radius, size polydispersity, and background parameters, while the ensemble standard deviation across trees provides built-in uncertainty quantification (UQ). On synthetic data, we show that combining fit-quality metrics (R 2 , MAE) with thresholds on prediction uncertainty reliably identifies accurate parameter estimates without access to ground truth. We then apply the trained model to 365 experimental SAXS profiles of citrate-reduced gold nanoparticles synthesized using an automated droplet-flow microreactor with in situ SAXS at a synchrotron beamline, classifying the results into high- and low-confidence subsets based on UQ metrics. Finally, we integrate RF-based SAXS analysis into a simulated closed-loop optimization campaign using Gaussian process Bayesian optimization to minimize nanoparticle polydispersity, benchmarking against conventional automated Levenberg–Marquardt fitting. The RF-guided campaign exhibits substantially faster convergence and lower relative opportunity cost (∼0.07 vs ∼0.3), demonstrating that uncertainty-aware machine-learning SAXS analysis significantly enhances the efficiency and robustness of autonomous nanomaterials synthesis workflows.
Bayesian optimization↗
Unveiling mesoscopic structures in distorted lamellar phases through deep learning-based small angle neutron scattering analysis
Hypothesis: The formation of distorted lamellar phases, distinguished by their arrangement of crumpled, stacked layers, is frequently accompanied by the disruption of long-range order, leading to the formation of interconnected network structures commonly observed in the sponge phase. Nevertheless, traditional scattering functions grounded in deterministic modeling fall short of fully representing these intricate structural characteristics. Our hypothesis posits that a deep learning method, in conjunction with the generalized leveled wave approach used for describing structural features of distorted lamellar phases, can quantitatively unveil the inherent spatial correlations within these phases. Experiments and Simulations: This report outlines a novel strategy that integrates convolutional neural networks and variational autoencoders, supported by stochastically generated density fluctuations, into a regression analysis framework for extracting structural features of distorted lamellar phases from small angle neutron scattering data. To evaluate the efficacy of our proposed approach, we conducted computational accuracy assessments and applied it to the analysis of experimentally measured small angle neutron scattering spectra of AOT surfactant solutions, a frequently studied lamellar system. Findings: The findings unambiguously demonstrate that deep learning provides a dependable and quantitative approach for investigating the morphology of wide variations of distorted lamellar phases. It is adaptable for deciphering structures from the lamellar to sponge phase including intermediate structures exhibiting fused topological features. In conclusion, this research highlights the effectiveness of deep learning methods in tackling complex issues in the field of soft matter structural analysis and beyond.
36 MATERIALS SCIENCE↗
Neutron Total Scattering Analysis of Materials for the Nuclear Fuel Cycle
Here, this paper reviews recent experimental efforts at the University of Tennessee and Oak Ridge National Laboratory to comprehensively characterize the structural details of materials relevant for the nuclear fuel cycle by employing advanced neutron scattering techniques. For the study of nuclear ceramics, neutron scattering offers distinct advantages over traditional laboratory or synchrotron X-ray diffraction, including enhanced sensitivity to elements with a low atomic mass, such as oxygen, nitrogen, and carbon. The key to these efforts is the recent advancement in the neutron scattering infrastructure at the high-flux diffractometers at the Spallation Neutron Source. The high neutron flux at these instruments enables neutron total scattering, a nondestructive bulk technique that simultaneously captures both short-range structural effects through pair distribution function analysis and long-range order through diffraction pattern analysis. This approach is particularly important for a comprehensive description of defective, disordered, or amorphous nuclear materials. The case studies presented here include analyses of the local defect structure in hyperstoichiometric uranium oxides and short-range order of ion-irradiated ceramics. This advanced analytical methodology will improve our understanding of the behavior of materials in extreme environments and contribute to the development of more resilient nuclear materials.
Neutron scattering↗
Small-Angle X-ray Scattering Analysis of Colloidal Crystals and Replica Materials Made from L-Arginine-Stabilized Silica Nanoparticles
Colloidal crystals made from sub-100 nm silica nanoparticles have provided a versatile platform for the template-assisted synthesis of three-dimensionally interconnected semiconducting, metallic, and magnetic replicas. However, the detailed structure of these materials has not yet been characterized. Here, we investigated the structures of colloidal crystalline films and germanium replicas by scanning electron microscopy and small angle X-ray scattering. The structures of colloidal crystals made by evaporative assembly depends on the size of L-arginine-capped silica nanoparticles. Particles smaller than ~31 nm diameter assemble into non-close-packed arrangements (bcc) whereas particles larger than 31 nm assemble into random close-packed structures with disordered hexagonal phase. Polycrystalline films of these materials retain their structures and long-range order upon infiltration at high temperature and pressure, and the structure is preserved in Ge replicas. The shear force during deposition and dispersity of silica nanoparticles contributes to the size-based variation in the structure and to the size of crystal domains in the colloidal crystal films.
36 MATERIALS SCIENCE↗
The Influence of Structural Dynamics in Two-Dimensional Hybrid Organic–Inorganic Perovskites on Their Photoluminescence Efficiency — Neutron Scattering Analysis
Two-dimensional hybrid organic–inorganic perovskites (HOIPs) have emerged as promising materials for light-emitting diode applications. In this study, by using time-of-flight neutron spectroscopy we identified and quantitatively separated the lattice vibrational and molecular rotational dynamics of two perovskites, butylammonium lead iodide (BA)2PbI4 and phenethyl-ammonium lead iodide (PEA) 2 PbI 4 . By examining the corresponding temperature dependence, we found that the lattice vibrations, as evidenced by neutron spectra, are consistent with the lattice dynamics obtained from Raman scattering. We revealed that the rotational dynamics of organic molecules in these materials tend to suppress their photoluminescence quantum yield (PLQY) while the vibrational dynamics did not show predominant correlations with the same. Additionally, we observed photoluminescence emission peak splitting for both systems, which becomes prominent above certain critical temperatures where the suppression of PLQY begins. This study suggests that the rotational motions of polarized molecules may lead to a reduction in exciton binding energy or the breaking of degeneracy in exciton binding energy levels, enhancing non-radiative recombination rates, and consequently reducing photoluminescence yield. These findings offer a deeper understanding of fundamental interactions in 2D HOIPs and could guide the design of more efficient light-emitting materials for advanced technological applications.
Rajeev, Haritha Sindhu [Univ. of Virginia, Charlot↗
Halo-independent dark matter electron scattering analysis with in-medium effects
Not Available
72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗
Density functional modeling and total scattering analysis of the atomic structure of a quaternary Ca
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Gong, Kai↗
Quantitative assessment of fitting errors associated with streak camera noise in Thomson scattering data analysis
Thomson scattering measurements in high energy density experiments are often recorded using optical streak cameras. In the low-signal regime, noise introduced by the streak camera can become an important and sometimes the dominant source of measurement uncertainty. In this paper, we present a formal method of accounting for the presence of streak camera noise in our measurements. We present a phenomenological description of the noise generation mechanisms and present a statistical model that may be used to construct the covariance matrix associated with a given measurement. This model is benchmarked against simulations of streak camera images. We demonstrate how this covariance may then be used to weight fitting of the data and provide quantitative assessments of the uncertainty in the fitting parameters determined by the best fit to the data and build confidence in the ability to make statistically significant measurements in the low-signal regime, where spatial correlations in the noise become apparent. These methods will have general applicability to other measurements made using optical streak cameras.
47 OTHER INSTRUMENTATION↗
Small Angle Scattering Data Analysis Assisted by Machine Learning Methods
Small angle scattering (SAS) is a widely used technique for characterizing structures of wide ranges of materials. For such wide ranges of applications of SAS, there exist a large number of ways to model the scattering data. While such analysis models are often available from various suites of SAS data analysis software packages, selecting the right model to start with poses a big challenge for beginners to SAS data analysis. Here, we present machine learning (ML) methods that can assist users by suggesting scattering models for data analysis. A series of one-dimensional scattering curves have been generated by using different models to train the algorithms. The performance of the ML method is studied for various types of ML algorithms, resolution of the dataset, and the number of the dataset. The degree of similarities among selected scattering models is presented in terms of the confusion matrix. The scattering model suggestions with prediction scores provide a list of scattering models that are likely to succeed. Therefore, if implemented with extensive libraries of scattering models, this method can speed up the data analysis workflow by reducing search spaces for appropriate scattering models.
72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗
Efficient analysis of small-angle scattering curves for large biomolecular assemblies using Monte Carlo methods
Structure elucidation from small-angle scattering curves of large biomolecular assemblies is notoriously challenging. This is because the simulation of high-resolution features in the structure of large macromolecular assemblies, such as de novo protein assemblies, is computationally demanding when it needs to cover a broad range of length scales. Conventional methods, such as the numerical approximation to the Debye equation or the use of spherical harmonics, do not scale well as the size of the assembly increases, which limits their application to small structures (e.g. individual proteins). This work explores the effectiveness of a Monte Carlo method to simulate and fit scattering curves for large biomolecular assemblies spanning over ranges covering atomic and molecular detail (e.g. spacing and orientation of proteins in an assembly) as well as large-scale (hundreds of nanometres) features. Owing to its speed and scalability, it can be combined with a fitting algorithm to extract structural features from experimental small-angle scattering curves in biomolecular assemblies that are otherwise intractable for interpretation. This work first demonstrates the effectiveness of the tool using experimental small-angle X-ray scattering (SAXS) data from tile-like proteins that assemble into 1D tube-like macromolecular structures. Here, the diameter distribution of tubes is extracted from SAXS fits, and this is quantitatively compared with distributions from electron microscopy. SAXS data are also obtained from 2D sheet-like protein assemblies, and the proposed method is used to quantify structural features such as the separation distance between protein building blocks and the flexing of the sheet. An open-source implementation of the methodology is provided for use in a broad range of biological systems involving multi-scale scattering analysis.
37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
Nanostructuring and Underscreening of Bisalt Electrolytes with Dual-Anion Effects: Insights from Small-Angle Scattering Prepeak Analysis
Using small-angle neutron scattering, we studied the nanostructure of a prototypical D(H)-bonded network electrolyte, alkaline sodium aluminate bisalt, at concentrations up to solute volume fraction of ~0.5. Analysis of the structure factor prepeak at 0.1 < Q < 1.0 Å-1 showed that its evolution is associated with nanoscopic species distribution in water-anion network., with differences in characteristic distance d between OD- and Al(OD)4- anions related to their distinct sizes and interactions. When the solute volume fraction approached 0.5, a common/maximum value of ~4.5 Å was found for the correlation length ? that characterizes the electrostatic force in concentrated electrolyte solution before precipitation occurred. This implies that the solutions’ morphology and behavior at high concentrations may be governed by geometric factors, rather than the chemistry of the specific anion. Furthermore, the scaling of ? with concentration yielded an exponent of 1.25(1), suggesting that ? is limited to a few Å. By combining the current observations of structural heterogeneity at the nanoscale, with dynamic heterogeneity at the microscopic scale from our previous quasi-elastic neutron scattering study, we have established a structural origin of local “caging” and restricted structural relaxation processes. These local solvent-solute interactions not only control dynamics heterogeneity in concentrated electrolytes but also are responsible for crystallization processes in industrial setting, such as aluminum production and radioactive waste treatment.
Wang, Hsiu-Wen↗
Water Migration and Swelling in Engineered Barrier Materials for Radioactive Waste Disposal
Deep, underground repositories are needed to isolate radioactive waste from the biosphere. Because bentonite is an integral component of many multibarrier repository systems, information on the hydraulic behavior of bentonite is crucial for modeling the long-term viability of such systems. In this paper the hydraulic behavior of bentonite samples was analyzed as a function of aggregate size, and samples were subjected to hydrothermal treatments involving contact with NaCl, KCl, and deionized water. Neutron and X-ray imaging were used to quantify water sorption into packed bentonite samples and bentonite swelling into the water column. The distance between the original clay-water interface and the wetting front was determined as a function of time. Average water uptake exhibited a square-root-of-time dependence in freshly prepared samples, but more variable rates were observed for samples previously in contact with water. The radiography was supported by small-angle neutron scattering analysis and ultra-small-angle neutron scattering analysis of aggregate size distributions and by inelastic neutron scattering to understand the physicochemical environment of the sorbed water. Results showed that hydrothermal treatment with KCl had the greatest effect of increased water transport in the bentonite, possibly as a result of the interaction of K + with smectite layers in the clay.
12 MANAGEMENT OF RADIOACTIVE AND NON-RADIOACTIVE W↗
A Parallel Machine Learning Workflow for Neutron Scattering Data Analysis
As part of a larger effort, this work-in-progress reports the possible advantages of modifying conventional workflows used to generate labelled training samples and train machine learning (ML) models on them. We compare results from three different workflows using neutron scattering data analysis as the motivating application and report about 20% improvement in speedup, with no appreciable loss of model accuracy, over a baseline workflow.
Wang, Tianle↗