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Search indexed NASA NTRS and DOE OSTI research on propulsion, heat transfer, battery materials and energy systems. Follow report and document links to the original sources.

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At least 109 records · Page 6
All sourcesNASA NTRSDOE OSTI
DOE OSTI2021

The local structure of 0.5Ba(Zr 0.2 Ti 0.8 )O 3 -0.5(Ba 0.7 Ca 0.3 )TiO 3 from neutron total scattering measurements and multi-edge X-ray absorption analysis

Neutron total scattering measurements and multi-edge X-ray absorption analysis were performed on a 0.5Ba(Zr 0.2 Ti 0.8 )O 3 -0.5(Ba 0.7 Ca 0.3 )TiO 3 powder sample. The TiIII-II edges showed that titanium is distorted in the rhombohedral [111] direction (in a BaTiO 3 -like chemical and bonding environment). Extended X-ray fine structure analysis (EXAFS) was performed on the zirconium K-edge and it was found that zirconium resides in the center of the ZrO 6 octahedra in cubic symmetry. Furthermore, the local structure was initially modelled with the orthorhombic Amm2 space group with unsatisfactory results. To better model the local structure, the results from the EXAFS analysis were incorporated into the small-box pair distribution function (PDF) refinements and the best fit of the observed data was a combination of barium calcium titanate (BCT) and barium calcium zirconate (BCZ) phases.

36 MATERIALS SCIENCE↗
DOE OSTI2020

Low-temperature spin dynamics in the TmFeO 3 orthoferrite with a non-Kramers ion

We investigate the magnetic dynamics of the orthorhombic perovskite TmFeO 3 at low temperatures, below the spin reorientation transition at TSR≈80 K, by means of time-of-flight neutron spectroscopy. We find that the magnetic excitation spectrum combines two emergent collective modes associated with different magnetic sublattices. The Fe subsystem orders below TN~632 K into a canted antiferromagnetic structure and exhibits sharp, high-energy magnon excitations. We describe them using linear spin-wave theory, and reveal a pronounced anisotropy between in- and out-of-plane exchange interactions, which was mainly neglected in previous reports on the spin dynamics in orthoferrites. At lower energies, we find two crystalline electrical field (CEF) excitations of Tm3+ ions at energies of ~2 and 5 meV. In contrast to the sister compound YbFeO 3 , where the Yb 3+ ions form quasi-one-dimensional chains along the c axis, the Tm excitations show dispersion along both directions in the (0KL) scattering plane. Analysis of the neutron scattering polarization factor reveals a longitudinal polarization of the 2 meV excitation. To evaluate the effect of the CEF on the Tm 3+ ions, we perform point-charge model calculations, and their results quantitatively capture the main features of Tm single-ion physics, such as energies, intensities, and polarization of the CEF transitions, and the type of magnetic anisotropy.

75 CONDENSED MATTER PHYSICS, SUPERCONDUCTIVITY AND↗
DOE OSTI2025

Tutorial: Machine-Learning-Based CREASE-2D Analysis of 2D SAXS Profiles to Characterize Anisotropic Nanostructures in Soft Materials

We present a tutorial to guide users on how to extend the Computational Reverse Engineering Analysis of Scattering Experiments-2D (CREASE-2D) framework to interpret their experimental two-dimensional small-angle scattering (SAS) data from soft materials (e.g., polymers, peptide amphiphiles, biomolecular fibrils). Unlike most traditional SAS analysis approaches, which typically rely on azimuthally averaged onedimensional (1D) profiles, CREASE-2D utilizes the complete 2D scattering profile to reveal information about anisotropy in the structure. In past applications, CREASE has provided insights into complex structural features, including the cross-sectional shapes of assembled nanostructures and dispersity in these features, which are difficult to discern with existing analytical models. While (1D- ) CREASE has been applied to SANS and SAXS data, this tutorial shares the steps for implementing CREASE-2D using an example of a dipeptide solution system, for which we have SAXS data. We present details for these steps involved in using CREASE-2D to interpret SAXS profiles: how to preprocess SAXS data, define relevant structural features, generate three-dimensional real-space structures for specific values of these features, train a machine learning (ML) surrogate model to predict scattering profiles for given structural features, and optimize these features using genetic algorithms (GA). Then, we use these steps to interpret complex 2DSAXS data collected from dipeptide solutions that, in microscopy images, exhibit nanoscale structures that could be elliptical tubes/ flat tapes/cylinders or a combination of these cross sections. Open-source codes, computational hardware, and software requirements, as well as the strengths and limitations of this protocol, are also presented. We expect researchers working with (soft) biomaterials, peptide amphiphiles, amphiphilic polymer solutions, polymer nanocomposites, and blends of particles/polymers will find this CREASE-2D method and this tutorial of use.

CREASE↗
DOE OSTI2021

Sentinel-1 Input Data for PSInSAR Analysis

Files used to perform the Persistent Scatterer InSAR analysis with SARPROZ. The data is sourced from ESAs Sentinel-1 project and covers Brady Hot Springs and Desert Peak geothermal areas. The original titles are included for the Sentinel-1 data. The naming guide is included as a link in this submission. The data contains SAR (Radar) data from the Sentinel 1A satellite between July 2017 and December 2019. The data is necessary to replicate our results, and can be used for further PSInSAR, DInSAR and other modern interferometric analyses to determine line of sight, vertical and, possibly, East-West horizontal displacement. These displacement analysis show the soil deformation in time (including average displacement velocity), which can be used to indicate subsoil phenomena: temperature changes, subsidence, uplift, and inform other analyses like seismicity and porosity.

15 GEOTHERMAL ENERGY↗
DOE OSTI2022

Bibliometric review and recent advances in total scattering pair distribution function analysis: 21 years in retrospect

Global research activities have been driven by the quest to develop and characterize novel materials for technological advancements. The total scattering pair distribution function (TSPDF) is a powerful and versatile characterization technique for examining the structural details of diverse complex materials including liquid, amorphous, disordered crystalline, and nanostructured materials. Thus, it is critical to keep track of research progress, identify research gaps, and future research directions of the application of the TSPDF technique in materials development and discovery. In this work, a bibliometric analysis of literature regarding the TSPDF technique between 2000 and 2021 was conducted using datasets retrieved from the Web of Science database. The research trends based on publication outputs, research subject distribution, co-authorships among institutions, countries/regions, co-citation of referenced sources, and keyword co-occurrence are evaluated and discussed herein. The impact of the TSPDF technique is projected to increase due to its importance in probing emerging functional materials, and the advances in specialized facilities and instrumentation among the scientific communities engaged with it. Finally, current and emerging research hotspots related to TSPDF technique such as catalysis, computer modeling and simulation, pharmaceutics, machine learning, hydrogen storage, battery materials, and layered structured materials are also identified and discussed.

36 MATERIALS SCIENCE↗
DOE OSTI2025

Benchmarking universal machine learning interatomic potentials for rapid analysis of inelastic neutron scattering data

The accurate calculation of phonons and vibrational spectra remains a significant challenge, requiring highly precise evaluations of interatomic forces. Traditional methods based on the quantum description of the electronic structure, while widely used, are computationally expensive and demand substantial expertise. Emerging universal machine learning interatomic potentials (uMLIPs) offer a transformative alternative by employing pre-trained neural network surrogates to predict interatomic forces directly from atomic coordinates. This approach dramatically reduces computation time and minimizes the need for technical knowledge. In this paper, we produce a phonon database comprising nearly 5000 inorganic crystals to benchmark the performance of several leading uMLIPs. We further assess these models in real-world applications by using them to analyze experimental inelastic neutron scattering data collected on a variety of materials. Through detailed comparisons, we identify the strengths and limitations of these uMLIPs, providing insights into their accuracy and suitability for fast calculations of phonons and related properties, as well as the potential for real-time interpretation of neutron scattering spectra. Our findings highlight how the rapid advancement of AI in science is revolutionizing experimental research and data analysis.

inelastic neutron scattering↗
DOE OSTI2022

Analysis of coherent Thomson scattering from a low temperature plasma

In this work, the spectrum of coherent Thomson scattering (CTS) induced by a periodic ponderomotive perturbation in a low-density low temperature plasma is considered. The analysis is performed for the case when the period of the resulting optical lattice is less than the Debye screening length in the plasma by solving an electron Boltzmann equation, where the total force is the sum of the periodic force due to the optical lattice and the electrostatic force due to self-consistent electric field in the plasma. An analogy between the CTS spectra calculated here and coherent Rayleigh scattering spectra in a neutral gas is established. For relatively low intensity for the optical lattice, the calculated CTS spectra are nearly Gaussian with widths slightly wider than the incoherent Thomson widths. We demonstrate that at higher intensities the line shape narrows and saturates to a width approximately half of that found at low lattice intensities. The proportionality of the spectral width to the square root of the electron temperature allows one to extract the electron temperature from the saturated spectra. Possible application of CTS for remote measuring the electron temperature in plasma is discussed.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗
DOE OSTI2024

Structural characterization of high-protein system through ultra-small and small-angle X-ray scattering

High-protein systems exhibit a hierarchical structure consisting of interconnected multiscale length assemblies. Recent investigations are directed toward identifying these structural units under various processing and environmental conditions to establish structure–function correlations. Ultra-small and small-angle X-ray scattering (USAXS/SAXS) has become a crucial tool for characterizing the structures of proteins and their clusters/aggregates, ranging from nanometers to micrometers, with minimal disruption to their original state. Here, this review first describes the facilities and principles of X-ray scattering, followed by discussions on the analysis of scattering data, including the interpretation of fitting models. It then delves into the main applications of USAXS/SAXS in plant and dairy protein-rich systems. Future strategies to enhance the utilization of scattering techniques for elucidating the structure of high-protein systems are also included.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2023

X-Ray Scattering Data Processing and Analysis

Semicrystalline polymers have a degree of crystallinity that depends on chain structure and crystallization conditions. Thermomechanical properties are influenced by the degree of crystallinity and crystalline morphology. A common method used to understand crystalline content, crystal and amorphous layer thickness, and layer thickness distributions, is X-ray scattering (XS). This report uses XS to first investigate crystalline orientation in high density polyethylene (HDPE) samples. Then, a single orientation is analyzed via XS in a temperature study to investigate crystalline behavior under changing temperatures. Lastly, block copolymer filaments extruded at varying speeds are analyzed. The X-ray data are used to gain a baseline understanding of crystalline behavior in these unique filaments.

36 MATERIALS SCIENCE↗
DOE OSTI2026

Four-Dimensional Quasielastic Neutron Scattering (4D-QENS) Analysis of Correlated Ionic Conduction in Sr⁢Cl2

Methods of elucidating the mechanisms of fast-ion conduction in solid-state materials are pivotal for advancements in energy technologies such as batteries, fuel cells, sensors, and supercapacitors. In this study, we examine the ionic conduction pathways in single crystal Sr⁢Cl2, which is a fast-ion conductor above 900 K, using four-dimensional quasielastic neutron scattering (4D-QENS). We explore both coherent and incoherent neutron scattering at temperatures above the transition temperature into the superionic phase to explore the correlated motion of hopping anions. Refinements of the incoherent QENS yield residence times and jump probabilities between lattice sites in good agreement with previous studies, confirming that ionic hopping along nearest-neighbor directions is the most probable conduction pathway. However, the coherent QENS reveals evidence of de Gennes narrowing, indicating the importance of ionic correlations in the conduction mechanism. This highlights the need for improvements both in the theory of ionic transport in fluorite compounds and the modeling of coherent 4D-QENS in single crystals.

Coles, Jared [Materials Science Division, Argonne ↗
DOE OSTI2025

Simulation and analysis of small angle scattering (SAS) patterns of Ni-based superalloy microstructures generated by a phase-field model

This paper investigates the relationship between microstructural features and small-angle scattering (SAS) patterns in Ni-based superalloys using a combined phase-field and SAS simulation approach coupled with microstructure analyses. The simulated SAS patterns accurately capture key experimental observations previously reported in the literature, including the time-dependent transition from circular to square-shaped precipitates and the development of anisotropic SAS patterns. Importantly, our analysis reveals the correlations between characteristic length scales extracted from SAS profiles and microstructural descriptors, such as precipitate size and inter-precipitate distance. These findings provide a comprehensive understanding of the link between SAS profiles and microstructure evolution in Ni-based superalloys, offering valuable insights for materials characterization and design.

Microstructure↗
DOE OSTI2021

Understanding magnetic phase coexistence in Ru 2 Mn 1-x Fe x Sn Heusler alloys: A neutron scattering, thermodynamic, and phenomenological analysis

The random substitutional solid solution between the antiferromagnetic (AFM) full-Heusler alloy Ru 2 MnSn and the ferromagnetic (FM) full-Heusler alloy Ru 2 FeSn provides a rare opportunity to study FM-AFM phase competition in a near-lattice-matched, cubic system, with full solubility. At intermediate x in Ru 2 Mn 1-x Fe x Sn this system displays suppressed magnetic ordering temperatures, spatially coexisting FM and AFM order, and strong coercivity enhancement, despite rigorous chemical homogeneity. Here, we construct the most detailed temperature- and x-dependent understanding of the magnetic phase competition and coexistence in this system to date, combining wide-temperature-range neutron diffraction and small-angle neutron scattering with magnetometry and specific heat measurements on thoroughly characterized polycrystals. A complete magnetic phase diagram is generated, showing FM-AFM coexistence between x ≈ 0.30 and x ≈ 0.70. Furthermore, important insight is gained from the extracted length scales for magnetic phase coexistence (25–100 nm), the relative magnetic volume fractions and ordering temperatures, and remarkable x-dependent trends in magnetic and electronic contributions to specific heat. An unusual feature in the magnetic phase diagram (an intermediate FM phase) is also shown to arise from an extrinsic effect related to a minor Ru-rich secondary phase. The established magnetic phase diagram is then discussed with the aid of phenomenological modeling, clarifying the nature of the mesoscale phase coexistence with respect to the understanding of disordered Heisenberg models.

36 MATERIALS SCIENCE↗
DOE OSTI2023

Mechanism of structural colors in binary mixtures of nanoparticle-based supraballs

Inspired by structural colors in avian species, various synthetic strategies have been developed to produce noniridescent, saturated colors using nanoparticle assemblies. Nanoparticle mixtures varying in particle chemistry and size have additional emergent properties that affect the color produced. For complex multicomponent systems, understanding the assembled structure and a robust optical modeling tool can empower scientists to identify structure-color relationships and fabricate designer materials with tailored color. Here, we demonstrate how we can reconstruct the assembled structure from small-angle scattering measurements using the computational reverse-engineering analysis for scattering experiments method and use the reconstructed structure in finite-difference time-domain calculations to predict color. We successfully, quantitatively predict experimentally observed color in mixtures containing strongly absorbing nanoparticles and demonstrate the influence of a single layer of segregated nanoparticles on color produced. The versatile computational approach that we present is useful for engineering synthetic materials with desired colors without laborious trial-and-error experiments.

36 MATERIALS SCIENCE↗
DOE OSTI2020

Cross-Cutting Software Solutions in Support of Experimental Analysis Challenges at National Scattering Facilities

Rapid improvements in instrumentation at neutron and light source combined with new capabilities in computing, data analytics, and machine learning offer unprecedented scientific capabilities to address fundamental and applied science questions in a wide area of subjects from quantum materials to biological systems. In order to make these capabilities accessible to the broader scientific community, significant developments are needed related to workflow spanning from experimental control to advanced materials simulations as well as the underpinning scientific software. In this chapter, we explore these needs and recent developments at the Spallation Neutron Source (SNS) at Oak Ridge National Laboratory. A central part of the workflow at the SNS is the data reduction framework Mantid which is developed as a partnership between European and US neutron-scattering facilities.

Proffen, Thomas↗
DOE OSTI2023

Ab Initio Evaluation of Plutonium Dioxide $S(α,β)$ and Thermal Neutron Cross Sections

Plutonium Dioxide (PuO 2 ) is an advanced fuel-cycle nuclear fuel material for thermal and fast reactors, with a melting point at 3261.8 K and thermal conductivity above 3 Wm -1 K -1 for temperatures below 1500 K. Plutonium from spent UO 2 is reprocessed to form chemically stable mixed oxide (MOX) fuel. In contrast to metallic fuels, the low PuO 2 conductivity results in high thermal gradients between fuel center and surface, resulting in structural annealing and efficient fission product migration over burnup. Crystal binding affects the doppler broadening of epithermal resonances for nuclear fuels and scattering behavior of thermal neutrons. Currently, there is no ENDF/B Thermal Scattering Law, i.e., $S(α,β)$, evaluation for PuO 2 , only free atom cross sections for Plutonium and Oxygen, respectively, which neglect lattice contributions to total cross section. Herein, ab initio lattice dynamics (AILD) techniques are employed to calculate the phonon density of states (DOS) using spin-orbit-coupling density functional theory (DFT) to predict the PuO2 paramagnetic ground-state structure. These results highlight the evaluation of $S(α,β)$ for Plutonium and Oxygen, respectively in PuO 2 and consequential generation of thermal neutron scattering cross sections for high fidelity criticality safety analysis and reactor calculations.

11 NUCLEAR FUEL CYCLE AND FUEL MATERIALS↗
DOE OSTI2024

Qualitative and quantitative enhancement of parameter estimation for model-based diagnostics using automatic differentiation with an application to inertial fusion

Parameter estimation using observables is a fundamental concept in the experimental sciences. Mathematical models that represent the physical processes can enable reconstructions of the experimental observables and greatly assist in parameter estimation by turning it into an optimization problem which can be solved by gradient-free or gradient-based methods. In this work, the recent rise in flexible frameworks for developing differentiable scientific computing programs is leveraged in order to dramatically accelerate data analysis of a common experimental diagnostic relevant to laser–plasma and inertial fusion experiments, Thomson scattering. A differentiable Thomson-scattering data analysis tool is developed that uses reverse-mode automatic differentiation (AD) to calculate gradients. By switching from finite differencing to reverse-mode AD, three distinct outcomes are achieved. First, gradient descent is accelerated dramatically to the extent that it enables near real-time usage in laser–plasma experiments. Second, qualitatively novel quantities which require $\mathcal{O}(10^3)$ parameters can now be included in the analysis of data which enables unprecedented measurements of small-scale laser–plasma phenomena. Third, uncertainty estimation approaches that leverage the value of the Hessian become accurate and efficient because reverse-mode AD can be used for calculating the Hessian.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2025

Quantifying dispersity in size and shape of nanoparticles from small-angle scattering data using machine learning based CREASE

Here, 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.

75 CONDENSED MATTER PHYSICS, SUPERCONDUCTIVITY AND↗
DOE OSTI2023

GSAS-II (GENERAL STRUCTURE AND ALAYSIS SYSTEM-II)

GSAS-II is a comprehensive data analysis and simulation software package for determining atomistic structures from single-crystal and powder diffraction data. X-ray, neutron and electron data may be used. Constant-wavelength, time-of-flight and pink-beam sources may be employed and analyses may combine different types of data. The package is oriented towards materials characterization problems, but protein crystallography, pair-distribution function analysis, small-angle scattering and reflectometry analysis are all included. GSAS-II provides tools for indexing and solving structures from powder diffraction data, in addition to structure, data and results visualization tools

TOBY, BRIANH↗