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Results for “Structural anisotropy”

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 73 records · Page 4
All sourcesNASA NTRSDOE OSTI
DOE OSTI2023

Second-harmonic signature of chiral spin structures in W/Pt/Co heterostructures with tunable magnetic anisotropy

Abstract Second-harmonic Hall voltage (SHV) measurement method has been widely used to characterize the strengths of spin–orbit torques (SOTs) in heavy metal/ferromagnet thin films saturated in the single-domain regime. Here, we show that the magnetic anisotropy of a W/Pt/Co trilayer can be robustly tuned from in-plane to out-of-plane by varying W, Pt, or Co thicknesses. Moreover, in samples with easy-cone anisotropy, SHV measurements exhibit anomalous ‘humps’ in the multidomain regime accessed by applying a nearly out-of-plane external magnetic field. These hump features can only be explained as a result of the formation of Néel-type domain walls, efficiently driven by nevertheless small SOTs in this double heavy metal heterostructure with canceling spin Hall angles.

Physics↗
DOE OSTI2026

Structural origin of resonant diffraction in RuO2

We report Ru L3-edge resonant X-ray diffraction studies on single crystal and (001) oriented epitaxial films of RuO2. We investigate the distinct Q = (100) and (001) Bragg-forbidden reflections as a function of incident energy, azimuthal angle, and temperature. The results show that the observed resonant diffraction in RuO2 is fully consistent with a resonant charge anisotropy signal of structural origin permitted by the parent (non-magnetic) rutile P42/mnm space group. These results significantly constrain the magnetic contribution to the resonant diffraction signal and indicate the unlikely existence of k = 0 antiferromagnetic order in RuO2.

36 MATERIALS SCIENCE↗
DOE OSTI2025

Advances in relaxation and memory effects of magnetic nanoparticles for biomedical applications

Functionalized magnetic nanoparticles are pivotal in magnetic resonance imaging, computed tomography, controlled drug delivery, and hyperthermia treatments due to their exceptional magnetic relaxation and functional properties. The magnetic core composition and structure significantly affects the complex magnetic properties of these nanoparticles necessitating a thorough examination of magnetism fundamentals related to these systems. One important aspect is the ability of magnetic nanoparticles to retain previous magnetic state configurations known as memory effect, primarily governed by domain structure and magnetic anisotropy. Despite its relevance to advanced applications, comprehensive studies on magnetic relaxation and memory effects remain limited. Here, the present review aims to bridge this gap by investigating relaxation mechanisms, synthesis strategies, and applications, fostering further innovation. It investigates the memory effects and their dependence on particle composition and morphology along with key synthesis techniques for large-scale production in industrial adoption. Structured into focused sections on magnetic properties and their influence on biomedical and technological applications, this review provides essential insights into memory effects, magneto-relaxation mechanisms, influencing factors, and both experimental and theoretical methodologies. It also delves into computational modelling and AI-driven design, which are revolutionizing the prediction, discovery, and optimization of materials with tailored properties.

36 MATERIALS SCIENCE↗
DOE OSTI2022

Quasi-One-Dimensional Metallicity in Compressed CsSnI 3

Low-dimensional metal halides exhibit strong structural and electronic anisotropies, making them candidates for accessing unusual electronic properties. Here, in this work, we demonstrate pressure-induced quasi-one-dimensional (quasi-1D) metallicity in δ-CsSnI 3 . With the application of pressure up to 40 GPa, the initially insulating δ-CsSnI 3 transforms to a metallic state. Synchrotron X-ray diffraction and Raman spectroscopy indicate that the starting 1D chain structure of edge-sharing Sn–I octahedra in δ-CsSnI 3 is maintained in the high-pressure metallic phase while the SnI 6 octahedral chains are distorted. Our experiments combined with first-principles density functional theory calculations reveal that pressure induces Sn–Sn hybridization and enhances Sn–I coupling within the chain, leading to band gap closure and formation of conductive SnI 6 distorted octahedral chains. In contrast, the interchain I...I interactions remain minimal, resulting in a highly anisotropic electronic structure and quasi-1D metallicity. Our study offers a high-pressure approach for achieving diverse electronic platforms in the broad family of low-dimensional metal halides.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2024

On the detectability of the moving lens signal in CMB experiments

Abstract Upcoming cosmic microwave background (CMB) experiments are expected to detect new signals probing interaction of CMB photons with intervening large-scale structure. Among these the moving-lens effect, the CMB temperature anisotropy induced by cosmological structures moving transverse to our line of sight, is anticipated to be measured to high significance in the near future. In this paper, we investigate two possible strategies for the detection of this signal: pairwise transverse-velocity estimation and oriented stacking. We expand on previous studies by including in the analysis realistic simulations of competing signals and foregrounds. We confirm that the moving lens effect can be detected at ≥ 10σlevel by a combination of CMB-S4 and LSST surveys. We show that the limiting factors in the detection depend on the strategy: for the stacking analysis, correlated extragalactic foregrounds, namely the cosmic infrared background and thermal Sunyaev Zel'dovich effect, play the most important role. The addition of foregrounds make the signal-to-noise ratio be most influenced by large and nearby objects. As for the pairwise detection, halo lensing and pair number counts are the main issues. In light of our findings, we elaborate on possible strategies to improve the analysis approach for the moving lens detection with upcoming experiments. We also deliver to the community all the simulations and tools we developed for this study.

Astronomy & Astrophysics↗
DOE OSTI2023

Achieving High Strength and Creep Resistance in Inconel 740H Superalloy through Wire-Arc Additive Manufacturing and Thermodynamic-Guided Heat Treatment

Inconel 740H superalloy is commonly used in advanced ultra-supercritical power plants since it possesses excellent strength and creep resistance. This study investigates the microstructure and mechanical properties of Inconel 740H superalloy fabricated using wire-arc additive manufacturing. The as-printed microstructure consisted of columnar γ grains with the Laves phase and (Nb, Ti)C carbides as secondary phases. The anisotropy in grain structure increased from the bottom to the top regions, while the hardness was highest in the middle portion of the build. To guide the post-heat treatment design, thermodynamic and kinetic simulations were employed to predict the temperature and time. Complete recrystallization with the Laves phase dissolution occurred throughout the build after homogenization at 1200 °C for 2 h. The peak hardness was achieved after aging at 760 °C for 12 h with the M 23 C 6 carbides decorating the grain boundaries and γ’ precipitates in the grain interior. The yield strength (655 MPa) and ductility (29.5%) in the post-heat treated condition exceeded the design targets (620 MPa, 20%). Stress rupture tests at 750 °C showed that the high-temperature performance was at par with the wrought counterparts. The fracture mode after rupture was identified to be intergranular with the presence of grain boundary cavities along with grain boundary sliding.

36 MATERIALS SCIENCE↗
DOE OSTI2023

Band gap dependence of exciton Lande factor in lead halide perovskites

The optical properties of lead halide perovskite semiconductors in vicinity of the bandgap are controlled by excitons, so that investigation of their fundamental properties is of critical importance. The exciton Landé or g-factor g X is the key parameter, determining the exciton Zeeman spin splitting in magnetic fields. The exciton, electron, and hole carrier g-factors provide information on the band structure, including its anisotropy, and the parameters contributing to the electron and hole effective masses. Here, g X is measured by reflectivity in magnetic fields up to 60 T for lead halide perovskite crystals. The materials band gap energies at a liquid helium temperature vary widely across the visible spectral range from 1.520 up to 3.213 eV in hybrid organic–inorganic and fully inorganic perovskites with different cations and halogens: FA 0.9 Cs 0.1 PbI 2.8 Br 0.2 , MAPbI 3 , FAPbBr 3 , CsPbBr 3 , and MAPb(Br 0.05 Cl 0.95 ) 3 . The exciton g-factors are found to be nearly constant, ranging from +2.3 to +2.7. Thus, the strong dependences of the electron and hole g-factors on the bandgap roughly compensate each other when combining to the exciton g-factor. The same is true for the anisotropies of the carrier g-factors, resulting in a nearly isotropic exciton g-factor. The experimental data are compared favorably with model calculation results.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗
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 OSTI2026

Origins of anisotropic linear magnetoresistance with isotropic mobility in Cd 3 As 2 films on GaAs$[110]$

Thin film synthesis allows for the potential to orient crystals in different orientations, permitting measurement of orientation-dependent material aspects such as band structures and transport anisotropy. Here, Dirac semimetal Cd 3 As 2 films are epitaxially grown on GaAs$[110]$ substrates, which has a $[001]$ orientation in-plane. Films contain domains of two different c-axis orientations resulting from an aligned a-axis in-plane. Magnetoresistance measurements performed along both $[1\bar{1}0]$ and $[001]$ substrate directions reveal similar mobility and carrier concentration, but much larger magnetoresistance along the $[001]$ direction, which can be explained by the guiding center diffusion model as arising from anisotropic disorder and different atomic spacings.

36 MATERIALS SCIENCE↗
DOE OSTI2025

Vector meson production in ultraperipheral heavy ion collisions

We review model calculations of exclusive vector meson production in ultraperipheral heavy ion collisions. We highlight differences and similarities between different dipole models and leading twist shadowing calculations. Recent color glass condensate calculations are presented with focus on effects from nuclear structure and azimuthal anisotropies driven by interference effects.

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗
DOE OSTI2023

Weak Dispersion of Exciton Landé Factor with Band Gap Energy in Lead Halide Perovskites: Approximate Compensation of the Electron and Hole Dependences

The optical properties of lead halide perovskite semiconductors in vicinity of the bandgap are controlled by excitons, so that investigation of their fundamental properties is of critical importance. The exciton Landé or g-factor g X is the key parameter, determining the exciton Zeeman spin splitting in magnetic fields. The exciton, electron, and hole carrier g-factors provide information on the band structure, including its anisotropy, and the parameters contributing to the electron and hole effective masses. Here, g X is measured by reflectivity in magnetic fields up to 60 T for lead halide perovskite crystals. The materials band gap energies at a liquid helium temperature vary widely across the visible spectral range from 1.520 up to 3.213 eV in hybrid organic–inorganic and fully inorganic perovskites with different cations and halogens: FA 0.9 Cs 0.1 PbI 2.8 Br 0.2 , MAPbI 3 , FAPbBr 3 , CsPbBr 3 , and MAPb(Br 0.05 Cl 0.95 ) 3 . The exciton g-factors are found to be nearly constant, ranging from +2.3 to +2.7. Thus, the strong dependences of the electron and hole g-factors on the bandgap roughly compensate each other when combining to the exciton g-factor. The same is true for the anisotropies of the carrier g-factors, resulting in a nearly isotropic exciton g-factor. The experimental data are compared favorably with model calculation results.

36 MATERIALS SCIENCE↗
DOE OSTI2022

A molecular dynamics survey of grain boundary energy in uranium dioxide and cerium dioxide

Uranium dioxide (UO 2 ) is the primary fuel material that is used in current nuclear reactors. As one of the most fundamental material parameters, grain boundary (GB) energy strongly influences many fuel properties, and the influences depend on the characters and properties of individual GBs. Using molecular dynamics simulations, a high throughput survey of GB energy in UO 2 was carried out for the purpose of elucidating the roles of GB geometry such as misorientation and inclination, as well as the bonding nature of UO 2 , in affecting GB energy. GB energies in CeO 2 were calculated as well for comparison with UO2toinvestigate the generality of GB energy anisotropy in fluorite phase oxides. The results show significant GB energy anisotropy in both UO 2 and CeO 2 that is associated with the cubic symmetry of the fluorite structure. More interestingly, the GB anisotropy is found to be dependent not only on the crystal structure but also the ionic bonding. As such, the GB energy anisotropy in fluorite oxides has significant differences compared with that in fcc metals. We report that the data obtained and the increased knowledge on GB anisotropy will facilitate GB engineering for nuclear fuels with improved properties

11 NUCLEAR FUEL CYCLE AND FUEL MATERIALS↗
DOE OSTI2022

Optically Induced Anisotropy in Time-Resolved Scattering: Imaging Molecular-Scale Structure and Dynamics in Disordered Media with Experiment and Theory

Time-resolved scattering experiments enable imaging of materials at the molecular scale with femtosecond time resolution. However, in disordered media they provide access to just one radial dimension thus limiting the study of orientational structure and dynamics. Here we introduce a rigorous and practical theoretical framework for predicting and interpreting experiments combining optically induced anisotropy and time-resolved scattering. Using impulsive nuclear Raman and ultrafast x-ray scattering experiments of chloroform and simulations, we demonstrate that this framework can accurately predict and elucidate both the spatial and temporal features of these experiments.

75 CONDENSED MATTER PHYSICS, SUPERCONDUCTIVITY AND↗
DOE OSTI2020

On the elastic anisotropy of the entropy-stabilized oxide (Mg, Co, Ni, Cu, Zn)O compound

Herein, we study the elastic properties of the entropy-stabilized oxide (Mg, Co, Ni, Cu, Zn)O using experimental and first principles techniques. Our measurements of the indentation modulus on grains with a wide range of crystallographic orientations of the entropy-stabilized oxide revealed a high degree of elastic isotropy at ambient conditions. First principles calculations predict mild elastic anisotropy for the paramagnetic structure, which decreases when the system is considered to be non-magnetic. When the antiferromagnetic state of CoO, CuO, and NiO is accounted for in the calculations, a slight increase in elastic anisotropy is observed, suggesting a coupling between magnetic ordering and the orientation dependent elastic properties. Furthermore, an examination of the local structure reveals that the isotropy is favored through local ionic distortions of Cu and Zn—due to their tendencies to form tenorite and wurtzite phases. The relationships between the elastic properties of the multicomponent oxide and those of its constituent binary oxides are reviewed. These insights open up new avenues for controlling isotropy for technological applications through tuning composition and structure in the entropy-stabilized oxide or the high-entropy compounds in general.

Entropy↗
DOE OSTI2020

Development of Thermal Tomography Imaging System for In-Service Nondestructive Evaluation of Additively Manufactured Components

Additive manufacturing (AM) is an emerging method for cost-efficient fabrication of nuclear reactor parts. AM of metallic structures for nuclear energy applications is currently based on laser powder bed fusion (LPBF) process, which can introduce internal material flaws, such as pores and anisotropy. Integrity of AM structures needs to be evaluated nondestructively because material flaws could lead to premature failures due to exposure to high temperature, radiation and corrosive environment in a nuclear reactor. Thermal tomography (TT) provides a capability for non-destructive evaluation of sub-surface defects in arbitrary size structures. We investigate TT of AM stainless steel 316L specimen with imprinted internal porosity defects using relatively low-cost, small form factor infrared (IR) camera based on uncooled micro bolometer detector. Sparse coding related K-means singular value decomposition (SVD) machine learning, image processing algorithms are developed to improve quality of TT images through removal of Additive white Gaussian noise without blurring the images. Following initial qualification of an AM component for deployment in a nuclear reactor, a compact TT can also be used for in-service nondestructive evaluation (NDE). With capability to perform in-service NDE of the AM component lifecycle, TT data can be used for development of a component digital twin. MIT research reactor (MITR) has been identified as a representative environment to perform investigation of feasibility of in-service NDE during shutdown. Preliminary study of radiation level in the MITR viewport, with geometric parameters potentially compatible with compact TT system, has been conducted.

36 MATERIALS SCIENCE↗
DOE OSTI2020

Detection of Defects in Additively Manufactured Metallic Materials with Machine Learning of Pulsed Thermography Images

Additive manufacturing (AM) is an emerging method for cost-efficient fabrication of nuclear reactor parts. AM of metallic structures for nuclear energy applications is currently based on laser powder bed fusion (LPBF) process, which can introduce internal material flaws, such as pores and anisotropy. Integrity of AM structures needs to be evaluated nondestructively because material flaws could lead to premature failures due to exposure to high temperature, radiation and corrosive environment in a nuclear reactor. Quality control (QC) requires nondestructive evaluation (NDE) of actual AM structures. Pulsed thermography is a potentially promising QC technique because it is scalable to arbitrary structure size. However, detection sensitivity of this method is limited by noises. We investigate separation of signal from noise in thermography images using several machine learning (ML) methods, including new spatio-temporal blind source separation (STBSS) and spatio-temporal sparse dictionary learning (STSDL) methods. Performance of the ML methods is benchmarked using thermography data obtained from imaging stainless steel 316L and Inconel 718 specimens produced LPBF method with imprinted calibrated porosity defects. The ML methods are ranked by F-score and execution runtime. The ML methods with higher accuracy require longer run time. However, this runtime is sufficiently short to perform QC within a realistic time frame.

22 GENERAL STUDIES OF NUCLEAR REACTORS↗
DOE OSTI2020

Pulsed Thermal Tomography Nondestructive Examination of Additively Manufactured Reactor Materials (Second Annual Progress Report)

Additive manufacturing (AM) is an emerging method for cost-efficient fabrication of nuclear reactor parts. AM of metallic structures for nuclear energy applications is currently based on laser powder bed fusion (LPBF) process, which can introduce internal material flaws, such as pores and anisotropy. Integrity of AM structures needs to be evaluated nondestructively because material flaws could lead to premature failures due to exposure to high temperature, radiation and corrosive environment in a nuclear reactor. Thermal tomography (TT) provides a capability for non-destructive evaluation of sub-surface defects in arbitrary size structures. Thermal tomography is a computational method for heat diffusion-based imaging of solids, which provides 3D visualization of data from flash thermography measurements. We investigate thermal tomography imaging and nondestructive evaluation of stainless steel and nickel super alloy metallic structures produced with laser powder bed fusion (LPBF) additive manufacturing (AM) process. Metallic structures produced with LPBF contain defects, and there are limited capabilities to evaluate these structures non-destructively. Thermal tomography reconstruction of 3D apparent spatial effusivity provides information about AM structure geometry and internal material flaws. We study performance of thermal tomography in imaging of metallic structures through COMSOL computer simulations of transient heat transfer, and through reconstruction of data obtained from experimental measurements. Reconstruction of internal defects is investigated using a stainless steel 316L specimen with flat bottom hole (FBH) indentations, and Inconel 718 plate produced with laser powder bed fusion (LPBF) method, which contains imprinted hemispherical shape low density regions containing non-sintered metallic powder. The FBH’s have the same sizes as the imprinted defects in the LPBF specimens, but offer better imaging contrast. Thermal tomography reconstructions provide visualizations of internal defects, and allow for estimation of their sizes and locations. Detection sensitivity of TT is limited by noises. We investigate separation of signal from noise in thermography images using several machine learning (ML) methods, including new spatio-temporal blind source separation (STBSS) and spatio-temporal sparse dictionary learning (STSDL) methods. Performance of the ML methods is benchmarked using thermography data obtained from imaging stainless steel 316L and Inconel 718 specimens produced LPBF method with imprinted calibrated porosity defects. The ML methods are ranked by F-score and execution runtime. Finally, we investigate TT of AM stainless steel 316L specimen with imprinted internal porosity defects using relatively low-cost, small form factor infrared (IR) camera based on uncooled micro bolometer detector. Sparse coding related K-means singular value decomposition (SVD) machine learning, image processing algorithms are developed to improve quality of TT images through removal of Additive white Gaussian noise without blurring the images. Following initial qualification of an AM component for deployment in a nuclear reactor, a compact TT can also be used for in-service nondestructive evaluation (NDE). With capability to perform in-service NDE of the AM component lifecycle, TT data can be used for development of a component digital twin.

22 GENERAL STUDIES OF NUCLEAR REACTORS↗