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At least 523 records · Page 29
All sourcesNASA NTRSDOE OSTI
DOE OSTI2025

Strength, deformation, and the fcc–hcp phase transition in condensed Kr and Xe to the 100 GPa pressure range

The rare gas solids exhibit systematic differences in crystal structure, phase transition conditions, bond strength, and other physical properties. The physical properties of heavy rare gas solids krypton and xenon are modified by the martensitic phase transition from face-centered cubic to hexagonal close packed structure over a broad pressure range. Crystal structure, strength, and plastic deformation of krypton and xenon have been investigated at 300 K using compression in the diamond-anvil cell with synchrotron angle-dispersive x-ray diffraction and complementary ruby fluorescence spectroscopy for Xe. Stacking faults indicative of the fcc–hcp phase transition are observed at pressures at and above 1.23 ± 0.05 and 1.9 ± 0.6 GPa in Kr and Xe, respectively. The transition remains incomplete in both solids to pressures greater than 100 GPa. Strength determined from stress measurements in Pt and ruby standards at pressures up to 111 GPa and complemented by observations of strain and texture measurements obtained by x-ray diffraction in the radial geometry to 100 GPa indicates similar or higher strength than Ar at all conditions, with significant stiffening at 15–20 GPa. Radial diffraction data reveal the persistence of broad highly textured fcc diffraction lines to 101 GPa in Xe, suggesting that the axial measurements may underestimate the metastable persistence of the fcc phase due to biased sampling of hcp crystallites resulting from preferred crystallite orientation. Kr and Xe are compared with He, Ne, and Ar for a systematic understanding of physical properties and phase equilibria of rare gas solids.

Compressive stress↗
DOE OSTI2025

Femtosecond temperature measurements of laser-shocked copper deduced from the intensity of the x-ray thermal diffuse scattering

We present 50-fs, single-shot measurements of the x-ray thermal diffuse scattering (TDS) from copper foils that have been shocked via nanosecond laser ablation up to pressures above ∼135 GPa. We hence deduce the x-ray Debye–Waller factor, providing a temperature measurement. The targets were laser-shocked with the DiPOLE 100-X laser at the High Energy Density endstation of the European X-ray Free-Electron Laser. Single x-ray pulses, with a photon energy of 18 keV, were scattered from the samples and recorded on Varex detectors. Despite the targets being highly textured (as evinced by large variations in the elastic scattering) and with such texture changing upon compression, the absolute intensity of the azimuthally averaged inelastic TDS between the Bragg peaks is largely insensitive to these changes, and allowing for both Compton scattering and the low-level scattering from a sacrificial ablator layer provides a reliable measurement of $T/Θ^2_D$, where Θ D is the Debye temperature. We compare our results with the predictions of the SESAME 3336 and LEOS 290 equations of state for copper and find good agreement within experimental errors. We, thus, demonstrate that single-shot temperature measurements of dynamically compressed materials can be made via thermal diffuse scattering of XFEL radiation.

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗
DOE OSTI2026

Symmetry-protected topological polarons

Emergent quasiparticles in solids often exhibit unique topological properties as a result of the complex interplay between charge, orbital, spin, and lattice degrees of freedom. Among these quasiparticles, the polaron occupies a special place as the first known manifestation of the interaction between a fermion and a boson field. While polarons have been investigated for almost a century, whether these quasiparticles exhibit topological properties and why remain open questions. Here, we establish the universal symmetry principles governing the topology of polar textures in large polarons. Using a group-theoretic analysis, we identify four distinct classes of polar textures in time-reversal-invariant systems, and we show that they carry integer topological charges. We validate this classification by performing state-of-the-art first-principles calculations of materials representative of each class. For these materials, we compute the fingerprints of polaron topology in Huang diffuse scattering and propose ultrafast electron and X-ray scattering experiments to detect these quasiparticles.

75 CONDENSED MATTER PHYSICS, SUPERCONDUCTIVITY AND↗
DOE OSTI2025

Testing the Froggatt-Nielsen mechanism with lepton flavor and number violating processes

The Froggatt-Nielsen (FN) mechanism offers an elegant explanation for the observed masses and mixings of Standard Model fermions. In this work, we systematically study FN models in the lepton sector, identifying a broad range of charge assignments (“textures”) that naturally yield viable masses and mixings for various neutrino mass generation mechanisms. Using these textures, we consider higher-dimensional operators consistent with a FN origin and find that natural realizations predict distinct patterns in lepton flavor- and number-violating observables. For Dirac and Majorana neutrinos, FN-related correlations can lead to detectable rates of charged lepton flavor violation at next-generation low-energy experiments. Majorana and type-I seesaw models predict measurable rates of neutrinoless double beta decay. Determination of inverted neutrino mass ordering would exclude the Dirac neutrino FN scenario. Only a small minority of purely leptonic FN models predict detectable flavor violation at future muon colliders, though it is possible that a combined analysis with the quark sector will reveal motivated signals. These findings highlight the power of the FN mechanism to link neutrino mass generation to testable leptonic observables, offering new pathways for the experimental exploration of lepton number and underscoring the importance of next-generation low-energy probes.

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗
DOE OSTI2020

Magnetic switching in Weyl semimetal-superconductor heterostructures

Here, we present a switching mechanism that utilizes the proximity coupling between the surface spin texture of a Weyl semimetal and a superconductor in a Weyl semimetal-superconductor Weyl semimetal trilayer heterostructure. We show that the superconductivity in the middle layer can be fully suppressed by the surface spin texture of the Weyl semimetals in the presence of an external magnetic field, but it can be recovered again by only changing the field direction. The restoration of the middle-layer superconductivity indicates a sharp transition to a low-resistance state. This sharp switching effect, realizable using a Weyl semimetal because of its strong spin-momentum locking and surface spin polarization, is a promising avenue for novel superconducting spin-valve applications.

75 CONDENSED MATTER PHYSICS, SUPERCONDUCTIVITY AND↗
DOE OSTI2020

Crystal orientation dependence of interfacial magnetic anisotropy at heavy-metal/magnetic-garnet interfaces

Ferrimagnetic insulators capped with a heavy metal are becoming an increasingly interesting materials system in spintronics due to their unique ability for electrical manipulation and detection of magnetic states and spin textures via spin-orbit torques. The ability to engineer magnetic anisotropy is a powerful tool for tuning the recently discovered phenomena in these bilayers such as electrical switching or the stabilization of topological magnetic textures. Here, we observe large shifts in the magnetic anisotropy in Tm 3 Fe 5 O 13 and Y 3 Fe 5 O 12 thin films due to heavy-metal capping layers, which strongly depends on the orientation of the substrate and therefore the orientation of the epitaxial films. This work suggests large Rashba spin-orbit coupling at the metal/ferrimagneticinsulator interface, which can be engineered in spintronic devices that utilize spin-orbit torques for electrical control of the magnetization in magnetic insulators.

75 CONDENSED MATTER PHYSICS, SUPERCONDUCTIVITY AND↗
DOE OSTI2022

Rare-earth monopnictides: Family of antiferromagnets hosting magnetic Fermi arcs

We report since the discovery of topological insulators a great deal of research effort has been devoted to magnetic topological materials, in which nontrivial spin properties can be controlled by magnetic fields, culminating in a wealth of fundamental phenomena and possible applications. The main focus was on ferromagnetic materials that can host Weyl fermions and therefore spin-textured Fermi arcs. The recent discovery of Fermi arcs and new magnetic band splitting in the antiferromagnet (AFM) NdBi has opened up new avenues for exploration. Here we show that these uncharted effects are not restricted to this specific compound, but also emerge in CeBi and NdSb when they undergo paramagnetic to AFM transition. Our data show that the Fermi arcs in NdSb have twofold symmetry, leading to strong anisotropy that may enhance effects of spin textures on transport properties. Our findings thus demonstrate that the RBi and RSb series are materials that host magnetic Fermi arcs and may be a potential platform for modern spintronics.

75 CONDENSED MATTER PHYSICS, SUPERCONDUCTIVITY AND↗
DOE OSTI2024

Giant spin transport anisotropy in magnetic topological insulators

Here, we report on exceptionally long spin transport and giant spin lifetime anisotropy in the gapped surface states of three-dimensional (3D) magnetic topological insulators (MTIs). We examine the properties of these states using the three-dimensional Fu-Kane-Mele Hamiltonian model in the presence of a magnetic exchange field. The corresponding spin textures of surface states, which are well reproduced by an effective two-band model, hint at a considerable enhancement of the lifetime of out-of-plane spins compared to in-plane spins. This is confirmed by large-scale spin transport simulations for 3D MTIs with disorder. The energy dependence of the spin lifetime anisotropy arises directly from the nontrivial spin texture of the surface states, and is correlated with the onset of the quantum anomalous Hall phase. Our findings suggest different spin filtering capabilities of the gapped surface MTI states, which could be explored by nonlocal spin valve measurements.

75 CONDENSED MATTER PHYSICS, SUPERCONDUCTIVITY AND↗
DOE OSTI2021

Dynamic Rashba-Dresselhaus Effect

The Rashba-Dresselhaus effect is the splitting of doubly degenerate band extrema in semiconductors, accompanied by the emergence of counterrotating spin textures and spin-momentum locking. Here we investigate how this effect is modified by lattice vibrations. We show that, in centrosymmetric nonmagnetic crystals, for which a bulk Rashba-Dresselhaus effect is symmetry-forbidden, electron-phonon interactions can induce a phonon-assisted, dynamic Rashba-Dresselhaus spin splitting in the presence of an out-of-equilibrium phonon population. In particular, we show how Rashba, Dresselhaus, or Weyl spin textures can selectively be established by driving coherent infrared-active phonons, and we perform ab initio calculations to quantify this effect for halide perovskites.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗
DOE OSTI2022

A room temperature polar magnetic metal

The emergence of long-range magnetic order in noncentrosymmetric compounds has stimulated interest in the possibility of exotic spin transport phenomena and topologically protected spin textures for applications in next-generation spintronics. Polar magnets, with broken symmetries of spatial inversion and time reversal, usually host chiral spin textures. This work reports on a wurtzite-structure polar magnetic metal, identified as AA'-stacked (Fe 0.5 Co 0.5 ) 5 GeTe 2 , which exhibits a Néel-type skyrmion lattice as well as a Rashba-Edelstein effect at room temperature. Atomic resolution imaging of the structure reveals a structural transition as a function of Co-substitution, leading to the emergence of the polar phase at 50% Co. We report this discovery reveals an unprecedented layered polar magnetic system for investigating intriguing spin topologies, and it ushers in a promising new framework for spintronics.

36 MATERIALS SCIENCE↗
DOE OSTI2022

Rashba effects in lead-free ferroelectric semiconductor [CH 3 PH 3 ]SnBr 3

Here, first-principles density functional theory computations are used to predict Rashba effects cofunctional with ferroelectricity in a recently synthesized lead-free hybrid organic-inorganic perovskite MPSnBr 3 (MP=methylphosphonium, [CH 3 PH 3 ] + ). The ground state of the material is polar monoclinic with calculated spontaneous polarization of 3.01 µC/cm 2 . It exhibits near band edges spin-splitting of up to 3.3 meV and Rashba coefficient up to 0.62 eV Å. The spin textures have different topology in the conduction and valence band, which originates from the difference in the spin-momentum coupling strengths. They occur in two orthogonal planes of the Brillouin zone and are coupled to the direction of spontaneous polarization. These features persist at a finite temperature of 293 K. At 333 K, that is above monoclinic to orthorhombic phase transition, spontaneous polarization is reduced to 0.15 µC/cm 2 , while the maximum spin splitting and Rashba coefficient reduce slightly to the value of 2.9 meV and 0.41 eV Å, respectively. The spin textures remain coupled with the polarization direction. We investigate the dependence of the aforementioned properties on the choice of computational methodology to extend the first-principles predictions to include finite temperature effects. We find that the predictions are sensitive to the methodology. Our study reveals the potential of MPSnBr 3 for low-temperature applications in spintronics and quantum computing.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2023

Inducing skyrmion flop transitions in Co 8 Zn 8 Mn 4 at room temperature

Magnetic skyrmions are topologically-protected spin textures that manifest in certain noncentrosymmetric ferromagnets under the right conditions of temperature and field. In thin-film skyrmion hosts, demagnetization effects combined with geometric confinement can result in two distinct types of spin textures: those with their axis of symmetry in the plane of the film (IP) and those with their axis pointing out-of-plane (OOP). Here we present Lorentz transmission electron microscopy evidence in conjunction with numerical modeling showing a flop transition between IP and OOP skyrmions in Co 8 ⁢Zn 8 ⁢Mn 4 at room temperature. Further, we show that this skyrmion flop transition is controllable via the angle of the external field relative to the film normal and we illustrate how this transition depends on thickness. Finally, we propose a skyrmion-writing device that utilizes the details of this transition.

36 MATERIALS SCIENCE↗
DOE OSTI2024

Large Rashba spin splittings in bulk and monolayer of BiAs

There is great interest in developing new materials with Rashba split bands near the Fermi level for spintronics. Here, using first-principles calculations, we predict BiAs as a semiconductor with large Rashba splitting in bulk and monolayer forms. Bulk BiAs has a layered crystal structure with two atoms in a rhombohedral primitive cell, derived from the structure of the parent Bi and As elemental phases. It is a narrow band gap semiconductor, and it shows a combination of Rashba and Dresselhaus spin splitting with a characteristic spin texture around the L point in the Brillouin zone of the hexagonal conventional unit cell. It has sizable Rashba energies and Rashba coupling constants in the valence and conduction bands at the band edges. The 2D monolayer of BiAs has a much larger band gap at Γ, with a circular spin texture characteristic of a pure Rashba effect. The Rashba energy and Rashba coupling constant of monolayer BiAs are large compared to other known 2D materials and rapidly increase under biaxial tensile strain.

36 MATERIALS SCIENCE↗
DOE OSTI2024

Androgynous { 10 1 ¯ 2 } twin in zinc

Under ambient conditions, Zn is a hexagonal metal with a large c / a ratio of 1.856. Plastic deformation is predominantly accommodated by basal 〈 a 〉 slip and compression twins on the { 10 1 ¯ 2 } planes. Increasing hydrostatic pressure drastically reduces the c / a ratio of Zn and, when a critical threshold of c / a = 3 at about 10 GPa is crossed, the { 10 1 ¯ 2 } twins are predicted to change from compressive to tensile in nature. What happens at the transition point, when c / a = 3 , remains unknown. Here, we strain-cycle a textured polycrystalline sample of pure Zn at uniform hydrostatic pressures ranging between 2 and 17 GPa, over which the c / a ratio crosses the c / a = 3 compressive-tensile transition for { 10 1 ¯ 2 } twins. During deformation, the state of the sample is monitored through x-ray diffraction to extract texture and internal strain evolution. By comparing the experimental results with the predictions of an elastoviscoplastic polycrystal simulation, we confirm the androgynous nature of { 10 1 ¯ 2 } twin response at low and high pressures. When c / a = 3 , polycrystalline Zn does not display any evidence of twinning and its plastic behavior is controlled by mostly basal and pyramidal 〈 c + a 〉 slip activity, with a very small contribution of prismatic 〈 a 〉 slip. Evidence for the activity of other { 10 1 ¯ n } twinning modes, which have been suggested for Zn under high pressure, are not observed. Published by the American Physical Society 2024

Merkel, Sébastien (ORCID:000000032767581X)↗
DOE OSTI2023

MILK : a Python scripting interface to MAUD for automation of Rietveld analysis

Modern diffraction experiments ( e.g. in situ parametric studies) present scientists with many diffraction patterns to analyze. Interactive analyses via graphical user interfaces tend to slow down obtaining quantitative results such as lattice parameters and phase fractions. Furthermore, Rietveld refinement strategies ( i.e. the parameter turn-on-off sequences) tend to be instrument specific or even specific to a given dataset, such that selection of strategies can become a bottleneck for efficient data analysis. Managing multi-histogram datasets such as from multi-bank neutron diffractometers or caked 2D synchrotron data presents additional challenges due to the large number of histogram-specific parameters. To overcome these challenges in the Rietveld software Material Analysis Using Diffraction ( MAUD ), the MAUD Interface Language Kit ( MILK ) is developed along with an updated text batch interface for MAUD . The open-source software MILK is computer-platform independent and is packaged as a Python library that interfaces with MAUD . Using MILK , model selection ( e.g. various texture or peak-broadening models), Rietveld parameter manipulation and distributed parallel batch computing can be performed through a high-level Python interface. A high-level interface enables analysis workflows to be easily programmed, shared and applied to large datasets, and external tools to be integrated with MAUD . Through modification to the MAUD batch interface, plot and data exports have been improved. The resulting hierarchical folders from Rietveld refinements with MILK are compatible with Cinema: Debye–Scherrer , a tool for visualizing and inspecting the results of multi-parameter analyses of large quantities of diffraction data. In this manuscript, the combined Python scripting and visualization capability of MILK is demonstrated with a quantitative texture and phase analysis of data collected at the HIPPO neutron diffractometer.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2023

Artifact identification in X-ray diffraction data using machine learning methods

In situ synchrotron high-energy X-ray powder diffraction (XRD) is highly utilized by researchers to analyze the crystallographic structures of materials in functional devices ( e.g. battery materials) or in complex sample environments ( e.g. diamond anvil cells or syntheses reactors). An atomic structure of a material can be identified by its diffraction pattern along with a detailed analysis of the Rietveld refinement which yields rich information on the structure and the material, such as crystallite size, microstrain and defects. For in situ experiments, a series of XRD images is usually collected on the same sample under different conditions ( e.g. adiabatic conditions) yielding different states of matter, or is simply collected continuously as a function of time to track the change of a sample during a chemical or physical process. In situ experiments are usually performed with area detectors and collect images composed of diffraction patterns. For an ideal powder, the diffraction pattern should be a series of concentric Debye–Scherrer rings with evenly distributed intensities in each ring. For a realistic sample, one may observe different characteristics other than the typical ring pattern, such as textures or preferred orientations and single-crystal diffraction spots. Textures or preferred orientations usually have several parts of a ring that are more intense than the rest, whereas single-crystal diffraction spots are localized intense spots owing to diffraction of large crystals, typically >10 µm. In this work, an investigation of machine learning methods is presented for fast and reliable identification and separation of the single-crystal diffraction spots in XRD images. The exclusion of artifacts during an XRD image integration process allows a precise analysis of the powder diffraction rings of interest. When it is trained with small subsets of highly diverse datasets, the gradient boosting method can consistently produce high-accuracy results. The method dramatically decreases the amount of time spent identifying and separating single-crystal diffraction spots in comparison with the conventional method.

73 NUCLEAR PHYSICS AND RADIATION PHYSICS↗
DOE OSTI2024

Identifying Outliers in AI-based Image Compression

Image compression using artificial intelligence (AI) is becoming increasingly prevalent across various fields, including scientific research. Scientific instruments can generate hundreds of images per second, and effectively compressing these images with high compression ratios is crucial for facilitating scientific discoveries. However, automatically detecting outlier cases, where compression may not have succeeded or where interesting scientific phenomena are present, poses a significant challenge. To address this, we have developed a methodology based on unsupervised machine learning techniques for detecting outlier compressed images. This methodology utilizes metrics such as peak signal-to-noise ratio (PSNR), structural similarity index measure (SSIM), structural texture similarity index measure (STSIM), and deep image and structural texture similarity index (DISTS). We have evaluated our methodology on several unlabeled datasets, including microscopy and x-ray images, and have successfully identified multiple outlier images using our proposed approach. Furthermore, our approach has enabled us to identify image semantics that are valuable for post-experiment analysis by scientists.

Data Analysis↗
DOE OSTI2021

AutoAtlas: Neural Network for 3D Unsupervised Partitioning and Representation Learning

Here we present a novel neural network architecture called AutoAtlas for fully unsupervised partitioning and representation learning of 3D brain Magnetic Resonance Imaging (MRI) volumes. AutoAtlas consists of two neural network components: one neural network to perform multi-label partitioning based on local texture in the volume, and a second neural network to compress the information contained within each partition. We train both of these components simultaneously by optimizing a loss function that is designed to promote accurate reconstruction of each partition, while encouraging spatially smooth and contiguous partitioning, and discouraging relatively small partitions. We show that the partitions adapt to the subject specific structural variations of brain tissue while consistently appearing at similar spatial locations across subjects. AutoAtlas also produces very low dimensional features that represent local texture of each partition. We demonstrate prediction of metadata associated with each subject using the derived feature representations and compare the results to prediction using features derived from FreeSurfer anatomical parcellation. Since our features are intrinsically linked to distinct partitions, we can then map values of interest, such as partition-specific feature importance scores onto the brain for visualization.

42 ENGINEERING↗