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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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SF-25-076 pyAXEAP1-GUI package is an X-ray emission spectroscopy (XES) data analysis package in the Python language. It can transfer the collected images from a 2D detector into spectra and display XES and RXES data as 3Dimages, 2D images and 1D profiles.
Synchrotron X-ray diffraction measurements were performed on 57 Fe 68 Pd 32 at multiple pressures and two temperatures in a diamond-anvil cell. Between 4 and 11GPa, the thermal expansion was zero or slightly negative. This pressure-induced Invar effect was studied further with 57 Fe nuclear forward scattering and nuclear resonant inelastic X-ray scattering to obtain information on the pressure-induced changes in both the magnetization and the phonon density of states. Magnetic entropy and phonon entropy were obtained from these results, the latter with additional measurements from inelastic neutron scattering to account for the contributions from Pd atoms. The dependencies of these entropies on pressure gave the magnetic and phonon contributions to thermal expansion. These canceled in the region of pressure-induced Invar behavior, even though they individually increased by more than a factor of 2 below the Curie pressure. The behavior of phonons gives evidence for spin-phonon interactions in Fe 68 Pd 32 . A general explanation of the pressure-induced Invar effect is presented, showing that Invar behavior is typically expected at a pressure P* below a magnetic transition at pressure PC. The difference in pressure P C − P* scales with the fractional reduction in magnetic exchange interaction divided by the average Gr¨uneisen parameter.
X-ray absorption near edge structure (XANES) spectroscopy is a powerful technique for characterizing the chemical state and symmetry of individual elements within materials, but requires collecting data at many energy points which can be time-consuming. While adaptive sampling methods exist for efficiently collecting spectroscopic data, they often lack domain-specific knowledge about the structure of XANES spectra. Here we demonstrate a knowledge-injected Bayesian optimization approach for adaptive XANES data collection that incorporates understanding of spectral features like absorption edges and pre-edge peaks. We show this method accurately reconstructs the absorption edge of XANES spectra using only 15–20% of the measurement points typically needed for conventional sampling, while maintaining the ability to determine the x-ray energy of the sharp peak after the absorption edge with errors less than 0.03 eV, the absorption edge with errors less than 0.1 eV; and overall root-mean-square errors less than 0.005 compared to traditionally sampled spectra. Our experiments on battery materials and catalysts demonstrate the method’s effectiveness for both static and dynamic XANES measurements, improving data collection efficiency and enabling better time resolution for tracking chemical changes. This approach advances the degree of automation in XANES experiments, reducing the common errors of under- or over-sampling points near the absorption edge and enabling dynamic experiments that require high temporal resolution or limited measurement time.
The search for Kitaev’s quantum spin liquid in real materials has recently expanded with the prediction that honeycomb lattices of divalent, high-spin cobalt ions could host the dominant bond-dependent exchange interactions required to stabilize the elusive entangled quantum state. The layered honeycomb Na 3 Co 2 SbO 6 has been singled out as a leading candidate provided that the trigonal crystal field acting on Co 3d orbitals, which enhances non-Kitaev exchange interactions between $J$ eff = $\frac{1}{2}$ spin-orbital pseudospins, is reduced. Here we show that applied pressure leads to anisotropic compression of the layered structure, significantly reducing the trigonal distortion of CoO 6 octahedra. Ferromagnetic correlations between pseudospins are enhanced in the spin-polarized (3 Tesla) phase up to about 60 GPa. Higher pressures drive a high-spin to low-spin transition destroying the $J$ eff = $\frac{1}{2}$ moments required to map the spin Hamiltonian into Kitaev’s model. The spin transition strongly suppresses the low-temperature magnetic susceptibility and appears to stabilize a paramagnetic phase driven by frustration. The possible emergence of frustrated magnetism of localized $S$ = $\frac{1}{2}$ moments opens the door for exploration of novel magnetic quantum states in compressed honeycomb lattices of divalent cobaltates.
SF-26-118 pySimpleMask is a tool for creating masks and Q-partition maps for X-ray scattering patterns, supporting SAXS, WAXS, and XPCS data reduction. It ships both a desktop GUI and a headless Python API that can drive the full pipeline from scripts.
Shear induced frictional networks have been proposed to be responsible for the emergence of discontinuous shear thickening (DST) in complex fluids. However, little experimental evidence exists to support this model directly. Here, in this study, using x-ray photon correlation spectroscopy (XPCS), we show the existence of an intrinsic heterodyne feature during shear cessation, which originates from the relative motion of mobile particles against an aggregated or jammed network induced by shear thickening. Upon removing the shear, the shear stress dissipates rather quickly in a two-step fashion, whereas the heterogeneous particle dynamics persist much longer with the relative velocity decaying slowly with time as 𝑡 −1 . More importantly, both continuous shear thickening (CST) and DST show similar heterodyne features, indicating the intrinsic mechanisms causing shear thickening are similar in nature
Debris generated from total hip arthroplasty (THA) components made from metal alloys can cause, in some cases, inflammatory cell (e.g., macrophages) responses that lead to adverse local tissue reactions (ALTR) and implant failure. The lack of information on intracellular chemical alterations of metal debris has hindered the understanding of the pathogenesis of ALTR. The goal of this study was to characterize intracellular debris within macrophages using Synchrotron imaging and spectroscopy. We studied periprosthetic tissues of two retrieved THAs with (1) a metal-on-metal (MoM) articulation and (2) a metal-on-polyethylene (MoP) articulation exhibiting corrosion of the metal femoral head. The MoM-THA exhibited different valence states of chromium- and cobalt-containing debris, suggesting three different moieties: Cr 2 O 3 , CrPO 4 , and an alloy-oxide mixture. The findings further suggest that Cr 2 O 3 formed in the tribological interfaces of the implant, while CrPO 4 is a by-product of the phagocytosis process of cobalt alloy-containing debris. Titanium debris appeared to occur in a mixed crystalline/amorphous oxide state. It remains unclear if this chemical state results from the tribochemical processes at the implant surface or intracellular alterations. The MoP-THA specimen exhibited no intracellular particulate debris associated with macrohpages, indicating that the ALTR may be entirely triggered by metal ionic species in this case. A better understanding of in vivo chemical alteration of implant debris will aid in assessing the risk for ALTR during implant design and material choice. However, various techniques are needed to accurately determine the interaction between metal particles and the inta- and extra-cellular environment.
Full-field ultra-high-speed (UHS) X-ray imaging experiments have been well established to characterize various processes and phenomena. However, the potential of UHS experiments through the joint acquisition of X-ray videos with distinct configurations has not been fully exploited. In this paper, we investigate the use of a deep learning-based spatio-temporal fusion (STF) framework to fuse two complementary sequences of X-ray images and reconstruct the target image sequence with high spatial resolution, high frame rate and high fidelity. We applied a transfer learning strategy to train the model and compared the peak signal-to-noise ratio (PSNR), average absolute difference (AAD) and structural similarity (SSIM) of the proposed framework on two independent X-ray data sets with those obtained from a baseline deep learning model, a Bayesian fusion framework and the bicubic interpolation method. The proposed framework outperformed the other methods with various configurations of the input frame separations and image noise levels. With three subsequent images from the low-resolution (LR) sequence of a four times lower spatial resolution and another two images from the high-resolution (HR) sequence of a 20 times lower frame rate, the proposed approach achieved average PSNRs of 37.57 dB and 35.15 dB, respectively. When coupled with the appropriate combination of high-speed cameras, the proposed approach will enhance the performance and therefore the scientific value of UHS X-ray imaging experiments.
Traditional Laue diffraction pattern indexing often struggles with noisy data, weak signals, peak overlap and missing reflections, particularly from complex or deformed microstructures. Here, we introduce LaueMatching, a high-throughput indexing algorithm designed to overcome these limitations. LaueMatching utilizes a fundamentally different approach based on direct pattern correlation: experimentally pre-processed images are compared against a comprehensive pre-computed library of simulated diffraction patterns corresponding to a dense grid of possible orientations. This approach bypasses the need for explicit peak identification and fitting, steps that are often a failure point for traditional methods. The algorithm rapidly and robustly indexes multiple crystallographic orientations and crystal systems simultaneously, even from challenging patterns. LaueMatching's effectiveness and accuracy have been rigorously tested and validated on diverse experimental (Ni, Al, EuAl 2 O 4 ) and simulated diffraction patterns, demonstrating high-fidelity orientation refinement. Code to implement this approach on both CPU and GPU resources can be downloaded from https://github.com/AdvancedPhotonSource/LaueMatching.
A central paradigm of nonequilibrium physics concerns the dynamics of heterogeneity and disorder, impacting processes ranging from the behavior of glasses to the emergent functionality of active matter. Understanding these complex mesoscopic systems requires probing the microscopic trajectories associated with irreversible processes, the role of fluctuations and entropy growth, and the timescales on which nonequilibrium responses are ultimately maintained. Approaches that illuminate these processes in model systems may enable a more general understanding of other heterogeneous nonequilibrium phenomena, and potentially define ultimate speed and energy cost limits for information processing technologies. Here, we apply ultrafast single-shot X-ray photon correlation spectroscopy to resolve the nonequilibrium, heterogeneous, and irreversible mesoscale dynamics during a light-induced phase transition in a (PbTiO 3 ) 16 /(SrTiO 3 ) 16 superlattice. Such ferroelectric superlattice systems are a useful platform to study phase transitions and topological dynamics due to their high degree of tunability. This provides an approach for capturing the nucleation of the light-induced phase, the formation of transient mesoscale defects at the boundaries of the nuclei, and the eventual annihilation of these defects, even in systems with complex polarization topologies. We identify a nonequilibrium correlation response spanning >10 orders of magnitude in timescales, with multistep behavior similar to the plateaus observed in supercooled liquids and glasses. We further show how the observed time-dependent long-time correlations can be understood in terms of stochastic and non-Markovian dynamics of domain walls, encoded in waiting-time distributions with power-law tails. This work defines possibilities for probing the nonequilibrium and correlated dynamics of disordered and heterogeneous media.
Here, we report the observation of an electronic reconstruction in dimensionally controlled ruthenate heterostructures synthesized by pulsed laser deposition. High structural and electronic quality of superlattices comprised of a single SrRuO 3 layer inter-spaced with varying thicknesses of insulating SrTiO 3 layers was verified by reflection high energy electron diffraction, atomic force microscopy, x-ray diffraction, reciprocal space mapping, and x-ray absorption spectroscopy. X-ray absorption spectroscopy evidences a confinement-driven evolution of the Ru electronic configuration from the 𝑑 5 $\underline{L}$ to the 𝑑 4 state. Significant increases of the spin-orbit coupling are observed in connection with the configuration changes supporting recent works identifying large enhancement of the magnetic anisotropy. The growth of high quality two-dimensional confined ruthenate layers under precisely controlled environments highlights the potential to directly manipulate interlayer coupling and selectively perturb the electronic state in ruthenates in analogy to superconducting Sr 2 RuO 4 .
Alkali-activated slag (AAS) is a promising low-CO 2 alternative cement consisting of several disordered phases of similar composition. Although their local atomic arrangements are known to influence macroscopic behavior, determination of structural changes in response to external stimuli remains a challenge. Here, X-ray diffraction-computed tomography (XRD-CT), pair distribution function-CT (PDF-CT), and nanoprobe X-ray fluorescence (nano-XRF) have been used to uncover how an increase of magnesium in AAS affects the atomic structure and spatial arrangement of phases after aggressive carbonation (100% dry CO 2 ), conditions experienced in applications such as oil and gas wells and geological storage of CO 2 . From PDF-CT it is found that a higher magnesium content decreases the average nanoscale crystallite size of disordered calcium carbonate. At the same time, higher magnesium content is correlated with a less decalcified C-(N)-A-S-H gel, as determined via analysis of Ca-Si atom-atom correlations from PDF-CT and Ca/Si ratios from nano-XRF. Finally, nano-XRF reveals that the disordered (i.e., amorphous) calcium carbonate is stabilized by the presence of silicates.
With the development of layer‐by‐layer growth techniques such as molecular beam epitaxy (MBE), it is now possible to construct materials with atomic‐level precision, with the layer sequence primarily determined by the growth recipe. However, materials can still restructure in the high‐temperature growth environment with surface thermodynamics strongly influencing growth behavior. Here, we demonstrate that growth on ${\rm SrTiO}_{3}$ (001), the principal platform for oxide electronics, does not occur in a layer‐by‐layer fashion but follows a more complex process in which a ${\rm TiO}_{2}$ plane continually diffuses toward the growth surface. Employing in situ synchrotron X‐ray scattering combined with ab initio thermodynamic calculations, we discover the existence of a stable ${\rm TiO}_{2}$ double‐layer structure on the pristine substrate and the occurrence of dynamic layer rearrangement during homoepitaxial growth by oxide MBE. Our findings suggest that the current methods used to precisely control ${\rm SrTiO}_{3}$ surfaces are limited, as well as our ability to dictate the composition of ultrathin films, resulting in important ramifications regarding the surface reactivity of perovskite materials grown on ${\rm SrTiO}_{3}$.
Dark-field X-ray microscopy is a lens-based technique that enables real-space imaging of heterogeneous micro- and meso-scale ordered materials. However, achieving accurate three-dimensional (3D) reconstruction often requires meticulous sample alignment or rastering, requiring complex rotational setups and extended acquisition times. To address these challenges, we introduce a structured illumination technique optimized for 3D imaging of ordered materials at sub-micrometer length scales. Our approach employs a coded aperture to spatially modulate the incident X-ray beam, enabling 3D structural reconstruction from images captured at various aperture positions. Unlike current 3D imaging approaches, which often rely on rotational or rastering methods, our technique uses scanning X-ray silhouettes of the coded aperture for depth resolution along the diffraction axis. This eliminates the need for sample rotation or rastering, resulting in a highly stable and efficient imaging modality. We validated the efficacy of this approach through experimental imaging of an isolated twin domain within a bulk single crystal of an iron pnictide using a dark-field X-ray microscope. This advancement aligns with the enhanced brightness upgrades of modern synchrotron radiation facilities, unlocking new possibilities for high-resolution imaging of ordered materials.
Extended X-ray absorption fine structure (EXAFS) is a widely used technique for atomic structure determination. Fourier transformation connects EXAFS in k space and R space. However, determining the appropriate k-range for the transformation can be challenging, but critical for the first-shell fit. In this study, we present an automatic method to determine the k-range using the Larch package and a Python program. The first step is to estimate spectral noise across a series of k-ranges with a fixed minimum value and identify the optimal maximum value in the k-range (k max ). The k max is determined by an empirical noise threshold that marks the point where the noise level in the Fourier transformed spectrum changes dramatically. Using the obtained k max value, the first shell is modeled to determine the minimum k value (k min ) by optimizing the background function through alignment of the spectrum with theory. The optimal k min corresponds to the point of the minimum R-factor, which quantifies the difference between the experimental and fitted spectrum. Our method was tested on various typical datasets and yielded suitable k-ranges for Fourier transformation and accurate first-shell fits. This approach helps avoid unreliable, irreproducible data analysis, especially for noisy data from diluted samples, and enables robust automatic first-shell EXAFS fitting.
Primitive mid-ocean ridge basalts (MORBs) exhibit Fe isotopic compositions heavier than the upper mantle by +0.074 ± 0.028 ‰ for δ 56 Fe. The processes responsible for this isotopic difference remain unclear. Modeling of Fe isotope fractionation during mantle partial melting requires reliable equilibrium Fe isotope fractionation factors between minerals and melts, for which consistent data are still lacking. Here, in this study, we used Nuclear Resonant Inelastic X-ray Scattering (NRIXS) technique to measure Fe force constants for a MORB glass (ALV 519-4-1) and natural mantle minerals (olivine, orthopyroxene, clinopyroxene, and spinel) to determine the equilibrium Fe isotope fractionation factors between them. The force constants determined in this study, in increasing order, are 167 ± 26 N/m for spinel, 175 ± 17 N/m for olivine, 176 ± 20 N/m for MORB glass, 205 ± 26 N/m for clinopyroxene, and 219 ± 36 N/m for orthopyroxene. We evaluated the previously proposed mechanisms for the heavy Fe isotopic composition of MORBs, including (i) mantle partial melting, (ii) mantle lithological heterogeneity, with pyroxenite in the source, (iii) mantle metasomatism by low-degree melts, and (iv) fractional crystallization of olivine from melts. For (i), we used the pMELTS program to simulate adiabatic decompression melting of mantle peridotites, and calculated Fe isotope fractionation based on Fe 3+ –Fe 2+ equilibrium-controlled fractionation, where Fe 3+ forms stronger bonds and is more incompatible than Fe 2+ . At 10 wt% peridotite melting, corresponding to MORB generation, only +0.03 ‰ Fe isotope fractionation between the melt and the original bulk composition (Δ 56 Fe = δ 56 Fe melt - δ 56 Fe 0 ) was produced, insufficient to account for the observed MORB-upper mantle difference. For (ii), melting of pyroxenites yields smaller Fe isotope fractionation than melting of peridotites, making it unlikely the cause for the MORB-upper mantle isotopic difference. For (iii), both the Fe 3+ /ΣFe ratio and the δ 56 Fe of melts increase with the degree of partial melting, indicating that low-degree melts are not isotopically heavy enough to significantly alter the isotopic composition of lithospheric mantle through metasomatism. For (iv), equilibrium isotope fractionation between olivine and melt is near zero. These results suggest that equilibrium Fe isotope fractionation alone cannot explain the MORB isotopic signature, highlighting the potential role of kinetic isotope fractionation. Using a diffusion model, we calculated kinetic Fe and Mg isotope fractionations associated with (iv) olivine crystallization from a melt, and found that the predicted Fe and Mg isotope fractionations were inconsistent with observations in MORBs. Qualitatively, two processes could have induced kinetic Fe isotope fractionation during MORB generation: (a) Fe-Mg interdiffusion between melt and solid during melt migration and (b) reactive melt-rock interactions during melt focusing. However, a quantitative understanding of their role in modifying the melt isotopic composition remains limited and requires further investigation.
Ferromagnetism is an essential ingredient for anomalous Hall effect. SrRuO 3 is a representative ferromagnetic oxide that exhibits anomalous Hall effect even down to the monolayer confinement limit. Paramagnetic metal SrIrO 3 , on the other hand, becomes an antiferromagnetic insulator when confined to a monolayer. Here, in this study, we show that, by forming a confined bilayer structure of SrRuO 3 and SrIrO 3 monolayers with SrTiO 3 spacers, the anomalous Hall effect is significantly enhanced while the ferromagnetism as well as the perpendicular magnetic anisotropy are suppressed. These effects originate from interfacial Ru–Ir hybridization that modifies the electronic structure in the vicinity of the Fermi level. Our work demonstrates that confined bilayer heterostructure is an useful design for exploring the synergistic combination of interfacial coupling and quantum confinement of complex oxides.
Subsurface processes in sandstones are controlled by porosity, permeability, and deformation mechanisms, all of which are controlled by a complex interplay of crystallographic rock texture, structure, and micromechanics. Texture, structure, and micromechanics have historically been studied using optical and electron microscopy of thin-sections. Here, we employed a new combination of in situ X-ray tomography and ray diffraction microscopy to study crystallographic texture, structure, and grain stresses in 3D. We examined these features in a sample of Nugget sandstone, a sandstone constituting hydrocarbon reservoirs across the American West. Our aims are threefold. First, we demonstrate the utility of X-ray diffraction microscopy probes for revealing texture, structure, and stress transmission in 3D. Second, we apply these techniques to Nugget sandstone and discuss findings in the context of prior work. Third, we study grain stress tensor evolution during mechanical compression to examine whether their heterogeneity and orientation evolution reflect that of inter-particle forces in granular materials. Our results show: (a) larger grains featured higher intra-granular misorientations, possibly from an increased prevalence of cements; (b) pores closed parallel to the loading direction and opened normal to loading; (c) grain stresses featured heterogeneity and orientations similar to inter-particle forces in non-cohesive granular materials; (d) grains featured compressive stresses in the loading direction and tensile stresses orthogonal to the loading direction, the latter resisting sample dilation and grain separation. Our work demonstrates the first known application of multi-modal X-ray tomography and diffraction microscopy to sandstone, providing new 3D insight into the nature of quartz cement and stress evolution.