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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 829 records · Page 46
All sourcesNASA NTRSDOE OSTI
DOE OSTI2019

Shear strain-induced anisotropic domain evolution in mixed-phase BiFeO 3 epitaxial films

Understanding and controlling the domain evolution under external stimuli in multiferroic thin films is critical to realizing nano-electronic devices, including for non-volatile memory, data storage, sensors, and optoelectronics. In this article, we studied the shear-strain effect on the domain evolution with temperature in highly strained BiFeO 3 thin films on rhombohedral LaAlO 3 sub-strates using a high-resolution synchrotron X-ray diffraction three dimensional-reciprocal space mapping (3D-RSM) technique. The results revealed significant biaxial, anisotropic, evolution behaviors of the mixed-phase ( M C + R' / T' phases) BiFeO 3 ferro-electric domains along the in-plane [100] and [010] axes. These biaxial, anisotropic, evolution behaviors were attributed to the shear-strain-modulated transition pathways of the mixed-phase ferroelectric domains. This viewpoint was further verified in the BiFeO 3 /LaSrAlO 4 (001) system in which no anisotropic evolution behaviors of the mixed-phase domains were found. This work sheds light on the quantitative analysis of domain evolution in multi-domain systems and demonstrates that the shear-strain effect could act as an effective tool to manipulate the domain behavior and control novel functionalities of ferroelectric thin films.

36 MATERIALS SCIENCE↗
DOE OSTI2019

Impact of non-Maxwellian electron velocity distribution functions on inferred plasma parameters in collective Thomson scattering

Optical collective Thomson scattering provides precise density and temperature measurements in numerous plasma-physics experiments. The accuracy of such measurements depends on the core assumption that the underlying electron distribution functions in under-dense laser-produced plasmas are Maxwellian. A statistically based, quantitative analysis of the errors in the measured electron density and temperature is presented when synthetic data calculated using a non-Maxwellian electron distribution function is fit assuming a Maxwellian electron distribution. Such analysis can lead to errors of up to 50% in temperature and 30% in density, in the specific case of super-Gaussian distributions characteristic of inverse bremsstrahlung heating. Furthermore including the proper family of non-Maxwellian electron distribution functions, as a fitting parameter, in Thomson-scattering analysis removes the model-dependent errors in the inferred parameters at a minimal cost to the statistical uncertainty.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗
DOE OSTI2019

Curvature distribution and autocorrelations in elliptic cylinders and cones

Not all micro-vessels (MV) are traditionally circular and there are examples of elliptic cylindrical MVs in life sciences, particularly if projected with a slant. Similarly, certain biological structures, ferroelectric liquid crystals, aluminum oxide clusters and witherite crystallites’ crosssection appear to be elliptical cones. We studied the mean curvature (H) distribution of these elliptic morphological structures with geometric parameter such as eccentricity; e (ratio of semi-minor to semi-major axes) and a measure of how much diagonal section deviates from circularity and height (h) in case of cones. By means of topographical cues, we defined the curvature-curvature autocorrelation function (gk) and applied this notion to mean curvature (H) of circular and elliptical cylinders and cones. The Fourier transform of correlation function, i.e. “curvature factor” is analogous to “structure factor (or Patterson function)” in X-ray and neutron scattering intensity. It elucidates critically important information related to surface curvature fluctuation relevant to shape (geometry), network and phase transformation. The latter is induced by cells under mechanical stress, occurring in many soft systems (polymeric liquid crystals, foams, bubbles) and biological tissues, particularly cell walls of primary and branched vessels bed in microvasculature that distributes blood within tissue during hypertension and migraines. This perspective is useful in a sustained release of angiogenic/vasculogenic factors and relevant for precision medicine and engineered microvessels and tissues in vitro and in vivo extended cellular processes. The quantitative analysis carried out in this work facilitates our understanding of the mechanical mechanisms associated with thrombosis during surgery that typically occur in bent or stretched MVs due to microenvironment such as localized shear stresses and biochemical factors.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗
DOE OSTI2020

Reconstruction of the interatomic forces from dynamic scanning transmission electron microscopy data

We explore the possibility for reconstruction of the generative physical models describing interactions between atomic units in solids from observational electron microscopy data. In this work, scanning transmission electron microscopy (STEM) is used to observe the dynamic motion of Si atoms at the edge of monolayer graphene under continuous electron beam illumination. The resulting time-lapsed STEM images represent the snapshots of observed chemical states of the system. We use two approaches: potential of mean force calculation using a radial distribution function and a direct fitting of the graphene–Si interatomic pairwise potentials with force matching, to reconstruct the force fields in the materials. These studies lay the foundation for quantitative analysis of materials energetics from STEM data through the sampling of the metastable states in the chemical space of the system.

42 ENGINEERING↗
DOE OSTI2020

Effect of hydride vapor phase epitaxy growth conditions on the degree of atomic ordering in GaInP

We report a systematic study of CuPt-type ordering in hydride vapor phase epitaxy (HVPE)-grown Ga 0.5 In 0.5 P. Selected-area electron diffraction reveals ordering in samples grown on (001) GaAs substrates offcut toward (1$\overline{1}$1)B. The ordering is single-variant, occurring only on (1$\overline{1}$1)B planes and not on ($\overline{1}$11)B. Quantitative analysis of the order parameter by high-resolution x-ray diffraction (HRXRD) indicates that ordering increases with deposition temperature in samples grown at 600–700 °C with a constant gas-phase V/III ratio ~3. Ordering increases with V/III ratio in the range of 1.3–6.7 at a constant deposition temperature of 650°C. Photoluminescence measurements correlate the order parameter with Ga0.5In0.5P bandgap contraction, though the contraction is larger than expected based on the magnitude of order parameters measured by HRXRD. A possible reason for this discrepancy is that the photoluminescence emission occurs in the lower bandgap ordered domains, which are small and evenly dispersed throughout the material. We also show that the degree of ordering decreases with growth rate, disappearing at ~60 µm/h. The HVPE-grown material exhibits a generally weaker ordering than organometallic vapor phase epitaxy (OMVPE)-grown material, likely due to the moderate V/III ratios employed, in contrast to the V/III ratios in the 100s typical of OMVPE. However, the tendency for ordering to increase with V/III ratio suggests that the same dimer-induced stress mechanism used to explain the occurrence of ordering in OMVPE-grown material also applies to HVPE. The tendencies for ordering to increase with deposition temperature and decrease with growth rate show that kinetics limit the degree of ordering, also in agreement with OMVPE trends.

74 ATOMIC AND MOLECULAR PHYSICS↗
DOE OSTI2021

X-ray multi-probe data acquisition: A novel technique for laser pump x-ray transient absorption spectroscopy

We report the development and implementation of a novel data acquisition (DAQ) technique for synchrotron-based laser pump X-ray Transient Absorption (XTA) spectroscopy, called X-ray Multi-Probe DAQ (XMP DAQ). This technique utilizes high performance analog to digital converters and home-built software to efficiently measure and process the XTA signal from all x-ray pulses between laser excitations. XMP DAQ generates a set of time resolved x-ray absorption spectra at thousands of different pump–probe time delays simultaneously. Two distinct XMP DAQ schemes are deployed to accommodate different synchrotron storage ring filling patterns. Current Integration (CI) DAQ is a quasi-analog technique that implements a fitting procedure to extract the time resolved absorption intensity from the averaged fluorescence detector response. The fitting procedure eliminates issues associated with small drifts in the voltage baseline and greatly enhances the accuracy of the technique. Photon Counting (PC) DAQ is a binary technique that uses a time resolved histogram to calculate the XTA spectrum. While PC DAQ is suited to measure XTA data with closely spaced x-ray pulses (~10 ns) and a low count rate (<1 detected photon/pulse), CI DAQ works best for widely spaced pulses (tens of ns or greater) with a high count rate (>1 detected photon/pulse). XMP DAQ produces a two-dimensional XTA dataset, enabling efficient quantitative analysis of photophysical and photochemical processes from the sub-nanosecond timescale to 100 μs and longer.

47 OTHER INSTRUMENTATION↗
DOE OSTI2021

Model fusion with physics-guided machine learning: Projection-based reduced-order modeling

The unprecedented amount of data generated from experiments, field observations, and large-scale numerical simulations at a wide range of spatiotemporal scales has enabled the rapid advancement of data-driven and especially deep learning models in the field of fluid mechanics. Although these methods are proven successful for many applications, there is a grand challenge of improving their generalizability. This is particularly essential when data-driven models are employed within outer-loop applications like optimization. In this work, we put forth a physics-guided machine learning (PGML) framework that leverages the interpretable physics-based model with a deep learning model. Leveraging a concatenated neural network design from multi-modal data sources, the PGML framework is capable of enhancing the generalizability of data-driven models and effectively protects against or inform about the inaccurate predictions resulting from extrapolation. We apply the PGML framework as a novel model fusion approach combining the physics-based Galerkin projection model and long- to short-term memory (LSTM) network for parametric model order reduction of fluid flows. We demonstrate the improved generalizability of the PGML framework against a purely data-driven approach through the injection of physics features into intermediate LSTM layers. Our quantitative analysis shows that the overall model uncertainty can be reduced through the PGML approach, especially for test data coming from a distribution different than the training data. Moreover, we demonstrate that our approach can be used as an inverse diagnostic tool providing a confidence score associated with models and observations. The proposed framework also allows for multi-fidelity computing by making use of low-fidelity models in the online deployment of quantified data-driven models.

42 ENGINEERING↗
DOE OSTI2021

Quantifying a partial polyamorphic transition in a cerium-based metallic glass during cooling

Cerium-based metallic glasses are prototype polyamorphous systems with pressure-induced polyamorphic transitions extensively reported. Cooling typically has a similar effect on materials as compression with regard to reducing volume. However, previous studies show dramatically different behavior of Ce-based metallic glasses between cooling and compression, whose origin remains unclear. Here, using in situ low-temperature synchrotron high-energy x-ray diffraction, the structural evolution of a Ce68Al10Cu20Co2 metallic glass is accurately determined and analyzed by a structure factor and a reduced pair distribution function (PDF) during cooling from 298 to 83 K. An unusually large linear thermal expansion coefficient is revealed, which is associated with both continuous but inconsistent structural changes between the two subpeaks of the first atomic shell in terms of average bond lengths and coordination numbers. These phenomena are suggested to be attributed to a gradual 4f electron delocalization of only a minimal amount (∼2.6% at 83 K) of Ce atoms by quantitative analysis of the PDF data. However, a previously expected global polymorphic transition from a low-density amorphous state to a high-density amorphous state with an abrupt volume collapse is not observed. Moreover, electrical resistivity also shows a continuous increase during cooling without any sharp change. It is clarified that cryogenic temperatures could facilitate but are not powerful enough alone to trigger a global polymorphic transition in the Ce68Al10Cu20Co2 metallic glass, suggesting a wide distribution of its local atomic environment.

Chen, Zhi↗
DOE OSTI2022

Three-dimensional hot-spot x-ray emission tomography from cryogenic deuterium–tritium direct-drive implosions on OMEGA

A three-dimensional model of the hot-spot x-ray emission has been developed and applied to the study of low-mode drive asymmetries in direct-drive inertial confinement fusion implosions on OMEGA with cryogenic deuterium–tritium targets. Here, the steady-state model assumes an optically thin plasma and the data from four x-ray diagnostics along quasi-orthogonal lines of sight are used to obtain a tomographic reconstruction of the hot spot. A quantitative analysis of the hot-spot shape is achieved by projecting the x-ray emission into the diagnostic planes and comparing this projection to the measurements. The model was validated with radiation-hydrodynamic simulations assuming a mode-2 laser illumination perturbation resulting in an elliptically shaped hot spot, which was accurately reconstructed by the model using synthetic x-ray images. This technique was applied to experimental data from implosions in polar-direct-drive illumination geometry with a deliberate laser-drive asymmetry, and the hot-spot emission was reconstructed using spherical-harmonic modes of up to ℓ = 3. A 10% stronger drive on the equator relative to that on the poles resulted in a prolate-shaped hot spot at stagnation with a large negative A 2,0 coefficient of A 2,0 = –0.47 ± 0.03, directly connecting the modal contribution of the hot-spot shape with the modal contribution in laser-drive asymmetry.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗
DOE OSTI2024

Neural network denoising of x-ray images from high-energy-density experiments

Noise is a consistent problem for x-ray transmission images of High-Energy-Density (HED) experiments because it can significantly affect the accuracy of inferring quantitative physical properties from these images. We consider experiments that use x-ray area backlighting to image a thin layer of opaque material within a physics package to observe its hydrodynamic evolution. The spatial variance of the x-ray transmission across the system due to changing opacity serves as an analog for measuring density in this evolving layer. The noise in these images adds nonphysical variations in measured intensity, which can significantly reduce the accuracy of our inferred densities, particularly at small spatial scales. Denoising these images is thus necessary to improve our quantitative analysis, but any denoising method also affects the underlying information in the image. In this paper, we present a method for denoising HED x-ray images via a deep convolutional neural network model with a modified DenseNet architecture. In our denoising framework, we estimate the noise present in the real (data) images of interest and apply the inferred noise distribution to a set of natural images. These synthetic noisy images are then used to train a neural network model to recognize and remove noise of that character. We show that our trained denoiser network significantly reduces the noise in our experimental images while retaining important physical features.

47 OTHER INSTRUMENTATION↗
DOE OSTI2025

Incubating advances in integrated photonics with emerging sensing and computational capabilities

As photonic technologies grow in multidimensional aspects, integrated photonics holds a unique position and continuously presents enormous possibilities for research communities. Applications include data centers, environmental monitoring, medical diagnosis, and highly compact communication components, with further possibilities continuously growing. Herein, we review state-of-the-art integrated photonic on-chip sensors that operate in the visible to mid-infrared wavelength region on various material platforms. Among the different materials, architectures, and technologies leading the way for on-chip sensors, we discuss the optical sensing principles that are commonly applied to biochemical and gas sensing. Our focus is on passive optical waveguides, including dispersion-engineered metamaterial-based structures, which are essential for enhancing the interaction between light and analytes in chip-scale sensors. We harness a diverse array of cutting-edge sensing technologies, heralding a revolutionary on-chip sensing paradigm. Our arsenal includes refractive-index-based sensing, plasmonics, and spectroscopy, which forge an unparalleled foundation for innovation and precision. Furthermore, we include a brief discussion of recent trends and computational concepts, incorporating Artificial Intelligence & Machine Learning (AI/ML) and deep learning approaches over the past few years to improve the qualitative and quantitative analysis of sensor measurements.

Jain, Sourabh (ORCID:0000000279923275)↗
DOE OSTI2025

Scattering-based structural inversion of soft materials via Kolmogorov–Arnold networks

Small-angle scattering techniques are indispensable tools for probing the structure of soft materials. However, traditional analytical models often face limitations in structural inversion for complex systems, primarily due to the absence of closed-form expressions of scattering functions. To address these challenges, we present a machine learning framework based on the Kolmogorov–Arnold Network (KAN) for directly extracting real-space structural information from scattering spectra in reciprocal space. This model-independent, data-driven approach provides a versatile solution for analyzing intricate configurations in soft matter. By applying the KAN to lyotropic lamellar phases and colloidal suspensions—two representative soft matter systems—we demonstrate its ability to accurately and efficiently resolve structural collectivity and complexity. Here, our findings highlight the transformative potential of machine learning in enhancing the quantitative analysis of soft materials, paving the way for robust structural inversion across diverse systems.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2019

The energetic basis for hydroxyapatite mineralization by amelogenin variants provides insights into the origin ofamelogenesis imperfecta

Small variations in the primary amino acid sequence of extracellular matrix proteins can have profound effects on the biomineralization of hard tissues. For example, a change in one amino acid within the amelogenin protein can lead to drastic changes in enamel phenotype, resulting in amelogenesis imperfecta, enamel that is defective and easily damaged. Despite the importance of these undesirable phenotypes, there is very little understanding of how single amino acid variation in amelogenins can lead to malformed enamel. Here, we aim to develop a thermodynamic understanding of how protein variants can affect steps of the biomineralization process. High-resolution, in situ atomic force microscopy (AFM) showed that altering one amino acid within the murine amelogenin sequence (natural variants T21 and P41T, and experimental variant P71T) resulted in an increase in the quantity of protein adsorbed onto hydroxyapatite (HAP) and the formation of multiple protein layers. Quantitative analysis of the equilibrium adsorbate amounts revealed that the protein variants had higher oligomer–oligomer binding energies. MMP20 enzyme degradation and HAP mineralization studies showed that the amino acid variants slowed the degradation of amelogenin by MMP20 and inhibited the growth and phase transformation of HAP. We propose that the protein variants cause malformed enamel because they bind excessively to HAP and disrupt the normal HAP growth and enzymatic degradation processes. The in situ methods applied to determine the energetics of molecular level processes are powerful tools toward understanding the mechanisms of biomineralization.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2021

Application of molecular convergent close-coupling cross sections in a collisional radiative model for the triplet system of molecular hydrogen

Collisional radiative (CR) models for molecular hydrogen are of high relevance for performing qualitative and quantitative analysis of excited-state population densities measured in plasmas or predicting the dependence of plasma emission on parameter variations. Although the development of such models for H 2 started decades ago, major uncertainties still exist regarding the most important set of input parameters, namely the cross sections for electron-impact excitation. The deviations between cross sections from different datasets are particularly pronounced in the energy region close to the threshold energy, strongly increasing the uncertainty of CR models applied to low-temperature plasmas. This paper presents experimental validation of a set of newly calculated non ro-vibrationally resolved electron-impact cross sections calculated for the triplet system of H 2 using the molecular convergent close-coupling method in the adiabatic-nuclei formulation. These cross sections are implemented into a CR model based on the flexible solver Yacora. A first comparison of CR calculations with the different datasets to experimentally-determined population densities is performed at a planar ICP discharge for varying pressure (between 1 and 10 Pa) and RF power (between 700 and 1100 W). For the experimentally-accessible electron temperature and density range (2.5–10 eV and 1.8–3.3 × 10 16 m –3 , respectively), very good agreement between the model and experiment is obtained using the new data set, in contrast to previously used cross sections.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗
DOE OSTI2021

Point-contact spectroscopy of heavy fermion superconductors Ce 2 PdIn 8 and Ce 3 PdIn 11 in comparison with CeCoIn 5

Here, we report point-contact spectroscopy measurements on heavy fermion cousins CeCoIn 5 , Ce 2 PdIn 8 and Ce 3 PdIn 11 to systematically study the hybridization between f and conduction electrons. Below a temperature T *, the spectrum of each compound exhibits an evolving Fano-like conductance shape, superimposed on a sloping background, that suggests the development of hybridization between local f and itinerant conduction electrons in the coherent heavy fermion state below T *. We present a quantitative analysis of the conductance curves with a two-channel model to compare the tunneling process between normal metallic silver particles in our soft point-contact and heavy-fermion single crystals CeCoIn 5 , Ce 2 PdIn 8 and Ce 3 PdIn 11 .

36 MATERIALS SCIENCE↗
DOE OSTI2018

Reverse-engineering of graphene on metal surfaces: a case study of embedded ruthenium

Using scanning tunneling microscopy, X-ray photoelectron spectroscopy, and X-ray absorption spectroscopy, we show that Ru forms metallic nanoislands on graphite, covered by a graphene monolayer. These islands are air-stable, contain 2-4 layers of Ru, and have diameters on the order of 10 nm. To produce these nanoislands two conditions must be met during synthesis. The graphite surface must be ion-bombarded, and subsequently held at elevated temperature (1000-1180 K) during Ru deposition. A coincidence lattice forms between the graphene overlayer and the Ru island top. Its characteristics – coincidence lattice constant, corrugation amplitude, and variation of carbon lattice appearance within the unit cell – closely resemble the well-established characteristics of single-layer graphene on the (0001) surface of bulk Ru. Quantitative analysis of the graphene lattice in relation to the coincidence lattice on the island tops shows that the twodimensional lattice constant of the underlying metal equals that of bulk Ru(0001), within experimental error. The embedded Ru islands are energetically favored over on-top (adsorbed) islands, based on density-functional-theory calculations for Ru films with 1-3 Ru layers. We propose a formation mechanism in which Ru atoms intercalate via defects that act as entry portals to the carbon galleries, followed by nucleation and growth in the galleries. In this model, high deposition temperature is necessary to prevent blockage of entry portals.

36 MATERIALS SCIENCE↗
DOE OSTI2018

Measuring implosion velocities in experiments and simulations of laser-driven cylindrical implosions on the OMEGA laser

Laser-driven magnetized liner inertial fusion (MagLIF) on OMEGA involves cylindrical implosions, a preheat beam, and an applied magnetic field. Initial experiments excluded the preheat beam and magnetic field to better characterize the implosion. X-ray self-emission as measured by framing cameras was used to determine the shell trajectory. The 1-D code LILAC was used to model the central region of the implosion, and results were compared to 2-D simulations from the HYDRA code. Post-processing of simulation output with SPECT3D and Yorick produced synthetic x-ray images that were used to compare the simulation results with the x-ray framing camera data. Quantitative analysis shows that higher measured neutron yields correlate with higher implosion velocities. The future goal is to further analyze the x-ray images to characterize the uniformity of the implosions and apply these analysis techniques to integrated laser-driven MagLIF shots to better understand the effects of preheat and the magnetic field.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗
DOE OSTI2016

Dark matter vs. astrophysics in the interpretation of AMS-02 electron and positron data

We perform a detailed quantitative analysis of the recent AMS-02 electron and positron data. We investigate the interplay between the emission from primary astrophysical sources, namely Supernova Remnants and Pulsar Wind Nebulae, and the contribution from a dark matter annihilation or decay signal. Our aim is to assess the information that can be derived on dark matter properties when both dark matter and primary astrophysical sources are assumed to jointly contribute to the leptonic observables measured by the AMS-02 experiment. We investigate both the possibility to set robust constraints on the dark matter annihilation/decay rate and the possibility to look for dark matter signals within realistic models that take into account the full complexity of the astrophysical background. Our results show that AMS-02 data enable to probe efficiently vast regions of the dark matter parameter space and, in some cases, to set constraints on the dark matter annihilation/decay rate that are comparable or even stronger than the ones derived from other indirect detection channels.

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗