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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 667 records · Page 37
All sourcesNASA NTRSDOE OSTI
DOE OSTI2022

Confined Growth of DNA-Assembled Superlattice Films

Here, we study the assembly of DNA-functionalized nanocubes under lateral confinement in microscale square trenches on a DNA-functionalized substrate. Microfocus small-angle X-ray scattering (SAXS) and scanning electron microscopy (SEM) are used to characterize the superlattices (SLs). The results indicate that nanocubes form simple-cubic SLs with square-prism morphology and a (100) out-of-plane orientation to maximize DNA bonding. In-plane, SLs align with the template, exposing their {100} side facets, and the degree of alignment depends on trench size. Interestingly, the distribution of in-plane orientations determined from SAXS and SEM do not agree, indicating that the internal and external structures of the SLs differ. To understand this discrepancy, X-ray ptychography is employed to image the internal structures of the SLs, revealing that SLs which appear to be single-crystalline in SEM may have subsurface grain boundaries, depending on trench size. SEM reveals that the SLs grow via nucleation and growth of randomly oriented domains, which then coalesce; this mechanism explains the observed dependence of alignment and defect structure on size. Interestingly, crystallization occurs via an unusual growth mode, whereby continuous SL layers grow on top of several misoriented islands. Overall, this work elucidates the effect of lateral confinement on the crystallization of DNA-functionalized nanoparticles and shows how X-ray ptychography can be used to gain insight into nanoparticle crystallization.

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

A comparison of machine learning methods to classify radioactive elements using prompt-gamma-ray neutron activation data

The detection of illicit radiological materials is critical to establishing a robust second line of defence in nuclear security. Neutron-capture prompt-gamma activation analysis (PGAA) can be used to detect multiple radioactive materials across the entire Periodic Table. However, long detection times and a high rate of false positives pose a significant hindrance in the deployment of PGAA-based systems to identify the presence of illicit substances in nuclear forensics. In the present work, six different machine-learning algorithms were developed to classify radioactive elements based on the PGAA energy spectra. The model performance was evaluated using standard classification metrics and trend curves with an emphasis on comparing the effectiveness of algorithms that are best suited for classifying imbalanced datasets. We analyse the classification performance based on Precision, Recall, F1-score, Specificity, Confusion matrix, ROC-AUC curves, and Geometric Mean Score (GMS) measures. The tree-based algorithms (Decision Trees, Random Forest and AdaBoost) have consistently outperformed Support Vector Machine and K-Nearest Neighbours. Based on the results presented, AdaBoost is the preferred classifier to analyse data containing PGAA spectral information due to the high recall and minimal false negatives reported in the minority class.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2023

Spectral theorems for generalized Weyl nodes with impurities in a magnetic field

Here, we prove a few spectral theorems for the density of states of a Weyl node with arbitrary topology. We show that the density of extended states of a Weyl node with random impurity potentials remains gapless in the presence of a magnetic field. Therefore, a magnetic field precludes Anderson localization in Weyl semimetals, when internode transitions are suppressed for smooth enough potentials. We also provide a rigorous quantum mechanical proof of the chiral magnetic effect for arbitrary topology of a Weyl node.

75 CONDENSED MATTER PHYSICS, SUPERCONDUCTIVITY AND↗
DOE OSTI2022

Global calculation of two-neutrino double- β decay within the finite amplitude method in nuclear density functional theory

Two-neutrino double-beta (2νββ) decay has been used to constrain the neutron-proton part of effective interactions, which in turn is used to compute the nuclear matrix elements for neutrinoless double-beta decay, the observation of which would have important consequences for fundamental physics. We carefully examine 2νββ matrix elements within the proton-neutron quasiparticle random-phase approximation with nuclear energy density functionals. Here we work with functionals that are fit globally to single-beta-decay half-lives and charge-exchange giant-resonance energies, but not to 2νββ half-lives themselves, to evaluate the 2νββ nuclear matrix elements for all important nuclei, including those whose half-lives have not yet been measured. Such a comprehensive evaluation in large model spaces without configuration truncation requires an efficient computational scheme; we employ a double contour integration within the finite amplitude method. The results generally reproduce the nuclear matrix element extracted from half-lives well, without the use of any of those half-lives in the fitting procedure. We present predictions of the matrix elements in a total of 27 nuclei with half-lives that are still unmeasured.

73 NUCLEAR PHYSICS AND RADIATION PHYSICS↗
DOE OSTI2025

Machine learning inversion from scattering for mechanically driven polymers

A machine learning inversion method is developed for analyzing scattering functions of mechanically driven polymers and extracting the corresponding feature parameters, which include energy parameters and conformation variables. The polymer is modeled as a chain of fixed-length bonds constrained by bending energy, and it is subject to external forces such as stretching and shear. We generate a data set consisting of random combinations of energy parameters, including bending modulus, stretching and shear force, along with Monte Carlo-calculated scattering functions and conformation variables such as end-to-end distance, radius of gyration and off-diagonal component of the gyration tensor. The effects of the energy parameters on the polymer are captured by the scattering function, and principal component analysis ensures the feasibility of the machine learning inversion. Finally, we train a Gaussian process regressor using part of the data set as a training set and validate the trained regressor for inversion using the rest of the data. The regressor successfully extracts the feature parameters.

Gaussian process regressors↗
DOE OSTI2021

Multi-Fidelity Active Subspaces for Wind Farm Uncertainty Quantification

Wind plants operate in stochastic environments characterized by complex turbulent flow dynamics and high-dimensional random variables. A key step in uncertainty quantification studies is sensitivity analysis and dimension reduction that can facilitate the development of surrogate models to be used for forward and inverse propagation or optimization under uncertainty. Prior work has shown active subspaces are an effective tool for identifying important directions in the space of stochastic inputs; however, they have only been applied to single-fidelity wind plant models. In this study, we investigate the efficacy of a multi-fidelity active subspace method for analyzing the uncertainty in wind plant power output. The multi-fidelity active subspace estimator offers the promise of increased accuracy in identifying active subspaces as compared to a single-fidelity estimator for the same computational cost, or a reduction in cost for the same accuracy. This makes the study of uncertainty in larger wind plants and with higher fidelity physics tractable. The multi-fidelity active subspace method is applied to gridded and existing wind plant layouts with single and multiple inflow conditions and its performance for surrogate modeling and uncertainty propagation is compared against a single-fidelity active subspace method. This multi-fidelity approach yields substantial computational speedups of 2x - 3.4x across the test cases along with acceptable accuracy in surrogate modeling and computing statistical moments.

active subspace↗
DOE OSTI2020

Method for conducting in situ high-temperature digital image correlation with simultaneous synchrotron measurements under thermomechanical conditions

This work presents a novel method of obtaining in situ strain measurements at high temperature by simultaneous digital image correlation (DIC), which provides the total strain on the specimen surface, and synchrotron x-ray diffraction (XRD), which provides lattice strains of crystalline materials. DIC at high temperature requires specialized techniques to overcome the effects of increased blackbody radiation that would otherwise overexpose the images. The technique presented herein is unique in that it can be used with a sample enclosed in an infrared heater, remotely and simultaneously with synchrotron XRD measurements. The heater included a window for camera access, and the light of the heater lamps is used as illumination. High-temperature paint is used to apply a random speckle pattern to the sample to allow the tracking of displacements and the calculation of the DIC strains. An inexpensive blue theatrical gel filter is used to block interfering visible and infrared light at high temperatures. This technique successfully produces properly exposed images at 870 °C and is expected to perform similarly at higher temperatures. The average strains measured by DIC were validated by an analytical calculation of the theoretical strain. Simultaneous DIC and XRD strain measurements of Inconel 718 (IN718) tensile test specimens were performed under thermal and mechanical loads and evaluated. Finally, this approach uses the fact that with DIC, the total strain is measured, including plastic strain, while with XRD, only elastic strain is captured. The observed differences were discussed with respect to the effective deformation mechanisms.

47 OTHER INSTRUMENTATION↗
DOE OSTI2021

Widespread co‐occurrence of Sebacinales and arbuscular mycorrhizal fungi in switchgrass roots and soils has limited dependence on soil carbon or nutrients

Societal Impact Statement This work addresses a novel group of Sebacinales mycorrhizal fungi being considered for development as inoculants in managed ecosystems because of their potential access to nutrients in soil organic matter. By comparing the diversity, distributions, and abundances of the Sebacinales with the more common arbuscular mycorrhizal fungi in switchgrass, a biofuel crop, we demonstrate that current suggestions for a Sebacinales revolution in agriculture should be tempered by their ecology. In particular, Sebacinales are rare compared to arbuscular mycorrhizal fungi, and are only weakly associated with soil carbon, suggesting that ideas about improved soil carbon cycling associated with Sebacinales need to be sufficiently studied across a range of environmental conditions prior to their consideration for broad‐spectrum soil inoculants. Summary Arbuscular mycorrhizal (AM) fungi are widespread and important root symbionts, but recent work suggests that Sebacinales fungi may play an equally important role in both plant success and ecosystem carbon and nutrient cycling based on their worldwide occurrence and putative access to organic matter. However, the ecological impacts of Sebacinales will depend on their abundance and distribution relative to AM fungi and environmental soil carbon and nutrient patterns, which remain unexplored. We characterized Glomeromycota and Sebacinales fungi in switchgrass ( Panicum virgatum L.) roots and soils across 14 sites with diverse soil conditions. We examined group richness differences, co‐occurrence patterns, and how the relative abundance of these fungi related to soil carbon, nutrient stoichiometry, and host size. Sebacinales were widespread, but less diverse, common, and abundant than Glomeromycota. Moreover, co‐occurrences were predominantly random, suggesting relatively few interactions between these groups. Sebacinales increased relative to Glomeromycota in soils with more carbon, but explanatory power was limited. Based on our findings, we suggest that Sebacinales are likely complementary to AM fungi in roots. Expectations that Sebacinales have large effects on soil carbon and nutrient cycling may need to be reconsidered, at least based on their limited abundances relative to AM fungi in switchgrass. This is an important consideration as Sebacinales are candidates for use as inoculants in managed ecosystems.

Lee, Marissa R.↗
DOE OSTI2020

Tailoring ductile-phase toughened tungsten hierarchical microstructures for plasma-facing materials

Biological materials, such as bones, nacre, etc., have been found to show attractive and unique combinations of stiffness, strength and fracture toughness. Research has been conducted in various engineering areas to mimic the naturally hierarchical microstructures or nanostructures of these materials to produce materials with optimum stiffness, high strength and fracture toughness for their intended structural applications. In the same objective, this work applies a multiscale microstructural approach recently developed to investigate the deformation and fracture behavior of ductile phase toughened tungsten (W) materials such as tungsten-nickel/iron (W-Ni/Fe) composites that possess lamellar-like and hierarchical “brick-and-mortar” (BAM) microstructures. First, the approach is used to simulate tensile loading of W-Ni/Fe specimens cut out from hot-rolled W-Ni/Fe plates which possess lamellar-like microstructures. The finite element model of the gage section of the specimen consists of a W-Ni/Fe dual-phase microstructural domain in which the constitutive behaviors of W and Ni/Fe phases are described by an elastic-plastic damage model. The predicted material stress-strain response and crack pattern development as a function of loading are then compared to the corresponding experimental results to determine the constitutive model parameters for W and Ni/Fe. Subsequently, the model parameters are used to analyze W-Ni/Fe BAM microstructures that are artificially created to investigate the effects of microstructural features and morphologies on the composite stress-strain response, damage, and fracture patterns. The modeling shows that regular BAM microstructures that experience bridging mechanism combined with crack penetration across W-phase regions exhibit significantly higher strengths than the rather random lamellar-like microstructures. More significantly, however adjusting the brick’s length-to-height ratio, the BAM microstructure can be designed to allow a more distributed damage zone which leads to increased strength, ductility and fracture energy.

36 MATERIALS SCIENCE↗
DOE OSTI2022

Entangled core/shell magnetic structure driven by surface magnetic symmetry-breaking in Cr 2 O 3 nanoparticles

Bulk Cr 2 O 3 is an antiferromagnetic (AFM) oxide that exhibits the magnetoelectric effect at room temperature, with neither spontaneous magnetization nor net electric polarization. These physical properties stem from a subtle competition between exchange and crystal field interactions. In this article, we exploit the symmetry breaking at the surface of Cr 2 O 3 nanoparticles for unbalancing this delicate physical equilibrium. The emerging weak ferromagnetic signal we observe persists up to near room temperature (≈ 270 K) at which the antiferromagnetic order disappears. In addition, an exchange-bias effect, that rapidly decreases on heating from low temperature up to 30 K, is resistant to thermal disorder above 200 K. In conclusion, our findings point to the possible formation of an entangled core/shell magnetic structure, where pinned uncompensated spins at the shell are randomly distributed in a low-temperature spin-glass ordering, with low net magnetic moment and an ordering temperature governed by the AFM Néel temperature.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2025

Fluctuations in Hill’s equation parameters and application to cosmic reheating

Cosmic inflation provides a compelling framework for explaining several observed features of our Universe, but its viability depends on an efficient reheating phase that converts the inflaton’s energy into Standard Model particles. This conversion often proceeds through nonperturbative mechanisms such as parametric resonance, which is described by Hill’s equation. In this work, we investigate how stochastic fluctuations in the parameters of Hill’s equation can influence particle production during reheating. We show that such fluctuations can arise from couplings to light scalar fields and can significantly alter the stability bands in the resonance structure, thereby enhancing the growth of fluctuations and broadening the region of efficient energy transfer. Using random matrix theory and stochastic differential equations, we decompose the particle growth rate into deterministic and noise-induced components and demonstrate analytically and numerically that even modest noise leads to substantial particle production in otherwise stable regimes. Furthermore, these results suggest that stochastic effects can robustly enhance the efficacy of reheating across a wide swath of parameter space, with implications for early Universe cosmology, UV completions involving multiple scalar fields, and the resolution of the cosmological moduli problem.

Cosmology↗
DOE OSTI2026

Perspective on “Active Brownian particles moving in a random Lorentz gas”

Self-propelled active matter can exhibit vastly different behavior than systems with purely Brownian motion. In Eur. Phys. J. E 40, 23 (2017), Zeitz, Wolf, and Stark compared an active matter particle with a Brownian particle moving in a random obstacle array. They showed that near the obstacle percolation density, both Brownian and active particles exhibit the same subdiffusive behavior, but the active particle reaches a steady state more rapidly. They also found that for high activity, the active particle has a lower effective diffusion than the Brownian particle due to the increased self-trapping effect generated by the activity. This result opens new directions for the study of active matter in disordered media, including bacteria in porous media, active colloids on quenched disorder, and active particles in crowded environments.

36 MATERIALS SCIENCE↗
DOE OSTI2022

Robustness of helical edge states under edge reconstruction

Abstract The helical edge states of time-reversal invariant two-dimensional topological insulators are protected against backscattering in idealized models. In more realistic scenarios with a shallow confining potential at the sample boundary, additional strongly interacting edge states may arise, that could interfere with the topological protection of edge conduction. We find that interaction effects within the reconstructed edges are well described by the Luttinger liquid model. While interactions between this Luttinger liquid and the helical edge states can in principle give rise to dynamical spin polarization and the breaking of time-reversal symmetry, we demonstrate that random spin-orbit coupling strongly suppresses such dynamical spin polarization, resulting in the persistence of near quantized edge conduction.

John, Niels↗
DOE OSTI2020

Characterization of Extreme Wave Conditions for Wave Energy Converter Design and Project Risk Assessment

Best practices and international standards for determining n-year return period extreme wave (sea states) conditions allow wave energy converter designers and project developers the option to apply simple univariate or more complex bivariate extreme value analysis methods. The present study compares extreme sea state estimates derived from univariate and bivariate methods and investigates the performance of spectral wave models for predicting extreme sea states at buoy locations within several regional wave climates along the US East and West Coasts. Two common third-generation spectral wave models are evaluated, a WAVEWATCH III® model with a grid resolution of 4 arc-minutes (6–7 km), and a Simulating WAves Nearshore model, with a coastal resolution of 200–300 m. Both models are used to generate multi-year hindcasts, from which extreme sea state statistics used for wave conditions characterization can be derived and compared to those based on in-situ observations at National Data Buoy Center stations. Comparison of results using different univariate and bivariate methods from the same data source indicates reasonable agreement on average. Discrepancies are predominantly random. Large discrepancies are common and increase with return period. There is a systematic underbias for extreme significant wave heights derived from model hindcasts compared to those derived from buoy measurements. This underbias is dependent on model spatial resolution. However, simple linear corrections can effectively compensate for this bias. A similar approach is not possible for correcting model-derived environmental contours, but other methods, e.g., machine learning, should be explored.

16 TIDAL AND WAVE POWER↗
DOE OSTI2022

A generalized 3D elastic model for nanoscale, self-assembled oxide-metal thin films with pillar-in-matrix configurations

In recent years, functional oxide-metal based vertically aligned nanocomposite (VAN) thin films have gained interest due to their intriguing physical properties and multifunctionalities stemming from the complex interactions between the two phases in the film and the substrate. In this work, we develop a model for studying the energetics of these thin film systems, including the effects of both lattice mismatch and capillary forces due to interface curvature. Each phase is incorporated into the model using a phase indicator function, and we introduce the capillary forces as body forces using a vector density representation of the interface. The model is implemented using the finite element method to study the deformation of the thin film which is composed of Au nanopillars embedded in a La 0.7 Sr 0.3 MnO 3 (LSMO) matrix on an SrTiO 3 (STO) substrate. Further, the results suggest that the total energy is lowest for random configurations of pillars compared to ordered square and hexagonal lattice configurations, consistent with the random distribution of pillars found in experiments. Furthermore, we find that the interfacial energy dominates the total energy of each configuration, suggesting that interfacial energy in the system is an important design parameter for nanocomposite growth, along with the lattice mismatch.

36 MATERIALS SCIENCE↗
DOE OSTI2021

Error Characteristics and Scale Dependence of Current Satellite Precipitation Estimates Products in Hydrological Modeling

Satellite precipitation estimates (SPEs) are promising alternatives to gauge observations for hydrological applications (e.g., streamflow simulation), especially in remote areas with sparse observation networks. However, the existing SPEs products are still biased due to imperfections in retrieval algorithms, data sources and post-processing, which makes the effective use of SPEs a challenge, especially at different spatial and temporal scales. In this study, we used a distributed hydrological model to evaluate the simulated discharge from eight quasi-global SPEs at different spatial scales and explored their potential scale effects of SPEs on a cascade of basins ranging from approximately 100 to 130,000 km 2 . The results indicate that, regardless of the difference in the accuracy of various SPEs, there is indeed a scale effect in their application in discharge simulation. Specifically, when the catchment area is larger than 20,000 km2, the overall performance of discharge simulation emerges an ascending trend with the increase of catchment area due to the river routing and spatial averaging. Whereas below 20,000 km 2 , the discharge simulation capability of the SPEs is more randomized and relies heavily on local precipitation accuracy. Our study also highlights the need to evaluate SPEs or other precipitation products (e.g., merge product or reanalysis data) not only at the limited station scale, but also at a finer scale depending on the practical application requirements. Here we have verified that the existing SPEs are scale-dependent in hydrological simulation, and they are not enough to be directly used in very fine scale distributed hydrological simulations (e.g., flash flood). More advanced retrieval algorithms, data sources and bias correction methods are needed to further improve the overall quality of SPEs.

DTVGM↗
DOE OSTI2023

Multi-scale investigation of short-range order and dislocation glide in MoNbTi and TaNbTi multi-principal element alloys

Refractory multi-principal element alloys (RMPEAs) are promising materials for high-temperature structural applications. Here, we investigate the role of short-range ordering (SRO) on dislocation glide in the MoNbTi and TaNbTi RMPEAs using a multi-scale modeling approach. Monte carlo/molecular dynamics simulations with a moment tensor potential show that MoNbTi exhibits a much greater degree of SRO than TaNbTi and the local composition has a direct effect on the unstable stacking fault energies (USFEs). From mesoscale phase-field dislocation dynamics simulations, we find that increasing SRO leads to higher mean USFEs and stress required for dislocation glide. The gliding dislocations experience significant hardening due to pinning and depinning caused by random compositional fluctuations, with higher SRO decreasing the degree of USFE dispersion and hence, amount of hardening. Finally, we show how the morphology of an expanding dislocation loop is affected by the applied stress.

36 MATERIALS SCIENCE↗
DOE OSTI2021

Trapping interlayer excitons in van der Waals heterostructures by potential arrays

Transition metal dichalcogenide heterostructures can host interlayer excitons (IXs), which consist of electrons and holes spatially separated in different layers. IXs possess permanent dipoles and have proven to offer a wealth of novel physics. Here we develop a discrete, random-walk model which includes annihilation and repulsion interactions among IXs. Using this model, we simulate the trapping of IXs in traps of different depths, densities, and shapes. Our results show that dipole-dipole interactions play an important role in regulating IX trapping. The effects of dipole interactions can be mitigated with small, deep traps which are realizable with atomic defects and moire potentials.

2-dimensional systems↗