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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 1,459 records · Page 81
All sourcesNASA NTRSDOE OSTI
DOE OSTI2015

Direct observations of field-induced assemblies in magnetite ferrofluids

Evolution of microstructures in magnetite-based ferrofluids with weak dipolar moments (particle size ≤ 10 nm) is studied with an emphasis on examining the effects of particle concentration (ϕ) and magnetic field strength (H) on the structures. Nanoparticles are dispersed in water at three different concentrations, ϕ = 0.15%, 0.48%, and 0.59% (w/v) [g/ml%] and exposed to uniform magnetic fields in the range of H = 0.05–0.42 T. Cryogenic transmission electron microscopy is employed to provide in-situ observations of the field-induced assemblies in such systems. As the magnetic field increases, the Brownian colloids are observed to form randomly distributed chains aligned in the field direction, followed by head-to-tail chain aggregation and then lateral aggregation of chains termed as zippering. By increasing the field in low concentration samples, the number of chains increases, though their length does not change dramatically. Increasing concentration increases the length of the linear particle assemblies in the presence of a fixed external magnetic field. Thickening of the chains due to zippering is observed at relatively high fields. Through a systematic variation of concentration and magnetic field strength, this study shows that both magnetic field strength and change in concentration can strongly influence formation of microstructures even in weak dipolar systems. Additionally, the results of two commonly used support films on electron microscopy grids, continuous carbon and holey carbon films, are compared. Holey carbon film allows us to create local regions of high concentrations that further assist the development of field-induced assemblies. The experimental observations provide a validation of the zippering effect and can be utilized in the development of models for thermophysical properties such as thermal conductivity.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗
DOE OSTI2015

Synthesis and characterization of visible-active molybdenum disulfide (2H-MoS{sub 2}) nanospheres

In current study, a novel 2H-MoS{sub 2} nanospheres were successfully synthesized and underwent structural- as well as optical-property characterizations. The MoS{sub 2} were prepared by one pot hydrothermal approach through adopting L-cysteine as environmentally-benignchalcogenide precursor. TEM image shows that the as-synthesized MoS{sub 2} appear to be spherical in shape with size distribution in the range of 120 nm – 180 nm. HRTEM lattice-fringes imaging further elucidate that the interlayer spacing at the edges is equal to be 0.62 nm that correspond to (002) plane stacking. Also, the HRTEM image clearly-illustrate that the internal microstructure of MoS{sub 2} composed of randomly-arrayed alternating layers, which render the postulation that the formation of nanosphere is driven by self-assembly of individual layers into globular morphology. XRD diffractogram that appear to be broad and unresolved reveal the partially crystalline nature of the sample. Optical-absorption spectra depicts the sample is visible active with featureless absorption, which can attribute to indirect transition of the excitions generated. By using Tauc plot, the bandgap energy is determined to be 1.75 eV, which reflect the nanospheres are indeed visible-active nanostructures.

77 NANOSCIENCE AND NANOTECHNOLOGY↗
DOE OSTI2020

The influence of packing structure and interparticle forces on ultrasound transmission in granular media

Ultrasound propagation through externally stressed, disordered granular materials was experimentally and numerically investigated. Experiments employed piezoelectric transducers to excite and detect longitudinal ultrasound waves of various frequencies traveling through randomly packed sapphire spheres subjected to uniaxial compression. The experiments featured in situ X-ray tomography and diffraction measurements of contact fabric, particle kinematics, average per-particle stress tensors, and interparticle forces. The experimentally measured packing configuration and inferred interparticle forces at different sample stresses were used to construct spring networks characterized by Hessian and damping matrices. The ultrasound responses of these network were simulated to investigate the origins of wave velocity, acoustic paths, dispersion, and attenuation. Results revealed that both packing structure and interparticle force heterogeneity played an important role in controlling wave velocity and dispersion, while packing structure alone quantitatively explained most of the observed wave attenuation. This research provides insight into time- and frequency-domain features of wave propagation in randomly packed granular materials, shedding light on the fundamental mechanisms controlling wave velocities, dispersion, and attenuation in such systems.

36 MATERIALS SCIENCE↗
DOE OSTI2025

Mapping wall-to-wall fractional cover of Arctic tundra plant functional types in Alaska using 20-m spatial resolution satellite imagery and harmonized plot observations

Estimates of fractional cover (fCover) across given land surfaces are used to assess, and often model, vegetation composition and diversity, which are crucial for understanding the health and functioning of terrestrial ecosystems. Remote sensing provides a useful means for scaling local, plot-measured fCover estimates to regional scales. Leveraging a recently synthesized and harmonized plot database, this study generated wall-to-wall maps of fCover for six Alaskan-Arctic plant functional types (PFT), including non-vascular plants, forbs, graminoids, and deciduous and evergreen shrubs, using 20-m satellite data (Sentinel-1, Sentinel-2, ArcticDEM) using a machine learning regression approach, specifically the random forest (RF) algorithm, which is well-suited for handling nonlinear relationships and high-dimensional satellite datasets. This study additionally addressed the spatio-temporal inconsistencies e.g., sampling scale, plot size, and collection year in plot measured fCover by adopting a multivariate outlier detection approach—Cook’s distance—to identify high-quality plots for model training and validation. Our approach achieves high accuracy (R 2 = 0.59–0.93, root mean squared errors = 0.02–0.10 for all PFTs) between plot-observed and satellite-derived fCover when using high-quality plot samples. The mapped fCover characterizes the spatial patterns of different PFTs across the tundra biome at a 20-m resolution, providing key information needed for improved representation of Arctic tundra vegetation in terrestrial biosphere models to better understand climate-vegetation feedback across the Arctic tundra.

Arctic tundra↗
DOE OSTI2021

A review of imputation strategies for isobaric labeling-based shotgun proteomics

The throughput efficiency and increased depth of coverage provided by isobaric-labeled proteomics measurements have led to increased usage of these techniques. However, the structure of missing data is uniquely different than unlabeled studies. In this review, we compare the efficacy of nine imputation methods on a CPTAC proteomics iTRAQ dataset. Imputation methods were evaluated with regard to accuracy, variability, statistical hypothesis test inference and run time over datasets consisting of varying number of iTRAQ plexes and percentages of missing data. In general, expectation maximization and random forest imputation methods yielded the best performances, and constant-based methods performed poorly consistently across all dataset sizes and percentages of missing values. For datasets with small sample sizes and higher percentages of missing data, results indicate that statistical inference with no imputation may be preferable. Based on the findings in this review, there are core imputation methods that perform higher for isobaric-labeled proteomics data, but great care and consideration as to whether imputation should be used should be given for datasets comprised of a small number of samples, as well as to factors such as computational time and reproducibility of imputation values.

Bramer, Lisa M.↗
DOE OSTI2019

Prediction of atomization energy using graph kernel and active learning

Data-driven prediction of molecular properties presents unique challenges to the design of machine learning methods concerning data structure/dimensionality, symmetry adaption, and confidence management. In this paper, we present a kernel-based pipeline that can learn and predict the atomization energy of molecules with high accuracy. The framework employs Gaussian process regression to perform predictions based on the similarity between molecules, which is computed using the marginalized graph kernel. To apply the marginalized graph kernel, a spatial adjacency rule is first employed to convert molecules into graphs whose vertices and edges are labeled by elements and interatomic distances, respectively. We then derive formulas for the efficient evaluation of the kernel. Specific functional components for the marginalized graph kernel are proposed, while the effects of the associated hyperparameters on accuracy and predictive confidence are examined. We show that the graph kernel is particularly suitable for predicting extensive properties because its convolutional structure coincides with that of the covariance formula between sums of random variables. Using an active learning procedure, we demonstrate that the proposed method can achieve a mean absolute error of 0.62 ± 0.01 kcal/mol using as few as 2000 training samples on the QM7 dataset.

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

Gut microbiome changes with micronutrient supplementation in children with attention–deficit/hyperactivity disorder: the MADDY study

Micronutrients have demonstrated promise in managing inattention and emotional dysregulation in children with attention-deficit/hyperactivity disorder (ADHD). The biological mechanism by which micronutrients improve these symptoms remains unclear. One plausible pathway is through the gut-brain axis, the bi-directional communication network that links the gastrointestinal tract with the brain. This study examines changes in gut microbiome composition and diversity after micronutrients supplementation in children with ADHD (N=44) and sheds light on potential mechanisms responsible for the response to micronutrients as measured by clinician-rated global impression. Participants from this investigation represent a sub-group of the Micronutrients for ADHD in Youth (MADDY) study, a double blind randomized controlled study in which participants received either micronutrients or a placebo for 8 weeks, followed by an 8-week open label extension with micronutrients for all participants. Stool samples collected at baseline, week 8, and week 16 were analyzed using 16S rRNA amplicon sequencing targeting the V4 hypervariable region. Pairwise compositional analyses served as the primary means for investigating changes in gut microbiome composition between micronutrients versus placebo groups and responders versus non-responders. A significant change in microbial evenness, as measured by alpha diversity, was observed following micronutrients, and the phylum Actinobacteriota decreased in the micronutrients group compared to placebo. Additionally, two bacterial families: Rikenellaceae and Oscillospiraceae, exhibited a significant increase in change of gut microbiome composition following micronutrients between responders and non-responders. These findings suggest that micronutrients modulated the composition of the gut microbiome and point towards specific bacterial changes associated with response to micronutrients.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2018

Magnetic excitations of the Cu 2 + quantum spin chain in Sr 3 CuPtO 6

Here, we report the magnetic excitation spectrum as measured by inelastic neutron scattering for a polycrystalline sample of Sr 3 CuPtO 6 . Modeling the data by the 2+4 spinon contributions to the dynamical susceptibility within the chains, and with interchain coupling treated in the random phase approximation, accounts for the major features of the powder-averaged structure factor. The magnetic excitations broaden considerably as temperature is raised, persisting up to above 100 K and displaying a broad transition as previously seen in the susceptibility data. No spin gap is observed in the dispersive spin excitations at low momentum transfer, which is consistent with the gapless spinon continuum expected from the coordinate Bethe ansatz. However, the temperature dependence of the excitation spectrum gives evidence of some very weak interchain coupling.

1-dimensional spin chains↗
DOE OSTI2024

Stochastic minibatch approach to the ptychographic iterative engine

The ptychographic iterative engine (PIE) is a widely used algorithm that enables phase retrieval at nanometer-scale resolution over a wide range of imaging experiment configurations. By analyzing diffraction intensities from multiple scanning locations where a probing wavefield interacts with a sample, the algorithm solves a difficult optimization problem with constraints derived from the experimental geometry as well as sample properties. The effectiveness at which this optimization problem is solved is highly dependent on the ordering in which we use the measured diffraction intensities in the algorithm, and random ordering is widely used due to the limited ability to escape from stagnation in poor-quality local solutions. In this study, we introduce an extension to the PIE algorithm that uses ideas popularized in recent machine learning training methods, in this case minibatch stochastic gradient descent. Our results demonstrate that these new techniques significantly improve the convergence properties of the PIE numerical optimization problem.

47 OTHER INSTRUMENTATION↗
NASA NTRS1972

Effect of polarity of electric current on friction behavior of two gallium-lubricated tantalum slipring assemblies

Computer-processed data from low-speed (10 rpm) slipring experiments with two similar (but of opposite polarity) gallium-lubricated tantalum slipring assemblies (hemisphere against disk) carrying 50 amperes dc in vacuum (10 to the minus 9th power torr) showed that the slipring assembly with the anodic hemisphere had significantly lower peak-to-peak values and standard deviations of coefficient-of-friction samples (a measure of smoothness of operation) than the slipring assembly with the cathodic hemisphere. Similar data from an experiment with the same slipring assemblies running currentless showed more random differences in the frictional behavior between the two assemblies.

Przybyszewski, J.↗
NASA NTRS1984

A 154-day periodicity in the occurrence of hard solar flares?

An analysis of the temporal distribution of 139 solar flares monitored by the Gamma Ray Spectrometer aboard the Solar Maximum Mission is reported. It is found that, instead of being randomly distributed in time, these events have a tendency to occur in groups with a mean spacing of about 154 days (75 nHz) over the observing interval. A larger sample of flares with an X-ray classification of M 2.5 or larger recorded by the GOES satellite showed a similar regularity.

Rieger, E.↗
NASA NTRS1992

Would a Galactic bar destroy the globular cluster system?

Five different dynamical Galaxy models are presented for the Galactic potential which satisfy the observed rotation curve but contain a central bar so that the 3-kpc nonintersecting streamlines have a radial velocity of 50 km/s when viewed at 45 deg to the bar axis. The effect of the central bars on the destruction rates of globular clusters in the Galaxy is investigated. The method of Aguilar et al. (1988) is applied to these barred Galaxy models. The unknown tangential velocity components of each observed cluster are drawn randomly from an assumed distribution function. The cluster's orbit is integrated, and the bulge shocking rate is calculated. The median destruction rate of the cluster is computed by sampling a large number of such orbits. The addition of the rotating bar does not strongly affect the destruction rates of globular clusters. There is a small increase in the destruction rate for those clusters within about 2.5 kpc. Thus it is not possible to rule out the existence of a rotating bar on these grounds.

Long, Kevin↗
NASA NTRS2003

Random vibration test of Mars Exploration Rover spacecraft

The primary objective of the random vibration test was to identify any hardware problems, which might compromise the mission. The test objectives, configuration, and requirements are briefly described in this presentation, and a representative sample of the measured data is presented.

vibration testing MER↗
NASA NTRS2014

Simulation of the Formation of DNA Double Strand Breaks and Chromosome Aberrations in Irradiated Cells

The formation of DNA double-strand breaks (DSBs) and chromosome aberrations is an important consequence of ionizing radiation. To simulate DNA double-strand breaks and the formation of chromosome aberrations, we have recently merged the codes RITRACKS (Relativistic Ion Tracks) and NASARTI (NASA Radiation Track Image). The program RITRACKS is a stochastic code developed to simulate detailed event-by-event radiation track structure: [1] This code is used to calculate the dose in voxels of 20 nm, in a volume containing simulated chromosomes, [2] The number of tracks in the volume is calculated for each simulation by sampling a Poisson distribution, with the distribution parameter obtained from the irradiation dose, ion type and energy. The program NASARTI generates the chromosomes present in a cell nucleus by random walks of 20 nm, corresponding to the size of the dose voxels, [3] The generated chromosomes are located within domains which may intertwine, and [4] Each segment of the random walks corresponds to approx. 2,000 DNA base pairs. NASARTI uses pre-calculated dose at each voxel to calculate the probability of DNA damage at each random walk segment. Using the location of double-strand breaks, possible rejoining between damaged segments is evaluated. This yields various types of chromosomes aberrations, including deletions, inversions, exchanges, etc. By performing the calculations using various types of radiations, it will be possible to obtain relative biological effectiveness (RBE) values for several types of chromosome aberrations.

Plante, Ianik↗
DOE OSTI2016

Development of Surface Grain Relief on Self-Ion Irradiated Copper

The development of surface grain relief has been observed on self-ion irradiated pure polycrystalline copper. However, the mechanistic origin of this feature has yet to be determined. Two possible mechanisms, anisotropic creep relaxation of void swelling induced stresses and crystallographic orientation-dependent sputtering rate differences, are being investigated as the cause of this evolution. This work focuses on characterizing the surface development and investigating the mechanism of orientation-dependent sputtering. Based on the conditions under which these specimens were irradiated, preferential sputtering is not likely to be the cause of the observed grain relief. Surface grain relief can greatly influence the ability to non-destructively detect irradiation-induced effects in polycrystalline metals. Determining the origin of grain relief development is necessary before this surface feature can be used an indicator of irradiation-induced effects in pure copper and other alloys. The development of surface grain relief has been observed on self-ion irradiated pure polycrystalline copper irradiated to 100 dpa. This surface feature was first noted by measuring samples using transient grating spectroscopy and later confirmed using scanning electron microscopy. Calculations of single-crystal sputtering rates show that the difference in sputtering yields between a randomly-oriented solid and a low-index plane is not sufficient to account for the observed height difference. SEM images appear to show a maximum step-height difference of around 100 nm and differential sputtering can only account for differences on the order of 10 nm at most. This determination indicates that anisotropic creep relaxation may be the mechanistic origin of the observed surface features. If this is the case, which will be confirmed by future work, then TG spectroscopy may be an effective tool for the determination of the onset of certain radiation-damage evolution. For example, if the onset of creep relaxation is strongly correlated to a specific level of void swelling and if the surface features present as a result of creep relaxation can be observed using TG spectroscopy, then accurate determinations of particular levels of void swelling may be made. (authors)

36 MATERIALS SCIENCE↗
DOE OSTI2025

Mapping Rare Earths and Toxics in E-Waste via Hyperspectral Imaging and Machine Learning

Electronic waste (e-waste) presents a mounting challenge to environmental sustainability due to its complex composition, which includes high-value rare earth elements, hazardous organic compounds, and non-recyclable plastics. Accurate and scalable material classification is essential for enabling efficient resource recovery and safe recycling practices. This study introduces a confidence-aware classification pipeline that combines mid-infrared hyperspectral imaging (HSI), spectral angle mapping (SAM), and iterative machine learning to perform pixel-level material identification across e-waste devices. A curated spectral library encompassing artificial materials (e.g., plastic iron oxide, galvanized metals), minerals (e.g., allanite, hematite), and organic compounds (e.g., benzanthracene, toluene) was used to generate pseudo-labels, each assigned a confidence score based on SAM-derived spectral similarity. High-confidence samples from seven consumer electronics—digital cameras, keyboards, laptop fans, modems, motherboards, TV remotes, and speakers—were iteratively expanded and classified using models such as Support Vector Machine (SVM), Random Forest, Gradient Boosting Classifier, Partial Least Squares Discriminant Analysis (PLSDA) and Logistic Regression. The best-performing classifiers achieved macro F1 scores approaching 1.0. Results revealed widespread plastic content (dominated by plastic iron oxide), the presence of rare earth-bearing minerals like cerium-containing allanite, and pervasive detection of hazardous organics such as benzanthracene. Principal Component Analysis (PCA) visualizations and confusion matrices confirmed high separability and robust classification performance. This methodology enables precise, non-destructive, and scalable classification of heterogeneous e-waste streams. It supports automated, hazard-aware sorting in recycling workflows, facilitating selective recovery of critical materials and compliance with circular economy goals. The confidence-aware framework provides a foundation for real-time deployment in industrial settings, offering significant implications for smart e-recycling infrastructure and policy-driven material stewardship.

Circular economy↗
DOE OSTI2019

Multi-scale structural analysis of proteins by deep semantic segmentation

Abstract Motivation Recent advances in computational methods have facilitated large-scale sampling of protein structures, leading to breakthroughs in protein structural prediction and enabling de novo protein design. Establishing methods to identify candidate structures that can lead to native folds or designable structures remains a challenge, since few existing metrics capture high-level structural features such as architectures, folds and conformity to conserved structural motifs. Convolutional Neural Networks (CNNs) have been successfully used in semantic segmentation—a subfield of image classification in which a class label is predicted for every pixel. Here, we apply semantic segmentation to protein structures as a novel strategy for fold identification and structure quality assessment. Results We train a CNN that assigns each residue in a multi-domain protein to one of 38 architecture classes designated by the CATH database. Our model achieves a high per-residue accuracy of 90.8% on the test set (95.0% average per-class accuracy; 87.8% average per-structure accuracy). We demonstrate that individual class probabilities can be used as a metric that indicates the degree to which a randomly generated structure assumes a specific fold, as well as a metric that highlights non-conformative regions of a protein belonging to a known class. These capabilities yield a powerful tool for guiding structural sampling for both structural prediction and design. Availability and implementation The trained classifier network, parser network, and entropy calculation scripts are available for download at https://git.io/fp6bd, with detailed usage instructions provided at the download page. A step-by-step tutorial for setup is provided at https://goo.gl/e8GB2S. All Rosetta commands, RosettaRemodel blueprints, and predictions for all datasets used in the study are available in the Supplementary Information. Supplementary information Supplementary data are available at Bioinformatics online.

Eguchi, Raphael R.↗
DOE OSTI2016

Mapping growth windows in quaternary perovskite oxide systems by hybrid molecular beam epitaxy

Requisite to growing stoichiometric perovskite thin films of the solid-solution A ' 1-x A x B O 3 by hybrid molecular beam epitaxy is understanding how the growth conditions interpolate between the end members A'B O 3 and AB O 3 , which can be grown in a self-regulated fashion, but under different conditions. Using the example of La 1-x Sr x VO 3 , the two-dimensional growth parameter space that is spanned by the flux of the metal-organic precursor vanadium oxytriisopropoxide and composition, x , was mapped out. The evolution of the adsorption-controlled growth window was obtained using a combination of X-ray diffraction, atomic force microscopy, reflection high-energy electron-diffraction (RHEED), and Rutherford backscattering spectroscopy. It is found that the stoichiometric growth conditions can be mapped out quickly with a single calibration sample using RHEED. Once stoichiometric conditions have been identified, the out-of-plane lattice parameter can be utilized to precisely determine the composition x . This strategy enables the identification of growth conditions that allow the deposition of stoichiometric perovskite oxide films with random A-site cation mixing, which is relevant to a large number of perovskite materials with interesting properties, e.g., high-temperature superconductivity and colossal magnetoresistance, that emerge in solid solution A ' 1-x A x B O 3 .

36 MATERIALS SCIENCE↗