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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 55 records · Page 3
All sourcesNASA NTRSDOE OSTI
DOE OSTI2025

Surface orientation ambiguity for single molecules at dielectric interfaces

Fluorescent molecules emit light in a dipole radiation pattern that can be used to infer their orientation through defocused fluorescence microscopy. Proper measurement of the orientation requires mathematical modeling of the radiation pattern expected for a dipole in the geometry of interest and subsequent comparison against experimental data. We point out an ambiguity in common calculations of these patterns that appears to compromise orientation measurements for molecules that are especially near dielectric surfaces. This results in a rotation of the measured emission dipole toward the surface for near-interface molecules, which can be mistaken for a preferentially horizontal orientation among the emitters. The proper treatment for on-surface emitters requires consideration of finite-sized current elements between two dielectric media, and we show that the theoretical ambiguity can be lifted via finite-element modeling. A prescription is provided for correcting measured orientations at arbitrary interfaces.

Dey, E. [University of Texas, Arlington, TX (Unite↗
DOE OSTI2025

Paired Neural Network for Matching Experimental and Predicted Infrared Spectra

Here, we present a novel machine learning (ML)-based scoring technique for determining the similarity between experimental and predicted infrared (IR) spectra for identification purposes. IR spectroscopy is a powerful technique used to identify the molecular structure and composition of a sample by measuring the unique vibrational frequency pattern of the molecule’s functional groups. Molecular identifications are often made by comparing experimental and reference spectra. However, the limited number of reference spectra available in spectral libraries can confound the identification process. Alternative identification procedures rely on in silico techniques to simulate spectra for a wide range of molecules. However, scoring spectral similarity between an experimental query and computationally predicted reference remains a significant challenge. Our proposed ML-based scoring technique overcomes these barriers by accurately and efficiently determining spectral similarity.

Neural Network↗
DOE OSTI2026

Dynamic compression and spallation behavior of NbTaTiVZr high-entropy alloy

High-entropy alloys are a new class of materials with promising properties for aerospace and defense applications. The unique behavior of these materials is driven by the complex interactions between dissimilar atoms in a crystal and requires combined theoretical and experimental efforts to unlock their full potential. Here, we evaluate the relationship between the microstructure and the dynamic response of the equiatomic NbTaTiVZr alloy. Specifically, the shock Hugoniot, or equation of state, was measured up to a particle velocity of 0.5 mm μs −1 using gas gun plate impacts. Shock wave profile and incipient spallation experiments were used to characterize wave propagation and damage formation with post-mortem recovery experiments. Molecular dynamics simulations confirm the experimental findings and extend them up to 2.1 mm μs −1 particle velocity. Computational thermodynamic calculation of phase diagram simulations explain details of the material microstructure, which explains the measured strength and experimental damage patterns. Overall, this work provides detailed high-strain-rate characterization of a refractory high-entropy alloy, and more importantly, a framework and demonstration of the utility and necessity of a combined theoretical and experimental approach, outlining the importance of considering processing and manufacturing conditions when evaluating the performance of new materials.

36 MATERIALS SCIENCE↗
DOE OSTI2025

Accounting for electron-beam-induced warping of molecular nanocrystals in MicroED structure determination

High-energy electrons induce sample damage and motion at the nanoscale to fundamentally limit the determination of molecular structures by electron diffraction. Using a fast event-based electron counting (EBEC) detector, we characterize beam-induced, dynamic, molecular crystal lattice reorientations (BIRs). These changes are sufficiently large to bring reciprocal lattice points entirely in or out of intersection with the sphere of reflection, occur as early events in the decay of diffracted signal due to radiolytic damage, and coincide with beam-induced migrations of crystal bend contours within the same fluence regime and at the same illuminated location on a crystal. These effects are observed in crystals of biotin, a series of amino acid metal chelates, and a six-residue peptide, suggesting that incident electrons inevitably warp molecular lattices. The precise orientation changes experienced by a given microcrystal are unpredictable but are measurable by indexing individual diffraction patterns during beam-induced decay. Reorientations can often tilt a crystal lattice several degrees away from its initial position before irradiation, and for an especially beam-sensitive Zn(II)-methionine chelate, are associated with dramatic crystal quakes prior to 1 e − Å −2 electron beam fluence accumulates. Since BIR coincides with the early stages of beam-induced damage, it echoes the beam-induced motion observed in single-particle cryoEM. As with motion correction for cryoEM imaging experiments, accounting for BIR-induced errors during data processing could improve the accuracy of MicroED data.

Vlahakis, Niko (ORCID:0000000250920265)↗
DOE OSTI2026

Topography and functional traits shape the distribution of key shrub plant functional types in low-Arctic tundra

The expansion of shrubs in the Arctic tundra fundamentally modifies land-atmosphere interactions. However, it remains unclear how shrub distribution and expansion differ across key species due to challenges with discriminating tundra plant species at regional scales. Here, we combined multi-scale, multi-platform remote sensing and in situ trait measurements to elucidate the distribution patterns and primary controls of two representative deciduous-tall-shrub (DTS) genera, Alnus and Salix, in low-Arctic tundra. We show that topographic features were a key control on DTSs, creating heterogeneous, but predictable distributions of Alnus and Salix fractional cover (fCover). Alnus was more tolerant of elevation and slope and was found on hilly uplands (slope >10°) within a specific elevational band (200–400 m above sea level [MSL]). In contrast, Salix occurred at lower elevations (50–300 m MSL) on gentler slopes (3-10°) and required adequate soil moisture associated with its profligate water use. We also show that niche differentiation between Alnus and Salix changed with patch size, where larger patches were more specialized in resource requirements than individual plants of Alnus and Salix. To understand what constrains the growth of DTSs at locations with low fCover, we developed environmental limiting factor models, which showed that topography limits the upper bound of Alnus and Salix fCover in 69.2% and 48.7% of the landscape, respectively. These findings highlight a critical need to better understand and represent topography-controlled processes and functional traits in regulating shrub distribution, as well as a need for more detailed species classification to predict shrubification in the Arctic.

alder↗
DOE OSTI2025

Development of a wide bandwidth heterodyne dispersion interferometer for electron density measurement of atmospheric pressure plasmas

One of the challenges of electron density measurements of an atmospheric pressure plasma (APP) with a laser interferometer is the significant and unwanted phase shift caused by changes in the neutral gas density. These unwanted phase shifts can be mitigated and plasma density measured using an interferometer architecture called a dispersion interferometer (DI). A DI is composed of two nonlinear orientation patterned GaAs crystals for frequency doubling and measures the phase shift induced by a plasma in the interference signal between two second-harmonic beams. Measurement of plasma dynamics or a short plasma pulse in less than a millisecond is enabled with heterodyne detection in a DI using an acousto-optic cell with a frequency of 40 MHz. This heterodyne DI (HDI) is targeted to measure APPs in an electron density range of 10 20 –10 24 m −3 . Finally, the HDI achieves a line-integrated density resolution of 2 × 10 15 m −2 (a phase resolution of 0.005°) with a time constant of 1 μs using ensemble averaging techniques.

atmospheric pressure plasma↗
DOE OSTI2025

Exploration of a Combined LIBS and LA-ICP-MS Approach for Apatite Characterisation

A combined laser‐induced breakdown spectroscopy (LIBS) and laser ablation‐inductively coupled plasma‐mass spectrometry (LA‐ICP‐MS) method is demonstrated for comprehensive apatite analysis. These measurements provide elemental imaging that can be used as a screening technique for chemical selection of grains for subsequent analysis (e.g., U‐Pb geochronology) or can be used to understand elemental distributions within a single grain that would have direct textural‐chemical implications (e.g., zoning patterns). Adding LIBS as a simultaneous measurement, to LA‐ICP‐MS U‐Pb geochronology, allowed for the direct determination of F (H and O show promise for future applications) in addition to major and trace elements of interest. Here, the quantitative measurements were validated against a series of apatites with known values and used to characterise a wide range of samples. Fluorine detection limits were determined to be as low as 70 μg g ‐1 F (broadband CMOS detector) and 4.2 μg g ‐1 F (ICCD detector). U‐Pb age dating was simultaneously collected by LA‐ICP‐MS with the quantitative elemental data from LIBS, providing a comprehensive method for geochronology.

Apatite↗
DOE OSTI2025

Mesoscale Cellular Convection Detection and Classification Using Convolutional Neural Networks: Insights From Long-Term Observations at ARM Eastern North Atlantic Site

Marine boundary layer clouds are crucial in Earth's climate system. They frequently manifest as closed or open cell mesoscale cellular convection (MCC). MCC clouds are challenging to represent accurately in current climate models, highlighting the need for detailed observational data sets and in-depth analyses. This study utilizes over 8 years of observations from the U.S. Department of Energy (DOE) Atmospheric Radiation Measurement (ARM) User Facility Eastern North Atlantic (ENA) site at Graciosa Island, Azores, to investigate these clouds. We first apply a convolutional neural network with a U-Net architecture to classify open and closed cells, marking the first application of such an approach for automatically detecting MCC patterns from ground-based radar measurements. This method addresses some observational gaps in satellite data related to low temporal resolution, nighttime challenges, and limited vertical structure capture. The analysis of the MCC cases shows clear differences between closed and open MCCs: Closed MCC clouds are characterized by lower cloud tops and bases, shallower cloud geometrical depth, weaker horizontal wind speeds, stronger atmospheric stability, and a more homogeneous liquid water path than open MCCs. Finally, we demonstrate two potential applications of our radar-based MCC classifications: (a) facilitating the investigation of aerosol-cloud interactions and (b) exploring meteorological factors along with MCC's evolution by integrating satellite imagery and back-trajectory analysis. The identified MCC cases offer a valuable resource for the scientific community to study MCC processes further and improve climate model accuracy.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2025

STEM Ptychographic Holography of Electric and Magnetic Potentials

The development of fast an efficient direct electron imaging detectors have enabled the advancement of phase-imaging techniques in STEM such as iterative ptychography. As beneficial as these techniques are to imaging phase objects, the information recorded in the raw data is due to phase gradients across the probe, so it can be challenging to reconstruct slowly varying phase at lower spatial frequencies such as those induced by electric or magnetic potentials within the specimen. STEM holography [1,2] is an interferometric 4D-STEM technique where electrons in the beam are coherently divided into a superposition of two or more spatially separated probes which are then scanned over the specimen. For example, in a two-beam superposition, the two probes form overlapping bright field discs at the detector which then interfere (left of Fig. 1). Furthermore, if one probe passes through vacuum while the other transmits through the specimen, the resulting relative phase shift can be measured by recording shifts in the interference pattern. STEM holography is thus directly sensitive to the phase of the probe relative to the reference beam, and this phase can be measured regardless of the convergence angle of the probe, unlike single beam ptychography.

Biological Sciences↗
DOE OSTI2025

Entropy-Assisted Quality Pattern Identification in Finance

Short-term patterns in financial time series form the cornerstone of many algorithmic trading strategies, yet extracting these patterns reliably from noisy market data remains a formidable challenge. In this paper, we propose an entropy-assisted framework for identifying high-quality, non-overlapping patterns that exhibit consistent behavior over time. We ground our approach in the premise that historical patterns, when accurately clustered and pruned, can yield substantial predictive power for short-term price movements. To achieve this, we incorporate an entropy-based measure as a proxy for information gain: patterns that lead to high one-sided movements in historical data yet retain low local entropy are more “informative” in signaling future market direction. Compared to conventional clustering techniques such as K-means and Gaussian Mixture Models (GMMs), which often yield biased or unbalanced groupings, our approach emphasizes balance over a forced visual boundary, ensuring that quality patterns are not lost due to over-segmentation. By emphasizing both predictive purity (low local entropy) and historical profitability, our method achieves a balanced representation of Buy and Sell patterns, making it better suited for short-term algorithmic trading strategies. This paper offers an in-depth illustration of our entropy-assisted framework through two case studies on Gold vs. USD and GBPUSD. While these examples demonstrate the method’s potential for extracting high-quality patterns, they do not constitute an exhaustive survey of all possible asset classes.

Physics↗
DOE OSTI2025

Detection of Isotopes in Urban Source Search Low-Count Gamma Spectra Using Hopfield Neural Networks

Source search campaigns involve measurements of background gamma-ray spectra with a mobile detector-spectrometer traveling along arbitrarily chosen trajectories over a wide screening area. Radiation counts are typically measured with a tellurium-doped sodium iodide [NaI(Tl)] scintillator detector-spectrometer in short acquisition intervals, usually 1 s. The objective is to detect orphan isotopes with half-lives shorter than those of the isotopes in the natural background. In principle, radioisotopes can be identified by their unique gamma emission spectrum. However, detecting orphan isotopes in search data is challenging because low counts measured in short acquisition intervals result in incomplete spectral lines. In this study, we investigate the performance of a Hopfield neural network (HNN) that implements an auto-associative memory for the detection of isotopes of interest in an urban search campaign. The HNN is trained on one example of gamma spectra with well-resolved spectral lines of each isotope of interest. During testing, the auto-associative memory implementation of the HNN processes low-count gamma spectra with partially complete isotopic lines by matching incoming measurements to the closest one of its memory-stored patterns. The testing database consisted of almost 10 000 1-s gamma spectra, including measurements of orphan isotopes 137 Cs, 241 Am, and 131 I, obtained during two urban search surveys with a NaI(Tl) detector. The performance of the HNN detection algorithm was evaluated using precision, recall, and F1 scores, and benchmarked with a multiple linear regression (MLR) identification algorithm. In conclusion, the test results demonstrate that HNN outperforms MLR in the detection of all the isotopes of interest.

Auto associative memory↗
DOE OSTI2025

Simulation driven adaptive sampling for neutron-diffraction based strain mapping of additively manufactured parts

Neutron diffraction based strain mapping is a useful technique for measuring residual strains in additively manufactured (AM) metal parts. The measurement is traditionally done by scanning the sample in a point-wise raster pattern to extract the strain at each position. Since the overall scan can span several hours, adaptive sampling approaches using Bayesian optimization based on Gaussian process (BO-GP) regression have been introduced—demonstrating that even with a fraction of the typically made measurements the dominant strain patterns in the sample can be reconstructed. However, the parameters of the BO-GP algorithm have to be carefully chosen for best performance, and the movement time between arbitrary points can offset the time savings from a reduced number of measurement locations. In this paper, we propose algorithms to refine the BO-GP based methods by using simulations of strain patterns in AM parts based on the materials and the process used to print them. We demonstrate that the simulated strain patterns can be used to help choose better parameters for the BO-GP based framework—leading to low reconstruction error for the final strain pattern. Furthermore, we show that the strain mapping experiment can be initialized with a sampling pattern learnt from the simulation data and ordered to reduce movement time, dramatically enabling reduction in the overall time required to run the baseline BO-GP method.

Gaussian process regression↗
DOE OSTI2024

OpenPATH - Leveraging Technology to Measure Travel Behavior

Shifting transportation to more sustainable modes is a key piece of the decarbonization puzzle. However, mobility behavior and travel patterns are difficult to influence because they are difficult to measure. OpenPATH provides a tool to capture longitudinal behaviors through a smartphone application. Agencies interested in gathering data about a population's travel behavior can set up a deployment of the app customized to the needs of their community. Partners can choose between simple mode and purpose labels or surveys for each trip to balance the level of user engagement with the associated burden. The labels, trip surveys, and an initial demographic survey can all be tailored to the specific context of the deployment. The OpenPATH tool is unique in its open-source nature, ability to gather detailed longitudinal travel data, and design allowing direct engagement with travelers. A valuable technological advancement, this tool enables partners to measure the way changes in the transportation landscape impact their community. The suite of tools includes both public and administrator dashboards. The public dashboard supports continuous data analysis through charts presenting trip information updated daily. The administrator dashboard displays geospatial data and supports data export. Example applications have included e-bike programs; gathering valuable metrics on increased access to opportunities and reduction in VMT, and studies aimed at understanding existing mobility behavior to see where advancements such as electric vehicles could fit into these habits. OpenPATH collects travel data in association with an initial demographic survey, enabling detailed insight into the behavior patterns or impact of a certain program on different populations.

ADVANCED PROPULSION SYSTEMS↗
DOE OSTI2025

High resolution variability in wet deposition in the southeastern United States

Rainwater chemistry is determined by atmospheric pollutants and particles which vary spatially and temporally. Industrial and agricultural activities and meteorological events (e.g. sea breezes, severe weather, blowing dust) alter atmospheric particle and trace gas compositions. These gases and particles are scavenged by cloud and rain droplets that drive wet deposition. During an Intensive Operation Period (IOP) from April to October 2021, rainwater was collected at higher frequency intervals, usually daily, after precipitation events at three locations on the Savannah River Site (SRS). The farthest locations were separated by approximately 20 km. The mean concentration (μeq/L) of seven ions followed the Cl⁻ > SO 4 2− > Na⁺ > NO 3 ⁻ > K⁺ > Mg 2+ > Ca 2+ downward trend. Ion concentrations were compared to background ion concentrations from the National Atmospheric Deposition Program (NADP). The high frequency monthly averaged SRS data compared well with the monthly averaged NADP background but demonstrated extensive variability. In some months in 2021, the high frequency data compared better to the NADP site near the coast while in other months inland sites compared better. Strong spatial variability for ion concentrations was observed across SRS which was attributed to localized impacts in rainfall spatial variability. High frequency measurements allowed for comparison to regional weather patterns indicating influences from the Atlantic Ocean, Gulf of Mexico, and cities. This can account for spatial variability in the wet deposition flux. Sea breezes, Saharan dust, and anthropogenic sources were shown to impact wet deposition flux variability. Higher frequency precipitation chemistry sampling at numerous locations better captures ion concentration variability and improves measurement representativeness.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2026

Projective imaging of high-energy nuclei via coherent exclusive vector meson production in electron-nucleus collisions

One of the major goals of modern nuclear experiments is to study the distributions of gluons inside nuclei at high energy. A key measurement is the coherent exclusive vector meson (VM) production in diffractive electron-nucleus collisions, where the gluon spatial distribution inside the nucleus can be obtained through a Fourier transform of the squared nuclear momentum transfer (|t|) distribution. This research aims to overcome the two main obstacles of the |t| measurement: limited precision in measuring |t| arising from the momentum resolution of the outgoing electron and the overwhelming incoherent background. We demonstrate that by measuring the projected |t| distribution along the direction perpendicular to the electron scattering plane, the effect of the outgoing electron’s momentum resolution can be effectively mitigated, and the diffractive pattern is largely restored. Furthermore, we propose to measure the angular distribution of the VM’s decay daughters to statistically remove the incoherent background.

73 NUCLEAR PHYSICS AND RADIATION PHYSICS↗
DOE OSTI2024

Overview of the first Wendelstein 7-X long pulse campaign with fully water-cooled plasma facing components

After a long device enhancement phase, scientific operation resumed in 2022. The main new device components are the water cooling of all plasma facing components and the new water-cooled high heat flux divertor units. Water cooling allowed for the first long-pulse operation campaign. A maximum discharge length of 8 min was achieved with a total heating energy of 1.3 GJ. Safe divertor operation was demonstrated in attached and detached mode. Stable detachment is readily achieved in some magnetic configurations but requires impurity seeding in configurations with small magnetic pitch angle within the edge islands. Progress was made in the characterization of transport mechanisms across edge magnetic islands: Measurement of the potential distribution and flow pattern reveals that the islands are associated with a strong poloidal drift, which leads to rapid convection of energy and particles from the last closed flux surface into the scrape-off layer. Using the upgraded plasma heating systems, advanced heating scenarios were developed, which provide improved energy confinement comparable to the scenario, in which the record triple product for stellarators was achieved in the previous operation campaign. However, a magnetic configuration-dependent critical heating power limit of the electron cyclotron resonance heating was observed. Exceeding the respective power limit leads to a degradation of the confinement.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗
DOE OSTI2026

Measurement of the longitudinal flow-plane decorrelation using multiplane cumulants in $\sqrt{s_{NN}}$ = 200 GeV Au+Au, Ru+Ru, and Zr+Zr collisions

Measurements of the variation of anisotropic flow-plane angles (𝛹 𝑛 ) with rapidity, commonly known as the flow-plane decorrelation, provide important insights into the initial conditions of the matter produced in heavy-ion collisions. Here, in this paper, using data collected by the STAR experiment, we report the first measurement of the four-plane correlator observable 𝑇 𝑛 ⁡{𝑏⁢𝑎;𝑑⁢𝑐}=⟨⟨sin⁡[𝑛⁢(𝛹$^𝑏_𝑛$ − 𝛹$^𝑎_𝑛$)]⁢sin⁡[𝑛⁢(𝛹$^𝑑_𝑛$ − 𝛹$^𝑐_𝑛$)]⟩⟩, where superscripts 𝑎, 𝑏, 𝑐, and 𝑑 denote sequential pseudorapidity (𝜂) regions with 𝑎 corresponding to the most backward region, 𝑏 and 𝑐 close to mid-rapidity with 𝜂 𝑏 < 0 and 𝜂 𝑐 > 0, and 𝑑 being the most forward. The measurement is performed for the elliptic and triangular flow (i.e.~𝑛 = 2 and 3) in Au+Au and isobar (Ru+Ru, Zr+Zr) collisions at $\sqrt{s_{NN}}$=200~GeV. The goal of calculating the correlation of the flow-plane angle variations from backward to mid-central, and from mid-central to forward regions, is to probe the systematic variation of flow angle over a wide 𝜂 range. In mid-central collisions (10−30% centrality), we find 𝑇 2 ⁡{𝑏⁢𝑎;𝑑⁢𝑐} =−0.004 ±0.001⁢(s⁢t⁢a⁢t) ±0.002⁢(s⁢y⁢s⁢t) independent of the collision system. Such a small value of 𝑇 2 favors a random-walk'' variation of the flow-plane angles, where the rapidity correlation length is smaller than the entire region under study. These measurements provide new information on the decorrelation patterns in the system and offer a quantitative estimate of possible systematic variations in anisotropic flow angles such astwist’’ between forward and backward regions. This opens new opportunities for understanding the three-dimensional structure and the time evolution of the quark-gluon plasma created in heavy-ion collisions.

73 NUCLEAR PHYSICS AND RADIATION PHYSICS↗