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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 163 records · Page 9
All sourcesNASA NTRSDOE OSTI
DOE OSTI2020

Shock Hugoniot measurements of single-crystal 1,3,5-triamino-2,4,6-trinitrobenzene (TATB) compressed to 83 GPa

We present laser-driven shock Hugoniot measurements of single-crystal (SC) 1,3,5-triamino-2,4,6-trinitrobenzene (TATB) between 15 and 83 GPa, spanning pressures below and well above the Chapman–Jouguet pressure of ~28 GPa for TATB formulations (TATB grains mixed with plastic binders at 5–10 wt. %). The new SC data are generally ~3% more compressible than previously published data on neat and formulated TATB measured in gas-gun and explosive-driven experiments. An exception is at compressions in the density of ~1.5 (~30–40 GPa), where our new SC data exhibit significantly lower pressures than previous results on overdriven TATB formulations, suggesting that our SC samples remain largely unreacted below 35 GPa over the short nanosecond-time scales inherent to our laser-driven experiments. We report these novel equation-of-state measurements are a critical step toward understanding TATB in its most fundamental form and improving predictive modeling of TATB-based explosives.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2020

A Clustering-Based Scenario Generation Framework for Power Market Simulation with Wind Integration

A critical step in stochastic optimization models of power system analysis is to select a set of appropriate scenarios and significant numbers of scenario generation methods exist in the literature. This paper develops a clustering based scenario generation method, which aims to improve the performance of existing scenario generation techniques by grouping a set of correlated wind sites into clusters according to their cross-correlations. Copula based models are utilized to model spatiotemporal correlations and the Gibbs sampling is then used to generate scenarios for day-ahead markets. Our results show that the generated scenarios based on clustered wind sites outperform existing approaches in terms of reliability and sharpness and can reduce the total computational time for scenario generation and reduction significantly. The clustering-based framework can therefore provide a better support for real-world market simulations with high wind penetration.

data visualization↗
DOE OSTI2020

Characterizing TES Power Noise for Future Single Optical-Phonon and Infrared-Photon Detectors

In this letter, we present the performance of a 100 μm × 400 μm × 40 nm W Transition-Edge Sensor with a critical temperature of 40 mK. This device has a noise equivalent power of 1.5×10-18 W/Hz, in a bandwidth of 2.6 kHz, indicating a resolution for Dirac delta energy depositions of 40 ± 5 meV (rms). The performance demonstrated by this device is a critical step toward developing a O(100) meV threshold athermal phonon detector for low-mass dark matter searches.

46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND ↗
DOE OSTI2021

Designing high-power, octave spanning entangled photon sources for quantum spectroscopy

Entangled photon spectroscopy is a nascent field that has important implications for measurement and imaging across chemical, biology, and materials fields. Entangled photon spectroscopy potentially offers improved spatial and temporal-frequency resolutions, increased cross sections for multiphoton and nonlinear measurements, and new abilities in inducing or measuring quantum correlations. A critical step in enabling entangled photon spectroscopies is the creation of high-flux entangled sources that can use conventional detectors as well as provide redundancy for the losses in realistic samples. Here, we report a periodically poled, chirped, lithium tantalate platform that generates entangled photon pairs with ~10 -7 efficiency. For a near watt level diode laser, this results in a near μW-level flux. The single photon per mode limit that is necessary to maintain non-classical photon behavior is still satisfied by distributing this power over up to an octave-spanning bandwidth. The spectral–temporal photon correlations are observed via a Michelson-type interferometer that measures the broadband Hong–Ou–Mandel two-photon interference. A coherence time of 245 fs for a 10 nm bandwidth in the collinear case and a coherence time of 62 fs for a 125 nm bandwidth in the non-collinear case are measured using a CW pump laser and, essentially, collecting the full photon cone. We outline in detail the numerical methods used for designing and tailoring the entangled photons source, such as changing center wavelength or bandwidth, with the ultimate aim of increasing the availability of high-flux UV–Vis entangled photon sources in the optical spectroscopy community.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2024

The physical chemistry of solar fuels catalysis

The demonstration of effective light-driven electrochemical water splitting at the surface of a TiO 2 electrode in the seminal 1972 Honda–Fujishima publication spurred global research efforts to understand this effect and design advanced systems for solar H 2 generation from water as a carbon-free and sustainable fuel. In the 50+ years since that discovery, research investigating approaches toward the catalytic generation of the so-called “solar fuels,” i.e., not only H 2 from water but also CO 2 reduction products and NH 3 from N 2 , has expanded dramatically in scope and scale. The development of new experimental and computational methods and construction and commissioning of large-scale facilities have paved the way for important insights into the machinery of natural photosynthesis, leading to a greater understanding of the complexity of light-to-chemical energy conversion with an exquisite temporal and spatial resolution. Finally, these studies have provided the foundation for biologically inspired molecular and hybrid designs with the goal to promote the photochemical steps critical to solar fuels generation while avoiding unproductive or unnecessary pathways.

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

Prediction of vacancy defect diffusion paths in high entropy alloys via machine learning on molecular dynamics data

Identifying the diffusion path of point defects is a critical step in understanding their evolution and the mechanisms of related phenomena. Defect diffusion occurs at small length and time scales, with impacts on material properties that may continue to evolve over ns to μs, ms, and the continuum scale (s, min, etc., and cm, m, etc.). The time scale accessible to molecular dynamics (MD) simulations is limited by small step sizes, typically in the fs range. Thus, surrogate models of MD simulations through machine learning (ML)-based algorithms are of great interest, especially for complex systems such as high entropy alloys (HEAs). In this work, dynamics governing vacancy migration in HEA were approximated with graph convolutional network (GCN) models as ansatzes for kinetic Monte Carlo (KMC) rate catalogs. Network design considered that diffusion in crystalline solids generally depends on interactions between defects and their immediate neighbor atoms. Graphs represented the vacancy surroundings, MD-generated trajectories provided training and comparison datasets, and unsupervised GCN models approximated interatomic dynamics governing vacancy migration in HEAs as ansatzes for KMC. A proof-of-concept model trained on MD data for the Fe, Ni, Cr, Co, and Cu HEA environment was used with two different neighbor interactions to assess the feasibility of training a GCN to predict vacancy defect transition rates in the HEA environment. The resulting setup rapidly generated MD-formatted synthetic trajectories based on dynamics learned from the MD training set, with a time acceleration of roughly two orders of magnitude and a similar diffusion coefficient to MD observations. Additionally, Nudged Elastic Band (NEB) calculations were performed on randomly generated FeNiCrCoCu HEA structures to determine vacancy migration barriers across nearest-neighbor sites. Transition probabilities for each jump, categorized by atomic type, were extracted from these calculations. NEB-based and GCN-based approaches led to similar outcomes.

Reimer, C↗
DOE OSTI2026

Analog-to-digital converter based on voltage-controlled superconducting devices

The increasing demand for cryogenic electronics in superconducting and quantum computing systems calls for ultra-energy-efficient data conversion architectures that remain functional at deep cryogenic temperatures. Here, in this work, we present the first design of a voltage-controlled superconducting flash analog-to-digital converter (ADC) based on a voltage-controlled quantum-enhanced Josephson junction field-effect transistor (JJFET). Exploiting its strong gate tunability and transistor-like behavior, the JJFET offers a scalable alternative to conventional current-controlled superconducting devices while aligning naturally with CMOS-style design methodologies. Building on our previously developed Verilog-A compact model calibrated to experimental data, we design and simulate a three-bit JJFET-based flash ADC targeted for integration within cryogenic control and readout circuitry in quantum computing. The core comparator block is realized through careful bias current selection and augmented with a three-terminal nanocryotron to precisely define reference voltages. Cascaded JJFET comparators ensure robust voltage gain, cascadability, and logic-level restoration across stages. Simulation results demonstrate accurate quantization behavior with ultra-low power dissipation, underscoring the feasibility of voltage-driven superconducting mixed-signal circuits. This work establishes a critical step toward unifying superconducting logic and data conversion, paving the way for scalable cryogenic architectures in quantum–classical co-processors, low-power artificial intelligence accelerators, and next-generation energy-constrained computing platforms.

Analog-to-digital converter↗
DOE OSTI2020

Observing 3-hydroxyanthranilate-3,4-dioxygenase in action through a crystalline lens

The synthesis of quinolinic acid from tryptophan is a critical step in the de novo biosynthesis of nicotinamide adenine dinucleotide (NAD + ) in mammals. Herein, the nonheme iron-based 3-hydroxyanthranilate-3,4-dioxygenase responsible for quinolinic acid production was studied by performing time-resolved in crystallo reactions monitored by UV-vis microspectroscopy, electron paramagnetic resonance (EPR) spectroscopy, and X-ray crystallography. Seven catalytic intermediates were kinetically and structurally resolved in the crystalline state, and each accompanies protein conformational changes at the active site. Among them, a monooxygenated, seven-membered lactone intermediate as a monodentate ligand of the iron center at 1.59-Å resolution was captured, which presumably corresponds to a substrate-based radical species observed by EPR using a slurry of small-sized single crystals. Other structural snapshots determined at around 2.0-Å resolution include monodentate and subsequently bidentate coordinated substrate, superoxo, alkylperoxo, and two metal-bound enol tautomers of the unstable dioxygenase product. These results reveal a detailed stepwise O-atom transfer dioxygenase mechanism along with potential isomerization activity that fine-tunes product profiling and affects the production of quinolinic acid at a junction of the metabolic pathway.

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

Scientific machine learning for modeling and simulating complex fluids

The formulation of rheological constitutive equations—models that relate internal stresses and deformations in complex fluids—is a critical step in the engineering of systems involving soft materials. While data-driven models provide accessible alternatives to expensive first-principles models and less accurate empirical models in many engineering disciplines, the development of similar models for complex fluids has lagged. The diversity of techniques for characterizing non-Newtonian fluid dynamics creates a challenge for classical machine learning approaches, which require uniformly structured training data. Consequently, early machine-learning based constitutive equations have not been portable between different deformation protocols or mechanical observables. Here, we present a data-driven framework that resolves such issues, allowing rheologists to construct learnable models that incorporate essential physical information, while remaining agnostic to details regarding particular experimental protocols or flow kinematics. These scientific machine learning models incorporate a universal approximator within a materially objective tensorial constitutive framework. By construction, these models respect physical constraints, such as frame-invariance and tensor symmetry, required by continuum mechanics. We demonstrate that this framework facilitates the rapid discovery of accurate constitutive equations from limited data and that the learned models may be used to describe more kinematically complex flows. This inherent flexibility admits the application of these “digital fluid twins” to a range of material systems and engineering problems. We illustrate this flexibility by deploying a trained model within a multidimensional computational fluid dynamics simulation—a task that is not achievable using any previously developed data-driven rheological equation of state.

Science & Technology - Other Topics↗
DOE OSTI2022

Advanced Autonomous Welding for Refabrication and Follow-On Testing of Previously Irradiated Nuclear Fuel

The performance of follow-on experiments using irradiated nuclear fuel at any point in its lifecycle is a critical step in understanding phenomena and behavior. Transient experiments with high-burnup fuel can deepen the understanding of fuel fragmentation, relocations, and dispersal under loss-of-coolant accidents. An advanced autonomous welding process to refabricate commercial fuel rods inside a hot cell was created and tested to enable flexible experiment approaches on fuels irradiated in commercial and test reactors. Irradiated light water reactor fuel test pins from experiments performed at the Advanced Test Reactor (ATR) at Idaho National Laboratory were used to demonstrate the refabrication process. The welding process was found to be sensitive to welding parameters but flexible such that multiple passes could be performed on the same location until a hermetic weld was obtained. The refabrication of rodlets and successful welds was also found to be sensitive to the preparation of the irradiated cladding and endcaps. Thorough defueling of the fuel at the weld location and proper sizing of the endcaps and backing material mitigated these issues. The use of strategically located heat sinks in contact with the cladding and endcap materials also increased welding and refabrication success. For this work, the test pins were sectioned to remove the original endcaps and fuel was removed from both ends of each rodlet. The reassembly of the rodlets was then completed in four steps, which included the press fitting of new endcaps, the circumferential welding of rodlet endcaps to the cladding, rodlet pressurization in a pressure chamber, and seal welding the rodlet under pressure. The integrity of the refabricated rodlets was then verified via helium leak checking inside a vacuum chamber. The advanced welding system is capable of refabricating rodlets up to 380 mm in length, and repressurizing them up to 15 500 kPa. The refabricated lengths of the rodlets used in this work ranged from 149 to 165 mm and the refabricated fuel stack heights ranged from 70.4 to 79.8 mm. Here, the rodlets were pressurized with argon to an average pressure of 3617 kPa, and the average leak rate after refabrication was 6.7 • 10 -8 cm 3 • s -1 .

11 NUCLEAR FUEL CYCLE AND FUEL MATERIALS↗
DOE OSTI2026

Hanford Tank Waste Matrix Impact on Ion Exchange Performance Using Crystalline Silicotitanate

The removal of radiocesium from Hanford tank waste supernate is a critical step in preparing feed for low-activity waste immobilization. This study evaluated cesium ion exchange performance using crystalline silicotitanate (CST) media in a series of tests designed to evaluate the influence of waste matrix variability on capacity and kinetics. Tank waste supernate subsampled from five Hanford double-shell tanks encompassed a range of sodium, hydroxide, nitrate, and nitrite concentrations in order to assess the impact of feed variability on the performance of the ion exchange system. Both equilibrium and dynamic ion exchange tests were conducted to quantify cesium distribution coefficients and breakthrough behavior under prototypic operating conditions. Results indicated that effective cesium capacity varied by up to a factor of five across the matrices tested, with higher sodium concentrations significantly reducing uptake. Kinetic behavior was similarly matrix-dependent, with solution viscosity contributing to a twofold variation in mass-transfer rates. These results demonstrate the strong dependence of CST ion exchange performance on waste composition and must be incorporated into predictive models for future treatment system design and optimization.

Westesen, Amy M.↗
DOE OSTI2020

High precision magnetic field measurement and mapping of the LEReC 180 degree bending magnet using very low field NMR with hall combined probe (140 - 350 gauss)

The Relativistic Heavy Ion Collider (RHIC) at BNL are using the Low Energy RHIC Electron Cooling (LEReC) to conduct experiments that search for the quantum chromodynamic (QCD) critical point. The first ever electron cooling based on the RF acceleration of electron beams was experimentally demonstrated on April 5, 2019 at LEReC at BNL. The first critical step in obtaining successful 3D non-magnetized cooling of the Au ion bunches in the RHIC cooling section was matching the electron beam energy with a relative error less than 5*10 -4 to the ion beam energy. Part of the LEReC beamline is a dipole magnet that bends the electron beam 180 degree. One of the most outstanding measurement challenges is that the dipole field is so low (≈200 G). Most of the existing NMR probes can only measure fields >400 G. Lower signal-to-noise ratio at low fields is requires the use of larger sample volumes. Working with CAYLAR, a NMR probe has been redesigned and optimized for these low field measurements with high resolution. We report the methods, challenges, and results for extensive magnetic field mappings of the 180 dipole magnet. A combination of NMR and Hall sensors has been successfully implemented to measure uniform field regimes inside the magnet center area and non-uniform field regimes at the magnet ends. Detailed measurement and mapping have been performed at five radii and five heights along the beam trajectory. Meanwhile a finite element magnetic modeling simulation of the magnet using Opera software has been performed. The calculated and measured data are compared, and the calculated data is good reference for measured data over long length mapping from magnet edge to center. The measured magnetic measurement data is directly useful for beam instrumentation, diagnostics and operation.

Magnetic Field Measurement↗
DOE OSTI2021

Augmented signal processing in Liquid Argon Time Projection Chambers with a deep neural network

The Liquid Argon Time Projection Chamber (LArTPC) is an advanced neutrino detector technology widely used in recent and upcoming accelerator neutrino experiments. It features a low energy threshold and high spatial resolution that allow for comprehensive reconstruction of event topologies. In current-generation LArTPCs, the recorded data consist of digitized waveforms on wires produced by induced signal on wires of drifting ionization electrons, which can also be viewed as two-dimensional (2D) (time versus wire) projection images of charged-particle trajectories. For such an imaging detector, one critical step is the signal processing that reconstructs the original charge projections from the recorded 2D images. For the first time, we introduce a deep neural network in LArTPC signal processing to improve the signal region of interest detection. By combining domain knowledge (e.g., matching information from multiple wire planes) and deep learning, this method shows significant improvements over traditional methods. This work details the method, software tools, and performance evaluated with realistic detector simulations.

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗
DOE OSTI2021

Picosecond Timing Resolution Measurements of Low Gain Avalanche Detectors with a 120 GeV Proton Beam for the TOPSiDE Detector Concept

This paper presents results that take a critical step toward proving 10 ps timing resolution’s feasibility for particle identification in the TOPSiDE detector concept for the Electron-Ion Collider. Measurements of LGADs with a thickness of 35 μm and 50 μm are evaluated with a 120 GeV proton beam. The performance of the gain and timing response is assessed, including the dependence on the reverse bias voltage and operating temperature. The best timing resolution of UFSDs in a test beam to date is achieved using three combined planes of 35 μm thick LGADs at -30°C with a precision of 14.3 ± 1.5 ps.

Particle tracking detectors↗
DOE OSTI2026

First light from beam neutrinos on an LAPPD in ANNIE

The Accelerator Neutrino Neutron Interaction Experiment (ANNIE) is both a physics experiment and a technology testbed for next-generation light-based neutrino detection. In this paper, we report the first demonstration of a fully integrated Large Area Picosecond Photodetector (LAPPD) operating in a running neutrino beam experiment. Particular focus is given to the design, commissioning, and successful deployment of the Packaged ANNIE LAPPD (PAL), a waterproof, self-triggering module incorporating fast waveform digitization and precision timing synchronized to the ANNIE detector subsystems. We identify beam-correlated LAPPD data frames consistent with charged-current neutrino interactions observed in multiple detector subsystems, establishing the first detection of neutrino-induced Cherenkov light with an LAPPD. These results validate the system-level performance of LAPPDs under realistic experimental conditions — including long-term stability, timing synchronization, and event matching with conventional PMT and muon detector systems — marking a critical step toward their deployment in future large-scale neutrino and particle detectors.

Cherenkov detectors↗
DOE OSTI2023

Assessing variation in faecal glucocorticoid concentrations in gray whales exposed to anthropogenic stressors

Understanding how individual animals respond to stressors behaviourally and physiologically is a critical step towards quantifying long-term population consequences and informing management efforts. Glucocorticoid (GC) metabolite accumulation in various matrices provides an integrated measure of adrenal activation in baleen whales and could thus be used to investigate physiological changes following exposure to stressors. In this study, we measured GC concentrations in faecal samples of Pacific Coast Feeding Group (PCFG) gray whales (Eschrichtius robustus) collected over seven consecutive years to assess the association between GC content and metrics of exposure to sound levels and vessel traffic at different temporal scales, while controlling for contextual variables such as sex, reproductive status, age, body condition, year, time of year and location. We develop a Bayesian Generalized Additive Modelling approach that accommodates the many complexities of these data, including non-linear variation in hormone concentrations, missing covariate values, repeated samples, sampling variability and some hormone concentrations below the limit of detection. Estimated relationships showed large variability, but emerging patterns indicate a strong context-dependency of physiological variation, depending on sex, body condition and proximity to a port. Our results highlight the need to control for baseline hormone variation related to context, which otherwise can obscure the functional relationship between faecal GCs and stressor exposure. Therefore, extensive data collection to determine sources of baseline variation in well-studied populations, such as PCFG gray whales, could shed light on cetacean stress physiology and be used to extend applicability to less-well-studied taxa. GC analyses may offer greatest utility when employed as part of a suite of markers that, in aggregate, provide a multivariate measure of physiological status, better informing estimates of individuals’ health and ultimately the consequences of anthropogenic stressors on populations.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2025

Evaluating algorithmic bias on biomarker classification of breast cancer pathology reports

Objectives: This work evaluated algorithmic bias in biomarkers classification using electronic pathology reports from female breast cancer cases. Bias was assessed across 5 subgroups: cancer registry, race, Hispanic ethnicity, age at diagnosis, and socioeconomic status. Materials and Methods: We utilized 594 875 electronic pathology reports from 178 121 tumors diagnosed in Kentucky, Louisiana, New Jersey, New Mexico, Seattle, and Utah to train 2 deep-learning algorithms to classify breast cancer patients using their biomarkers test results. We used balanced error rate (BER), demographic parity (DP), equalized odds (EOD), and equal opportunity (EOP) to assess bias. Results: We found differences in predictive accuracy between registries, with the highest accuracy in the registry that contributed the most data (Seattle Registry, BER ratios for all registries >1.25). BER showed no significant algorithmic bias in extracting biomarkers (estrogen receptor, progesterone receptor, human epidermal growth factor receptor 2) for race, Hispanic ethnicity, age at diagnosis, or socioeconomic subgroups (BER ratio <1.25). DP, EOD, and EOP all showed insignificant results. Discussion: We observed significant differences in BER by registry, but no significant bias using the DP, EOD, and EOP metrics for socio-demographic or racial categories. This highlights the importance of employing a diverse set of metrics for a comprehensive evaluation of model fairness. Conclusion: A thorough evaluation of algorithmic biases that may affect equality in clinical care is a critical step before deploying algorithms in the real world. We found little evidence of algorithmic bias in our biomarker classification tool. Artificial intelligence tools to expedite information extraction from clinical records could accelerate clinical trial matching and improve care.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2022

Optimization of sporulation and purification methods for sporicidal efficacy assessment on Bacillus spores

Abstract Validating the efficacy of sporicidal agents is a critical step in current good manufacturing practices for disinfection requirements. A limitation is that the poor quality of spores can lead to false positive sporicidal results. The aim of this study was to explore optimal sporulation and purification methods in Bacillus spores. Spores of 7 Bacillus strains were produced in 5 different sporulation media. After density centrifugation, spore yields were measured by phase-contrast microscopy and enumeration assays. Effects of purification methods including heat, sonication and lysozyme, and maturation on spore qualities were determined by sodium hypochlorite sporicidal assay. Difco sporulation media was identified as the preferred sporulation medium for 4 out of 7 tested Bacillus strains. Sporulation rates in B. cereus, B. sphaericus, and B. thuringiensis were higher at 30°C than the rates at 37°C at a difference of 5%, 65%, and 20%, respectively. Bacillus licheniformis favored Mn2+-amended 10% Columbia Broth at 37°C for sporulation with 40–72% higher sporulation rates than other media. The maximum sporulation rates of B. cereus and B. thuringiensis were observed on double-strength Schaeffer's-glucose broth. All studied purification methods improved the spore purity with strain variations. However, intense heat (80°C for 20 min) and lysozyme (100 μg/mL) treatment impaired the spore quality of specific Bacillus strains by sensitizing them against sodium hypochlorite. The length of the maturation period had an impact on the spore resistance, and the most optimal maturation periods ranged from 7 to 21 days in Bacillus strains. The results of this study will pave the way for further evaluation of the sporicidal activity of disinfectants.

Biotechnology & Applied Microbiology↗