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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 145 records · Page 8
All sourcesNASA NTRSDOE OSTI
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↗
DOE OSTI2022

Inferring the shape of data: a probabilistic framework for analysing experiments in the natural sciences

A critical step in data analysis for many different types of experiments is the identification of features with theoretically defined shapes in N -dimensional datasets; examples of this process include finding peaks in multi-dimensional molecular spectra or emitters in fluorescence microscopy images. Identifying such features involves determining if the overall shape of the data is consistent with an expected shape; however, it is generally unclear how to quantitatively make this determination. In practice, many analysis methods employ subjective, heuristic approaches, which complicates the validation of any ensuing results—especially as the amount and dimensionality of the data increase. Here, we present a probabilistic solution to this problem by using Bayes’ rule to calculate the probability that the data have any one of several potential shapes. This probabilistic approach may be used to objectively compare how well different theories describe a dataset, identify changes between datasets and detect features within data using a corollary method called Bayesian Inference-based Template Search; several proof-of-principle examples are provided. Altogether, this mathematical framework serves as an automated ‘engine’ capable of computationally executing analysis decisions currently made by visual inspection across the sciences.

Science & Technology - Other Topics↗
DOE OSTI2024

Congruity of genomic and epidemiological data in modelling of local cholera outbreaks

Cholera continues to be a global health threat. Understanding how cholera spreads between locations is fundamental to the rational, evidence-based design of intervention and control efforts. Traditionally, cholera transmission models have used cholera case-count data. More recently, whole-genome sequence data have qualitatively described cholera transmission. Integrating these data streams may provide much more accurate models of cholera spread; however, no systematic analyses have been performed so far to compare traditional case-count models to the phylodynamic models from genomic data for cholera transmission. Here, we use high-fidelity case-count and whole-genome sequencing data from the 1991 to 1998 cholera epidemic in Argentina to directly compare the epidemiological model parameters estimated from these two data sources. We find that phylodynamic methods applied to cholera genomics data provide comparable estimates that are in line with established methods. Our methodology represents a critical step in building a framework for integrating case-count and genomic data sources for cholera epidemiology and other bacterial pathogens.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2025

Measuring the growth of Alfvén wave parametric decay instability using counter-propagating waves: Theory and simulations

The parametric decay instability (PDI) of Alfvén waves—where a pump Alfvén wave decays into a backward-propagating child Alfvén wave and a forward ion acoustic wave—is a fundamental nonlinear wave-wave interaction and holds significant implications for space and laboratory plasmas. However, to date there has been no direct experimental measurement of PDI. Here, we propose a novel and experimentally viable scheme to quantify the growth of Alfvén wave PDI on a linear device using a large pump Alfvén wave and a small counter-propagating seed Alfvén wave, with the seed-wave frequency tuned to match the backward Alfvén wave generated by standard PDI. Using hybrid simulations, we show that energy transfer from the pump to the seed reduces the latter's spatial damping. By comparing seed-wave amplitudes with and without the pump wave, this damping reduction can be used as a direct and reliable proxy for PDI growth. The method is validated in our simulations across a range of plasma and wave parameters and agrees well with theoretical predictions. Notably, the scheme exhibits no threshold for PDI excitation and is, in principle, readily implementable under current laboratory conditions. Finally, this scheme is a critical step toward solving the challenge of experimentally accessing Alfvén wave PDI and provides an elegant method that may be used to validate fundamental theories of parametric instabilities in controlled laboratory settings.

heliosphere↗
DOE OSTI2022

Gaps in topological magnon spectra: Intrinsic versus extrinsic effects

Determining and explaining the presence of a gap at a magnon crossing point is a critical step to characterize the topological properties of a material. An inelastic neutron scattering study of a single crystal is a powerful experimental technique to probe the magnetic excitation spectra of topological materials. Here, we show that when the scattering intensity rapidly disperses in the vicinity of a crossing point, such as a Dirac point, the apparent topological gap size is extremely sensitive to experimental conditions including sample mosaic, resolution, and momentum integration range. In this work, we demonstrate these effects using comprehensive neutron scattering measurements of CrCl 3 . Our measurements confirm the gapless nature of the Dirac magnon in CrCl 3 , but also reveal an artificial, i.e., extrinsic, magnon gap unless the momentum integration range is carefully controlled. Our study provides an explanation of the discrepancies between spectroscopic and first-principles estimates of Dirac magnon gap sizes and provides guidelines for accurate measurement of topological magnon gaps.

75 CONDENSED MATTER PHYSICS, SUPERCONDUCTIVITY AND↗
DOE OSTI2024

Moment extraction using an unfolding protocol without binning

Deconvolving (“unfolding”) detector distortions is a critical step in the comparison of cross-section measurements with theoretical predictions in particle and nuclear physics. However, most existing approaches require histogram binning while many theoretical predictions are at the level of statistical moments. We develop a new approach to directly unfold distribution moments as a function of another observable without having to first discretize the data. Our moment unfolding technique uses machine learning and is inspired by Boltzmann weight factors and generative adversarial networks (GANs). We demonstrate the performance of this approach using jet substructure measurements in collider physics. With this illustrative example, we find that our moment unfolding protocol is more precise than bin-based approaches and is as or more precise than completely unbinned methods.

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗
DOE OSTI2020

Femtosecond Compression Dynamics and Timing Jitter Suppression in a THz-driven Electron Bunch Compressor

Herein we present the first demonstration of THz driven bunch compression and timing stabilization of a relativistic electron beam. Quasi-single-cycle strong field THz radiation is used in a shorted parallel-plate structure to compress a few-fC beam with 2.5 MeV kinetic energy by a factor of 2.7, producing a 39 fs rms bunch length and a reduction in timing jitter by more than a factor of 2 to 31 fs rms. This THz driven technique offers a significant improvement to beam performance for applications like ultrafast electron diffraction, providing a critical step towards unprecedented timing resolution in ultrafast sciences, and other accelerator applications using femtosecond-scale electron beams.

43 PARTICLE ACCELERATORS↗
DOE OSTI2025

Window Observables for Benchmarking Parton Distribution Functions

Global analysis of collider and fixed-target experimental data and calculations from lattice quantum chromodynamics (QCD) are used to gain complementary information on the structure of hadrons. We propose novel “window observables” that allow for higher precision cross-validation between the different approaches, a critical step for studies that wish to combine the datasets. Global analyses are limited by the kinematic regions accessible to experiment, particularly in a range of Bjorken-𝑥, and lattice QCD calculations also have limitations requiring extrapolations to obtain the parton distributions. We provide two different window observables that can be defined within a region of 𝑥 where extrapolations and interpolations in global analyses remain reliable and where lattice QCD results retain sensitivity and precision.

lattice QCD↗