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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 253 records · Page 14
All sourcesNASA NTRSDOE OSTI
DOE OSTI2025

Boron isotopic analysis in bulk silicate materials using the Neoma MS/MS MC-ICP-MS

Boron (B) isotopes are a valuable tracer with applications ranging from geological, environmental, and nuclear studies because B isotopic fractionation is highly sensitive to chemical processes yielding distinct isotopic trends in natural and anthropogenic systems. Despite this wide applicability, there remain relatively few measurements on well-described reference materials and in some cases, poor agreement between various methods. We report a method for boron isotope ratio measurement in solution on the Neoma MS/MS MC-ICP-MS specifically targeting bulk silicates. We evaluate the performance of the method and instrument as it relates to the measurement of the absolute boron isotope ratio ( 10 B/ 11 B). The results indicate that the method produces data in agreement with literature values and that the sample–standard bracketing technique is appropriate for the Neoma MS/MS MC-ICP-MS which has been in use for decades on previous generation instruments. Careful tuning of the MS/MS lenses is required to obtain precision comparable to non MS/MS equipped MC-ICP-MS. With careful tuning, internal and external precisions of ∼0.3‰ were achieved. However, when the MS/MS is not properly tuned external precisions exceed 3‰. Nevertheless, our results for IAEA B-6, BCR-2, BHVO-2 and W-2a reference materials overlap the 1σ range of previously reported 10 B/ 11 B. Data are reported for total boron quantities down to a few tens of nanograms. Our procedure yielded blanks as low as 3 ng but up to 29 ng, making blank corrections important for small samples sizes in the few 10s of nanogram range. We report B isotope ratios for AGV-2G, SL-1G, GSC-2G, GSD-2G, GSE-2G, RLS-132, RLS-140, NKT-1G, and T1-G glass reference materials that have not been previously reported in the literature.

Scott, Sean R. [Pacific Northwest National Laborat↗
DOE OSTI2023

Exploring unsupervised top tagging using Bayesian inference

Recognizing hadronically decaying top-quark jets in a sample of jets, or even its total fraction in the sample, is an important step in many LHC searches for Standard Model and Beyond Standard Model physics as well. Although there exists outstanding top-tagger algorithms, their construction and their expected performance rely on Montecarlo simulations, which may induce potential biases. For these reasons we develop two simple unsupervised top-tagger algorithms based on performing Bayesian inference on a mixture model. In one of them we use as the observed variable a new geometrically-based observable \tilde{A}_{3} A ̃ 3 , and in the other we consider the more traditional \tau_{3}/\tau_{2} τ 3 / τ 2 N N -subjettiness ratio, which yields a better performance. As expected, we find that the unsupervised tagger performance is below existing supervised taggers, reaching expected Area Under Curve AUC \sim 0.80-0.81 ∼ 0.80 − 0.81 and accuracies of about 69% - − 75% in a full range of sample purity. However, these performances are more robust to possible biases in the Montecarlo that their supervised counterparts. Our findings are a step towards exploring and considering simpler and unbiased taggers.

Alvarez, Ezequiel↗
DOE OSTI2021

0D Nanocrystals as Light‐Driven, Localized Charge‐Injection Sources for the Contactless Manipulation of Atomically Thin 2D Materials

A contactless charge‐injection scheme that allows the local and quasi‐permanent manipulation of atomically thin 2D materials, such as monolayer (1L‐)MoS 2 , over spatial extents of several tens of micrometers, is reported. The possibility to precisely position and localize the charge‐injection source to the micrometer scale post‐fabrication allows the investigation of local unperturbed electronic structure of the 2D material. Thanks to this novel approach, the important impact of sample inhomogeneity on the charge‐carrier percolation that occurs over the entire extent of the 2D flake and proliferates up to 40 μm away from the localized charge injection is elucidated. The apparent driving force for carrier relocation is the initial inhomogeneous electronic landscape of the 2D material. These studies demonstrate that local and contactless charge injection with submicrometer precision delivers an alternative route for charge injection and indicates that local 2D material electronic structure can serve as a key element for novel nanoscale device design.

Ghini, Michele↗
DOE OSTI2025

Oxygen stable isotopes in the nuclear fuel cycle: Assessment of the potential for determining the fabrication and provenance history of anhydrous and hydrous uranium oxides

Determining the origin and history of interdicted nuclear materials is a central challenge in nuclear forensics. The oxygen stable isotope composition of uranium oxide compounds has emerged as a promising forensic signature, attracting increasing attention since the early 2000s. This review examines analytical techniques for measuring oxygen isotope compositions in uranium oxides and evaluates how the nuclear fuel production cycle introduces or modifies these isotopic signatures. The potential for forensic geolocation is explored through workflows that calibrate the relationship between environmental water oxygen isotopes and those found in uranium oxides. Key strengths and limitations of this approach are assessed, including gaps in knowledge related to isotope fractionation during specific stages of the fuel cycle, and processing facility water inputs. The importance of proper sample handling and storage under inert atmospheres, as well as a deeper understanding of both intra-sample oxygen isotope heterogeneity, and hydrous uranium oxide phase formation, is highlighted for improving the reliability of forensic interpretations. In conclusion, the development of uranium oxide standards with well-characterized δ 18 O values and international collaboration toward consensus on their use are identified as essential steps for advancing the field.

Attribution↗
DOE OSTI2026

Personal and environmental predictors of polycyclic aromatic hydrocarbon exposure identified through repeated silicone wristband sampling

This study integrates quantitative data on personal exposure to polycyclic aromatic hydrocarbons (PAHs) in 162 silicone wristbands with demographics, behavioral information, and housing characteristics to explore contributions to residential exposure in a superfund-adjacent community over the course of a year. Forty-six residents completed questionnaires and wore silicone wristbands as personal passive samplers for seven consecutive days on up to four separate occasions in alternating months between November 2022 and June 2023. It was hypothesized that individual behaviors and housing characteristics are sources of dependence and correlation between personal PAH exposures. 50 PAHs were detected at least once, 17 of which were alkylated PAHs. Exposure to PAHs of similar molecular weight was often correlated, notably between naphthalenes (2-rings) and higher molecular weight PAHs (3 or more rings). Generalized linear mixed models identified flooring type, participant age, and sampling month as important predictors of increased PAH exposure, and flooring type, and use of wood stoves or heavy machinery as predictors of increased naphthalene exposure relative to higher molecular weight PAHs. Individual chemical models based on concentration data and detection frequencies corroborated these findings across multiple PAHs. We demonstrate that personal exposure is not static and the degree of variability in personal exposure is individual. Hence, identification of influential exposure factors through repeated measures of chemical exposure and characterization of variability in personal exposure as performed in this study, is important in the development of exposure mitigation strategies.

Bonner, Emily↗
DOE OSTI2021

Radiolysis of thermally dehydrated gibbsite

Aluminum hydroxide (gibbsite, Al (OH) 3 ) powders and their thermally dehydrated forms were rehydrated and irradiated with gamma rays to examine the effects of the collapse of the crystalline gibbsite structure as it is converted to alumina on the transport of precursors of molecular hydrogen to the surface. The amount of hydrogen gas, H 2 , production from irradiated gibbsite and its transition phases with adsorbed water increased substantially with increasing temperature for dehydration of the samples, up to a point. Furthermore, the production of H 2 for samples without water was considerably lower than the hydrated samples signifying the importance of surface water and the transport of precursors to the surface. EPR spectroscopy showed that the major radiolytic products are trapped electrons and related O – centers. Thermal gravimetric analysis (TGA) measurements of gibbsite dehydration established the temperatures of phase transitions from gibbsite to alumina. Changes to the surface and structure of gibbsite and its transition phases following irradiation were analyzed using nitrogen adsorption, powder X-ray diffraction (pXRD), Raman spectroscopy, electron paramagnetic resonance (EPR), and scanning electron microscopy (SEM). pXRD and Raman spectra showed amorphorization at about 300 °C, coupled to a substantial increase in specific surface area due to increasing porosity.

38 RADIATION CHEMISTRY, RADIOCHEMISTRY, AND NUCLEA↗
DOE OSTI2024

Thermal Transport and Mechanical Stress Mapping of a Compression Bonded GaN/Diamond Interface for Vertical Power Devices

Bonding diamond to the back side of gallium nitride (GaN) electronics has been shown to improve thermal management in lateral devices; however, engineering challenges remain with the bonding process and characterizing the bond quality for vertical device architectures. Here, in this study, integration of these two materials is achieved by room-temperature compression bonding centimeter-scale GaN and a diamond die via an intermetallic bonding layer of Ti/Au. Recent attempts at GaN/diamond bonding have utilized a modified surface activation bonding (SAB) method, which requires Ar fast atom bombardment immediately followed by bonding within the same tool under ultrahigh vacuum (UHV) conditions. The method presented here does not require a dedicated SAB tool yet still achieves bonding via a room-temperature metal–metal compression process. Imaging of the buried interface and the total bonding area is achieved via transmission electron microscopy (TEM) and confocal acoustic scanning microscopy (C-SAM), respectively. The thermal transport quality of the bond is extracted from spatially resolved frequency-domain thermoreflectance (FDTR) with the bonded areas boasting a thermal boundary conductance of >100 MW/m 2 ·K. Additionally, Raman maps of GaN near the GaN–diamond interface reveal a low level of compressive stress, <80 MPa, in well-bonded regions. FDTR and Raman were coutilized to map these buried interfaces and revealed some poor thermally bonded areas bordered by high-stress regions, highlighting the importance of spatial sampling for a complete picture of bond quality. Overall, this work demonstrates a novel method for thermal management in vertical GaN devices that maintains low intrinsic stresses while boasting high thermal boundary conductances.

Raman↗
DOE OSTI2023

Cryo-EM structures reveal native GABA A receptor assemblies and pharmacology

Type A γ-aminobutyric acid receptors (GABA A Rs) are the principal inhibitory receptors in the brain and the target of a wide range of clinical agents, including anaesthetics, sedatives, hypnotics and antidepressants. However, our understanding of GABA A R pharmacology has been hindered by the vast number of pentameric assemblies that can be derived from 19 different subunits and the lack of structural knowledge of clinically relevant receptors. Here, we isolate native murine GABA A R assemblies containing the widely expressed α1 subunit and elucidate their structures in complex with drugs used to treat insomnia (zolpidem (ZOL) and flurazepam) and postpartum depression (the neurosteroid allopregnanolone (APG)). Using cryo-electron microscopy (cryo-EM) analysis and single-molecule photobleaching experiments, we uncover three major structural populations in the brain: the canonical α1β2γ2 receptor containing two α1 subunits, and two assemblies containing one α1 and either an α2 or α3 subunit, in which the single α1-containing receptors feature a more compact arrangement between the transmembrane and extracellular domains. Interestingly, APG is bound at the transmembrane α/β subunit interface, even when not added to the sample, revealing an important role for endogenous neurosteroids in modulating native GABA A Rs. Together with structurally engaged lipids, neurosteroids produce global conformational changes throughout the receptor that modify the ion channel pore and the binding sites for GABA and insomnia medications. Our data reveal the major α1-containing GABA A R assemblies, bound with endogenous neurosteroid, thus defining a structural landscape from which subtype-specific drugs can be developed.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2025

Neutrons reveal the dynamics of leaf thylakoids in living plants

The study is the first known exploration of photosynthetic membranes dynamics in living plants by high resolution quasielastic neutron scattering spectroscopy. We investigated the mobility and flexibility of thylakoid membranes in common duckweed (Landoltia punctata) and identified dynamics across various length scales corresponding to individual membranes and membranes stack. We employed classical models typically used to study lipid bilayers to characterize the undulation modes and rigidity of the membranes and reveal how structural variations influence the observed complex dynamics. Our findings show that the stacks of thylakoids in duckweed behave as rigid systems, exhibiting an effective bending coefficient in the lower range associated with surfactant membranes. In contrast, the single thylakoid leaflets display greater apparent flexibility and are well situated within the bi-continuous surfactant phase dynamics. While our observations enhance the understanding of the intricate architecture and mobility of photosynthetic cellular machinery, they also highlight the limitations of applying ideal lipid membranes models to describe complex biological systems. This work opens more questions and the need for further investigations across extended length and time scales, as well as the importance of rigorous sample preparation and experimental control.

Applied physics↗
DOE OSTI2020

Machine learning prediction of incidence of Alzheimer’s disease using large-scale administrative health data

Nationwide population-based cohort provides a new opportunity to build an automated risk prediction model based on individuals’ history of health and healthcare beyond existing risk prediction models. We tested the possibility of machine learning models to predict future incidence of Alzheimer’s disease (AD) using large-scale administrative health data. From the Korean National Health Insurance Service database between 2002 and 2010, we obtained de-identified health data in elders above 65 years (N = 40,736) containing 4,894 unique clinical features including ICD-10 codes, medication codes, laboratory values, history of personal and family illness and socio-demographics. To define incident AD we considered two operational definitions: “definite AD” with diagnostic codes and dementia medication (n = 614) and “probable AD” with only diagnosis (n = 2026). We trained and validated random forest, support vector machine and logistic regression to predict incident AD in 1, 2, 3, and 4 subsequent years. For predicting future incidence of AD in balanced samples (bootstrapping), the machine learning models showed reasonable performance in 1-year prediction with AUC of 0.775 and 0.759, based on “definite AD” and “probable AD” outcomes, respectively; in 2-year, 0.730 and 0.693; in 3-year, 0.677 and 0.644; in 4-year, 0.725 and 0.683. The results were similar when the entire (unbalanced) samples were used. Important clinical features selected in logistic regression included hemoglobin level, age and urine protein level. This study may shed a light on the utility of the data-driven machine learning model based on large-scale administrative health data in AD risk prediction, which may enable better selection of individuals at risk for AD in clinical trials or early detection in clinical settings.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2024

Validation of the VUV-reflective coating for next-generation liquid xenon detectors

Abstract Coating detector materials with films highly reflective in the vacuum ultraviolet region improves sensitivity of the next-generation rare-event detectors that use liquid xenon. In this work, we investigate the MgF 2 -Al-MgF 2 coating designed to achieve high reflectance at 175 nm, the mean wavelength of liquid xenon (LXe) scintillation. The coating was applied to an unpolished, passivated copper substrate mimicking a realistic detector component of the proposed nEXO experiment, as well as to two unpassivated substrates with “high” and “average” levels of polishing. After confirming the composition and morphology of the thin-film coating using TEM and EDS, the samples underwent reflectance measurements in LXe and gaseous nitrogen (GN2). Measurements in LXe exposed the coated samples to -100°C for several hours. No peeling of the coatings was observed after several thermal cycles. Polishing is found to strongly correlate with the measured specular reflectance (R spec ). In particular, 5.8(5)% specular spike reflectance in LXe was measured for the realistic sample at 20° of incidence, while the values for similar angles of incidence on the high and average polish samples are 62.3(1.3)% and 27.4(7)%, respectively. At large angles (66°–75°), theR spec in LXe for the three samples increases to 23(5)%, 80(8)%, and 84(18)%, respectively. The R spec at around 45° was measured in both GN2 and LXe for average polish sample and shows a reasonable agreement. Importantly, the total reflectance of the samples is comparable and estimated to be 92(8)%, 85(8)%, and 83(8)% in GN2 for the realistic, average, and high polish samples, respectively. This is considered satisfactory for the next-generation LXe experiments that could benefit from using reflective films, such as nEXO and DARWIN, thus validating the design of the coating.

Instruments & Instrumentation↗
DOE OSTI2023

A declining major merger fraction with redshift in the local Universe from the largest-yet catalogue of major and minor mergers in SDSS

It is difficult to accurately identify galaxy mergers and it is an even larger challenge to classify them by their mass ratio or merger stage. In previous work we used a suite of simulated mergers to create a classification technique that uses linear discriminant analysis to identify major and minor mergers. Here, we apply this technique to 1.3 million galaxies from the SDSS DR16 photometric catalogue and present the probability that each galaxy is a major or minor merger, splitting the classifications by merger stages (early, late, post-coalescence). We present publicly available imaging predictor values and all of the above classifications for one of the largest-yet samples of galaxies. We measure the major and minor merger fraction (f merg ) and build a mass-complete sample of galaxies, which we bin as a function of stellar mass and redshift. For the major mergers, we find a positive slope of fmerg with stellar mass and negative slope of f merg with redshift between stellar masses of 10.5 < M * (log M ⊙ ) < 11.6 and redshifts of 0.03 < z < 0.19. We are able to reproduce an artificial positive slope of the major merger fraction with redshift when we do not bin for mass or craft a complete sample, demonstrating the importance of mass completeness and mass binning. Further, we determine that the positive trend of the major merger fraction with stellar mass is consistent with a hierarchical assembly scenario. The negative trend with redshift requires that an additional assembly mechanism, such as baryonic feedback, dominates in the local Universe.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2022

Size-Dependent Nucleation in Crystal Phase Transition from Machine Learning Metadynamics

In this Letter, we present a framework that combines machine learning potential (MLP) and metadynamics to investigate solid-solid phase transition. Here, based on the spectral descriptors and neural networks regression, we develop a scalable MLP model to warrant an accurate interpolation of the energy surface where two phases coexist. Applying it to the simulation of B4–B1 phase transition of GaN under 50 GPa with different model sizes, we observe sequential change of the phase transition mechanism from collective modes to nucleation and growths. When the size is at or below 128 000 atoms, the nucleation and growth appear to follow a preferred direction. At larger sizes, the nuclei occur at multiple sites simultaneously and grow to microstructures by passing the critical size. The observed change of the atomistic mechanism manifests the importance of statistical sampling with large system size in phase transition modeling.

36 MATERIALS SCIENCE↗
DOE OSTI2020

Study of a linear surface wave plasma source for tin removal in an extreme ultraviolet source

Tin deposition mitigation employs hydrogen radicals and ions, formed in a hydrogen plasma, to interact with tin to form tin hydride (SnH 4 ) in the gaseous state, which is then pumped away. Surface wave plasma (SWP) technology developed at Illinois generates hydrogen radicals and ions, resulting in tin etch rates that are high enough to keep extreme ultraviolet (EUV) lithographic tools clean. An advantage of an SWP antenna is the ability to generate a high density of hydrogen radicals and hydrogen ions directly at the desired etching location. In situ etching of tin enables high availability EUV tools by maintaining high reflectivity of the multilayer mirror of the collector. Additionally, the SWP is characterized with low ion energies and low electron temperature, such that the multilayer mirror does not suffer any damage from sputtering or implantation of hydrogen ions during operation. Here, experiments elucidating the fundamental processes of tin removal are conducted by varying pressure, power, surface temperature, and gas flow rate in order to observe the etch rate behavior. Our results have shown that the presence of hydrogen ions increases etch rates because ion bombardment weakens Sn–Sn bonds, which, in turn, allows for a higher rate of chemical etching by the radicals. The ion bombardment reduces the number of radicals needed to etch a single tin atom to the range of 10 2 –10 3 . The linear SWP antenna yields plasma densities on the order of 10 17 to 10 18 m -3 and radical densities on the order of 10 18 to 10 19 m -3 , allowing for greater utilization of ion etch enhancement. Etch rates of up to 200 nm/min have been achieved. The surface temperature of the samples is an important factor in the etching process such that the decrease in the surface temperature increases the etch rates and decreases the hydrogen desorption rates. In addition, a kinetic etch model is developed to explain the behavior of etch rates as a function of surface temperature. Furthermore, results from experiments performed in an Illinois NXE:3100 chamber will be discussed.

42 ENGINEERING↗
DOE OSTI2025

Recovering new viruses from New Mexico soils

Here, we utilized metagenomic and size-filtered virome sequencing to recover 4,157 medium, high, or complete quality viral genomes from soils taken from three high elevation sites in New Mexico, USA. Among recovered viral genomes, 90% were from size-filtered samples, indicating the importance of this enrichment in assessments of complex viromes.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2021

Assessing Prior Emergent Constraints on Surface Albedo Feedback in CMIP6

An emergent constraint (EC) is a popular model evaluation technique, which offers the potential to reduce intermodel variability in projections of climate change. Two examples have previously been laid out for future surface albedo feedbacks (SAF) stemming from loss of Northern Hemisphere (NH) snow cover (SAF snow ) and sea ice (SAF ice ). These processes also have a modern-day analog that occurs each year as snow and sea ice retreat from their seasonal maxima, which is strongly correlated with future SAF across an ensemble of climate models. The newly released CMIP6 ensemble offers the chance to test prior constraints through out-of-sample verification, an important examination of EC robustness. Here, we show that the SAF snow EC is equally strong in CMIP6 as it was in past generations, while the SAF ice EC is also shown to exist in CMIP6, but with different, slightly weaker characteristics. We find that the CMIP6 mean NH SAF exhibits a global feedback of 0.25 ± 0.05 W m -2 K -1 , or ~61% of the total global albedo feedback, largely in line with prior generations despite its increased climate sensitivity. Additionally, the NH SAF can be broken down into similar contributions from snow and sea ice over the twenty-first century in CMIP6. Crucially, intermodel variability in seasonal SAF snow and SAF ice is largely unchanged from CMIP5 because of poor outlier simulations of snow cover, surface albedo, and sea ice thickness. These outliers act to mask the noted improvement from many models when it comes to SAF ice , and to a lesser extent SAF snow .

58 GEOSCIENCES↗
DOE OSTI2022

Conditional quantum plasmonic sensing

The possibility of using weak optical signals to perform sensing of delicate samples constitutes one of the main goals of quantum photonic sensing. Furthermore, the nanoscale confinement of electromagnetic near fields in photonic platforms through surface plasmon polaritons has motivated the development of highly sensitive quantum plasmonic sensors. Despite the enormous potential of plasmonic platforms for sensing, this class of sensors is ultimately limited by the quantum statistical fluctuations of surface plasmons. Indeed, the fluctuations of the electromagnetic field severely limit the performance of quantum plasmonic sensing platforms in which delicate samples are characterized using weak near-field signals. Furthermore, the inherent losses associated with plasmonic fields levy additional constraints that challenge the realization of sensitivities beyond the shot-noise limit. Here, we introduce a protocol for quantum plasmonic sensing based on the conditional detection of plasmons. We demonstrate that the conditional detection of plasmonic fields, via plasmon subtraction, provides a new degree of freedom to control quantum fluctuations of plasmonic fields. This mechanism enables improvement of the signal-to-noise ratio of photonic sensors relying on plasmonic signals that are comparable to their associated field fluctuations. Consequently, the possibility of using weak plasmonic signals to sense delicate samples, while preserving the sample properties, has important implications for molecule sensing, and chemical detection.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗
NASA NTRS1968

Deep gamma ray penetration in thick shields

Appropriate importance function and sampling scheme facilitates the application of the Monte Carlo method to problems involving the deep penetration of radiation.

Armstrong, T. W.↗