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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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All sourcesNASA NTRSDOE OSTI
DOE OSTI2022

Coordinated neutron and X-ray computed tomography of meteorites: Detection and distribution of hydrogen-bearing materials

The presence and distribution of hydrogen-bearing materials in meteorites are important constraints on processes in the early solar system, and the delivery of volatile constituents to growing planets. Here, in this paper, we show that coordinated neutron and X-ray computed tomography, NXCT, can reveal the presence and distributions of hydrogen-bearing materials in meteorites, and thus help constrain the presence and actions of water in the early solar system. NXCT is nearly nondestructive of meteorite samples. Neutron fluence in NXCT is approximately seven orders of magnitude less than in typical instrumental neutron activation analysis, and so produces little residual radioactivity and currently undetectable changes in isotope ratios. Heating during NXCT is minimal, but NXCT will overprint the record of cosmic ray exposure held in natural thermoluminescence. Two meteorites were examined. EET 87503 is a howardite, a regolith breccia inferred to be from the asteroid 4 Vesta, and contains fragments of eucrite basalt, diogenite pyroxenite, and H-rich carbonaceous chondrites. With NXCT, the chondrite fragments within the meteorite piece can be clearly located and characterized, in preparation for possible extraction and detailed analyses. Graves Nunataks (GRA) 06100 is a CR2 chondrite meteorite that contains abundant iron metal and H-bearing silicates from aqueous alteration. In NXCT, H-bearing altered material is clearly distinguished from metal, and its distribution in three dimensions is revealed as a constraint on the processes of alteration.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2023

Exploring the High-Pressure Phases of Carbon through X-ray Diffraction of Dynamic Compression Experiments on Sandia’s Z Pulsed Power Facility

The carbon phase diagram is rich with polymorphs which possess very different physical and optical properties ideal for different scientific and engineering applications. An understanding of the dynamically driven phase transitions in carbon is particularly important for applications in inertial confinement fusion, as well as planetary and meteorite impact histories. Experiments on the Z Pulsed Power Facility at Sandia National Laboratories generate dynamically compressed high-pressure states of matter with exceptional uniformity, duration, and size that are ideal for investigations of fundamental material properties. X-ray diffraction (XRD) is an important material physics measurement because it enables direct observation of the strain and compression of the crystal lattice, and it enables the detection and identification of phase transitions. Several unique challenges of dynamic compression experiments on Z prevent using XRD systems typically utilized at other dynamic compression facilities, so novel XRD diagnostics have been designed and implemented. We performed experiments on Z to shock compress carbon (pyrolytic graphite) samples to pressures of 150–320 GPa. The Z-Beamlet Laser generated Mn-Heα (6.2 keV) X-rays to probe the shock-compressed carbon sample, and the new XRD diagnostics measured changes in the diffraction pattern as the carbon transformed into its high-pressure phases. Quantitative analysis of the dynamic XRD patterns in combination with continuum velocimetry information constrained the stability fields and melting of high-pressure carbon polymorphs.

36 MATERIALS SCIENCE↗
DOE OSTI2022

Modeling Dust Production, Growth, and Destruction in Reionization-era Galaxies with the CROC Simulations: Methods and Parameter Exploration

We introduce a model for the explicit evolution of interstellar dust in a cosmological galaxy formation simulation. We post-process a simulation from the Cosmic Reionization on Computers project, integrating an ordinary differential equation for the evolution of the dust-to-gas ratio along pathlines in the simulation sampled with a tracer particle technique. This model incorporates the effects of dust grain production in asymptotic giant branch star winds and supernovae, grain growth due to the accretion of heavy elements from the gas phase of the interstellar medium, and grain destruction due to thermal sputtering in the high-temperature gas of supernova remnants due to thermal sputtering. A main conclusion of our analysis is the importance of a carefully chosen dust destruction model, for which different reasonable parameterizations can predict very different values at the ∼100 pc resolution of the interstellar medium (ISM) in our simulations. We run this dust model on the single most massive galaxy in a 10 h$^{−1}$ comoving megaparsec box, which attains a stellar mass of ∼2 × 10$^{9}$ M $_{⊙}$ by z = 5. We find that the model is capable of reproducing dust masses and dust-sensitive observable quantities broadly consistent with existing data from high-redshift galaxies. The total dust mass in the simulated galaxy is somewhat sensitive to parameter choices for the dust model, especially the timescale for grain growth due to accretion in the ISM. Consequently, observable quantity observations that can constrain galaxy dust masses at these epochs are potentially useful for placing constraints on dust physics.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2026

Deep nonparametric estimation of operators between infinite dimensional spaces

Learning operators between infinitely dimensional spaces is an important learning task arising in machine learning, imaging science, mathematical modeling and simulations, etc. This paper studies the nonparametric estimation of Lipschitz operators using deep neural networks. Non-asymptotic upper bounds are derived for the generalization error of the empirical risk minimizer over a properly chosen network class. Under the assumption that the target operator exhibits a low dimensional structure, our error bounds decay as the training sample size increases, with an attractive fast rate depending on the intrinsic dimension in our estimation. Our assumptions cover most scenarios in real applications and our results give rise to fast rates by exploiting low dimensional structures of data in operator estimation. We also investigate the influence of network structures (e.g., network width, depth, and sparsity) on the generalization error of the neural network estimator and propose a general suggestion on the choice of network structures to maximize the learning efficiency quantitatively.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2023

Technical descriptions of the experimental dynamical downscaling simulations over North America by the CAM–MPAS variable-resolution model

Abstract. Comprehensive assessment of climate datasets is important for communicating model projections and associated uncertainties to stakeholders. Uncertainties can arise not only from assumptions and biases within the model but also from external factors such as computational constraint and data processing. To understand sources of uncertainties in global variable-resolution (VR) dynamical downscaling, we produced a regional climate dataset using the Model for Prediction Across Scales (MPAS; dynamical core version 4.0) coupled to the Community Atmosphere Model (CAM; version 5.4), which we refer to as CAM–MPAS hereafter. This document provides technical details of the model configuration, simulations, computational requirements, post-processing, and data archive of the experimental CAM–MPAS downscaling data. The CAM–MPAS model is configured with VR meshes featuring higher resolutions over North America as well as quasi-uniform-resolution meshes across the globe. The dataset includes multiple uniform- (240 and 120 km) and variable-resolution (50–200, 25–100, and 12–46 km) simulations for both the present-day (1990–2010) and future (2080–2100) periods, closely following the protocol of the North American Coordinated Regional Climate Downscaling Experiment. A deviation from the protocol is the pseudo-warming experiment for the future period, using the ocean boundary conditions produced by adding the sea surface temperature and sea-ice changes from the low-resolution version of the Max Planck Institute Earth System Model (MPI-ESM-LR) in the Coupled Model Intercomparison Project Phase 5 to the present-day ocean state from a reanalysis product. Some unique aspects of global VR models are evaluated to provide background knowledge to data users and to explore good practices for modelers who use VR models for regional downscaling. In the coarse-resolution domain, strong resolution sensitivity of the hydrological cycles exists over the tropics but does not appear to affect the midlatitude circulations in the Northern Hemisphere, including the downscaling target of North America. The pseudo-warming experiment leads to similar responses of large-scale circulations to the imposed radiative and boundary forcings in the CAM–MPAS and MPI-ESM-LR models, but their climatological states in the historical period differ over various regions, including North America. Such differences are carried to the future period, suggesting the importance of the base state climatology. Within the refined domain, precipitation statistics improve with higher resolutions, and such statistical inference is verified to be negligibly influenced by horizontal remapping during post-processing. Limited (≈50 % slower) throughput of the current code is found on a recent many-core/wide-vector high-performance computing system, which limits the lengths of the 12–46 km simulations and indirectly affects sampling uncertainty. Our experience shows that global and technical aspects of the VR downscaling framework require further investigations to reduce uncertainties for regional climate projection.

58 GEOSCIENCES↗
DOE OSTI2024

Deep Nonparametric Estimation of Operators between Infinite Dimensional Spaces

Learning operators between infinitely dimensional spaces is an important learning task arising in machine learning, imaging science, mathematical modeling and simulations, etc. This paper studies the nonparametric estimation of Lipschitz operators using deep neural networks. Non-asymptotic upper bounds are derived for the generalization error of the empirical risk minimizer over a properly chosen network class. Under the assumption that the target operator exhibits a low dimensional structure, our error bounds decay as the training sample size increases, with an attractive fast rate depending on the intrinsic dimension in our estimation. Our assumptions cover most scenarios in real applications and our results give rise to fast rates by exploiting low dimensional structures of data in operator estimation. We also investigate the influence of network structures (e.g., network width, depth, and sparsity) on the generalization error of the neural network estimator and propose a general suggestion on the choice of network structures to maximize the learning efficiency quantitatively.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2021

Arctic Ice Nucleation Sampling during MOSAiC (INPMOSAIC2) Field Campaign Report

The accelerated rate of warming in the Arctic is of great concern due to potential impacts that include release of greenhouse gases from permafrost, melting glacial ice contributing to sea level rise, and declining sea ice cover exposing the darker ocean surface. These processes induce positive feedbacks and contribute to further warming that affects climate globally. Clouds play a crucial role in regulating the energy reaching the sea ice and snow surfaces, but the magnitude of their effects on surface temperature is not well constrained in the Arctic. Aerosols are an important contributor by serving as seeds for cloud particle formation, but even less is known about their overall impact and origin. In particular, aerosols that serve as ice nucleating particles (INPs) are vastly understudied, especially above the central Arctic Ocean. However, INPs likely play a significant role in arctic mixed-phase cloud (AMPC) microphysics and the resulting impacts of such clouds on the surface energy budget over the sea ice.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2023

Efficacy of one-time rotenone application for Westslope Cutthroat Trout restoration in Boulder Creek, Montana

Abstract Objective Although piscicides are an important tool for native fish management, our understanding of native fish population dynamics posttreatment (i.e., in putatively vacant habitat) is limited. Methods Here, we describe long-term trends in size and abundance of Cutthroat Trout Oncorhynchus clarkii in Lower Boulder Lake and Boulder Creek (Montana). Result No significant differences were found before and after treatment with rotenone in mean length and population density in Boulder Creek. Posttreatment genetic samples from Boulder Lake and upper Boulder Creek consisted entirely of the Westslope Cutthroat Trout Oncorhynchus clarkii lewisi used to refound the population. Middle and lower Boulder Creek genetic samples contained 99.4% and 98.4% Westslope Cutthroat Trout, indicating that some nonnative (Yellowstone Cutthroat Trout Oncorhynchus clarkii bouvieri hybrid) fish survived the treatment. Population demographics showed robust recovery posttreatment; however, genetic results revealed the limitations of a one-time treatment—that is, an incomplete fish kill. Conclusion We demonstrate that Cutthroat Trout populations achieve recovery after rotenone treatment in the long term, but one-time rotenone treatments on large drainages often fail to totally eradicate populations. Therefore, managers should plan on multiple treatments and utilize environmental DNA to help achieve total removal of target species.

Lampton, Jared↗
DOE OSTI2023

Surface analysis insight note: X‐ray photoelectron spectroscopy analysis of battery electrodes—Challenges with nickel–manganese–cobalt and Li examples using an Al Kα x‐ray source

X‐ray photoelectron spectroscopy (XPS) has become a highly important tool for the analysis of battery materials and components. However, both anecdotal and detailed analysis of selected parts of the literature indicate that many reports of XPS on battery electrodes have significant analysis or data flaws. In this paper, we highlight several of the common challenges that analysts face when using XPS for battery materials, pointing to recent literature that addresses many of the critical issues associated with sample preparation as well as data collection and analysis. A common error for battery materials (and other materials) involves ignoring peak overlaps and interferences. Specifically, when a “minor” peak associated with a component in relatively high concentration overlaps or contributes to the primary peak (or one recommended for quantitative analysis) from a different element in the material. Overlap issues apply to many battery electrodes composed of many elements with complex photoelectron peak structures, as well as those involving peaks with seemingly simpler spectral envelopes such as Li and F. Examples of issues associated with battery systems are highlighted by a discussion of challenges associated with XPS analysis of Li and nickel–manganese–cobalt (NMC) electrodes in battery systems. Lithium analysis has challenges associated with the preparation and an often‐unrecognized peak overlap with F. In our laboratory and in the literature, NMC electrodes are often examined and new XPS users do not always recognize interference of the Auger signal from F KLL (in or on the electrode) with Ni 2p photoelectron spectrum when generated with Al Kα X‐rays. The use of simulated spectra involving both F and NiO demonstrates the extent of F Auger contributions to the Ni 2p signal strength as a function of the F/Ni atom ratio in the material and suggests spectra information that can be used to identify how significant effects will be on the resultant spectra. Our analysis demonstrates that in many cases overlap issues are significant for real electrode materials.

25 ENERGY STORAGE↗
DOE OSTI2020

Dicarboxylic acid emissions from a GDI engine equipped with a catalytic gasoline particulate filter

Dicarboxylic acids play an important role in atmospheric chemistry, yet their emissions from primary sources, such as internal combustion engines, has not been extensively studied. In this paper, KOH impregnated quartz filters were loaded with exhaust gases from a gasoline direct injection (GDI) engine equipped with a catalytic gasoline particulate filter (GPF). All filters were analyzed for carboxylic and dicarboxylic acids using a derivatized gas chromatography-mass spectroscopy method. Exhaust gas was sampled from pre-GPF and post-GPF locations to determine the performance of the GPF regarding acid conversion. Lean and stoichiometric engine modes were considered with non-oxygenated gasoline and 10% splash blended ethanol in gasoline (E10) to examine the impact of stoichiometry and fuel type. Acid emissions represented as much as 0.51% of total unburned hydrocarbon emissions for total monocarboxylic acids and as much as 0.40% for total dicarboxylic acids. Individual acid concentrations were as high as 38 mg/kg-fuel for monocarboxylic acids and as high as 29 mg/kg-fuel for dicarboxylic acids. Overall, the study found that fuel oxygenates had mixed impact on the acid emissions. Engine-out monocarboxylic acids were reduced when using the E10 fuel by approximately 30–45% for the stoichiometric condition and increased marginally for the lean condition. Dicarboxylic acid emissions were generally insensitive to ethanol content. However, the engine condition significantly affected the acid emissions. Lean operation produced a factor of two to an order of magnitude higher emissions rates of both monocarboxylic and dicarboxylic acids than the stoichiometric condition. The catalytic GPF eliminated between 80 and 92% of the acids emitted from the engine, allowing some acids to be emitted into the environment.

42 ENGINEERING↗
DOE OSTI2020

The Complicated Link Between Material Properties and Microfracture Density for an Underground Explosion in Granite

Geophysical techniques are often implemented as quick and inexpensive ways to locate and characterize fractures in the subsurface, which is important for a number of geoscience fields. Seismic velocities are the most widely used proxies for identification of fractures, but the correlation is not always well-defined. In this study we present material property data: unconfined compressive strength (UCS), bulk density (ρ), Young's modulus (E), Poisson's ratio (ν), P wave velocity (V p ), and S wave velocity (V s ), in conjunction with microfracture densities measured on samples of granite collected before and after underground chemical explosions. Results indicate the relationship between fractures and material properties is complex, even in this single-lithology environment. We interpret that this complexity arises from varying fracture mechanisms (e.g. dilation-inducing fractures vs compression-inducing fractures) in different parts of the core, due to differences in stress conditions. Additional complexity may result from chemical interactions between the fresh fractures and the fluids in the area. Water content appears to have a significant, if not dominant, role in the unconfined compressive strength (UCS) of the samples. We suggest caution when using elastic property measurements as a proxy for fracturing in areas of explosion-induced damage, or in other areas where a variety of mechanisms induce fracturing.

58 GEOSCIENCES↗
DOE OSTI2022

Patterns and ecological drivers of viral communities in acid mine drainage sediments across Southern China

Recent advances in environmental genomics have provided unprecedented opportunities for the investigation of viruses in natural settings. Yet, our knowledge of viral biogeographic patterns and the corresponding drivers is still limited. Here, we perform metagenomic deep sequencing on 90 acid mine drainage (AMD) sediments sampled across Southern China and examine the biogeography of viruses in this extreme environment. The results demonstrate that prokaryotic communities dictate viral taxonomic and functional diversity, abundance and structure, whereas other factors especially latitude and mean annual temperature also impact viral populations and functions. In silico predictions highlight lineage-specific virus-host abundance ratios and richness-dependent virus-host interaction structure. Further functional analyses reveal important roles of environmental conditions and horizontal gene transfers in shaping viral auxiliary metabolic genes potentially involved in phosphorus assimilation. Our findings underscore the importance of both abiotic and biotic factors in predicting the taxonomic and functional biogeographic dynamics of viruses in the AMD sediments.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2026

ENVnet provides a global molecular resource of dissolved organic matter

Dissolved organic matter (DOM) is an important component of Earth's carbon cycle and one of the planet's most chemically diverse pools, yet the molecular structures of its constituents remain largely unresolved. This limitation has hindered our ability to link DOM composition to microbial processes and ecosystem function. Here we present ENVnet, a global molecular repository built from tandem mass spectrometry data collected across 13 terrestrial and aquatic environment types, including 419 newly generated samples that expand publicly available DOM metabolomics data and cover previously underrepresented environments. By computationally deconvolving chimeric mass spectra, a longstanding challenge in environmental metabolomics, we recover high-quality fragmentation data for >22,000 distinct molecular features (defined by a specific precursor mass and fragmentation pattern). Using ENVnet, we uncover conserved and environment-specific molecular patterns in DOM composition and underlying biogeochemical processes. We also use molecular features encoded in ENVnet to train predictive models of DOM persistence, allowing molecular-level assessment of microbial turnover in independent systems.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2023

A novel in-situ Raman spectroscopic cell for aqueous geochemistry at the solid–liquid interface

In-situ Raman spectroscopy has the potential to be a powerful technique for monitoring geochemical reactions at a solid–liquid interface in real time. In this article, we present the development and testing of an in-situ Raman spectroscopic cell, which can be used for reaction systems at moderate temperatures and pressure [<1000 psi (6.89 MPa), <100 °C, relevant to subsurface geologic systems] and can hold samples large enough for chemical mapping of heterogeneous rock surfaces. The system is validated by measuring the temperature-dependent conversion of gypsum to calcite over time. Near total conversion of gypsum to calcite on the mineral surface took 29 hours at room temperature and 150 minutes at 100 °C, corresponding to an 11.6-fold increase in the conversion rate. Further, we anticipate that this cell can be an important tool in quantifying the rates of carbon mineralization relevant to geologic carbon sequestration, particularly for the elevated rates recently observed in mafic/ultramafic rocks.

47 OTHER INSTRUMENTATION↗
DOE OSTI2025

Generation of strong fields with subcritical density plasmas to study the phase transitions of magnetized warm dense matter

Warm dense matter (WDM) is a regime where Fermi degenerate electrons play an important role in the macroscopic properties of a material. Recent experiments have brought us closer to understanding unmagnetized processes in WDM, but magnetized WDM remains unexplored because kilotesla magnetic fields are required. Although there are examples of field compression generating such fields by imploding pre-magnetized targets, these existing methods give no independent control over the parameters of the magnetized plasma and result in limited laser access for sample creation and diagnosis. In this paper, numerical simulations show that kilotesla magnetic fields can be obtained by shining laser beams onto the inner surface of a cylindrical target, rather than on the outer surface. This approach relies on field compression by a low-density, high-temperature plasma, rather than a high-density, low-temperature plasma, used in the more conventional approach. With this novel configuration, the region of peak magnetic field is mostly free of plasma, hence, other beams can reach a sample placed in the region of the peak field to form WDM and diagnose it.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗
DOE OSTI2023

Synchrotron light source X-ray detection with Low-Gain Avalanche Diodes

Abstract Low Gain Avalanche Diodes (LGADs) represent the state-of-the-art in timing measurements and will instrument the future Timing Detectors of ATLAS and CMS for the High-Luminosity LHC. While initially conceived as a sensor for charged particles, the intrinsic gain of LGADs makes it possible to detect low-energy X-rays with good energy resolution and excellent time resolution (tens of picoseconds). Using the Stanford Synchrotron Radiation Lightsource (SSRL) at SLAC, several LGADs designs were characterized with energies from 5 to 70 keV. The SSRL provides 10 ps pulsed X-ray bunches separated by 2 ns intervals with an energy dispersion (ΔE/E) of 10 -4 . LGADs from Hamamatsu Photonics (HPK) and Brookhaven National Laboratory (BNL) with different thicknesses ranging from 20 μm to 50 μm and different gain layer designs were read out using fast amplification boards and digitized with a high bandwidth and high sampling rate oscilloscope. PIN devices from HPK and AC-LGADs from BNL were characterized as well. A systematic and detailed characterization of the devices' energy linearity, resolution, and time resolution as a function of X-ray energy was performed for different biasing voltages at room temperature and are reported in this work. The charge collection and multiplication mechanism were simulated using Geant4 and TCAD Sentaurus, providing an important handle for interpreting the data.

Instruments & Instrumentation↗
DOE OSTI2024

New constraint on the Np 237 ( n , γ ) Np 238 integral cross section using the Godiva-IV critical assembly

Accurate knowledge of the 237 Np(n, γ) 238 Np cross section at fast neutron energies is important for applied nuclear science. The presently available experimental data has large disagreements in the fast neutron region. Perform a model-independent measurement of the 237 Np(n, γ) 238 Np integral cross section using a well characterized fast neutron source and compare the result with previous measurements and current nuclear data evaluations. Provide an integral measurement that can be used as a benchmark for current evaluations. Multiple samples of 237 Np were irradiated in the Godiva-IV critical assembly. Following the irradiation, the samples placed in a γ-ray counting setup and the γ-rays emitted from the decay of 238 Np were measured over a time period of approximately 7 days. Multiple γ-ray decay branches of 238 Np were observed. The observed activity of 238 Np was used to calculate the amount of 238 Np produced during the irradiation via the 237 Np(n, γ) 238 Np reaction and an integral cross section of 342(11) mb was measured for the Godiva-IV neutron spectrum. Further, the 238 Np half-life has been measured with a result of 50.31(5) hours. The 237 Np(n, γ) 238 Np integral cross section measured in this work is in agreement with overlapping 1σ error bands to ENDF/B-VIII.0. However, the measured value is 3σ away from the calculated integral cross section using JENDL-5. This measurement offers a reliable benchmark for future 237 Np(n, γ) 238 Np cross section evaluations.

21 SPECIFIC NUCLEAR REACTORS AND ASSOCIATED PLANTS↗
DOE OSTI2022

The air mycobiome is decoupled from the soil mycobiome in the California San Joaquin Valley

Abstract Dispersal is a key force in the assembly of fungal communities and the air is the dominant route of dispersal for most fungi. Understanding the dynamics of airborne fungi is important for determining their source and for helping to prevent fungal disease. This understanding is important in the San Joaquin Valley of California, which is home to 4.2 million people and where the airborne fungus Coccidioides is responsible for the most important fungal disease of otherwise healthy humans, coccidioidomycosis. The San Joaquin Valley is the most productive agricultural region in the United States, with the principal crops grown therein susceptible to fungal pathogens. Here, we characterize the fungal community in soil and air on undeveloped and agricultural land in the San Joaquin Valley using metabarcoding of the internal transcribed spacer 2 variable region of fungal rDNA. Using 1,002 individual samples, we report one of the most extensive studies of fungi sampled simultaneously from air and soil using modern sequencing techniques. We find that the air mycobiome in the San Joaquin Valley is distinct from the soil mycobiome, and that the assemblages of airborne fungi from sites as far apart as 160 km are far more similar to one another than to the fungal communities in nearby soils. Additionally, we present evidence that airborne fungi in the San Joaquin Valley are subject to dispersal limitation and cyclical intra‐annual patterns of community composition. Our findings are broadly applicable to understanding the dispersal of airborne fungi and the taxonomic structure of airborne fungal assemblages.

59 BASIC BIOLOGICAL SCIENCES↗