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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 343 records · Page 19
All sourcesNASA NTRSDOE OSTI
NASA NTRS1999

Physical and Chemical Characterization of Particles in the Upper Troposphere and Lower Stratosphere: Microanalysis of Aerosol Impactor Samples

Herein is reported activities to support the characterization of the aerosol in the upper troposphere (UT) and lower stratosphere (LS) collected during the Airborne Southern Hemisphere Ozone Experiment/Measurements for Assessing the Effects of Stratospheric Aircraft (ASHOE/MAESA) missions in 1994. Through a companion proposal, another group was to measure the size distribution of aerosols in the 0.008 to 2 micrometer diameter range and to collect for us impactor samples of particles larger than about 0.02 gm. In the first year, we conducted laboratory studies related to particulate deposition patterns on our collection substrates, and have performed the analysis of many ASHOE/MAESA aerosol samples from 1994 using analytical electron microscopy (AEM). We have been building an "aerosol climatology" with these data that documents the types and relative abundances of particles observed at different latitudes and altitudes. The second year (and non-funded extension periods) saw continued analyses of impactor aerosol samples, including more ASHOE/MAESA samples, some northern hemisphere samples from the NASA Stratospheric Photochemistry Aerosols and Dynamics Expedition (SPADE) program for comparison, and a few aerosol samples from the NASA Stratospheric TRacers of Atmospheric Transport (STRAT) program. A high-resolution field emission microscope was used for the analysis and re-analysis of a number of samples to determine if this instrument was superior in performance to our conventional electron microscope. In addition, some basic laboratory studies were conducted to determine the minimum detectable and analyzable particle size for different types of aerosols. In all, 61 aerosol samples were analyzed, with a total of over 30,000 individual particle analyses. In all analyzed samples, sulfate particles comprised the major aerosol number fraction. It must be stressed that particles composed of more than one species, for example sulfate and organic carbon, were classified according to the major fraction. Thus, many of the particles classified as sulfate may have contained significant mass fractions of carbonaceous or other material. These particles for the most part did not show two physical phases, however. Nonsulfate particles were classified according to the physical and chemical characteristics of each particle, and were grouped into the major nonsulfate particle classes, including C-rich, crustal, metallic, and salts. Our UT and LS sample analyses indicate a maximum for crustal and C-rich particle abundance in the Northern Hemisphere upper troposphere, and a salt particle maximum in the Southern Hemisphere upper troposphere. Metallic particles are clearly more prevalent in the troposphere than in the stratosphere, but interhemispheric differences appear small.

Sheridan, Patrick J.↗
DOE OSTI2024

NuGraph2: A Graph Neural Network for Neutrino Event Reconstruction

Neutrino experiments are set to probe some of the most important open questions in physics, from CP violation and the nature of dark matter. The technology of choice for many of these experiments is the liquid argon time projection chamber (LArTPC). In current LArTPC experiments, reconstruction performance often represents a limiting factor for the sensitivity. New developments are therefore needed to unlock the full potential of LArTPC experiments. NuGraph2 is a state of the art Graph Neural Network for reconstruction of data in LArTPC experiments. NuGraph2 utilizes a heterogeneous graph structure, with separate subgraphs of 2D nodes (hits in each plane) connected across planes via 3D nodes (space points). The model provides a consistent description of the neutrino interaction across all planes. NuGraph2 is a multi-purpose network, with a common message-passing attention engine connected to multiple decoders with different classification or regression tasks. These include the classification of detector hits according to the particle type that produced them (semantic segmentation) and the separation of hits from the neutrino interaction from hits due to noise or cosmic-ray background. Additional decoders are being developed, performing tasks such as the regression of the neutrino interaction vertex position. Performance results will be presented based on publicly available samples from MicroBooNE. These include both physics performance metrics, achieving 95% accuracy for semantic segmentation and 98% classification of neutrino hits, as well as computational metrics for training and for inference on CPU or GPU. The status of the NuGraph integration in the LArSoft software framework will be presented, as well as initial studies about model interpretability and injection of domain knowledge.

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗
DOE OSTI2024

NuGraph2: A Graph Neural Network for Neutrino Event Reconstruction

Neutrino experiments are set to probe some of the most important open questions in physics, from CP violation and the nature of dark matter. The technology of choice for many of these experiments is the liquid argon time projection chamber (LArTPC). In current LArTPC experiments, reconstruction performance often represents a limiting factor for the sensitivity. New developments are therefore needed to unlock the full potential of LArTPC experiments. NuGraph2 is a state of the art Graph Neural Network for reconstruction of data in LArTPC experiments. NuGraph2 utilizes a heterogeneous graph structure, with separate subgraphs of 2D nodes (hits in each plane) connected across planes via 3D nodes (space points). The model provides a consistent description of the neutrino interaction across all planes. NuGraph2 is a multi-purpose network, with a common message-passing attention engine connected to multiple decoders with different classification or regression tasks. These include the classification of detector hits according to the particle type that produced them (semantic segmentation) and the separation of hits from the neutrino interaction from hits due to noise or cosmic-ray background. Additional decoders are being developed, performing tasks such as the regression of the neutrino interaction vertex position. Performance results will be presented based on publicly available samples from MicroBooNE. These include both physics performance metrics, achieving 95% accuracy for semantic segmentation and 98% classification of neutrino hits, as well as computational metrics for training and for inference on CPU or GPU. The status of the NuGraph integration in the LArSoft software framework will be presented, as well as initial studies about model interpretability and injection of domain knowledge.

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗
DOE OSTI2024

NuGraph2: A Graph Neural Network for Neutrino Event Reconstruction

Neutrino experiments are set to probe some of the most important open questions in physics, from CP violation and the nature of dark matter. The technology of choice for many of these experiments is the liquid argon time projection chamber (LArTPC). In current LArTPC experiments, reconstruction performance often represents a limiting factor for the sensitivity. New developments are therefore needed to unlock the full potential of LArTPC experiments. NuGraph2 is a state of the art Graph Neural Network for reconstruction of data in LArTPC experiments [https://arxiv.org/abs/2403.11872]. NuGraph2 utilizes a heterogeneous graph structure, with separate subgraphs of 2D nodes (hits in each plane) connected across planes via 3D nodes (space points). The model provides a consistent description of the neutrino interaction across all planes. NuGraph2 is a multi-purpose network, with a common message-passing attention engine connected to multiple decoders with different classification or regression tasks. These include the classification of detector hits according to the particle type that produced them (semantic segmentation) and the separation of hits from the neutrino interaction from hits due to noise or cosmic-ray background. Additional decoders are being developed, performing tasks such as the regression of the neutrino interaction vertex position. Performance results will be presented based on publicly available samples from MicroBooNE. These include both physics performance metrics, achieving 95% accuracy for semantic segmentation and 98% classification of neutrino hits, as well as computational metrics for training and for inference on CPU or GPU. The status of the NuGraph integration in the LArSoft software framework will be presented, as well as initial studies about model interpretability and injection of domain knowledge.

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗
DOE OSTI2020

Comparing serial X-ray crystallography and microcrystal electron diffraction (MicroED) as methods for routine structure determination from small macromolecular crystals

Innovative new crystallographic methods are facilitating structural studies from ever smaller crystals of biological macromolecules. In particular, serial X-ray crystallography and microcrystal electron diffraction (MicroED) have emerged as useful methods for obtaining structural information from crystals on the nanometre to micrometre scale. Despite the utility of these methods, their implementation can often be difficult, as they present many challenges that are not encountered in traditional macromolecular crystallography experiments. Here, XFEL serial crystallography experiments and MicroED experiments using batch-grown microcrystals of the enzyme cyclophilin A are described. The results provide a roadmap for researchers hoping to design macromolecular microcrystallography experiments, and they highlight the strengths and weaknesses of the two methods. Specifically, we focus on how the different physical conditions imposed by the sample-preparation and delivery methods required for each type of experiment affect the crystal structure of the enzyme.

36 MATERIALS SCIENCE↗
DOE OSTI2025

Emulation of radiation transport in 3D stochastic media using 1D planar Monte Carlo stochastic media radiation transport algorithms

A subset of stochastic media radiation transport problems involves those in which radiation is incident on a thin slab of stochastic material. Particle tracking in 3D for such problems is expensive, and 1D planar models lack accuracy because they only allow the material to change in one dimension. Therefore, we propose dimensional emulation, which through a slight modification allows existing 1D planar geometry stochastic media radiation transport models to reproduce results from the equivalent 3D models by allowing the material to change in all three dimensions, reproducing the fidelity of the 3D model for the low computational cost of the 1D planar model. In this work, we apply dimensional emulation to three Monte Carlo stochastic media radiation transport models: Chord Length Sampling (CLS), the Local Realization Preserving method (LRP), and a variant of Conditional Point Sampling (CoPS). For a common Markovian benchmark set, the 3D emulation variants of these algorithms are numerically verified to reproduce the results of the 3D variants within statistics while running 1.3 to 2 times faster in the implementation within Sandia National Laboratories open-source research code PlaybookMC. The 3D emulation variants are also shown to yield a 72%–92% reduction in error for the thin slab problems in comparison to the 1D benchmark. As a result, the 3D emulation variant of CLS and CoPS-1 are shown to reproduce 3D CLS results that were used to approximate results for a 3D spherical inclusion geometry benchmark set.

73 NUCLEAR PHYSICS AND RADIATION PHYSICS↗
DOE OSTI2023

One-Pot Synthesis of Melamine Formaldehyde Resin-Derived N-Doped Porous Carbon for CO 2 Capture Application

The design and synthesis of porous carbons for CO 2 adsorption have attracted tremendous interest owing to the ever-soaring concerns regarding climate change and global warming. Herein, for the first time, nitrogen-rich porous carbon was prepared with chemical activation (KOH) of commercial melamine formaldehyde resin (MF) in a single step. It has been shown that the porosity parameters of the as-prepared carbons were successfully tuned by controlling the activating temperature and adjusting the amount of KOH. Thus, as-prepared N-rich porous carbon shows a large surface area of 1658 m 2 /g and a high N content of 16.07 wt%. Benefiting from the unique physical and textural features, the optimal sample depicted a CO 2 uptake of up to 4.95 and 3.30 mmol/g at 0 and 25 °C under 1 bar of pressure. More importantly, as-prepared adsorbents show great CO 2 selectivity over N 2 and outstanding recyclability, which was prominently important for CO 2 capture from the flue gases in practical application. An in-depth analysis illustrated that the synergetic effect of textural properties and surface nitrogen decoration mainly determined the CO 2 capture performance. However, the textural properties of carbons play a more important role than surface functionalities in deciding CO 2 uptake. In view of cost-effective synthesis, outstanding textural activity, and the high adsorption capacity together with good selectivity, this advanced approach becomes valid and convenient in fabricating a unique highly efficient N-rich carbon adsorbent for CO 2 uptake and separation from flue gases.

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

Understanding relative metal prices and availability: Combining physical and economic perspectives

Abstract Scientists typically use physical indicators, such as average crustal abundance, and energy requirements in production, as measures of the availability of mineral resources. Economists, on the other hand, typically rely on measures such as prices, and extraction costs. This study investigates the role of crustal abundance, energy requirements, and other physical indicators in determining relative metal prices. The relationship between prices, and physical indicators is estimated statistically for a sample of 22 chemical elements over the period 1970–2013 using an economic market model. The results suggest that energy requirements in production explain 43% of observed variation in metal prices, crustal abundance 21%, and other physical indicators (toxicity, native metal status, and melting point) combine to explain 12% of the observed differences in metal prices.

Watson, Brett J.↗
DOE OSTI2025

Position-Dependent Neutron Time-of-Flight Deviation at VULCAN Diffractometer

In neutron diffraction, it is critical to precisely measure the lattice spacing, as it is an indicator of a material’s physical characteristics, such as lattice strain, thermal expansion, and phase structures. In time-of-flight (TOF) measurements, the lattice spacing is determined by the recorded TOF from a well-calibrated instrument. However, changes in neutron time-of-flight are sensitive to many factors, such as the alignment of instrument optics, temperature, sample positions, sample dimensions, internal strains, and chemical or physical heterogeneities at the grain level. At VULCAN (SNS, ORNL), which is a high-flux engineering neutron diffractometer, we used a 1-mm-diameter diamond powder sample to scan for changes in TOF by measuring d-spacing values at different sample positions under several configurations. The 2D map of the TOF deviation or d-spacing deviation, in terms of lattice shift/lattice strain, is reported. The change in TOF is dependent on the scanned location in the beam as well as on detector locations. The results are informative for experimental planning, data interpretation, and future instrument design.

36 MATERIALS SCIENCE↗
DOE OSTI2026

Enabling Dynamic Probabilistic Risk Assessment of Physical Security Using EMRALD and MAAP

The optimization of physical security in nuclear power plants requires sophisticated methodologies that integrate operator actions and plant behavior through advanced simulation tools. To address this, Idaho National Laboratory [JL2.1]has developed the Modeling and Analysis for Safety and Security using the Dynamic EMRALD Framework (MASS-DEF) methodology, an approach that integrates force-on-force simulations, dynamic probabilistic risk assessment, and thermal-hydraulics modeling [JL3.1]to enhance security planning while reducing costs. We developed a tool that produces reduced order models using thermal hydraulic simulations from the Modular Accident Analysis Program (MAAP) [1]. These models can quickly evaluate reactor core behavior during attack simulations, and in so doing, address two barriers of traditional methods: (1) MAAP simulations are computationally intensive, and (2) attack scenarios must be run in a secure environment, which complicates analysis and validation. By precomputing scenario outcomes for a small number of modified parameters, the reduced order model significantly decreases the computational cost and enables offsite review of the results.

22 - GENERAL STUDIES OF NUCLEAR REACTORS↗
DOE OSTI2020

Das ist der HAMMER: consistent new physics interpretations of semileptonic decays

Abstract Precise measurements of $$b\rightarrow c\tau \bar{\nu }$$ b → c τ ν ¯ decays require large resource-intensive Monte Carlo (MC) samples, which incorporate detailed simulations of detector responses and physics backgrounds. Extracted parameters may be highly sensitive to the underlying theoretical models used in the MC generation. Because new physics (NP) can alter decay distributions and acceptances, the standard practice of fitting NP Wilson coefficients to SM-based measurements of the $$R(D^{(*)})$$ R ( D ( ∗ ) ) ratios can be biased. The newly developed software tool enables efficient reweighting of MC samples to arbitrary NP scenarios or to any hadronic matrix elements. We demonstrate how allows avoidance of biases through self-consistent fits directly to the NP Wilson coefficients. We also present example analyses that demonstrate the sizeable biases that can otherwise occur from naive NP interpretations of SM-based measurements. The library is presently interfaced with several existing experimental analysis frameworks and we provide an overview of its structure.

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗
DOE OSTI2025

Interpretable machine learning models classify minerals via spectroscopy

Developing methods to identify mineral species confidently and rapidly from Raman spectral analysis is critical to numerous fields. Traditionally, analysis relies on pattern matching the Raman spectrum of an unknown dataset with a supporting library of well-characterized spectral data, which may prove difficult for environmental samples that are poorly crystalline or phase mixtures. Here, we developed interpretable machine learning models that can classify uranium minerals by secondary oxyanion chemistry and other physicochemical properties based solely on Raman spectra. This new ML method produces a mineral profile of physical and chemical properties for an unknown sample and can rapidly classify or identify unknown minerals from Raman data, without the need for an exact pattern match in a spectral library. Training models are validated by 1. Strong correlation of high confidence model regions with published spectroscopic assignments and 2. Correct classification of a mineral not present in training data. Training data are from the Compendium of Uranium Raman and Infrared Experimental Spectra and available crystallographic information files within the open-source Smart Spectral Matching scientific framework. Physically meaningful classifier models can rapidly identify key structural and chemical information about unknown uranium minerals and the overall methodology is broadly applicable for mineral phases.

Machine learning↗
DOE OSTI2026

Porous reaction-bonded silicon nitride: Size effects for pressureless nitriding of binder jet additively manufactured silicon

The direct nitridation of binder jet additively manufactured silicon to produce porous reaction-bonded silicon nitride was explored to determine the maximum thickness of the sample that could be converted to >99% silicon nitride. The maximum thickness that could be nitrided was investigated by nitriding cubes of increasing physical size on the millimeter scale. Highly porous samples with high conversion ( > 99%) to silicon nitride were achieved in cubes with 35 mm edge length. It was shown that the silicon nitride phase content changes with increasing sample size.

Aguirre, Trevor [ORNL] (ORCID:0000000281469630)↗
NASA NTRSDate not supplied

Pneumatic Sampler (P-Sampler) for the Martian Moons Exploration (MMX)

The Martian Moons eXploration (MMX) mission, led by the Japanese Aerospace Exploration Agency (JAXA), will focus on the exploration of the two Martian moons – Phobos and Deimos (Figure 1). The spacecraft will perform close-up remote sensing and observations of both moons and collect a sample from Phobos for Earth sample return. MMX has set the two mission goals: (1) determining the origin of the Martian moons and (2) observing processes in the circumplanetary environment of Mars, based on remote sensing, in-situ observations, and laboratory analyses of returned samples of Phobos regolith [1-2]. To fulfill the mission goals, MMX employs a double sampling approach: Coring and Pneumatic Samplers (Figure 2).The Coring Sampler (C-Sampler), a core soil tube deployed by a robotic arm, providing access to the building blocks of Phobos beneath the surface (>2 cm), and also collect a mixture of near surface material. The P-Sampler, on the other hand, would selectively sample the surface veneer and provides reference of surface component with the C-Sampler. The double sampling system not only enhances the scientific merits of MMX but also reduces risks associated with the sampling of Phobos. Without enough knowledge of the physical and chemical properties, and the geotechnical conditions of the surface of Phobos (e.g., compositions, temperature gradient/variation, porosity, grain size distribution), having two sampling systems that utilize entirely different sampling approaches is prudent.

Dylan Van Dyne↗
DOE OSTI2022

Adaptive-in-temperature method for fast on-the-fly sampling of thermal neutron scattering data in MCNP6 [Slides]

The project objective and motivation is to develop thermal data libraries for selected isotopes in MCNP6 to support on-the-fly S(alpha, beta) sampling for temperature ranges applicable to nuclear criticality safety, thereby enhancing the physics treatment in MCNP6 so that it can perform fast on-the-fly sampling of S(alpha, beta) data at arbitrary temperature.

73 NUCLEAR PHYSICS AND RADIATION PHYSICS↗
DOE OSTI2022

A Study on Recycled Polymers Recovered from Multilayer Plastic Packaging Films by Solvent–Targeted Recovery and Precipitation (STRAP)

Multilayer plastic film use increased in multiple packaging applications due to its versatility and overall increased performance over monolayer structures. However, the performance gains from multiple layers also make recycling difficult because they contain multiple polymers that can be immiscible and burdensome to traditional mechanical recycling operations. A possible solution is the solvent-targeted recovery and precipitation (STRAP) process, but the effect on the retrieved polymer is still unknown. Here, the STRAP process is applied to two different multilayer films and samples of recovered polymers are evaluated for physical, molecular, and thermal properties. Changes in the molecular weight are not significant, but differences in thermal properties are reported along with the coprecipitation of different polymers. Solvent retention in the polymer matrix from STRAP reduced the glass transition temperature of samples, but enhanced drying recovered it. Heavy metals, such as Cd, Cr, and Pb are not detected, indicating regulatory compliance for different applications.

36 MATERIALS SCIENCE↗
DOE OSTI2021

Exploring DFT+U parameter space with a Bayesian calibration assisted by Markov chain Monte Carlo sampling

Abstract The density-functional theory is widely used to predict the physical properties of materials. However, it usually fails for strongly correlated materials. A popular solution is to use the Hubbard correction to treat strongly correlated electronic states. Unfortunately, the values of the Hubbard U and J parameters are initially unknown, and they can vary from one material to another. In this semi-empirical study, we explore the U and J parameter space of a group of iron-based compounds to simultaneously improve the prediction of physical properties (volume, magnetic moment, and bandgap). We used a Bayesian calibration assisted by Markov chain Monte Carlo sampling for three different exchange-correlation functionals (LDA, PBE, and PBEsol). We found that LDA requires the largest U correction. PBE has the smallest standard deviation and its U and J parameters are the most transferable to other iron-based compounds. Lastly, PBE predicts lattice parameters reasonably well without the Hubbard correction.

36 MATERIALS SCIENCE↗
DOE OSTI2020

Prospects for fundamental physics with LISA

In this paper, which is of programmatic rather than quantitative nature, we aim to further delineate and sharpen the future potential of the LISA mission in the area of fundamental physics. Given the very broad range of topics that might be relevant to LISA,we present here a sample of what we view as particularly promising fundamental physics directions. We organize these directions through a “science-first” approach that allows us to classify how LISA data can inform theoretical physics in a variety of areas. For each of these theoretical physics classes, we identify the sources that are currently expected to provide the principal contribution to our knowledge, and the areas that need further development. Overall, the classification presented here should not be thought of as cast in stone, but rather as a fluid framework that is amenable to change with the flow of new insights in theoretical physics.

79 ASTRONOMY AND ASTROPHYSICS↗