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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
NASA NTRS1995

Chemical Sensing of Unexploded Ordnance with the Mobile Underwater Survey System (MUDSS)

The ability to sense explosives residues in the marine environment is a critical tool for identification and classification of underwater unexploded ordnance (UXO). Trace explosives signatures of TNT and DNT have been extracted from multiple sediment samples adjacent to unexploded undersea ordnance at Halifax Harbor, Canada. The ordnance was hurled into the harbor during a massive explosion fifty years earlier, in 1945 after World War II had ended. Laboratory sediment extractions were made using the solid-phase microextraction (SPME) method in seawater, and detection using the Reversal Electron Attachment Detection (READ) technique and, in the case of DNT, a commercial gas-chromatography/mass spectrometer (GC/MS). Results show that, after more than 50 years in the environment, ordnance which appeared to be physically intact gave good explosives signatures at the parts-per-billion level, whereas ordnance which had been cracked open during the explosion gave no signatures at the 10 parts-per-trillion sensitivity level. These measurements appear to provide the first reported data of explosives signatures from undersea UXOs.

Darrach, M. R.↗
NASA NTRS1999

Chemical Sensing of Unexploded Ordnance with the Mobile Underwater Survey System (MUDSS)

The ability to sense explosives residues in the marine environment is a critical tool for identification and classification of underwater unexploded ordnance (UXO). Trace explosives signatures of TNT and DNT have been extracted from mulitple sediment samples adjacent to unexploded undersea ordnance at Halifax Harbor, Canada.

explosives electron attachment marine UXOs↗
DOE OSTI2018

A robust two-dimensional zirconium-based luminescent coordination polymer built on a V-shaped dicarboxylate ligand for vapor phase sensing of volatile organic compounds

Here we report herein a new two-dimensional zirconium-based luminescent coordination polymer Zr 6 (sdba) 4 (μ 3 -O) 4 (μ 3 - OH) 4 (HCOO) 2 (OH) 2 (H 2 O) 2 (1) [sdba = 4,4'-sulfonyldibenzoate] exhibiting selective fluorescence responses towards a variety of volatile organic compounds upon exposure in the vapor phase. Having a unique two-dimensional signal response towards aromatic molecules, including but not limited to nitroaromatic explosives, it is capable of identification of a diverse set of analytes. In addition, compound 1 shows a remarkably high sensitivity toward acetone vapors.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2014

Science and Technology Review (March 2014)

Over the past few years, Lawrence Livermore has experienced a surge of innovation in radiochemistry—the science of generating, identifying, isolating, and characterizing the radioactive isotopes of chemical elements. For example, Livermore researchers have developed a technique for producing double-sided target foils and built a target surface mapping tool, which increased the productivity of stockpile stewardship experiments. Radiochemists are also using the Laboratory’s particle accelerator to measure nuclear reaction products for reactor safety and nuclear forensics research. Another Livermore team is making synthetic nuclear debris samples with ingredients that more closely match debris from a real explosion for use in isotope identification exercises. Researchers are also refining the chemicals and protocols required to perform wet-chemistry experiments on transactinides, shortlived heavy elements such as flerovium and livermorium. A collaboration with two universities is developing a portable, automated system that can quickly capture, prepare, and chemically test radioisotopes.

29 ENERGY PLANNING, POLICY, AND ECONOMY↗
DOE OSTI2018

Machine Learning in High Energy Physics Community White Paper

Machine learning is an important applied research area in particle physics, beginning with applications to high-level physics analysis in the 1990s and 2000s, followed by an explosion of applications in particle and event identification and reconstruction in the 2010s. In this document we discuss promising future research and development areas in machine learning in particle physics with a roadmap for their implementation, software and hardware resource requirements, collaborative initiatives with the data science community, academia and industry, and training the particle physics community in data science. The main objective of the document is to connect and motivate these areas of research and development with the physics drivers of the High-Luminosity Large Hadron Collider and future neutrino experiments and identify the resource needs for their implementation. Additionally we identify areas where collaboration with external communities will be of great benefit.

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗
DOE OSTI2021

Full Moment Tensor Solutions of U.S. Underground Nuclear Tests for Event Screening and Yield Estimation

Moment tensor (MT) solutions are proving increasingly valuable in explosion monitoring, especially now that they are more routinely calculated for the unconstrained, full (six component) MT. In this study, we have calculated MTs for U.S. underground nuclear tests conducted at the Nevada National Security Site using seismic recordings primarily from the Livermore Nevada Network. We are able to determine them for 130 nuclear explosions from 1970 to 1996 for a range of yields and under a variety of material conditions, which we have supplemented with 10 additional chemical explosions at the test site. The result is an extensive database of MTs that can be used to assess the performance of important monitoring tasks such as event identification and yield determination. We test the explosion event screening on the fundamental lune of the MT eigensphere and find MT screening to be a robust discriminant between earthquakes and explosions. We then explore the estimation of moment-derived yield, in which we find that material properties are the largest contributor to differences in the estimated moment-to-yield ratio. Additionally, further research conducted on this dataset can be used to develop, test, and improve various explosion monitoring methodologies.

58 GEOSCIENCES↗
DOE OSTI2019

SN 2016coi (ASASSN-16fp): An Energetic H-stripped Core-collapse Supernova from a Massive Stellar Progenitor with Large Mass Loss

Herein, we present comprehensive observations and analysis of the energetic H-stripped SN 2016coi (a.k.a. ASASSN-16fp), spanning the γ -ray through optical and radio wavelengths, acquired within the first hours to ~420 days post explosion. Our observational campaign confirms the identification of He in the supernova (SN) ejecta, which we interpret to be caused by a larger mixing of Ni into the outer ejecta layers. By modeling the broad bolometric light curve, we derive a large ejecta-mass-to-kinetic-energy ratio ( M ej ~ 4–7 M ⊙ , E k ~ (7–8) × 10 51 erg). The small [Ca ii ] λλ 7291,7324 to [O i ] λλ 6300,6364 ratio (~0.2) observed in our late-time optical spectra is suggestive of a large progenitor core mass at the time of collapse. We find that SN 2016coi is a luminous source of X-rays ( L X > 10 39 erg s –1 in the first ~100 days post explosion) and radio emission ( L 8.5 GHz ~ 7 × 10 27 erg s –1 Hz –1 at peak). These values are in line with those of relativistic SNe (2009bb, 2012ap). However, for SN 2016coi, we infer substantial pre-explosion progenitor mass loss with a rate ~ (1–2) × and a sub-relativistic shock velocity v sh ~ 0.15 c , which is in stark contrast with relativistic SNe and similar to normal SNe. Finally, we find no evidence for a SN-associated shock breakout γ -ray pulse with energy E γ > 2 × 10 46 erg. While we cannot exclude the presence of a companion in a binary system, taken together, our findings are consistent with a massive single-star progenitor that experienced large mass loss in the years leading up to core collapse, but was unable to achieve complete stripping of its outer layers before explosion.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2012

Selective Response of Mesoporous Silicon to Adsorbants with Nitro Groups

We demonstrate that the electronic structure of mesoporous silicon is affected by adsorption of nitrobased explosive molecules in a compound-selective manner. This selective response is demonstrated by probing the adsorption of two nitro-based molecular explosives (trinitrotoluene and cyclotrimethylenetrinitramine) and a nonexplosive nitro-based aromatic molecule (nitrotoluene) on mesoporous silicon using soft X-ray spectroscopy. The Si atoms strongly interact with adsorbed molecules to form Si-O and Si-N bonds, as evident from the large shifts in emission energy present in the Si L2,3 X-ray emission spectroscopy (XES) measurements. Furthermore, we find that the energy gap (band gap) of mesoporous silicon changes depending on the adsorbant, as estimated from the Si L2,3 XES and 2p X-ray absorption spectroscopy (XAS) measurements. Our ab initio molecular dynamics calculations of model compounds suggest that these changes are due to spontaneous breaking of the nitro groups upon contacting surface Si atoms. This compound-selective change in electronic structure may provide a powerful tool for the detection and identification of trace quantities of airborne explosive molecules.

Environmental Molecular Sciences Laboratory↗
NASA NTRS1992

The 32nd CDC: System identification using interval dynamic models

Motivated by the recent explosive development of results in the area of parametric robust control, a new technique to identify a family of uncertain systems is identified. The new technique takes the frequency domain input and output data obtained from experimental test signals and produces an 'interval transfer function' that contains the complete frequency domain behavior with respect to the test signals. This interval transfer function is one of the key concepts in the parametric robust control approach and identification with such an interval model allows one to predict the worst case performance and stability margins using recent results on interval systems. The algorithm is illustrated by applying it to an 18 bay Mini-Mast truss structure.

Keel, L. H.↗
DOE OSTI2012

Materials characterisation with the associated particle technique

Since the last past years, the Nuclear Measurement Laboratory of CEA Cadarache, France, together with Partners from European and National projects, has been studying the application of fast neutron interrogation with the Associated Particle Technique for material identification in different areas of homeland and maritime security, and for the characterisation of the materials constituting radioactive waste. Fast 14 MeV neutrons are produced from the H-3(H-2,n)alpha fusion reaction in a sealed tube neutron generator embedding an alpha detector. The alpha particle is used to tag neutron direction and emission time, thus allowing the electronic selection of neutron-induced gamma spectra in the voxels of interest. Gamma rays emitted by tagged neutron interactions on the present nuclei (C, O, N, Fe, Al, Si, Cl, etc.) are recorded with spectroscopic detectors and analysed to determine elemental proportions, thus allowing material identification. Investigations have been conducted for the detection of explosives, illicit drugs and contraband materials in cargo containers, for the inspection of objects lying on the sea floor suspected to contain explosives like mines, bombs, torpedoes, etc., for the recognition of an improvised chemical device, and for material identification in radioactive waste packages. Recently the detection of special nuclear materials is being investigated using time correlation analysis between induced fission particles, instead of gamma-ray spectroscopy. The paper presents an overview of these studies and last results. (authors)

46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND ↗
DOE OSTI2016

Design and expected performance of a fast neutron attenuation probe for light element density measurements

In this paper, we present the design and expected performance of a proof-of-concept 32 channel material identification system. Our system is based on the energy-dependent attenuation of fast neutrons for four elements: hydrogen, carbon, nitrogen and oxygen. We describe a new approach to obtaining a broad range of neutron energies to probe a sample, as well as our technique for reconstructing the molar densities within a sample. The system's performance as a function of time-of-flight energy resolution is explored using a Geant4-based Monte Carlo. Our results indicate that, with the expected detector response of our system, we will be able to determine the molar density of all four elements to within a 20–30% accuracy in a two hour scan time. In many cases this error is systematically low, thus the ratio between elements is more accurate. This degree of accuracy is enough to distinguish, for example, a sample of water from a sample of pure hydrogen peroxide: the ratio of oxygen to hydrogen is reconstructed to within 8±0.5% of the true value. Lastly, with future algorithm development that accounts for backgrounds caused by scattering within the sample itself, the accuracy of molar densities, not ratios, may improve to the 5–10% level for a two hour scan time.

46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND ↗
DOE OSTI2017

Neutrinos from type Ia supernovae: The gravitationally confined detonation scenario

Despite their use as cosmological distance indicators and their importance in the chemical evolution of galaxies, the unequivocal identification of the progenitor systems and explosion mechanism of normal type Ia supernovae (SNe Ia) remains elusive. The leading hypothesis is that such a supernova is a thermonuclear explosion of a carbon-oxygen white dwarf, but the exact explosion mechanism is still a matter of debate. Observation of a galactic SN Ia would be of immense value in answering the many open questions related to these events. One potentially useful source of information about the explosion mechanism and progenitor is the neutrino signal because the neutrinos from the different mechanisms possess distinct spectra as a function of time and energy. Here in this paper, we compute the expected neutrino signal from a gravitationally confined detonation (GCD) explosion scenario for a SN Ia and show how the flux at Earth contains features in time and energy unique to this scenario. We then calculate the expected event rates in the Super-K, Hyper-K, JUNO, DUNE, and IceCube detectors and find both Hyper-K and IceCube will see a few events for a GCD supernova at 1 kpc or closer, while Super-K, JUNO, and DUNE will see events if the supernova is closer than ~0.3 kpc. The distance and detector criteria needed to resolve the time and spectral features arising from the explosion mechanism, neutrino production, and neutrino oscillation processes are also discussed. The neutrino signal from the GCD is then compared with the signal from a deflagration-to-detonation transition (DDT) explosion model computed previously. We find the overall event rate is the most discriminating feature between the two scenarios followed by the event rate time structure. Using the event rate in the Hyper-K detector alone, the DDT can be distinguished from the GCD at 2σ if the distance to the supernova is less than 2.3 kpc for a normal mass ordering and 3.6 kpc for an inverted ordering.

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗
DOE OSTI2023

Moment tensor event identification for collapses

SUMMARY We introduce a seismic identification method for collapse events using moment tensors (MTs). We start by computing full (six-element) MT solutions for 43 identified collapse events from around the world, and statistically characterizing the population on the MT hypersphere. We then test a large data set of over 1000 full MTs for the western U.S. against the distribution of collapses using a MT-based identification method similarly as used for testing explosions. Known collapses and explosions are readily identified, along with other anomalous events in the Geysers and central California coast. Misidentification rates are determined for various screening angles with optimal misidentification rates between earthquakes and collapses on the order of 3 per cent. The method is demonstrated to be very effective at identifying non-earthquake sources with a 97–98 per cent accuracy. It is likely to be transportable to other regions, and can be used for event identification anywhere full MT solutions are routinely calculated.

58 GEOSCIENCES↗
DOE OSTI2023

Multi-Functional Applications of H-Glass Embedded with Stable Plasmonic Gold Nanoislands

Metal nanoparticles (MNPs) are synthesized using various techniques on diverse substrates that significantly impact their properties. However, among the substrate materials investigated, the major challenge is the stability of MNPs due to their poor adhesion to the substrate. Herein, it is demonstrated how a newly developed H-glass can concurrently stabilize plasmonic gold nanoislands (GNIs) and offer multifunctional applications. The GNIs on the H-glass are synthesized using a simple yet, robust thermal dewetting process. The H-glass embedded with GNIs demonstrates versatility in its applications, such as i) acting as a room temperature chemiresistive gas sensor (70% response for NO 2 gas); ii) serving as substrates for surface-enhanced Raman spectroscopy for the identifications of Nile blue (dye) and picric acid (explosive) analytes down to nanomolar concentrations with enhancement factors of 4.8 × 10 6 and 6.1 × 10 5 , respectively; and iii) functioning as a nonlinear optical saturable absorber with a saturation intensity of 18.36 × 10 15 W m -2 at 600 nm, and the performance characteristics are on par with those of materials reported in the existing literature. In conclusion, this work establishes a facile strategy to develop advanced materials by depositing metal nanoislands on glass for various functional applications.

36 MATERIALS SCIENCE↗
DOE OSTI2014

Identification of a jet-driven supernova remnant in the Small Magellanic Cloud: Possible evidence for the enhancement of bipolar explosions at low metallicity

Recent evidence has suggested that the supernova remnant (SNR) 0104–72.3 in the Small Magellanic Cloud (SMC) may be the result of a 'prompt' Type Ia SN on the basis of enhanced iron abundances and its association with a star-forming region. In this paper, we present evidence that SNR 0104–72.3 arose from a jet-driven bipolar core-collapse (CC) SN. Specifically, we use serendipitous Chandra data of SNR 0104–72.3 taken because of its proximity to the calibration source SNR E0102–72.3. We analyze 56 Advanced CCD Imaging Spectrometer (ACIS) observations of SNR 0104–72.3 to produce imaging and spectra with an effective exposure of 528.6 ks. We demonstrate that SNR 0104–72.3 is highly elliptical relative to other nearby young SNRs, suggesting a CC SN origin. Furthermore, we compare ejecta abundances derived from spectral fits to nucleosynthetic yields of Type Ia and CC SNe, and we find that the iron, neon, and silicon abundances are consistent with either a spherical CC SN of a 18-20 M {sub ☉} progenitor or an aspherical CC SN of a 25 M {sub ☉} progenitor. We show that the star formation history at the site of SNR 0104–72.3 is also consistent with a CC origin. Given the bipolar morphology of the SNR, we favor the aspherical CC SN scenario. This result may suggest jet-driven SNe occur frequently in the low-metallicity environment of the SMC, consistent with the observational and theoretical work on broad-line Type Ic SNe and long-duration gamma-ray bursts.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2025

Explainable Machine Learning for Functional Data

Black-box machine learning models are recognized as useful tools for prediction applications, but the algorithmic complexity of some models causes interpretation challenges. Explainability methods have been proposed to provide insight into these models, but there is little research focused on supervised modeling with functional data inputs. We argue that, especially in applications of high consequence, it is important to explicitly model the functional dependence in a black-box analysis to not obscure or misrepresent patterns in explanations. As such, we propose the V ariable importance E xplainable E lastic S hape A nalysis (VEESA) pipeline for training supervised machine learning models with functional inputs. The pipeline is an analysis process that includes the data preprocessing, modeling, and post-hoc explanations. The preprocessing is done using elastic functional principal components analysis, which accounts for vertical and horizontal variability in functional data and, ultimately, allows for explanations in the original data space that identify the important functional variability without bias due to correlated variables. Here, we demonstrate the pipeline on two high-consequence applications: explosives classification for national security and inkjet printer identification in forensic science. The applications exhibit the VEESA pipeline’s ability to provide an understanding of the characteristics of the functional data useful for prediction. Code for implementing the pipeline is available in the veesa R package (and supplemental python code).

Elastic Shape Analysis↗
DOE OSTI2015

Enabling Explosives and Contraband Detection with Neutron Resonant Attenuation. Year 1 of 3 Summary

Material Identification by Resonant Attenuation is a technique that measures the energy-dependent attenuation of 1-10 MeV neutrons as they pass through a sample. Elemental information is determined from the neutron absorption resonances unique to each element. With sufficient energy resolution, these resonances can be used to categorize a wide range of materials, serving as a powerful discrimination technique between explosives, contraband, and other materials. Our proposed system is unique in that it simultaneously down-scatters and time tags neutrons in scintillator detectors oriented between a d-T generator and sample. This allows not only for energy measurements without pulsed neutron beams, but for sample interrogation over a large range of relevant energies, vastly improving scan times. Our system’s core advantage is a potential breakthrough ability to provide detection discrimination of threat materials by their elemental composition (e.g. water vs. hydrogen peroxide) without opening the container. However, several technical and computational challenges associated with this technique have yet to be addressed. There are several open questions: what is the sensitivity to different materials, what scan times are necessary, what are the sources of background, how do each of these scale as the detector system is made larger, and how can the system be integrated into existing scanning technology to close current detection gaps? In order to prove the applicability of this technology, we will develop a validated model to optimize the design and characterize the uncertainties in the measurement, and then test the system in a real-world scenario. This project seeks to perform R&D and laboratory tests that demonstrate proof of concept (TRL 3) to establishing an integrated system and evaluating its performance (TRL 4) through both laboratory tests and a validated detector model. The validated model will allow us to explore our technology’s benefits to explosive detection in various applications.

46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND ↗