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At least 1,441 records · Page 80
All sourcesNASA NTRSDOE OSTI
DOE OSTI2016

Modeling the measured effect of a nitroplasticizer (BDNPA/F) on cookoff of a plastic bonded explosive (PBX 9501)

Here, we have used a modified version of the Sandia Instrumented Thermal Ignition (SITI) experiment to develop a pressure-dependent, five-step ignition model for a plastic bonded explosive (PBX 9501) consisting of 95 wt% octahydro-1,3,5,7-tetranitro-1,3,5,7-tetrazoncine (HMX), 2.5 wt% Estane® 5703 (a polyurethane thermoplastic), and 2.5 wt% of a nitroplasticizer (NP): BDNPA/F, a 50/50 wt% eutectic mixture bis(2,2-dinitropropyl)-acetal (BDNPA) and bis(2,2-dinitropropyl)-formal (BDNPF). The five steps include desorption of water, decomposition of the NP to form NO2, reaction of the NO2 with Estane® and HMX, and decomposition of HMX. The model was fit using our experiments and successfully validated with experiments from five other laboratories with scales ranging from about 2 g to more than 2.5 kg of PBX. Our experimental variables included density, confinement, free gas volume, and temperature. We measured internal temperatures, confinement pressure, and ignition time. In some of our experiments, we used a borescope to visually observe the decomposing PBX. Our observations included the endothermic β–δ phase change of the HMX, a small exothermic temperature excursion in low-density unconfined experiments, and runaway ignition. We hypothesize that the temperature excursion in these low density experiments was associated with the NP decomposing exothermically within the PBX sample. This reactant-limited temperature excursion was not observed with our thermocouples in the high-density experiments. For these experiments, we believe the binder diffused to the edges of our high density samples and decomposed next to the highly conductive wall as confirmed by our borescope images.

42 ENGINEERING↗
DOE OSTI2025

A generalized and adaptable tensor-contraction-based cluster expansion formalism for multicomponent solids

Density functional theory (DFT)-based simulations of materials have first-principles accuracy, but are very computationally expensive. For simulating various properties of multi-component alloys, the cluster expansion (CE) technique has served as the standard workaround to improve computational efficiency. However, the standard CE technique is difficult to extend to exotic and/or low-symmetry lattices, often implemented via iteration over particular cluster types, which must be enumerated per lattice structure. In this work, we introduce the tensor cluster expansion (TCE), implemented in the open-source code tce-lib, which maps correlation functions to mixed tensor contractions, eliminating the need to iterate over cluster types and additionally making the calculation of correlation functions well-suited for massively parallel architectures like GPUs. We show that local interaction energies are an immediate consequence of the TCE formalism, yielding nearly $\mathcal{O}$(1) energy difference calculations. We then use this formalism to fit CE models for the TaW and CoNiCrFeMn systems, and use these models to respectively compute the enthalpy of mixing curve and Cowley short-range order parameters, showing excellent agreement with ground truth data.

Cluster expansion↗
DOE OSTI2023

Methodology for physics-informed generation of synthetic neutron time-of-flight measurement data

Accurate neutron cross section data are a vital input to the simulation of nuclear systems for a wide range of applications from energy production to national security. The evaluation of experimental data is a key step in producing accurate cross sections. There is a widely recognized lack of reproducibility in the evaluation process due to its artisanal nature and therefore there is a call for improvement within the nuclear data community. This can be realized by automating/standardizing viable parts of the process, namely, parameter estimation by fitting theoretical models to experimental data. This automation effort could greatly benefit from a synthetic data resource. This work leverages problem-specific physics, Monte Carlo sampling, and a general methodology for data synthesis to generate unlimited, labelled experimental cross-section data that is statistically indistinguishable to the observed data. Heuristic and, where applicable, rigorous statistical comparisons to observed data support this claim. The demonstration is based on/limited to transmission measurements at Rensselaer Polytechnic Institute (RPI) and energy-differential cross sections in the resolved resonance region (RRR). An open-source software is published alongside this article that executes the complete methodology to produce high-utility synthetic datasets. The goal of this work is to provide an approach and corresponding tool that will allow the evaluation community to begin exploring more data-driven, ML-based solutions to long-standing challenges in the field.

73 NUCLEAR PHYSICS AND RADIATION PHYSICS↗
DOE OSTI2015

No evidence of a threshold in traffic volume affecting road-kill mortality at a large spatio-temporal scale

Previous studies have found that the relationship between wildlife road mortality and traffic volume follows a threshold effect on low traffic volume roads. We aimed at evaluating the response of several species to increasing traffic intensity on highways over a large geographic area and temporal period. We used data of four terrestrial vertebrate species with different biological and ecological features known by their high road-kill rates: the barn owl (Tyto alba), hedgehog (Erinaceus europaeus), red fox (Vulpes vulpes) and European rabbit (Oryctolagus cuniculus). Additionally, we checked whether road-kill likelihood varies when traffic patterns depart from the average. We used annual average daily traffic (AADT) and road-kill records observed along 1000 km of highways in Portugal over seven consecutive years (2003–2009). We fitted candidate models using Generalized Linear Models with a binomial distribution through a sample unit of 1 km segments to describe the effect of traffic on the probability of finding at least one victim in each segment during the study. We also assigned for each road-kill record the traffic of that day and the AADT on that year to test for differences using Paired Student's t-test. Mortality risk declined significantly with traffic volume but varied among species: the probability of finding road-killed red foxes and rabbits occurs up to moderate traffic volumes (< 20,000 AADT) whereas barn owls and hedgehogs occurred up to higher traffic volumes (40,000 AADT). Perception of risk may explain differences in responses towards high traffic highway segments. Road-kill rates did not vary significantly when traffic intensity departed from the average. In summary, we did not find evidence of traffic thresholds for the analysed species and traffic intensities. We suggest mitigation measures to reduce mortality be applied in particular on low traffic roads (< 5000 AADT) while additional measures to reduce barrier effects should take into account species-specific behavioural traits. - Highlights: • Traffic and road-kills were analysed along 1000 km of highways over seven years. • Mortality risk declined significantly with traffic volume. • Perception of risk may explain different responses towards high traffic sections. • Reducing barrier effects should take into account species behavioural traits.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2014

Standing on the Shoulders of Giants: Results From the Radiation Oncology Academic Development and Mentorship Assessment Project (ROADMAP)

Purpose: To analyze survey information regarding mentorship practices and cross-correlate the results with objective metrics of academic productivity among academic radiation oncologists at US Accreditation Council for Graduate Medical Education (ACGME)-accredited residency training programs. Methods and Materials: An institutional review board-approved survey for the Radiation Oncology Academic Development and Mentorship Assessment Project (ROADMAP) was sent to 1031 radiation oncologists employed at an ACGME-accredited residency training program and administered using an international secure web application designed exclusively to support data capture for research studies. Data collected included demographics, presence of mentorship, and the nature of specific mentoring activities. Productivity metrics, including number of publications, number of citations, h-index, and date of first publication, were collected for each survey respondent from a commercially available online database, and m-index was calculated. Results: A total of 158 academic radiation oncologists completed the survey, 96 of whom reported having an academic/scientific mentor. Faculty with a mentor had higher numbers of publications, citations, and h- and m-indices. Differences in gender and race/ethnicity were not associated with significant differences in mentorship rates, but those with a mentor were more likely to have a PhD degree and were more likely to have more time protected for research. Bivariate fit regression modeling showed a positive correlation between a mentor's h-index and their mentee's h-index (R{sup 2} = 0.16; P<.001). Linear regression also showed significant correlates of higher h-index, in addition to having a mentor (P=.001), included a longer career duration (P<.001) and fewer patients in treatment (P=.02). Conclusions: Mentorship is widely believed to be important to career development and academic productivity. These results emphasize the importance of identifying and striving to overcome potential barriers to effective mentorship.

62 RADIOLOGY AND NUCLEAR MEDICINE↗
DOE OSTI2014

Low-Dose Hyper-Radiosensitivity Is Not a Common Effect in Normal Asynchronous and G2-Phase Fibroblasts of Cancer Patients

Purpose: In our previous study, using the micronucleus assay, a low-dose hyper-radiosensitivity (HRS)-like phenomenon was observed for normal fibroblasts of 2 of the 40 cancer patients investigated. In this article we report, for the first time, the survival response of primary fibroblasts from 25 of these patients to low-dose irradiation and answer the question regarding the effect of G2-phase enrichment on HRS elicitation. Methods and Materials: The clonogenic survival of asynchronous as well as G2-phase enriched fibroblast populations was measured. Separation of G2-phase cells and precise cell counting was performed using a fluorescence-activated cell sorter. Sorted and plated cells were irradiated with single doses (0.1-4 Gy) of 6-MV x-rays. For each patient, at least 4 independent experiments were performed, and the induced-repair model was fitted over the whole data set to confirm the presence of HRS effect. Results: The HRS response was demonstrated for the asynchronous and G2-phase enriched cell populations of 4 patients. For the rest of patients, HRS was not defined in either of the 2 fibroblast populations. Thus, G2-phase enrichment had no effect on HRS elicitation. Conclusions: The fact that low-dose hyper-radiosensitivity is not a common effect in normal human fibroblasts implies that HRS may be of little consequence in late-responding connective tissues with regard to radiation fibrosis.

62 RADIOLOGY AND NUCLEAR MEDICINE↗
DOE OSTI2015

On the increased efficiency in InGaN-based multiple quantum wells emitting at 530–590 nm with AlGaN interlayers

InGaN/AlGaN/GaN-based multiple quantum wells (MQWs) with AlGaN interlayers (ILs) are investigated, specifically to examine the fundamental mechanisms behind their increased radiative efficiency at wavelengths of 530–590 nm. The Al z Ga 1-z N (z~0.38) IL is ~1–2 nm thick, and is grown after and at the same growth temperature as the ~3 nm thick InGaN quantum well (QW). This is followed by an increase in temperature for the growth of a ~10 nm thick GaN barrier layer. The insertion of the AlGaN IL within the MQW provides various benefits. First, the AlGaN IL allows for growth of the In x Ga 1-x N QW well below typical growth temperatures to achieve higher x (up to~0.25). Second, annealing the IL capped QW prior to the GaN barrier growth improves the AlGaN IL smoothness as determined by atomic force microscopy, improves the InGaN/AlGaN/GaN interface quality as determined from scanning transmission electron microscope images and x-ray diffraction, and increases the radiative efficiency by reducing non-radiative defects as determined by time-resolved photoluminescence measurements. Finally, the AlGaN IL increases the spontaneous and piezoelectric polarization induced electric fields acting on the InGaN QW, providing an additional red-shift to the emission wavelength as determined by Schrodinger-Poisson modeling and fitting to the experimental data. The relative impact of increased indium concentration and polarization fields on the radiative efficiency of MQWs with AlGaN ILs is also explored, along with implications to conventional longer wavelength emitters.

14 SOLAR ENERGY↗
DOE OSTI2019

Atomistic modeling of out-of-pile xenon diffusion by vacancy clusters in UO 2

We report that diffusion of Xe atoms in UO 2 fuel is important for nuclear fuel performance, and is enabled by interaction with U and O vacancies. Previous work using atomistic calculations based on density functional theory (DFT) and empirical potentials (EP) focused on the role of small vacancy clusters (Xe U2Oy , y = 0, 1, 2) for Xe transport, but the model predictions failed to reproduce the experimental observations by consistently underestimating Xe diffusivity. In this work, where we focused on out-of-pile conditions (i.e. in the absence of irradiation), we have explored two of the uncertainties associated with the model: the DFT methodology and the types of clusters considered. Additionally, we found that using the energy barriers obtained with GGA + U DFT allows to reconcile simulation results and experimental observations in the intrinsic regime (i.e. out-of-pile conditions). The Xe UO cluster is the preferred configuration at high temperature, while the mobile Xe U2O cluster takes over below 1720 K. The latter cluster is also the main contributor to Xe diffusion and good agreement is obtained with current models empirically fitted to experiments for diffusion at high temperature. A simple expression that captures most of Xe diffusivity is proposed, relying on Xe UO and Xe U2O clusters only. We also determined the out-of-pile concentration and diffusivity of extended clusters Xe U z O y , 3 ≤ x ≤ 9, and determined that these do not contribute significantly to the overall diffusivity under intrinsic conditions. However, we speculate that the Xe U 4 O 3 and Xe U 8 O 9 clusters, which have relatively low migration barrier, may play a role under irradiation conditions, because the radiation-induced enhanced vacancy concentration would rapidly increase the fraction of large Xe-vacancy clusters.

11 NUCLEAR FUEL CYCLE AND FUEL MATERIALS↗
DOE OSTI2018

Photothermal deflection technique investigation of thermal annealing effects of AlGaAsSb/GaSb laser structure: Non-radiative recombination parameters enhancement

Highlights: • Photothermal deflection technique is performed to mesuaure the nonradiative recombination parameters of AlGaAsSb/GaSb. • After annealing time process, nonradiative recombination parameters of AlGaAs / GaSb have been enhanced. • The largest nonradiative recombination values have been found for AlGaAsSb/GaSb annealed for 60 min - Abstract: Photothermal deflection technique (PTD) is used to investigate the effects of thermal annealing time on nonradiative recombination parameters i.e. nonradiative lifetime of minority carriers, electronic diffusion coefficient, and surface and interface recombination velocity of AlGaAsSb/GaSb laser structure. The extraction of the electronic parameters are performed by PTD technique through extensive modeling by fitting the experimental photothermal signal to the corresponding theoretical ones. We have found that nonradiative recombination parameters have been enhanced by annealing time. These results are very promising for improvement of active layer used for vertical-cavity surface-emitting lasers (VCSELs).

75 CONDENSED MATTER PHYSICS, SUPERCONDUCTIVITY AND↗
DOE OSTI2025

Explaining drivers of housing prices with nonlinear hedonic regressions

Housing markets play a critical role in shaping the spatial and demographic evolution of urban areas. Simulating housing price dynamics can enhance projections of future urban development outcomes. However, traditional hedonic regressions for housing prices, which neglect nonlinear interactions among explanatory variables, often exhibit limited predictive performance. While machine learning (ML) methods can provide a more flexible representation of the relationships between predictors, they are often regarded as “black boxes” due to their complexity and lack of transparency. Interpretable ML techniques provide a promising route by combining the flexibility of ML methods with approaches to analyze the relationships between inputs and outputs. In this study, we employ interpretable ML to analyze the patterns driving the housing market in Baltimore, Maryland, USA. We train an Artificial Neural Network (ANN) to predict Baltimore housing prices based on structural characteristics (e.g., home size, number of stories) and locational attributes (e.g., distance to the city center). We then conduct sensitivity and Partial Dependence Plot (PDP) analyses to interpret the fitted ANN model. We find that the ML model achieves higher predictive accuracy and explains 16 % more of housing price variance than a traditional linear regression model. The interpretable ML model also reveals more nuanced and realistic nonlinear relationships between housing sales price and predictors as well as interactive effects underlying Baltimore home price dynamics. For instance, while the linear model indicates a steady housing price increase over time, our interpretable ML model detects a post-2008 decline, with smaller properties experiencing the sharpest drop.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2015

Digital pulse processing for NaI(Tl) detectors

We apply two different post-processing techniques to digital pulses induced by photons in a NaI(Tl) detector and compare the obtained energy resolution to the standard analog approach. Our digital acquisition approach is performed using a single-stage acquisition with a fast digitizer. Both the post-processing techniques we propose rely on signal integration. In the first, the pulse integral is calculated by directly numerically integrating the pulse digital samples, while in the second the pulse integral is estimated by a model-based fitting of the pulse. Our study used a 7.62 cm×7.62 cm cylindrical NaI(Tl) detector that gave a 7.60% energy resolution (at 662 keV), using the standard analog acquisition approach, based on a pulse shaping amplifier. The new direct numerical integration yielded a 6.52% energy resolution. The fitting approach yielded a 6.55% energy resolution, and, although computationally heavier than numerical integration, is preferable when only the early samples of the pulse are available. We also evaluated the timing performance of a fast–slow detection system, encompassing an EJ-309 and a NaI(Tl) scintillator. Here, we use two techniques to determine the pulse start time: constant fraction discrimination (CFD) and adaptive noise threshold timing (ANT), for both the analog and digital acquisition approach. With the analog acquisition approach, we found a system time resolution of 5.8 ns and 7.3 ns, using the constant fraction discrimination and adaptive noise threshold timing, respectively. With the digital acquisition approach, a time resolution of 1.2 ns was achieved using the ANT method and 3.3 ns using CFD at 50% of the maximum, to select the pulse start time. Finally, the proposed direct digital readout and post-processing techniques can improve the application of NaI(Tl) detectors, traditionally considered ‘slow’, for fast counting and correlation measurements, while maintaining a good measurement of the energy resolution.

46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND ↗
DOE OSTI2018

Attribution of gamma-ray background collected by a mobile detector system to its surroundings using panoramic video

Mobile radiation detector systems are important tools for detecting radiological and nuclear sources outside of regulatory control, but due to their mobility, they are subject to complex and varying backgrounds in most realistic operational scenarios. Recent work has found correlations between non-radiological contextual information and gamma-ray spectral features that can be used to decrease false alarm rates, however a more complete understanding of background source terms has been elusive. A measurement campaign developed a full radiological characterization of a controlled facility that roughly corresponded to two city blocks. As part of the campaign, the Radiological Multi-sensor Analysis Platform (RadMAP) collected extensive multi-sensor data. In this work, RadMAP’s panoramic video data were used to visually identify several different materials and quantify the detector response to each material. A linear model was fit between the material responses and radiological features, and the results demonstrate reasonable agreement with ground truth flux measurements for each material. This paper will describe the data collection, processing, and analysis of the gamma-ray and video data from RadMAP. The paper will conclude with perspectives on the applicability of such a method to less controlled environments both with respect to achieving better understanding the sources of variability of background radiation in urban environments and whether such methods could be leveraged in operational scenarios.

46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND ↗
DOE OSTI2020

A functional form for liquid scintillator pulse shapes

Digitization of detector signals enables analysis of the original waveform to extract timing, particle identification, and energy deposition information. Here we present the use of analytical functions based on sigmoids to model and fit such pulse shapes from liquid organic scintillators, though the method should also be applicable to other detector systems. Neutron and gamma interactions in NE213 detectors were digitized from the phototube anode and fit using a sigmoid-based function. The acuity of the fit in extracting timing information and performing neutron-gamma pulse-shape discrimination are presented and discussed.

46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND ↗
DOE OSTI2024

Investigating the composition of the $K$$^{*}_{0}$(700) state with π ± $K$$^{0}_{S}$ correlations at the LHC

The first measurements of femtoscopic correlations with the particle pair combinations π ± $K$$^{0}_{S}$ in pp collisions at $\sqrt{s}$ = 13 TeV at the Large Hadron Collider (LHC) are reported by the ALICE experiment. Using the femtoscopic approach, it is shown that it is possible to study the elusive $K$$^{*}_{0}$(700) particle that has been considered a tetraquark candidate for over forty years. Source and final-state interaction parameters are extracted by fitting a model assuming a Gaussian source to the experimentally measured two-particle correlation functions. The final-state interaction in the π ± $K$$^{0}_{S}$ system is modeled through a resonant scattering amplitude, defined in terms of a mass and a coupling parameter, The extracted mass and Breit–Wigner width, derived from the coupling parameter, of the final-state interaction are found to be consistent with previous measurements of the $K$$^{*}_{0}$ (700). The small value and increase of the correlation strength with increasing source size support the hypothesis that the $K$$^{*}_{0}$(700) is a four-quark state, i.e. a tetraquark state of the form (q 1 , $\bar{q}$ 2 , q 3 , $\bar{q}$ 3 ) in which q 1 , q 2 and q 3 indicate the flavor of the valence quarks of the π and $K$$^{0}_{S}$. This latter trend is also confirmed via a simple geometric model that assumes a tetraquark structure of the $K$$^{*}_{0}$(700) resonance.

13 TeV pp collisions↗
DOE OSTI2020

The maximum carboxylation rate of Rubisco affects CO2 refixation in temperate broadleaved forest trees

Mesophyll resistance to CO 2 diffusion (rm) and the maximum carboxylation rate of Rubisco (Vcmax) affect photosynthetic rates, and can potentially also influence the percentage of respiratory and photorespiratory CO 2 being refixated (Pr) by mesophyll cells. Here we investigated how various leaf anatomical traits (e.g. leaf mass per area [LMA] and leaf dry matter content [LDMC]) influenced rm in leaves of mature forest trees. We further explored how rm and Vcmax in turn affected Pr, and if these traits varied among species and leaves along a light gradient. Photosynthetic CO 2 response of leaves grown in high-, medium-, and low-light environments was measured, from Pinus sylvestris [Scots pine], Picea abies [Norway spruce], Quercus robur [English oak], and Betula pendula [Silver birch] in southern Sweden. A modified version of the Farquhar-von Caemmerer-Berry model was fitted to the leaf gas exchange data to estimate Vcmax, rm and Pr. We found that of all leaf traits measured, only LMA for Q. robur was significantly higher in leaves from high-light environments. When comparing species, both rm and LMA were significantly higher in the conifers, and rm had a negative correlation with Vcmax. We found that Pr was similar between different species and functional groups, with an average of 73.2% (and SD of ±10.4) across all species. There was a strong, positive correlation between Pr and Vcmax in broadleaves, and we hypothesise that this effect might derive from a higher CO 2 drawdown near Rubisco in leaves with high Vcmax.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2023

Using electronic health record metadata to predict housing instability amongst veterans

Housing instability is considered a significant life stressor and preemptive screening should be applied to identify those at risk for homelessness as early as possible so that they can be targeted for specialized care. We developed models to classify patient outcomes for an established VA Homelessness Screening Clinical Reminder (HSCR), which identifies housing instability, in the two months prior to its administration. Logistic Regression and Random Forest models were fit to classify responses using the last 18 months of document activity. We measure concentration of risk across stratifications of predicted probability and observe an enriched likelihood of finding confirmed false negative responses from veterans with diagnosed housing instability. Positive responses were 34 times more likely to be detected within the top 1 % of patients predicted at risk than from those randomly selected. There is a 1 in 4 chance of detecting false negatives within the top 1 % of predicted risk. Machine learning methods can classify between episodes of housing instability using a data-driven approach that does not rely on variables curated from domain experts. This method has the potential to improve clinicians’ ability to identify veterans who are experiencing housing instability but are not captured by HSCR.

60 APPLIED LIFE SCIENCES↗