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At least 271 records · Page 15
All sourcesNASA NTRSDOE OSTI
DOE OSTI2021

Estimating the Acute Health Impacts of Fire-Originated PM 2.5 Exposure During the 2017 California Wildfires: Sensitivity to Choices of Inputs

Exposure to wildfire smoke increases the risk of respiratory and cardiovascular hospital admissions. Health impact assessments, used to inform decision-making processes, characterize the health impacts of environmental exposures by combining preexisting epidemiological concentration–response functions (CRFs) with estimates of exposure. These two key inputs influence the magnitude and uncertainty of the health impacts estimated, but for wildfire-related impact assessments the extent of their impact is largely unknown. We first estimated the number of respiratory, cardiovascular, and asthma hospital admissions attributable to fire-originated PM 2.5 exposure in central California during the October 2017 wildfires, using Monte Carlo simulations to quantify uncertainty with respect to the exposure and epidemiological inputs. We next conducted sensitivity analyses, comparing four estimates of fire-originated PM 2.5 and two CRFs, wildfire and nonwildfire specific, to understand their impact on the estimation of excess admissions and sources of uncertainty. We estimate the fires accounted for an excess 240 (95% CI: 114, 404) respiratory, 68 (95% CI: -10, 159) cardiovascular, and 45 (95% CI: 18, 81) asthma hospital admissions, with 56% of admissions occurring in the Bay Area. Although differences between impact assessment methods are not statistically significant, the admissions estimates' magnitude is particularly sensitive to the CRF specified while the uncertainty is most sensitive to estimates of fire-originated PM 2.5 . Not accounting for the exposure surface's uncertainty leads to an underestimation of the uncertainty of the health impacts estimated. Employing context-specific CRFs and using accurate exposure estimates that combine multiple data sets generates more certain estimates of the acute health impacts of wildfires.

58 GEOSCIENCES↗
DOE OSTI2020

Formation energy puzzle in intermetallic alloys: Random phase approximation fails to predict accurate formation energies

We performed density functional calculations to estimate the formation energies of intermetallic alloys. We used two semilocal approximations, the generalized gradient approximation (GGA) by Perdew-Burke- Ernzerhof (PBE) and the strongly constrained and appropriately normed (SCAN) meta-GGA. In addition, we utilized two nonlocal DFT functionals, the hybrid HSE06, and the state-of-the-art random phase approximation (RPA). The nonlocal functionals such as HSE06 and RPA yield accurate formation energies of binary alloys with completely-filled d-band metals, where semilocal functionals underperform. The accuracy at the nonlocal functionals is greatly reduced when a partially-filled d-band metal is present in an alloy, while PBE-GGA outperforms in these cases. We show that the accurate prediction of formation energies by any DFT method depends on its ability to predict the accurate electronic properties, e.g., valence d-band contribution to the density of states (DOS). The SCAN meta-GGA often corrects the PBE-DOS, however, it does not provide accurate formation energies compared to PBE. This is assumed to be due to the lack of proper error cancellation that should be expected due to the similar bulk nature of both alloys and their constituents, which may improve with the modification of meta-GGA ingredients. RPA yields too negative formation energies of alloys with partially-filled d-band metals. RPA results can be corrected by restoring the exchange-correlation kernel, thereby improving the short-range electron-electron correlation in metallic densities.

36 MATERIALS SCIENCE↗
DOE OSTI2023

Simple and accurate estimation of metal, semiconductor, and insulator work functions

Work-function Φ measures the work needed for an electron to escape a solid. For simple metals, it is almost exclusively the result of the surface dipole created by spill-out of electrons from bulk into the vacuum as shown in the seminal work by Lang and Kohn [Phys. Rev. B 3, 1215 (1971)] using a jellium model. Despite a half century of intense efforts, however, such understanding has not been extended past metals. Here we present a universal model for Φ which contains a previously unrecognized bulk contribution Φ I [Phys. Rev. Lett. 121, 196802 (2018)]. By incorporating Φ I into the jellium model, we find that it dictates the dipole due to charge spill out and provides a description which applies to a wide range of materials, ranging from metals, semiconductors, to insulators.

36 MATERIALS SCIENCE↗
DOE OSTI2024

Investigations of Relative Stability and Distribution of Mo Species in Pore Space of Mo/ZSM-5 Systems

Reduced forms of molybdenum (Mo) carbides anchoring in pore space of zeolites are considered as activated catalysts for the methane dehydroaromatization reaction. However, the atomic structure of the active sites and their stability under reactive conditions are still not clearly understood. Herein, we employ a synergistic theoretical and experimental approach to investigate this research gap. The Raman data shows the existence of the distributed binuclear and mononuclear Mo oxides in the pores that are corroborated by the hydrogen temperature-programmed reduction experiments. The binding free energies of mono- and bi-nuclear Mo carbides in straight and sinusoidal channels and their intersections as a function of temperature and anchoring sites have been determined. The catalysts with Mo coordinated at two anchoring sites are more stable compared to those located at the rings with one Al substitution. The activation energies of the catalysts migration to adjacent rings were estimated as a function of temperature and kinetics parameters of such migrations were determined. The migration is facilitated if there is one anchoring site shared between two rings. This process can contribute to an agglomeration mechanism leading to loss of catalytic activity.

Myshakin, Evgeniy↗
DOE OSTI2018

Adaptive Sampling of Large Deviations

We introduce and test an algorithm that adaptively estimates large deviation functions characterizing the fluctuations of additive functionals of Markov processes in the long-time limit. These functions play an important role for predicting the probability and pathways of rare events in stochastic processes, as well as for understanding the physics of nonequilibrium systems driven in steady states by external forces and reservoirs. The algorithm uses methods from risk-sensitive and feedback control to estimate from a single trajectory a new process, called the driven process, known to be efficient for importance sampling. Its advantages compared to other simulation techniques, such as splitting or cloning, are discussed and illustrated with simple equilibrium and nonequilibrium diffusion models.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗
DOE OSTI2017

Experimental estimation of the bisulfite isomer quotient as a function of temperature: Implications for sulfur isotope fractionations in aqueous sulfite solutions

Bisulfite (HSO 3 - ) and sulfite (SO 3 2- ) compounds play key roles in numerous geochemical and biochemical processes extending from the atmosphere to the subseafloor biosphere. Despite decades of spectroscopic investigations, the molecular composition of HSO 3 - in solution remains uncertain and, thus, the role of bisulfite in (bio)chemical and isotope fractionation processes is unclear. We report new experimental estimates for the bisulfite isomer quotient (Qi= [(HO)SO 2 - ]/[(HS)O3-]; [] = concentration) as a function of temperature from the interpretation of Raman spectra collected from aqueous NaHSO 3 solutions contained in fused silica capsules. In pure NaHSO 3 solutions (1Na + :1HSO 3 - , stoichiometric) over [NaHSO 3 ] = 0.2–0.4 m (moles/kg H 2 O), the following relationship is obtained: ln(Q i )=[formula omitted]-1.9642(±0.1081),where T = 278–358 K (5–85 °C) (based on 50 determinations; additional significant figures are provided to avoid rounding errors). This relationship suggests that the minor isomer, (HS)O 3 - , may comprise 23–38% (±3%) of the mole fraction of HSO 3 - over 5–85 °C, respectively, which is higher in relative abundance than has generally been understood previously. We additionally provide estimates for Q i in NaHSO 3 solutions containing a total ionic strength of µ = 1.0 m (0.2 m NaHSO 3 + 0.8 m NaCl) and different pH (3.3–4.5), but do not appear to resolve any significant differences in Q i as a function of these additional variables. These new values of Q i are employed to re-assess the bulk sulfur isotope fractionations among bisulfite and other S(IV) compounds as a function of temperature, which appear to be highly dependent on the amount of (HS)O 3 - present. Our new constraints on the bisulfite isomer quotient may allow for a detailed assessment of the molecular composition and isotope mass balance of S(IV) solutions containing HSO 3 - , and may be useful in the further investigation of the mechanisms and isotope fractionations associated with a number of processes that involve bisulfite compounds. Furthermore, these may include the intracellular enzymatic transformations of sulfite and bisulfite compounds that occur as part of dissimilatory sulfate reduction, which is a major and geologically important form of anaerobic respiration.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2025

Optimizing stochastic algorithms for hadron correlation function computations in lattice QCD using a localized distillation basis

Distillation is a quark-smearing method for the construction of a broad class of hadron operators useful in lattice QCD computations and defined via a projection operator into a vector space of smooth gauge-covariant fields. A new orthonormal basis for this space is constructed which builds in locality. This basis is useful for the construction of stochastic methods to estimate the correlation functions computed in Monte Carlo calculations relevant for hadronic physics.

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗
DOE OSTI2021

Deep Learning Approaches to Surrogates for Solving the Diffusion Equation for Mechanistic Real-World Simulations

In many mechanistic medical, biological, physical, and engineered spatiotemporal dynamic models the numerical solution of partial differential equations (PDEs), especially for diffusion, fluid flow and mechanical relaxation, can make simulations impractically slow. Biological models of tissues and organs often require the simultaneous calculation of the spatial variation of concentration of dozens of diffusing chemical species. One clinical example where rapid calculation of a diffusing field is of use is the estimation of oxygen gradients in the retina, based on imaging of the retinal vasculature, to guide surgical interventions in diabetic retinopathy. Furthermore, the ability to predict blood perfusion and oxygenation may one day guide clinical interventions in diverse settings, i.e., from stent placement in treating heart disease to BOLD fMRI interpretation in evaluating cognitive function (Xie et al., 2019; Lee et al., 2020). Since the quasi-steady-state solutions required for fast-diffusing chemical species like oxygen are particularly computationally costly, we consider the use of a neural network to provide an approximate solution to the steady-state diffusion equation. Machine learning surrogates, neural networks trained to provide approximate solutions to such complicated numerical problems, can often provide speed-ups of several orders of magnitude compared to direct calculation. Surrogates of PDEs could enable use of larger and more detailed models than are possible with direct calculation and can make including such simulations in real-time or near-real time workflows practical. Creating a surrogate requires running the direct calculation tens of thousands of times to generate training data and then training the neural network, both of which are computationally expensive. Often the practical applications of such models require thousands to millions of replica simulations, for example for parameter identification and uncertainty quantification, each of which gains speed from surrogate use and rapidly recovers the up-front costs of surrogate generation. We use a Convolutional Neural Network to approximate the stationary solution to the diffusion equation in the case of two equal-diameter, circular, constant-value sources located at random positions in a two-dimensional square domain with absorbing boundary conditions. Such a configuration caricatures the chemical concentration field of a fast-diffusing species like oxygen in a tissue with two parallel blood vessels in a cross section perpendicular to the two blood vessels. To improve convergence during training, we apply a training approach that uses roll-back to reject stochastic changes to the network that increase the loss function. The trained neural network approximation is about 1000 times faster than the direct calculation for individual replicas. Because different applications will have different criteria for acceptable approximation accuracy, we discuss a variety of loss functions and accuracy estimators that can help select the best network for a particular application. We briefly discuss some of the issues we encountered with overfitting, mismapping of the field values and the geometrical conditions that lead to large absolute and relative errors in the approximate solution.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2017

A nondestructive method to estimate the chlorophyll content of Arabidopsis seedlings

Chlorophyll content decreases in plants under stress conditions, therefore it is used commonly as an indicator of plant health. Arabidopsis thaliana offers a convenient and fast way to test physiological phenotypes of mutations and treatments. But, chlorophyll measurements with conventional solvent extraction are not applicable to Arabidopsis leaves due to their small size, especially when grown on culture dishes. We provide a nondestructive method for chlorophyll measurement whereby the red, green and blue (RGB) values of a color leaf image is used to estimate the chlorophyll content from Arabidopsis leaves. The method accommodates different profiles of digital cameras by incorporating the ColorChecker chart to make the digital negative profiles, to adjust the white balance, and to calibrate the exposure rate differences caused by the environment so that this method is applicable in any environment. We chose an exponential function model to estimate chlorophyll content from the RGB values, and fitted the model parameters with physical measurements of chlorophyll contents. As further proof of utility, this method was used to estimate chlorophyll content of G protein mutants grown on different sugar to nitrogen ratios. Our method is a simple, fast, inexpensive, and nondestructive estimation of chlorophyll content of Arabidopsis seedlings. This method lead to the discovery that G proteins are important in sensing the C/N balance to control chlorophyll content in Arabidopsis.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2019

Estimating physics models and quantifying their uncertainty using optimization with a Bayesian objective function

This paper reports a verification study for a method that fits functions to sets of data from several experiments simultaneously. The method finds a maximum a posteriori probability (MAP) estimate of a function subject to constraints (e. g., convexity in the study), uncertainty about the estimate, and a quantitative characterization of how data from each experiment constrains that uncertainty. While the present work focuses on a model of the Equation Of State (EOS) of gasses produced by detonating a high explosive, the method can be applied to a wide range of physics processes with either parametric or semi-parametric models. As a verification exercise, a reference EOS is used and artificial experimental data sets are created using numerical integration of ordinary differential equations and pseudo-random noise. The method yields an estimate of the EOS that is close to the reference, and identifies how each experiment most constrains the result.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗
DOE OSTI2025

Single spin asymmetry in forward p A collisions from the Pomeron-odderon interference

Working in the hybrid framework of the high energy p A collisions we identify a new contribution to transverse single spin asymmetry (SSA). The phase necessary for the SSA is provided by the Pomeron-odderon interference in the dense nuclear target. The complete formula for the p A → h X polarized cross section also contains the transversity distribution for the polarized projectile as well as the real part of the twist-three fragmentation function. We numerically estimate the asymmetry A N and its nuclear dependence. Based on a model computation we find that A N can be a percent level in the forward and low- P h ⊥ region. For large nuclei we find significant suppression, with A N ∝ A − 7 / 6 parametrically. As a notable feature we find a node of A N as a function of the P h ⊥ around the values of the initial saturation scale that could be used to test this mechanism experimentally. Published by the American Physical Society 2025

Benić, Sanjin↗
DOE OSTI2026

Optical functions of crystalline and amorphous TeO2

The optical functions of crystalline paratellurite (α-TeO2) and amorphous TeO2 were determined using several optical techniques: (1) standard spectroscopic two-modulator generalized ellipsometry (2-MGE) of paratellurite (200–850 nm, 6.2–1.46 eV), (2) near-normal-incidence two-modulator generalized ellipsometry microscopy (2-MGEM) of paratellurite (577 nm, 2.15 eV), (3) Mueller matrix transmission of paratellurite (320–798 nm, 3.87–1.55 eV), and (4) polarized transmission of paratellurite (323.6–334.3 nm, 3.83–3.71 eV). The 2-MGE measurements yielded highly accurate values of the dielectric functions and error estimates from 1.46 to 6.2 eV for both paratellurite and amorphous TeO2, whereas the polarization-dependent transmission yielded more accurate values of the absorption coefficient below the band edge of paratellurite. The 2-MGEM measured the diattenuation of paratellurite, which is related to the birefringence. Mueller matrix transmission measurements of paratellurite of a (001) cut crystal as a function of angle of incidence were used to determine both the birefringence and the rotary power as a function of photon energy.

Jellison Jr, Gerald [ORNL]↗
DOE OSTI2021

Model-based real-time surface heat flux and temperature estimation for the DIII-D tokamak

A control-oriented model for monitoring of wall power flux densities on the DIII-D tokamak has been successfully implemented and validated experimentally. Future reactors will have to withstand severe steady state high heat flux loads on plasma-facing components (PFCs). Due to the difficulty of directly-measuring local heat fluxes on these components, monitoring and protection of PFCs during the plasma discharge can benefit from simplified physics-based real-time (RT) functional models to estimate and guide heat load control. As a first step into the development, a control-oriented model for monitoring of wall power flux densities and temperatures on DIII-D tokamak has been successfully implemented. The paper discusses the experimental demonstration and comparison of the 2-D model-based wall heat flux algorithm on the DIII-Dinner wall limiter (IWL) against infra-red camera heat flux measurements for limited plasma configurations. The paper also reports on the benchmarking of the field line tracing environment, SMITER, developed at ITER organization on DIII-D tokamak against experimental IR diagnostic data and the derivation of the component shaping weighting factors for the 2-D model-based approach. Here, the extension of the model-based approach for surface temperature estimation on the DIII-D IWL is also presented.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗
DOE OSTI2018

Energy of Plasma Flows Accelerated in a Current Sheet as a Function of the Current Flowing through the Sheet

The energy of directed plasma flows accelerated in a current sheet is studied experimentally as a function of the current flowing through the sheet. It is found that the plasma flow energy rapidly increases with increasing current amplitude, nearly according to a power law with an exponent of 1.8–1.9. Using relations for the neutral current sheet and the concept of plasma acceleration under action of the Ampère forces, the energy of directed plasma flows is estimated as a function of the total current flowing through the sheet. It is shown that, as the current rises, the ion energy grows due to an increase in both the Ampère forces and the width of the current sheet within which plasma is accelerated.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗
DOE OSTI2025

The DECADE cosmic shear project II: photometric redshift calibration of the source galaxy sample

We present the photometric redshift characterization and calibration for the Dark Energy Camera All Data Everywhere (DECADE) weak lensing dataset: a catalog of 107 million galaxies observed by the Dark Energy Camera (DECam) in the northern Galactic cap. The redshifts are estimated from a combination of wide-field photometry, deep-field photometry with associated redshift estimates, and a transfer function between the wide field and deep field that is estimated using a source injection catalog. We construct four tomographic bins for the galaxy catalog, and estimate the redshift distribution, $n(z)$, within each one using the Self-organizing Map Photo-Z (SOMPZ) methodology. Our estimates include the contributions from sample variance, zeropoint calibration uncertainties, and redshift biases, as quantified for the deep-field dataset. The total uncertainties on the mean redshifts are $σ_{\langle}$$_z$$_{\rangle} ≈ 0.01$. The SOMPZ estimates are then compared to those from the clustering redshift method, obtained by cross-correlating our source galaxies with galaxies in spectroscopic surveys, and are shown to be consistent with each other.

Anbajagane, D. [Univ. of Chicago, IL (United State↗
DOE OSTI2014

Functional Data Analysis in NTCP Modeling: A New Method to Explore the Radiation Dose-Volume Effects

Purpose/Objective(s): To describe a novel method to explore radiation dose-volume effects. Functional data analysis is used to investigate the information contained in differential dose-volume histograms. The method is applied to the normal tissue complication probability modeling of rectal bleeding (RB) for patients irradiated in the prostatic bed by 3-dimensional conformal radiation therapy. Methods and Materials: Kernel density estimation was used to estimate the individual probability density functions from each of the 141 rectum differential dose-volume histograms. Functional principal component analysis was performed on the estimated probability density functions to explore the variation modes in the dose distribution. The functional principal components were then tested for association with RB using logistic regression adapted to functional covariates (FLR). For comparison, 3 other normal tissue complication probability models were considered: the Lyman-Kutcher-Burman model, logistic model based on standard dosimetric parameters (LM), and logistic model based on multivariate principal component analysis (PCA). Results: The incidence rate of grade ≥2 RB was 14%. V{sub 65Gy} was the most predictive factor for the LM (P=.058). The best fit for the Lyman-Kutcher-Burman model was obtained with n=0.12, m = 0.17, and TD50 = 72.6 Gy. In PCA and FLR, the components that describe the interdependence between the relative volumes exposed at intermediate and high doses were the most correlated to the complication. The FLR parameter function leads to a better understanding of the volume effect by including the treatment specificity in the delivered mechanistic information. For RB grade ≥2, patients with advanced age are significantly at risk (odds ratio, 1.123; 95% confidence interval, 1.03-1.22), and the fits of the LM, PCA, and functional principal component analysis models are significantly improved by including this clinical factor. Conclusion: Functional data analysis provides an attractive method for flexibly estimating the dose-volume effect for normal tissues in external radiation therapy.

62 RADIOLOGY AND NUCLEAR MEDICINE↗