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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 757 records · Page 42
All sourcesNASA NTRSDOE OSTI
DOE OSTI2021

Quantification of Intrafraction and Interfraction Tumor Motion Amplitude and Prediction Error for Different Liver Tumor Trajectories in Cyberknife Synchrony Tracking

To research the fiducial-based, real-time tracking intrafraction (during the fraction [intra-]) and interfraction (between fractions [inter-]) tumor respiration amplitude, motion trajectory, and prediction error and quantify their relationships for different types of motion trajectories during Cyberknife-based stereotactic ablation radiotherapy.

62 RADIOLOGY AND NUCLEAR MEDICINE↗
DOE OSTI2021

Quantification of Oxygen Depletion During FLASH Irradiation In Vitro and In Vivo

Delivery of radiation at ultrahigh dose rates (UHDRs), known as FLASH, has recently been shown to preferentially spare normal tissues from radiation damage compared with tumor tissues. However, the underlying mechanism of this phenomenon remains unknown, with one of the most widely considered hypotheses being that the effect is related to substantial oxygen depletion upon FLASH, thereby altering the radiochemical damage during irradiation, leading to different radiation responses of normal and tumor cells. Testing of this hypothesis would be advanced by direct measurement of tissue oxygen in vivo during and after FLASH irradiation.

62 RADIOLOGY AND NUCLEAR MEDICINE↗
DOE OSTI2022

AI-Based Quantification of Planned Radiation Therapy Dose to Cardiac Structures and Coronary Arteries in Patients With Breast Cancer

The purpose of this work is to develop and evaluate an automatic deep learning method for segmentation of cardiac chambers and large arteries, and localization of the 3 main coronary arteries in radiation therapy planning on computed tomography (CT). In addition, a second purpose is to determine the planned radiation therapy dose to cardiac structures for breast cancer therapy.

62 RADIOLOGY AND NUCLEAR MEDICINE↗
DOE OSTI2020

Consequences of product inhibition in the quantification of kinetic parameters

While the potential for product inhibition in catalytic reactions is well known, the impact of neglected inhibition on measured kinetic parameters is often overlooked. The presence of product inhibition, most often caused by the competitive adsorption of products with reactants on catalytic active sites, is difficult to determine a priori for an arbitrary catalytic system. The significance of product inhibition relies on the concentration of the products, their adsorption thermodynamics on catalytically relevant sites, and process parameters such as temperature and pressure. When inhibition is significant, however, apparent activation energies and reaction orders vary from the differential-reactor apparent activation energy by a factor of 1/(1 - δ), where δ is the total inhibition order (e.g., the factor (1 - δ) = 1.6 for a system with product inhibition of -0.6 order). This is illustrated here with the kinetics of NO oxidation over Cu ion clusters (Cu x O y ) in Cu-SSZ-13, for which the product NO 2 inhibits the forward reaction. Furthermore, in the presence of inhibition, when only reactants are fed to a flow reactor or placed in a batch reactor, there is often no practical conversion that is low enough to guarantee differential behavior. Inclusion of products in the feed solves this problem, allowing accurate determination of kinetic parameters such as apparent reaction orders and activation energies. We also demonstrate that evaluation of the necessity of co-feeding products to assure measurement of differential-reactor data in a given catalytic system is straightforward from a plot of the log of the rate (or conversion) versus the log of the space time in a flow reactor or elapsed time in a batch reactor. We encourage inclusion of this test in all kinetic analyses that are reasonably approximated by power law rate expressions.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2021

Identification and quantification of distinct active sites in Hf-Beta zeolites for transfer hydrogenation catalysis

Despite the significant progress made in characterizing different framework heteroatom sites that exist in Lewis acidic zeolites with probe molecule adsorption and spectroscopy, methods to reliably quantify site counts remain indefinite and have been primarily limited to Sn and Ti Lewis acid sites. Here, methods to quantify framework Lewis acidic Hf 4+ sites in zeolite Beta (Hf-Beta) with two Lewis base titrants (pyridine, deuterated acetonitrile) were developed using infrared (IR) spectroscopy. Lewis acid site counts for Hf-Beta zeolites were validated by measuring integrated molar extinction coefficients (IMECs; ε, cm μmol –1 ) on Sn-Beta zeolites using identical Lewis base titrants to benchmark site counts with established literature procedures to quantify Lewis acid sites in Beta zeolites from IR spectra. Highlighting the importance of benchmarking active site counts against well-established experimental protocols, IMECs of CD 3 CN bound to open (ε(Sn; 2316 cm –1 ): 1.80 ± 0.25) and closed (ε(Sn; 2308 cm –1 ): 3.76 ± 0.33) Sn sites were ~1.8x larger than those previously reported while total Lewis acid site counts agreed with those measured by pyridine (ε(Sn; 1451 cm –1 ): 1.58 ± 0.16) on six different Sn-Beta zeolites. IMECs measured for IR peaks reflecting pyridine bound to Lewis acidic Hf sites (ε(Hf; 1448 cm –1 ): 1.54 ± 0.21) and CD 3 CN bound to open (ε(Hf; 2313 cm –1 ): 2.40 ± 0.22) and closed (ε(Hf; 2307 cm –1 ): 3.55 ± 0.41) Hf sites, gave similar counts for the total number of Lewis acidic sites across six Hf-Beta zeolites (Si/Hf = 100–413). Consistent with previous reports with Sn-Beta catalysts where open Sn sites are responsible for catalytic turnover, apparent first and zero-order MPVO rate constants (0.01–1 M cyclohexanone in 2-butanol; per total Hf, 373 K) correlated with the total number of open Hf sites, per total Hf, but not with the total number of closed Hf sites or total Lewis acid site counts. Measured initial MPVO rates (0.1 M cyclohexanone in 2-butanol, per open Hf, 373 K) were ~25x higher on hydrophobic Hf-Beta-F than on hydrophilic Hf-Beta-OH zeolites. Overall, the apparent first-order MPVO rate constants (2-butanol solvent, per open Hf, 373 K) were ~6x higher on Hf-Beta-F than on Hf-Beta-OH zeolites. The characterization methods reported here enable normalization of MPVO turnover rates on Sn- and Hf-Beta zeolites by their number of open sites. Finally, this enables performing quantitative rate comparisons across Lewis acid zeolites of varying active site identity, solvation, and pore topology used in liquid-phase catalysis.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2024

Quantification of active sites in yttrium containing dealuminated Beta zeolites during conversion of ethanol and acetaldehyde to butadiene

Here, in this work, yttrium containing dealuminated Beta zeolites (Y/deAlBeta) were synthesized and characterized by various spectroscopic techniques to improve understanding of ethanol upgrading over these materials. Characterization results indicate yttrium atoms partially condense with framework silanol nests formed during dealumination of parent Al-Beta supports. Active sites for conversion of ethanol and acetaldehyde to butadiene were quantified on a series of Y/deAlBeta catalysts (0.1–10 wt% yttrium) via ex situ chemisorption and transmission Fourier transformed infrared (FTIR) spectroscopy measurements by first measuring the integrated molar extinction coefficient (IMEC) for pyridine bound to Lewis acidic yttrium sites. In situ titrations with pyridine demonstrate that the number of sites quantified by ex situ chemisorption IR is quantitatively similar to the number of sites that catalyze butadiene formation, which varies (from 0.05 to 0.35) across the series of catalysts. In situ pyridine titrations impact butadiene site time yields (STY), but not crotonaldehyde STY, indicating that a distribution of yttrium sites is present, and that discrete yttrium site types participate in distinct steps in the pathway from ethanol to butadiene. Apparent kinetic parameters including activation energies and reaction orders were measured, these suggest differences in reactant (or reactant-derived intermediate) surface coverages result in higher STYs (per mol Y or per Lewis acidic Y site) for samples with low Y loadings relative to those with higher Y loadings. Isotopic labeling experiments evince the existence of other kinetically relevant steps in addition to the crotonaldehyde transformation to crotyl alcohol. Together, these findings provide further guidance into the heterogeneities in site structures in yttrium-containing zeolites and their relevance for the various steps in the pathway from ethanol to C 4 products useful for production of sustainable aviation fuel and renewable butadiene.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2022

Bioconversion and quantification of humic substances from low rank coals using indigenous fungal isolates

The major portion of coals around the world belongs to lignitic origin, and burning of these Low Rank Coals (LRCs) not only produces energy, but also release harmful gases into the environment. On the other hand, coal is a mega source of fossilized carbon compounds that can be utilized in diverse range of carbon/organic industries. Biosolubilization is one of the safest and economical way to consume these LRCs for the harvest of humic and fulvic acids for industrial applications. Present studies deal with solubilization of coal samples from five different mines of Punjab, Pakistan by indigenously isolated fungus. From a number of indigenously isolated fungal isolates, an isolate NF-2 was selected based on its solubilization potential on solid Sabouraud medium. The intensity of coal biosolubilization was found maximum for coal sample B-1 (59%) and lowest for sample B-26 (17.2%) with 5% (w/v) pulp density at 28 ± 2°C. Humification index value (A 4 /A 6 ) for biosolubilized products of coal sample B-1 was found 6.18 which was much higher than the A 4 /A 6 value (4.27) of commercially available standard humic acid. Biosolubilized products from coal sample B-1 was found maximum with 8681 mg/L humic acid concentration as determined by standard curve method. FTIR and FE-SEM analyses of sample B-1 treated with NF-2 confirmed the structural break down and solubilization of coal. Molecular identification of fungal isolate NF-2 showed 100% similarity to Aspergillus flavus. Here, this work paves the way to utilize lignitic coals for the environment friendly and low-cost production of humic acids by fungal biosolubilization as an alternate to chemical methods.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2019

Adversarial uncertainty quantification in physics-informed neural networks

Here, we introduce a deep learning framework for quantifying and propagating uncertainty in systems governed by non-linear differential equations using physics-informed neural networks. Specifically, we employ latent variable models to construct probabilistic representations for the system states, and put forth an adversarial inference procedure for training them on data, while constraining their predictions to satisfy given physical laws expressed by partial differential equations. Such physics-informed constraints provide a regularization mechanism for effectively training deep generative models as surrogates of physical systems in which the cost of data acquisition is high, and training data-sets are typically small. This provides a flexible framework for characterizing uncertainty in the outputs of physical systems due to randomness in their inputs or noise in their observations that entirely bypasses the need for repeatedly sampling expensive experiments or numerical simulators. Moreover, we demonstrate the effectiveness of our approach through a series of examples involving uncertainty propagation in non-linear conservation laws, and the discovery of constitutive laws for flow through porous media directly from noisy data.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2020

A generalized approximate control variate framework for multifidelity uncertainty quantification

We describe and analyze a variance reduction approach for Monte Carlo (MC) sampling that accelerates the estimation of statistics of computationally expensive simulation models using an ensemble of models with lower cost. These lower cost models — which are typically lower fidelity with unknown statistics — are used to reduce the variance in statistical estimators relative to a MC estimator with equivalent cost. We derive the conditions under which our proposed approximate control variate framework recovers existing multifidelity variance reduction schemes as special cases. We demonstrate that existing recursive/nested strategies are suboptimal because they use the additional low-fidelity models only to efficiently estimate the unknown mean of the first low-fidelity model. As a result, they cannot achieve variance reduction beyond that of a control variate estimator that uses a single low-fidelity model with known mean. However, there often exists about an order-of-magnitude gap between the maximum achievable variance reduction using all low-fidelity models and that achieved by a single low-fidelity model with known mean. We show that our proposed approach can exploit this gap to achieve greater variance reduction by using non-recursive sampling schemes. The proposed strategy reduces the total cost of accurately estimating statistics, especially in cases where only low-fidelity simulation models are accessible for additional evaluations. Several analytic examples and an example with a hyperbolic PDE describing elastic wave propagation in heterogeneous media are used to illustrate the main features of the methodology.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗
DOE OSTI2026

High-resolution modeling of indoor radon exposure with uncertainty quantification in Utah

Indoor radon accounts for 37% of population-level exposure to ionizing radiation in the United States. However, radon metrics are typically reported at coarse spatial scales, potentially obscuring meaningful local variation. We developed a high-resolution modeling framework to estimate indoor radon concentrations across Utah while explicitly quantifying predictive uncertainty. A total of 19,497 residential radon measurements collected between 2006 and 2017 were combined with environmental and housing characteristics and analyzed using a geospatial neural network that accommodates spatial dependence and nonlinear associations. Predictions were generated on a uniform hexagonal grid at 0.73 km2 resolution (H3 level 8). Out-of-sample predictions aggregated to the H3 level 8 grid showed good agreement with observed concentrations (Pearson r=0.64), while household-level predictions exhibited more moderate agreement (r=0.45). The model produced well-calibrated uncertainty estimates, with 24.1% of held-out observations exceeding the predicted 75th-percentile threshold. Maps of predicted radon concentrations and the probability of exceeding the U.S. EPA action level of 148 Bq/m3 (4 pCi/L) revealed substantial fine-scale spatial heterogeneity that was not apparent in conventional coarse-resolution summaries, with greater local variability observed in densely monitored urban counties than in sparsely sampled regions. High-resolution radon models that explicitly quantify uncertainty provide a useful framework for characterizing the spatial distribution of indoor radon and identifying areas of elevated exceedance risk. These findings highlight the value of fine-scale monitoring data and uncertainty-aware modeling approaches for radon exposure assessment, environmental risk characterization, and radon-related health research.

Wu, Yunhan [ORNL] (ORCID:0000000178842994)↗
DOE OSTI2019

A unified approach to evaluating precipitation frequency estimates with uncertainty quantification: Application to Florida and California watersheds

Although a variety of climate datasets are now available, including observational and model products, there remains a pressing need for the development of a unified, automated and application-focused approach for evaluating and comparing climate extremes within and across these datasets. Precipitation frequency (PF) es- timates for extreme precipitation events are useful decision-relevant quantities among water managers. Our objective and quantitative framework for computing PF estimates of any duration uses known techniques in a novel combination. Our framework consists of three steps: (1) use of a regionalization-based statistical method for precipitation frequency estimation at observed sites; this involves hierarchical clustering (here we used Ward’s method) for delineating homogeneous regions, and regional frequency analysis using L-moments; (2) use of kriging for spatial mapping because of its advantages over other conventional interpolation methods; (3) use of z-scores accounts for estimation uncertainties when comparing PF estimates between datasets. The 24-h PF estimates in the Kissimmee-Southern Florida watershed (flat topography) and the Sacramento-San Joaquin watershed (complex topography) demonstrate the applicability of this approach in regions of any geographical complexity. Our results are shown to be reasonably calibrated using the probability integral transform (PIT) histogram of the best fitted distribution. Our results are compared with that of the NOAA Atlas 14 reports and show that for most of the stations the two estimates are statistically indistinguishable; and, the confidence intervals of our estimates are narrower than those of the NOAA estimates. Our approach is applicable to a variety of datasets and provides a baseline for assessing performance of climate models in historical simulations – a necessary first step towards analyzing future projections.

58 GEOSCIENCES↗
DOE OSTI2024

High resolution identification and quantification of diffuse deep groundwater discharge in mountain rivers using continuous boat-mounted helium measurements

Discharge of deeply sourced groundwater to streams is difficult to locate and quantify, particularly where both discrete and diffuse discharge points exist, but diffuse discharge is one of the primary controls on solute budgets in mountainous watersheds. The noble gas helium is a unique identifier of deep groundwater discharge because groundwater with long residence times is commonly enriched in helium. In this study, a portable mass spectrometer was used to measure longitudinal variation in dissolved helium concentrations in two mountainous rivers at high spatial resolution not feasible with traditional sampling techniques. Helium profiles were then simulated using a mass-balance model to quantify longitudinal variation in groundwater discharge to the receiving rivers. Results indicate helium concentrations were enriched by multiple orders of magnitude above atmospheric equilibrium in both rivers and that this persisted for up to 18 km below observed pulse inputs in the Colorado River. Helium mass-balance models match observed longitudinal patterns with the exception of sharp initial increases in helium observed in the rivers. Increased longitudinal groundwater discharge rates correspond to mapped geologic structures in both watersheds that likely transport deep geothermal water. Models show variable sensitivity to spatial assignment of input variables representing the groundwater source, illustrating the importance of collecting data from discrete groundwater discharges where possible. The methodology shows promise for field experiments designed to assess air–water exchange rates and to quantify total groundwater discharge from a combination of discrete and diffuse sources.

Deep groundwater↗