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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 505 records · Page 28
All sourcesNASA NTRSDOE OSTI
DOE OSTI2014

Dynamic nuclear renography kinetic analysis: Four-compartment model for assessing kidney function

Dynamic nuclear renography method produces TACs of kidneys and bladder. Multiple TACs data can be further analyzed to obtain the overview of urinary system's condition. Tracer kinetic analysis was performed using four-compartment models. The system's model consist of four irreversible compartment with four transport constants (k1, k2, k3 and k4). The mathematical expressions of tracer's distributions is fitted to experimental data (TACs) resulting in model constants. This transport constants represent the urinary system behavior, and later can be used for analyzing system's condition. Different intervals of kinetics parameter are clearly shown by abnormal system with respect to the normal one. Furthermore, the system with delayed uptake has 82% lower uptake parameters (k1 and k2) than normal one. Meanwhile, the system with prolonged clearance time has its kinetics parameters k3 or k4 lower than the others. This model is promising for quantitatively describe urinary system's function especially if supplied with more data.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2017

Investigation of Cody–Lorentz and Tauc–Lorentz Models in Characterizing Dielectric Function of (HfO{sub 2}){sub x}(ZrO{sub 2}){sub 1–x} Mixed Thin Film

In the present study, Tauc–Lorentz and Urbach tail assisted Cody–Lorentz models were intuitively derived, explained, and employed to simulate the imaginary dielectric function of the atomic layer deposited (HfO{sub 2}){sub x}(ZrO{sub 2}){sub 1–x} in the energy range of 0.7–9.3 eV. A fitting procedure is carried out using the Levenberg–Marquardt method for minimizing mean square errors. Both models support the experimental data derived by vacuum ultraviolet spectroscopic ellipsometry. For a zirconium content of 60 and 75%, a weak absorption profile is observed in the visible region and elaborately explained by the Lorentz oscillator model. In order to calculate the band gap of different compositions, Cody plots and Tauc plots are presented. The results show that the Cody–Lorentz model is more accuracy due to its exponential absorption below the band gap energy and modified density of states.

75 CONDENSED MATTER PHYSICS, SUPERCONDUCTIVITY AND↗
DOE OSTI2020

The Sensitivity of Chemical Kinetic Models on Flame Transfer Functions in Acoustic Fluctuation Environments with Varying Equivalence Ratios

Various factors affect the heat release response of flames – thermodynamics, hydrodynamics, acoustics, etc. The coupling of heat release rate and acoustics, in particular, is essential to the design of combustion devices so as to avoid adverse effects during their operation. Modeling flames in acoustic environments requires an accurate understanding of the fundamental physics that occurs when flames interact with a fluctuating pressure field. Over the past couple of decades, researchers have been quantifying the impact of this fluctuating pressure field on the unsteady heat release rate. The flame transfer functions computed have also been shown to depend on the level of detail in the chemical kinetic model. This paper attempts to bridge the gap between these studies to understand how sensitive the flame transfer function is to various kinetic models. The problem is studied by performing multiple one-dimensional simulations using the low-Mach solver NGA. The detailed GRI-Mech 3.0 will be used for comparison. Also, the pressure amplitude and reactant equivalence ratio are varied to understand how the environment affects this sensitivity.

Girdhar, Anant↗
DOE OSTI2010

The NJL-jet model for quark fragmentation functions

A description of fragmentation functions which satisfy the momentum and isospin sum rules is presented. We concentrate on the pion fragmentation function, taking into account cascade-like processes in a generalized jet-model approach. Numerical results obtained in this NJL-jet model are presented.

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗
DOE OSTI2021

Hydrodechlorination of 1,2-Dichloroethane on Platinum Catalysts: Insights from Reaction Kinetics Experiments, Density Functional Theory, and Microkinetic Modeling

Catalytic hydrodechlorination is a promising strategy for treating industrial 1,2-dichloroethane wastes, for which Pt and Pt-based alloy catalysts are widely used. Here, we performed a detailed mechanistic study for 1,2-dichloroethane hydrodechlorination on Pt using a synergistic approach combining density functional theory (DFT) calculations, reaction kinetics experiments, and microkinetic modeling. Using planewave DFT calculations, we evaluated the reaction energy and activation energy barrier of each elementary step involved in the reaction network on Pt(111). The calculated energetics were then incorporated into a comprehensive mean-field microkinetic model accounting for a total of 65 elementary steps. The model-predicted reaction rates were compared with the results from our reaction kinetics experiments on SiO2-supported Pt catalysts. Our results indicated that the hydrodechlorination of 1,2-dichloroethane on Pt(111) starts with a H-removal step; then, it proceeds through a sequence of alternating dechlorination and dehydrogenation steps until vinylidene (CH 2 C*) is formed; finally, CH 2 C* is hydrogenated to the final product, ethane, sequentially via vinyl (CH 2 CH*), ethylene, and ethyl (CH 3 CH 2 *) intermediates. After model parameter adjustments, we achieved good agreement between our theoretical model and experimental results; the adjustments to the calculated parameters are consistent with the typically anticipated coverage effects. Furthermore, our study offers valuable mechanistic insights, which are useful for improving catalysts for this chemistry.

1,2-dichloroethane↗
DOE OSTI2022

Estimation of conditional cumulative incidence functions under generalized semiparametric regression models with missing covariates, with application to analysis of biomarker correlates in vaccine trials

Herein, this article presents generalized semiparametric regression models for conditional cumulative incidence functions with competing risks data when covariates are missing by sampling design or happenstance. A doubly robust augmented inverse probability weighted (AIPW) complete-case approach to estimation and inference is investigated. This approach modifies IPW complete-case estimating equations by exploiting the key features in the relationship between the missing covariates and the phase-one data to improve efficiency. An iterative numerical procedure is derived to solve the nonlinear estimating equations. The asymptotic properties of the proposed estimators are established. A simulation study examining the finite-sample performances of the proposed estimators shows that the AIPW estimators are more efficient than the IPW estimators. The developed method is applied to the RV144 HIV-1 vaccine efficacy trial to investigate vaccine-induced IgG binding antibodies to HIV-1 as correlates of acquisition of HIV-1 infection while taking account of whether the HIV-1 sequences are near or far from the HIV-1 sequences represented in the vaccine construct.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2022

Application of the metabolic modeling pipeline in KBase to categorize reactions, predict essential genes, and predict pathways in an isolate genome

The DOE Systems Biology Knowledgebase (KBase) platform offers a range of powerful tools for the reconstruction, refinement, and analysis of genome-scale metabolic models built from microbial isolate genomes. In this chapter, we describe and demonstrate these tools in action with an analysis of isoprene production in the Bacillus subtilis DSM genome. Two different methods are applied to build initial metabolic models for the DSM genome, then the models are gapfilled in three different growth conditions. Next, flux balance analysis (FBA) and flux variability analysis (FVA) techniques are applied to both study the growth of these models in minimal media and classify reactions within each model based on essentiality and functionality. The models are applied with the FBA method to predict essential genes, which are then compared to an updated list of essential genes obtained for B. subtilis 168, a very similar strain to the DSM isolate. The models are also applied to simulate Biolog growth conditions, and these results are compared with Biolog data collected for B. subtilis 168. Finally, the DSM metabolic models are applied to explore the pathways and genes responsible for producing isoprene in this strain. These studies demonstrate the accuracy and utility of models generated from the KBase pipelines, as well as exploring the tools available for analyzing these models.

DOE knowledgebase↗
DOE OSTI2014

A computational tool integrating host immunity with antibiotic dynamics to study tuberculosis treatment

While active tuberculosis (TB) is a treatable disease, many complex factors prevent its global elimination. Part of the difficulty in developing optimal therapies is the large design space of antibiotic doses, regimens and combinations. Computational models that capture the spatial and temporal dynamics of antibiotics at the site of infection can aid in reducing the design space of costly and time-consuming animal pre-clinical and human clinical trials. The site of infection in TB is the granuloma, a collection of immune cells and bacteria that form in the lung, and new data suggest that penetration of drugs throughout granulomas is problematic. In this paper, we integrate our computational model of granuloma formation and function with models for plasma pharmacokinetics, lung tissue pharmacokinetics and pharmacodynamics for two first line anti-TB antibiotics. The integrated model is calibrated to animal data. We make four predictions. First, antibiotics are frequently below effective concentrations inside granulomas, leading to bacterial growth between doses and contributing to the long treatment periods required for TB. Second, antibiotic concentration gradients form within granulomas, with lower concentrations toward their centers. Third, during antibiotic treatment, bacterial subpopulations are similar for INH and RIF treatment: mostly intracellular with extracellular bacteria located in areas non-permissive for replication (hypoxic areas), presenting a slowly increasing target population over time. In conclusion, we find that on an individual granuloma basis, pre-treatment infection severity (including bacterial burden, host cell activation and host cell death) is predictive of treatment outcome.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2024

Reduction of spectroscopic overlap across the Z = 8 shell in neutron-rich nuclei

The recent discovery and spectroscopic measurements of 27 O and 28 O suggests the disappearance of the N = 20 shell structure in these neutron-rich oxygen isotopes. We measured one- and two-proton removal cross sections from 27 F and 29 N, respectively, extracting spectroscopic factors and comparing them to shell model overlap functions coupled with eikonal reaction model calculations. The invariant mass technique was used to reconstruct the two-body ( 24 O + n) and three-body ( 24 O + 2n) decay energies from knockout reactions of 27 F (106.2 MeV/u) and 29 Ne (112.8 MeV/u) beams impinging on a 9 Be target. The one-proton removal from 27 F strongly populated the ground state of 26 O and the extracted cross section of $3.4^{+0.3}_{-1.5}$ mb agrees with eikonal model calculations that are normalized by the shell model spectroscopic factors and account for the systematic reduction factor observed for single nucleon removal reactions within the models used. For the two-proton removal reaction from 29 Ne an upper limit of 0.08 mb was extracted for populating states in 27 O decaying though the ground state of 26 O. The measured upper limit for the population of the ground state of 26 O in the two-proton removal reaction from 29 Ne indicates a significant difference in the underlying nuclear structure of 27 F and 29 Ne.

73 NUCLEAR PHYSICS AND RADIATION PHYSICS↗
DOE OSTI2015

Demonstration of emulator-based Bayesian calibration of safety analysis codes: Theory and formulation

System codes for simulation of safety performance of nuclear plants may contain parameters whose values are not known very accurately. New information from tests or operating experience is incorporated into safety codes by a process known as calibration, which reduces uncertainty in the output of the code and thereby improves its support for decision-making. The work reported here implements several improvements on classic calibration techniques afforded by modern analysis techniques. The key innovation has come from development of code surrogate model (or code emulator) construction and prediction algorithms. Use of a fast emulator makes the calibration processes used here with Markov Chain Monte Carlo (MCMC) sampling feasible. This study uses Gaussian Process (GP) based emulators, which have been used previously to emulate computer codes in the nuclear field. The present work describes the formulation of an emulator that incorporates GPs into a factor analysis-type or pattern recognition-type model. This “function factorization” Gaussian Process (FFGP) model allows overcoming limitations present in standard GP emulators, thereby improving both accuracy and speed of the emulator-based calibration process. Calibration of a friction-factor example using a Method of Manufactured Solution is performed to illustrate key properties of the FFGP based process.

21 SPECIFIC NUCLEAR REACTORS AND ASSOCIATED PLANTS↗
DOE OSTI2021

Application of the Metabolic Modeling Pipeline in KBase to Categorize Reactions, Predict Essential Genes, and Predict Pathways in an Isolate Genome

The DOE Systems Biology Knowledgebase (KBase) platform offers a range of powerful tools for the reconstruction, refinement, and analysis of genome-scale metabolic models built from microbial isolate genomes. In this chapter, we describe and demonstrate these tools in action with an analysis of isoprene production in the Bacillus subtilis DSM genome. Two different methods are applied to build initial metabolic models for the DSM genome, then the models are gapfilled in three different growth conditions. Next, flux balance analysis (FBA) and flux variability analysis (FVA) techniques are applied to both study the growth of these models in minimal media and classify reactions within each model based on essentiality and functionality. The models are applied with the FBA method to predict essential genes, which are then compared to an updated list of essential genes obtained for B. subtilis 168, a very similar strain to the DSM isolate. The models are also applied to simulate Biolog growth conditions, and these results are compared with Biolog data collected for B. subtilis 168. Finally, the DSM metabolic models are applied to explore the pathways and genes responsible for producing isoprene in this strain. These studies demonstrate the accuracy and utility of models generated from the KBase pipelines, as well as exploring the tools available for analyzing these models.

Allen, Benjamin↗
DOE OSTI2022

Electrochemical CO2 Reduction over Metal-/Nitrogen-Doped Graphene Single-Atom Catalysts Modeled Using the Grand-Canonical Density Functional Theory

Renewably driven, electrochemical conversion of carbon dioxide into value-added products is expected to be a critical tool in global decarbonization. However, theoretical studies based on the computational hydrogen electrode largely ignore the nonlinear effects of the applied potential on the calculated results, leading to inaccurate predictions of catalytic behavior or mechanistic pathways. Here, we use grand canonical density functional theory (GC-DFT) to model electrochemical CO2 reduction (CO2R) over metal- and nitrogen-doped graphene catalysts (MNCs) and explicitly include the effects of the applied potential. We used GC-DFT to compute the CO2 to CO reaction intermediate energies at -0.3, -0.7, and -1.2 VSHE catalyzed by MNCs each doped with 1 of the 10 3d block metals coordinated by four pyridinic nitrogen atoms. Our results predict that Sc-, Ti-, Co-, Cu-, and Zn-N4Cs effectively catalyze CO2R at moderate to large reducing potentials (-0.7 to -1.2 VSHE). ZnN4C is a particularly promising electrocatalyst for CO2R to CO both at low and moderate applied potentials based on our thermodynamic analysis. Our findings also explain the observed pH independence of CO production over FeN4C and predict that the rate-determining step of CO2R over FeN4C is not *CO2- formation but rather *CO desorption. Additionally, the GC-DFT-computed density of states analysis illustrates how the electronic states of MNCs and adsorbates change non-uniformly with applied potential, resulting in a significantly increased *CO2- stability relative to other intermediates and demonstrating that the formation of the adsorbed *CO2- anion is critical to CO2R activation. This work demonstrates how GC-DFT paves the way for physically realistic and accurate theoretical simulations of reacting electrochemical systems.

CO2 reduction↗
DOE OSTI2013

Local structures of Sr{sub 2}FeMnO{sub 5+y} (y=0, 0.5) and Sr{sub 2}Fe{sub 1.5}Cr{sub 0.5}O{sub 5} from reverse Monte Carlo modeling of pair distribution function data and implications for magnetic order

The local structures of the oxygen deficient perovskites Sr{sub 2}FeMnO{sub 5}, Sr{sub 2}FeMnO{sub 5.5}, and Sr{sub 2}Fe{sub 1.5}Cr{sub 0.5}O{sub 5} have been analyzed using neutron pair distribution function data. The results show that locally all three structures are more complex than implied by their average cubic structures and that the distributions of oxygen vacancies are not completely random on a local level. For both Sr{sub 2}FeMnO{sub 5+y} compounds it is found that there is no short range ordering of the Fe and Mn cations. For Sr{sub 2}Fe{sub 1.5}Cr{sub 0.5}O{sub 5} there is evidence to suggest that the Fe/Cr distribution is not completely random and is locally ordered such that there are fewer Fe--Fe nearest neighbor pairs than in a random distribution. Reverse Monte Carlo modeling of the pair distribution function data has provided the Fe--O, Mn--O, and Cr--O bond length distributions and information on the coordination numbers of the Fe, Mn, and Cr cations. In Sr{sub 2}FeMnO{sub 5} it is found that the Fe{sup 3+} cations are most often in 4-fold coordination but there is also a large amount of Fe{sup 3+} in 5-fold coordination and a small amount in 6-fold coordination. The Mn{sup 3+} is split between 5-fold and 6-fold coordination. The Mn--O bond length distributions indicate that the Mn{sup 3+}O{sub 6} octahedra and Mn{sup 3+}O{sub 5} square pyramids are locally Jahn-Teller distorted. In Sr{sub 2}FeMnO{sub 5.5} the Fe{sup 3+} is almost entirely 5 coordinate while the Mn{sup 4+} is almost entirely 6 coordinate. The Cr{sup 3+} in Sr{sub 2}Fe{sub 1.5}Cr{sub 0.5}O{sub 5} is almost entirely 6-fold coordinated, giving the Fe{sup 3+} an average coordination number of 4.67. In Sr{sub 2}FeMnO{sub 5} and Sr{sub 2}Fe{sub 1.5}Cr{sub 0.5}O{sub 5} the Fe{sup 3+} and Sr{sup 2+} cations undergo local displacements which are driven by the oxygen vacancies, while the Mn{sup 3+} and Cr{sup 3+} cations remain near their positions in the average structures. In Sr{sub 2}FeMnO{sub 5.5} these cations are not significantly displaced. The local coordination geometries are used to explain previously observed but yet poorly understood magnetic properties of these materials. - Graphical abstract: The actual bond angle distributions in the cubic perovskite Sr{sub 2}FeMnO{sub 5} obtained from reverse Monte Carlo modeling of the local structure using neutron pair distribution function data. Highlights: Black-Right-Pointing-Pointer No long range ordering of oxygen vacancies, but short range order is present. Black-Right-Pointing-Pointer No short range Fe/Mn order but short range Fe/Cr order is present. Black-Right-Pointing-Pointer Fe tends to have lower coordination numbers while Mn and Cr have higher ones. Black-Right-Pointing-Pointer Local bond distances and bond angles have been determined. Black-Right-Pointing-Pointer Local structures can help explain long range magnetic ordering behavior.

36 MATERIALS SCIENCE↗
DOE OSTI2020

The Effects of Atmospheric Models on the Estimation of Infrasonic Source Functions at the Source Physics Experiment

We invert infrasound signals for an equivalent seismoacoustic source function using different atmospheric models to produce the necessary Green’s functions. The infrasound signals were produced by a series of underground chemical explosions as part of the Source Physics Experiment (SPE). In a previous study, we inverted the infrasound data using so-called predictive atmospheric models, which were based on historic, regional-scaled, publicly available weather observations interpolated onto a 3D grid. For the work presented here, we invert the same infrasound data, but using atmospheric models based on weather data collected in a time window that includes the approximate time of the explosion experiments, which we term postdictive models. We build two versions of the postdictive models for each SPE event: one that is based solely on the regional scaled observations, and one that is based on both regional scaled observations combined with on-site observations obtained by a weather sonde released at the time of the SPE. We then invert the observed data set three times, once for each atmospheric model type. We find that the estimated seismoacoustic source functions are relatively similar in waveform shape regardless of which atmospheric model that we used to construct the Green’s functions. However, we find that the amplitude of the estimated source functions is systematically dependent on the atmospheric model type: using the predictive atmospheric models to invert the data generally yields estimated source functions that are larger in amplitude than those estimated using the postdictive models.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2023

Functional characterization and regulatory modeling of lignocellulose deconstruction in the saprophytic bacterium Cellvibrio japonicus (Final Technical Report)

The goal of this project was to establish a fundamental systems-level model of lignocellulose deconstruction by saprophytic soil bacteria while simultaneously identifying and characterizing novel enzymes that can be used for applied bioenergy technologies. This project was significant because it elevated our mechanistic and regulatory understanding of bacterial plant biomass degradation and led to technologies to enable bottlenecks in biofuel production R&D. Furthermore, the completed work was novel because it integrated in vivo, in vitro, and in silico approaches with bioenergy-relevant substrates. We identified the required enzymes for lignocellulose degradation and mechanisms of regulation through a combination of transcriptomic profiling (RNAseq), enzymatic analysis, the functional characterization of mutants, and modeling of the regulatory networks used to detect and degrade lignocellulose. This project employed the lignocellulose-degrading bacterium Cellvibrio japonicus to dissect the functional properties and underlying regulatory networks saprophytic bacteria use to depolymerize and utilize lignocellulose. There were four Objectives to this project, the specific metrics of which are summarized in this report.

09 BIOMASS FUELS↗
DOE OSTI2015

Adsorption of a metalorganic complex at a metal surface: A density functional theory study vs. model description

A modification of the electronic and magnetic structure of a metalorganic complex by chemisorption at a metallic surface is addressed. The density functional theory (DFT) is applied to investigate a simplified form of a copper-dioxolene complex in a free state and connected to Au(111) surface with alkanethiol linkers. A systematic study of the dependence of the system electronic structure on the linker length is performed. It is found that the electronic structure of the complex is well preserved during the adsorption process. The magnetic moment of the Cu-dioxolene functional group is shown to be strictly correlated with the amount of the charge residing at the complex. On the basis of the DFT results, a model Hamiltonian of the adsorbed metalorganic system is proposed. The model is an extension of the Sandorfy's model of the alkanes and includes explicitly Coulomb interaction between electrons both within the alkane's backbone and the end group. We show that the latter feature is necessary to understand the evolution of the system's properties with the length of the linkers. The advantage of this approach is that it not only reproduces the main results of our DFT analysis but also provides a simple common basis to analyse a wide class of metal complexes bound to metal surfaces with alkanethiol linkers.

75 CONDENSED MATTER PHYSICS, SUPERCONDUCTIVITY AND↗