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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 289 records · Page 16
All sourcesNASA NTRSDOE OSTI
DOE OSTI2025

Optimizing ensemble NV − spin properties of fluorescent diamond microparticles by systematic low pressure high temperature annealing

Low pressure high temperature annealing is a means for driving nitrogen and defect diffusion in diamond to reduce internal lattice damage without the need for technically complicated high-pressure cells. Herein, we perform a systematic time (5, 15, and 30 min) and temperature (1200 °C–1800 °C) study of effects of low-pressure high temperature annealing on photoluminescence, spin concentrations, and spin relaxation properties of NV centers in ca. 3 μm synthetic type 1b diamond particles. Annealing in the temperature range of ca. 1400 °C–1700 °C for even 5 min leads to a higher optically detected magnetic resonance contrast as compared to standard annealing at 900 °C for 2 h. Particles annealed at 1700 °C for 5 min exhibit a contrast close to about 13% as compared to about 9% for those annealed at 900 °C for 2 h. A reduction in the zero-field splitting strain parameter from E ≈ 4.5 MHz to ≈ 2.5 MHz and spectral linewidth from Δν ≈ 7 MHz to ≈ 4 MHz are observed even after 5 min annealing at 1700 °C. Improvements in these spectral parameters resulted in a roughly 2-fold reduction in the noise level of temperature monitoring experiment utilizing an ensemble of NV centers in the particles. Annealing in the temperature range of 1600 °C for 15 or 30 min or 1700 °C for 5 min resulted in NV T 1 relaxation times approaching ca. 5 ms typically observed for bulk diamond. Quantitative electron paramagnetic resonance (EPR) allowed for estimations of thermal activation energies of paramagnetic center annihilation. Monitoring the primary defect concentration (P1 and other defects with half integer spins) and utilizing second order kinetic modeling, an activation energy of 3.63 ± 0.28 eV was estimated. Alternatively, using the NV half field EPR signal and first order kinetic modeling, a similar activation energy 3.89 ± 0.29 eV was estimated.

NV centers↗
DOE OSTI2021

Embedded Error Bayesian Calibration of Thermal Decomposition of Organic Materials

Organic materials are an attractive choice for structural components due to their light weight and versatility. However, because they decompose at low temperatures relative to tradiational materials they pose a safety risk due to fire and loss of structural integrity. To quantify this risk, analysts use chemical kinetics models to describe the material pyrolysis and oxidation using thermogravimetric analysis. This process requires the calibration of many model parameters to closely match experimental data. Previous efforts in this field have largely been limited to finding a single best-fit set of parameters even though the experimental data may be very noisy. Furthermore the chemical kinetics models are often simplified representations of the true de- composition process. The simplification induces model-form errors that the fitting process cannot capture. In this work we propose a methodology for calibrating decomposition models to thermogravimetric analysis data that accounts for uncertainty in the model-form and experimental data simultaneously. The methodology is applied to the decomposition of a carbon fiber epoxy composite with a three-stage reaction network and Arrhenius kinetics. The results show a good overlap between the model predictions and thermogravimetric analysis data. Uncertainty bounds capture devia- tions of the model from the data. The calibrated parameter distributions are also presented. In conclusion, the distributions may be used in forward propagation of uncertainty in models that leverage this material.

36 MATERIALS SCIENCE↗
DOE OSTI2020

Modeling and Manipulating Phenylpropanoid Pathway Flux for Bioenergy

We have made important strides toward our project goals over the past five years. First, we developed and published a novel isotopic feeding system to investigate metabolic fluxes of the lignin biosynthesis. Next, we have developed and published a kinetic model of the phenylpropanoid pathway. As an extension of that work, we developed a multilayer kinetic model of lignin biosynthesis. Next we conducted a systematic transcriptome and metabolome analysis for phenylpropanoid metabolism. Finally, we have identified three strategies that enhance the accumulation of 2-phenylethanol in plants.

09 BIOMASS FUELS↗
DOE OSTI2022

Catalyst and reactor design considerations for selective production of acids by oxidative cleavage of alkenes and unsaturated fatty acids with H 2 O 2

Oxidative cleavage of alkenes and unsaturated fatty acids with hydrogen peroxide gives an efficient and sustainable process to obtain mono- and di-acids for polymers and lubricants with fewer safety risks and less environmental impact than processes that utilize ozone or other inorganic oxidizers (e.g., permanganate, dichromate, etc.). Guided by insight into the mechanisms for competing reaction pathways (i.e., epoxidation of alkene on W–(η 2 -O 2 ) complexes vs. H 2 O 2 decomposition) and the apparent kinetics derived from kinetic experiments, here, we postulate that W-based heterogeneous catalysts can provide high performance and stable operations at low H 2 O 2 concentrations. Semi-batch reactors with continuous introduction of H 2 O 2 solutions offer the means to maintain low H 2 O 2 concentrations while providing sufficient quantities of H 2 O 2 to satisfy the reaction stoichiometry. We derived simple kinetic model equations for the epoxidation, ring-opening, oxidative cleavage, and oxidation steps and fit theses equations to batch experimental data to obtain kinetic parameters. Furthermore, this kinetic model describes the concentration profiles of reactant, oxidant, and products well as shown by agreement with experimental data. Further predictions of the optimal H 2 O 2 feed rate for semi-batch operation utilized by the proposed rate expressions and the reactor design equations suggest that low H 2 O 2 feed rate increases selectivity towards oxidative cleavage products and selective use of H 2 O 2 for oxidative cleavage pathway. Comparisons of oxidative cleavage of 4-octene in batch and semi-batch reactors show that semibatch reactors with optimized molar feed rates of H 2 O 2 increased oxidative cleavage product selectivities (76% to 99%; with an increase in butyric acid selectivity from 1% to 55%) and H 2 O 2 selectivity (3% to 30%). In addition, semibatch reaction conditions used avoid H 2 O 2 -mediated dissolution of W-atoms from the catalyst. Analysis of these findings suggest that solid oxide catalysts will be effective for continuous oxidative cleavage reactions if deployed within fixed-bed reactors that allow for distributed introduction of reactants and therefore low in situ concentrations of H 2 O 2 .

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

Data for Catalyst and Reactor Design Considerations for Selective Production of Acids by Oxidative Cleavage of Alkenes with H2O2

Oxidative cleavage of alkenes and unsaturated fatty acids with hydrogen peroxide gives an efficient and sustainable process to obtain mono- and di-acids for polymers and lubricants with fewer safety risks and less environmental impact than processes that utilize ozone or other inorganic oxidizers (e.g., permanganate, dichromate, etc.). Guided by insight into the mechanisms for competing reaction pathways (i.e., epoxidation of alkene on W–(η2-O2) complexes vs. H2O2 decomposition) and the apparent kinetics derived from kinetic experiments, here, we postulate that W-based heterogeneous catalysts can provide high performance and stable operations at low H2O2 concentrations. Semi-batch reactors with continuous introduction of H2O2 solutions offer the means to maintain low H2O2 concentrations while providing sufficient quantities of H2O2 to satisfy the reaction stoichiometry. We derived simple kinetic model equations for the epoxidation, ring-opening, oxidative cleavage, and oxidation steps and fit theses equations to batch experimental data to obtain kinetic parameters. This kinetic model describes the concentration profiles of reactant, oxidant, and products well as shown by agreement with experimental data. Further predictions of the optimal H2O2 feed rate for semi-batch operation utilized by the proposed rate expressions and the reactor design equations suggest that low H2O2 feed rate increases selectivity towards oxidative cleavage products and selective use of H2O2 for oxidative cleavage pathway. Comparisons of oxidative cleavage of 4-octene in batch and semi-batch reactors show that semi-batch reactors with optimized molar feed rates of H2O2 increased oxidative cleavage product selectivities (76% to 99%; with an increase in butyric acid selectivity from 1% to 55%) and H2O2 selectivity (3% to 30%). In addition, semi-batch reaction conditions used avoid H2O2-mediated dissolution of W-atoms from the catalyst. Analysis of these findings suggest that solid oxide catalysts will be effective for continuous oxidative cleavage reactions if deployed within fixed-bed reactors that allow for distributed introduction of reactants and therefore low in situ concentrations of H2O2.

Catalysis↗
DOE OSTI2021

Dynamics and chemical mode analysis of plasma thermal-chemical instability

The stability of the weakly ionized plasma and the transition from a stable homogeneous discharge to unstable filaments play an important role in gas laser physics, plasma-assisted combustion, chemical reforming, and material synthesis. Here, theoretical stability analysis and thermal-chemical mode analysis were performed to understand the mechanism of plasma thermal-chemical instability by using a zero-dimensional plasma system with both simplified and detailed chemical kinetics of H 2 /O 2 /N 2 mixtures. The plasma dynamic and kinetic models accounted for multiple physical mechanisms in the chemically-reactive weakly ionized plasma, including ionization, attachment/detachment, recombination, vibrational and electronic energy relaxation, convective and diffusive species/heat removal, Joule heating, and detailed chemical kinetics. An analytical criterion and the explosive mode species/temperature pointers were formulated while the representative active species were identified for different thermal-chemical modes. The results showed that in addition to the classical thermal-ionization mechanism, various chemical modes from chemical heat imbalance and elementary kinetics significantly modified the time dynamics and the stability of the weakly ionized plasma. The present analysis provides insights and guidance to control plasma instability using chemical kinetics.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗
DOE OSTI2024

Distinguishing Surface and Bulk Reactivity: Concentration-Dependent Kinetics of Iodide Oxidation by Ozone in Microdroplets

Iodine oxidation reactions play an important role in environmental, biological, and industrial contexts. The multiphase reaction between aqueous iodide and ozone is of particular interest due to its prevalence in the marine atmosphere and unique reactivity at the air–water interface. Here, we explore the concentration dependence of the I – + O 3 reaction in levitated microdroplets under both acidic and basic conditions. To interpret the experimental kinetics, molecular simulations are used to benchmark a kinetic model, which enables insight into the reactivity of the interface, the nanometer-scale subsurface region, and the bulk interior of the droplet. For all experiments, a kinetic description of gas- and liquid-phase diffusion is critical to interpreting the results. We find that the surface dominates the iodide oxidation kinetics under concentrated and acidic conditions, with the reactive uptake coefficient approaching an upper limit of 10 –2 at pH 3. In contrast, reactions in the subsurface dominate under more dilute and alkaline conditions, with inhibition of the surface reaction at pH 12 and an uptake coefficient that is 10× smaller. In conclusion, the origin of a changing surface mechanism with pH is explored and compared to previous ozone-dependent measurements.

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

Copper availability governs nitrous oxide accumulation in wetland soils and stream sediments

Denitrification is microbially-mediated through enzymes containing metal cofactors. Laboratory studies of pure cultures have highlighted that the availability of Cu, required for the multicopper enzyme nitrous oxide reductase, can limit N2O reduction. However, in natural aquatic systems, such as wetlands and hyporheic zones in stream beds, the role of Cu in controlling denitrification remains incompletely understood. In this study, we collected soils and sediments from three natural environments -- riparian wetlands, marsh wetlands, and a stream -- to investigate their nitrogen species transformation activity at background Cu levels and different supplemented Cu loadings. All of the systems contained solid-phase associated Cu below or around geological levels (40 - 280 nmol g-1) and exhibited low dissolved Cu (3-50 nM), which made them appropriate sites for evaluating the effect of limited Cu availability on denitrification. In laboratory incubation experiments, high concentrations of N2O accumulated in all microcosms lacking Cu amendment except for one stream sediment sample. With Cu added to provide dissolved concentrations at trace levels (10-300 nM), reduction rate of N2O to N 2 in the wetland soils and stream sediments was enhanced. A kinetic model could account for the trends in nitrogen species by combining the reactions for microbial reduction of NO 3 - to NO 2 -/N 2 O/N 2 and abiotic reduction of NO 2 - to N 2 . The model revealed that the rate of N 2 O to N 2 conversion increased significantly in the presence of Cu. For riparian wetland soils and stream sediments, the kinetic model also suggested that overall denitrification is driven by abiotic reduction of NO2- in the presence of inorganic electron donors. This study demonstrated that natural aquatic systems containing Cu at concentrations less than or equal to crustal abundances may display incomplete reduction of N 2 O to N 2 that would cause N 2 O accumulation and release to the atmosphere.

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

Enhancing Drinking Water Quality Modeling: Leveraging Physics Informed Neural Networks for Learning with Imperfect Reaction Models and Partial Data

Chemical kinetics models, typically formulated as systems of ordinary or partial differential equations, are valuable tools for simulating drinking water quality. However, these models often face inaccuracies due to discrepancies between the laboratory and the real-world conditions, as well as limitations in experimental analytical methods, hindering the accurate representation of the true underlying chemical mechanisms. In this study, we propose a Physics Informed Neural Network (PINN), using the eXtreme Theory of Functional Connections, to improve the prediction of chemical concentrations over time. The PINN method accounts for imperfect chemical models and incorporates partial data to improve predictions. Focusing on reactions describing water disinfection residual and disinfectant byproduct formation, which are crucial for public health and regulatory compliance, we demonstrate that the PINN model is able to accurately predict the concentrations of chemical species across various pH values. Notably, the model extends its accuracy to predict concentrations of chemical species not originally included in its training data. The developed method can be extended to a variety of chemical systems, offering a wide array of potential applications.

13 HYDRO ENERGY↗
DOE OSTI2025

Kinetic Deep Learning v0.1

Here, we present a method that uses protein levels to predict times series of metabolite concentrations. Understanding this type of pathway dynamics is important in order to predict the behavior of the pathway and, more pragmatically, to be able to design biological systems (such as strains bioengineered to produce chemical products) reliably. Typically, for this purpose, kinetic models consisting of differential equations based on the Michaelis-Menten dynamics have been used in the past. However, these methods can rarely produce good fits to measured data time series. Possibly, this happens because the kinetic constants are unknown or are different from the ones measured in vivo, or perhaps because Michaelis-Menten dynamics is not a satisfactory description. In order to improve the predictive nature of these kinetic models we have eliminated the Michaelis-Menten description of pathway dynamics and we have substituted it by algorithms that automatically learn these dynamics from previously obtained metabolomics and proteomics data using machine learning approaches. Specifically, kinetic deep learning uses deep learning to map proteomics time series to metabolite concentration time series, instead of learning the first metabolite derivative and integrating in (as in the first version of kinetic learning). This approach is shown to provide good to excellent results with a data set specifically collected for this purpose.

Garcia Martin, Hector [Joint BioEnergy Institute (↗
DOE OSTI2025

Portal matter models of kinetic mixing with two light dark gauge bosons

The kinetic mixing (KM) portal mandates the existence of at least one new gauge boson, the dark photon (DP) based on the group 𝑈⁢(1) 𝐷 , which mixes with the Standard Model (SM) photon via loops of other new heavy particles carrying both SM and dark charges called portal matter (PM). Arguments exist based on the renormalization group equations running of the 𝑈⁢(1) 𝐷 gauge coupling suggesting that at higher scales 𝑈⁢(1) 𝐷 becomes part of a more complex non-Abelian group, a simple example being just the SM-like 𝐺 𝐷 =𝑆⁢𝑈⁢(2)𝐼 ×𝑈⁢(1) 𝑌 𝐼 . In our past analyses, it was always assumed that 𝐺 𝐷 broke in a SM-like manner directly to the DP’s 𝑈⁢(1) 𝐷 which then subsequently broke at low energies ≲1 GeV. However, this need not be the case, and 𝐺 𝐷 can instead break to 𝑈⁢(1) 𝑇 3⁢𝐼 ×𝑈⁢(1) 𝑌 𝐼 , with 𝑇 3⁢𝐼 being the diagonal generator of 𝑆⁢𝑈⁢(2) 𝐼 , now producing two light gauge bosons which obtain masses at the ≲1 GeV scale. In this paper, we explore the phenomenology of a very simple realization of this kind of alternative setup employing non-Abelian KM and having a minimal, leptonlike PM sector, demonstrating its distinctive nature in comparison to the previously examined symmetry breaking path. The effects of interference between these gauge bosons on thermal dark matter annihilation, the production of new heavy gauge, and Higgs and PM states at colliders, as well as the corresponding signatures for the light dark gauge bosons are examined. Collider signatures of this setup are found to be particularly challenging.

extensions of Higgs sector↗
DOE OSTI2023

Catalytic Strategy for Conversion of Triacetic Acid Lactone to Potassium Sorbate

Here this study shows a new route to produce potassium sorbate (KS) from triacetic acid lactone (TAL), which is a chemical platform that can be biologically synthesized from natural sources. Sorbic acid and its potassium salt (KS) are widely used as preservatives in foods and pharmaceuticals. Three steps are used to produce KS from TAL: 1) hydrogenation of TAL into 4-hydroxy- 6-methyltetrahydro-2-pyrone (HMP), 2) dehydration of HMP to parasorbic acid (PSA), and 3) ring-opening and hydrolysis of PSA to KS. TAL can be fully hydrogenated over Ni/SiO2 to give near quantitative yields of HMP. A three-step reaction kinetics model was developed for dehydration of HMP into PSA. This model was used to show that the highest PSA yield occurs at low temperatures. An experimental PSA yield of 84.2% with respect to TAL was obtained which agreed with the prediction of the reaction kinetics model. KOH was used as a coreactant for the ring-opening hydrolysis of PSA to produce >99.9% yield of KS from PSA. Tetrahydrofuran (THF) was used to purify the TAL derived-KS (TAL-KS). The TAL-KS had a KS purity of 95.5%. The overall yield of TAL-KS with respect to TAL was calculated to be 77.3%. TAL-KS produced in this study had similar antimicrobial activities as commercial KS.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2023

Data for Catalytic Strategy for Conversion of Triacetic Acid Lactone to Potassium Sorbate

This study shows a new route to produce potassium sorbate (KS) from triacetic acid lactone (TAL), which is a chemical platform that can be biologically synthesized from natural sources. Sorbic acid and its potassium salt (KS) are widely used as preservatives in foods and pharmaceuticals. Three steps are used to produce KS from TAL: 1) hydrogenation of TAL into 4-hydroxy-6-methyltetrahydro-2-pyrone (HMP), 2) dehydration of HMP to parasorbic acid (PSA), and 3) ring-opening and hydrolysis of PSA to KS. TAL can be fully hydrogenated over Ni/SiO2 to give near quantitative yields of HMP. A three-step reaction kinetics model was developed for dehydration of HMP into PSA. This model was used to show that the highest PSA yield occurs at low temperatures. An experimental PSA yield of 84.2% with respect to TAL was obtained which agreed with the prediction of the reaction kinetics model. KOH was used as a coreactant for the ring-opening hydrolysis of PSA to produce >99.9% yield of KS from PSA. Tetrahydrofuran (THF) was used to purify the TAL derived-KS (TAL-KS). The TAL-KS had a KS purity of 95.5%. The overall yield of TAL-KS with respect to TAL was calculated to be 77.3%. TAL-KS produced in this study had similar antimicrobial activities as commercial KS.

Catalysis↗
DOE OSTI2021

Probing the Chemical Kinetics of Minimalist Functional Group Gasoline Surrogates

We report surrogate mixtures are routinely used for understanding gasoline fuel combustion in engine simulations. The general trend in surrogate formulation has been to increase the number of fuel components in a mixture to better emulate real fuel properties. Recently, a new surrogate design strategy based on functional group analysis of real gasolines was proposed using a minimal number of species [minimalist functional group (MFG)-approach]. MFG surrogates (having just one or two components) could experimentally capture the ignition delay time (IDT), threshold sooting index, and smoke point of different gasoline fuels with hundreds of components. However, other combustion characteristics were not explored, and kinetic modeling of MFG surrogates was not reported. These aspects are addressed in this paper, where the combustion behavior of MFG surrogates for various gasolines was assessed by simulating IDT, jet-stirred reactor oxidation, and premixed laminar flame speeds using chemical kinetic modeling. MFG simulations were compared with experimental data of the real gasolines as well as with the more complex multicomponent (five to nine species) surrogates. This study reveals that binary MFG surrogate mixtures are capable of accurately simulating the combustion behavior of more complex gasoline fuels with hundreds of components.

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

Electric field distribution in a non-self-sustained RF discharge with ionization generated by Ns discharge pulses

Abstract Electric field in a capacitively coupled, non-self-sustained RF discharge nitrogen plasma with external ionization generated by high-voltage ns pulses has been measured by ps Electric field induced second harmonic generation (EFISH). The measurements are made both in the bulk of the plasma and in the sheaths, using the absolute calibration by the Laplacian field between two plane electrodes. The results are compared with the kinetic modeling calculations. The RF electric field in the sheaths is significantly higher compared to that in the plasma, due to the displacement of the electrons by the drift oscillations and the resultant plasma self-shielding. However, the kinetic modeling predictions indicate that the electron impact ionization in the sheaths is largely ineffective, due to the low electron density. The reduction of the electric field in the plasma by the self-shielding in the sheaths is moderate, such that the energy is coupled to the plasma by the below-breakdown RF field. The peak RF field in the plasma is in the range of 15–25 Td, indicating the efficient vibrational excitation of N 2 by electron impact. The modeling predictions suggest that the targeted vibrational excitation of molecular species in a non-self-sustained RF discharge with external ionization is scalable to high pressures, electron densities, and discharge powers. The present approach can be extended to the vibrational excitation of other molecular species where the vibrational relaxation is relatively slow, such as CO, CO 2 , and H 2 .

Physics↗
DOE OSTI2021

Theoretical justification for heat flux limiter 0.15

In this section, we describe a semi-nonlocal kinetic model for the electron heat flux. The model has no free parameters. It takes the more physically motivated approach of placing a limiter on the perturbation to the electron distribution function, rather than the usual ad-hoc limiter on the heat flux. If the kinetic model is fitted by a heat flux limiter model, we find that the higher value of f e = 0.15 is a much closer fit than other models with f e ≃ 0.02.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗
DOE OSTI2021

Modeling Growth Kinetics, Interspecies Cell Fusion, and Metabolism of a Clostridium acetobutylicum / Clostridium ljungdahlii Syntrophic Coculture

ABSTRACT Clostridium acetobutylicum and Clostridium ljungdahlii grown in a syntrophic culture were recently shown to fuse membranes and exchange cytosolic contents, yielding hybrid cells with significant shifts in gene expression and growth phenotypes. Here, we introduce a dynamic genome-scale metabolic modeling framework to explore how cell fusion alters the growth phenotype and panel of metabolites produced by this binary community. Computational results indicate C. ljungdahlii persists in the coculture through proteome exchange during fusing events, which endow C. ljungdahlii cells with expanded substrate utilization, and access to additional reducing equivalents from C. acetobutylicum -evolved H 2 and through acquisition of C. acetobutylicum -native cofactor-reducing enzymes. Simulations predict maximum theoretical ethanol and isopropanol yields that are increased by 0.64 mmol and 0.39 mmol per mmol hexose sugar consumed, respectively, during exponential growth when cell fusion is active. This modeling effort provides a mechanistic explanation for the metabolic outcome of cellular fusion and altered homeostasis achieved in this syntrophic clostridial community. IMPORTANCE Widespread cell fusion and protein exchange between microbial organisms as observed in synthetic C. acetobutylicum / C. ljungdahlii culture is a novel observation that has not been explored in silico . The mechanisms responsible for the observed cell fusion events in this culture are still unknown. In this work, we develop a modeling framework that captures the observed culture composition and metabolic phenotype, use it to offer a mechanistic explanation for how the culture achieves homeostasis, and identify C. ljungdahlii as primary beneficiary of fusion events. The implications for the events described in this study are far reaching, with potential to reshape our understanding of microbial community behavior synthetically and in nature.

59 BASIC BIOLOGICAL SCIENCES↗