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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 271 records · Page 15
All sourcesNASA NTRSDOE OSTI
DOE OSTI2021

Yield and PDV Measurements of the Godiva-IV Critical Assembly

A high dynamic range, scinillator based detector, the MHD-240, was used to measure the gamma and neutron flux of several prompt critical bursts of the Godiva-IV assembly. The measurements provided the alpha and FWHM for several different burst temperatures. Calibrations of the MHD240 gamma and neutron sensitivity along with MCNP based simulation of the Godiva-IV assembly allowed us to also determine the total fission yield of the burst integrated over approximately 5 ms. In addition a Photo Doppler Velocimetry measurement was conducted to determine the surface motion vibrational frequency of Godiva-IV assembly during the bursts. The combined data were collected to be used as input to multi-physics simulation of the Godiva-IV assembly. This is a preliminary report, there is an ongoing effort to update the MCNP simulations used in the analysis and develop a correction for the room return component of the measured signal.

22 GENERAL STUDIES OF NUCLEAR REACTORS↗
DOE OSTI2022

Experimental Validation and Continuous Testing of an On-Purpose High-Yield Pitch Synthesis Process for Producing Carbon Fiber from US Domestic Coal

This project aims to develop technology that converts domestic United States (US) raw coal to high quality, high value, and marketable carbon fiber. More specifically, the project aims to significantly improve the selectivity and yield of carbon fiber produced per ton of coal over conventional coal pitch-based production by using low-severity direct coal conversion technology to maximize the yield of pitch from coal, suitable for production of carbon fiber. To meet the stated objective of developing a technology platform, capable of producing high quality, high-value and marketable carbon fiber from domestic US coal, the proposed scope of work involves testing of a low-severity direct coal liquefaction (LS-DCL) process approach. The isotropic pitch produced from the LS-DCL process will be thermally treated using conventional processes to convert to a liquid crystal or “mesophase” pitch as a precursor for structural carbon fibers. This mesophase pitch will then be melt-spun into fiber, oxygen stabilized, and carbonized using conventional processes. The resulting carbon fiber will be evaluated for mechanical properties and suitability for structural applications such as automotive parts and spars for wind turbine blades. The overall process is expected to significantly lower the cost of fiber compared to state of the art fibers produced from polyacrylonitrile (PAN) precursors.

01 COAL, LIGNITE, AND PEAT↗
DOE OSTI2023

Advancing energy storage through solubility prediction: leveraging the potential of deep learning

Solubility prediction plays a crucial role in energy storage applications, such as redox flow batteries, because it directly affects the efficiency and reliability. Researchers have developed various methods that utilize quantum calculations and descriptors to predict the aqueous solubilities of organic molecules. Notably, machine learning models based on descriptors have shown promise for solubility prediction. As deep learning tools, graph neural networks (GNNs) have emerged to capture complex structure–property relationships for material property prediction. Specifically, MolGAT, a type of GNN model, was designed to incorporate n-dimensional edge attributes, enabling the modeling of intricacies in molecular graphs and enhancing the prediction capabilities. In a previous study, MolGAT successfully screened 23 467 promising redox-active molecules from a database of over 500 000 compounds, based on redox potential predictions. This study focused on applying the MolGAT model to predict the aqueous solubility (log S) of a broad range of organic compounds, including those previously screened for redox activity. The model was trained on a diverse sample of 8494 organic molecules from AqSolDB and benchmarked against literature data, demonstrating superior accuracy compared with other state of the art graph-based and descriptor-based models. Subsequently, the trained MolGAT model was employed to screen redox-active organic compounds identified in the first phase of high-throughput virtual screening, targeting favorable solubility in energy storage applications. The second round of screening, which considered solubility, yielded 12 332 promising redox-active and soluble organic molecules suitable for use in aqueous redox flow batteries. Thus, the two-phase high-throughput virtual screening approach utilizing MolGAT, specifically trained for redox potential and solubility, is an effective strategy for selecting suitable intrinsically soluble redox-active molecules from extensive databases, potentially advancing energy storage through reliable material development. This indicates that the model is reliable for predicting the solubility of various molecules and provides valuable insights for energy storage, pharmaceutical, environmental, and chemical applications.

25 ENERGY STORAGE↗
DOE OSTI2020

Variable Chain Length Carboxylic Acids as Modifiers to Enhance the Antiviral Efficacy of Sodium Dodecyl Sulfate.

Non-toxic disinfectants composed of readily available commodity chemicals are needed for immediate response to the current COVID-19 pandemic. One such area is the active research field for food-grade sanitization. Combinations of levulinic acid, a five-carbon ketocarboxylic acid, and sodium dodecyl sulfate, have been frequently described for antibacterial use on food contact surfaces. Levulinic acid has been identified as a renewable feedstock but is not presently in commodity production. Other carboxylic acids, such as acetic acid, may be equally usable and food-safe. Acidic and buffered solutions were highly effective, yielding no countable surviving organisms. The high efficacy of each acid may suggest that carboxylic acid sanitizers in general have potential use against viruses.

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

Thresholding Analysis and Feature Extraction from 3D Ground Penetrating Radar Data for Noninvasive Assessment of Peanut Yield

This study explores the efficacy of utilizing a novel ground penetrating radar (GPR) acquisition platform and data analysis methods to quantify peanut yield for breeding selection, agronomic research, and producer management and harvest applications. Sixty plots comprising different peanut market types were scanned with a multichannel, air-launched GPR antenna. Image thresholding analysis was performed on 3D GPR data from four of the channels to extract features that were correlated to peanut yield with the objective of developing a noninvasive high-throughput peanut phenotyping and yield-monitoring methodology. Plot-level GPR data were summarized using mean, standard deviation, sum, and the number of nonzero values (counts) below or above different percentile threshold values. Best results were obtained for data below the percentile threshold for mean, standard deviation and sum. Data both below and above the percentile threshold generated good correlations for count. Correlating individual GPR features to yield generated correlations of up to 39% explained variability, while combining GPR features in multiple linear regression models generated up to 51% explained variability. The correlations increased when regression models were developed separately for each peanut type. This research demonstrates that a systematic search of thresholding range, analysis window size, and data summary statistics is necessary for successful application of this type of analysis. The results also establish that thresholding analysis of GPR data is an appropriate methodology for noninvasive assessment of peanut yield, which could be further developed for high-throughput phenotyping and yield-monitoring, adding a new sensor and new capabilities to the growing set of digital agriculture technologies.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2022

Experimental Investigation of Barium Sources and Fluid–Rock Interaction in Unconventional Marcellus Shale Wells Using Ba Isotopes

Produced waters from unconventional Marcellus Shale gas wells have anomalously high barium (Ba) concentrations and yield some of the isotopically heaviest Ba measured to date. Experiments were conducted to constrain the source of Ba in these fluids and the controls on barite (BaSO 4 ) precipitation and dissolution in oil and gas wells. Experiments simulating the acidizing stage evaluated the solubility of pure barite and drilling mud in 2 M HCl at 80 °C for periods of 2, 6, and 48 h and resulted in <0.01% barite dissolution with no appreciable change in δ 138 Ba ( 138 Ba/ 134 Ba normalized to NIST standard 3104a). Static autoclave experiments conducted at 66 °C and 20.7 MPa with combinations of ground Marcellus Shale solids and/or barite-bearing drilling mud with synthetic low-Ba fracturing fluid resulted in decreased Ba concentrations in the fluid, with the largest decrease in the shale-only run. Fluid δ 138 Ba values increased by about 0.5‰ as Ba concentrations decreased, consistent with closed-system Rayleigh fractionation. Flow-through experiments in Marcellus Shale core conducted for 28 days resulted in effluent Ba concentrations an order of magnitude lower than the influent, while sulfate concentrations increased over time. Effluent δ 138 Ba values increased over the first 12 days and plateaued at about 1‰ higher than the influent. Modeling suggests a combination of the release of labile shale Ba and barite precipitation. This work indicates that the processes of Ba release from fluid–shale interactions and barite precipitation in fractures and the well bore, while capable of producing high δ 138 Ba fluids, are unlikely to generate fluids with high-Ba concentrations and δ 138 Ba values like those in Marcellus-produced waters. As a result, we find that the release of sulfate from shale pyrite oxidation rapidly catalyzes barite precipitation and that dissolution of drilling mud barite or natural barite in the shale is unlikely to be the major source of Ba in Marcellus-produced waters.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2022

Correlating Broadband Photoluminescence with Structural Dynamics in Layered Hybrid Halide Perovskites

The emission of white light from a single material is atypical and is of interest for solid-state lighting applications. Broadband light emission has been observed in some layered perovskite derivatives, A 2 PbBr 4 (A = R-NH 3 + ), and correlates with static structural distortions corresponding to out-of-plane tilting of the lead bromide octahedra. While materials with different organic cations can yield distinct out-of-plane tilts, the underlying origin of the octahedral tilting remains poorly understood. Using high energy resolution (e.g., quasi-elastic) neutron scattering, this contribution details the rotational dynamics of the organic cations in A 2 PbBr 4 materials where A = n-butylammonium (nBA), 1,8-diaminooctammonium (ODA), and 4-aminobutyric acid (GABA). The organic cation dynamics differentiate (nBA) 2 PbBr 4 from (ODA)PbBr 4 or (GABA) 2 PbBr 4 in that the larger spatial extent of dynamics of nBA yields a larger effective cation radius. The larger effective volume of the nBA cation in (nBA) 2 PbBr 4 yields a closer to ideal A-site geometry, preventing the out-of-plane tilt and broadband luminescence. In all three compounds, we observe hydrogen dynamics attributed to rotation of the ammonium headgroup and at a time scale faster than the white light photoluminescence studied by time-correlated single photon counting spectroscopy. This supports a previous assignment of the broadband emission as resulting from a single ensemble, such that the emissive excited state experiences many local structures faster than the emissive decay. Furthermore, the findings presented here highlight the role of the organic cation and its dynamics in hybrid organic–inorganic perovskites and white light emission.

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

Morphological-informed Thermal Property Prediction in the Engineering Domain

Using first-principle, atomic scale methods to predict thermal carrier (electron and phonon) behavior at nanoscale yields highly resolved thermal properties. Density functional theory simulations can model myriad effects on the transport of phonons and electrons (e.g., phonon and electron coupling, electron correlation, defect presence, carrier scattering), and yield thermal properties for small geometric domains such as thermal and electrical conductivity, or heat capacity. While these thermal properties are accurate in atomistic and nanoscopic systems, they must be scaled up to the microstructural domain to be a useful predictor for experimental basis, as the microscale is where myriad changes and physical phenomenon occur within a material (e.g., grain boundaries, precipitate aggregations, interfaces, defect clusters). My talk will discuss the necessity of thermal property predictions for nuclear fuels applications and more. Additionally I will discuss the necessity in developing multi-physics, multi-scale methods for future applications in predictions for not only nuclear fuels, but other materials applications.

36 MATERIALS SCIENCE↗
DOE OSTI2025

Integrated thermal and biological conversion of microalgal proteins to lipids

Microalgal composition varies with cultivation strategy, and low-cost approaches often produce high-protein biomass. This presents challenges for biorefineries designed around static, lipid-rich feedstocks. In particular, hydrolysates from high-protein algae are nitrogen-rich and sugar-poor, limiting microbial conversion and reducing product yields. This study develops a sequential thermal conditioning and biological upgrading strategy to integrate high-protein hydrolysate processing within conventional lipid extraction and upgrading designs. Oxidative deconstruction was used to break down proteins into ammonium and short-chain carboxylates. Ammonium was subsequently removed to yield a nitrogen-depleted, carboxylate-rich medium suitable for microbial lipid production. Bioconversion trials with Cutaneotrichosporon oleaginosum showed lipid accumulation only from hydrolysates treated with both oxidative deconstruction and nitrogen removal, reaching 1.2 g/L lipids at 30 % intracellular content. This integrated approach enables protein-to-lipid conversion and improves flexibility to process variable algal feedstocks, advancing fuel-oriented microalgal biorefineries.

09 BIOMASS FUELS↗
DOE OSTI2022

Optical Trapping of High-Aspect-Ratio NaYF Hexagonal Prisms for kHz-MHz Gravitational Wave Detectors

Here, we present experimental results on optical trapping of Yb-doped β-NaYF subwavelength-thickness high-aspect-ratio hexagonal prisms with a micron-scale radius. The prisms are trapped in vacuum using an optical standing wave, with the normal vector to their face oriented along the beam propagation direction, yielding much higher trapping frequencies than those typically achieved with microspheres of similar mass. This platelike geometry simultaneously enables trapping with low photon-recoil-heating, high mass, and high trap frequency, potentially leading to advances in high frequency gravitational wave searches in the Levitated Sensor Detector, currently under construction. The material used here has previously been shown to exhibit internal cooling via laser refrigeration when optically trapped and illuminated with light of suitable wavelength. Employing such laser refrigeration methods in the context of our work may enable higher trapping intensity and thus higher trap frequencies for gravitational wave searches approaching the several hundred kilohertz range.

47 OTHER INSTRUMENTATION↗
DOE OSTI2023

Characterizing the flux effect on the irradiation embrittlement of reactor pressure vessel steels using machine learning

In-service exposure to high-energy neutrons embrittles reactor pressure vessel (RPV) steels. An increase in the yield stress (Δσ y ) results in a corresponding increase in the brittle to ductile transition temperature (ΔT c ). Most existing models underpredict ΔT c at higher fluence following accelerated irradiations in test reactors. High fluence, up to 10 20 n/cm 2 in some cases, will be reached over extended RPV vessel operation of 80 years, or more, at low service flux. Embrittlement has been extensively studied in accelerated, higher flux test reaction irradiations. However, the use of test reactor data naturally raises the question of flux effects. Here, this study used a machine learning approach trained on a set of hardening data, covering a wide range of flux, fluence, and steel compositions to determine the interactive effects of both irradiation and material variables on Δσ y . The analysis included machine learning-based cross-plots of the variable dependence of Δσ y for six core steels (i.e., CM6, LC, LD, LG, LH, and LI), with controlled differences in their Cu and Ni contents. A primary objective is to evaluate an effective to actual fluence (Φt e /Φt) ratio, as a function of flux, fluence, and steel composition. This is information critical to properly use intermediate flux-high fluence data in calibrating a low flux-high fluence embrittlement model. The predicted Φt e /Φt is reasonably consistent with estimates previously derived from a physics-based solute recombination trap model.

36 MATERIALS SCIENCE↗
DOE OSTI2024

Testing of Helium-Cooled Metal Molybdenum Disk Target

NorthStar Medical Radioisotopes LLC is planning to produce an important medical radioisotope, molybdenum-99 (Mo-99), through photonuclear reaction on molybdenum-100 (Mo-100). In this approach, molybdenum metal will be bombarded with a 40-MeV electron beam. Because enriched Mo-100 is expensive, it is desired to use as much beam power as possible to achieve maximum production yield and minimize target mass. This objective leads to very high beam power density (heat deposition in the target), which sets challenging requirements for cooling. Together with scientists at Los Alamos National Laboratory, a team at Argonne National Laboratory has developed and demonstrated a cooling approach using pressurized helium, which allows for efficient heat removal. One of the main challenges in this approach is the management of the heat load on the target window. The target window separates the high-pressure helium inside the target from the vacuum in the beamline, so it is constantly under stress from differential pressure. Also, the window is cooled only by the helium gas flowing on one side, making the window cooling even more challenging. High heat deposition in the target disks also imposes a strict requirement on performance of the helium cooling system and thickness of the target disks. The target disks are produced from metal powder via a press-and-sinter process. The resulting disks do not possess tensile strength as high as solid molybdenum and might not survive the vibration from the high-velocity helium coolant and high thermal stress from beam heating. An Argonne team of scientists performed a series of tests at Argonne’s Low Energy Accelerator Facility (LEAF) [3-7]. This report describes two series of tests for scale down production target designs that utilize full-scale 29 millimeters diameter, 0.75 mm thick press-and-sintered disks. We performed two thermal tests with different beam parameters and configurations of the disk laminations. We compared the results of the window temperature measurements and cooling system parameters obtained in the experiments with those predicted by analytical calculations and Computation Flow Dynamic (CFD) simulations. Results of the experiments and calculations are presented below.

36 MATERIALS SCIENCE↗
DOE OSTI2025

Identifying High Ionic Conductivity Compositions of Ionic Liquid Electrolytes Using Features of the Solvation Environment

Binary mixtures of ionic liquids with molecular solvents are gaining interest in electrochemical applications due to the improvement in their performance over neat ionic liquids. Dilution with suitable molecular solvents can reduce the viscosity and facilitate faster diffusion of ions, thereby yielding substantially higher ionic conductivity than that for a pure ionic liquid. Although viscosity and diffusion coefficients typically behave as monotonic functions of concentration, ionic conductivity often passes through a peak value at an optimum molar ratio of the molecular solvent to the ionic liquid. The ionic conductivity maximum is generally explained in terms of a balance between the ease of charge transport and the concentration of the charge carriers. In this work, fluctuation in the local environment surrounding an ion is invoked as a plausible explanation for the ionic conductivity mechanism with a binary mixture of 1-ethyl-3-methylimidazolium tetrafluoroborate and ethylene glycol as an example. The magnitude of the dynamism in the local environment is captured by measuring the spatial and temporal features of the solvation environment. Standard deviation in the number of ions in the solvation environment serves as a spatial feature, while the cage correlation lifetimes for oppositely charged ions within the first solvation shell serve as a temporal feature. Large standard deviations in the cluster ion population and short cage correlation lifetimes are indicators of highly dynamic ionic environment at the molecular level and consequently yield high ionic conductivity. Such compositions were found to be in good agreement with the optimum ionic liquid mole fractions obtained through experimental measurement. Short cage correlation lifetimes enable the identification of optimum mixture compositions using simulation trajectories significantly shorter than those required to implement the Nernst–Einstein or Einstein formalisms for calculating ionic conductivity. We validated the applicability of this approach across force fields and in six ionic liquid-molecular solvent electrolytes formed with combination of cations, anions, and solvents. We offer a computationally efficient approach of screening ionic liquid-molecular solvent binary mixture electrolytes to identify molar ratios that yield high ionic conductivity.

25 ENERGY STORAGE↗
DOE OSTI2020

Light‐driven catalysis with engineered enzymes and biomimetic systems

Abstract Efforts to drive catalytic reactions with light, inspired by natural processes like photosynthesis, have a long history and have seen significant recent growth. Successfully engineering systems using biomolecular and bioinspired catalysts to carry out light‐driven chemical reactions capitalizes on advantages offered from the fields of biocatalysis and photocatalysis. In particular, driving reactions under mild conditions and in water, in which enzymes are operative, using sunlight as a renewable energy source yield environmentally friendly systems. Furthermore, using enzymes and bioinspired systems can take advantage of the high efficiency and specificity of biocatalysts. There are many challenges to overcome to fully capitalize on the potential of light‐driven biocatalysis. In this mini‐review, we discuss examples of enzymes and engineered biomolecular catalysts that are activated via electron transfer from a photosensitizer in a photocatalytic system. We place an emphasis on selected forefront chemical reactions of high interest, including CH oxidation, proton reduction, water oxidation, CO 2 reduction, and N 2 reduction.

Edwards, Emily H.↗
DOE OSTI2020

Inverse design of two-dimensional graphene/h-BN hybrids by a regressional and conditional GAN

Design of materials with desired properties is currently laborious and heavily relies on intuition of researchers through a trial-and-error process. To tackle this challenge, in this work we propose a novel regressional and conditional generative adversarial network (RCGAN) for inverse design of representative two-dimensional materials, the graphene and boron-nitride (BN) hybrids. RCGAN incorporates a supervised regressor network, thus overcoming the common technical barrier in the traditional unsupervised GANs, which cannot generate data when fed with continuous and quantitative labels. RCGAN can autonomously generate graphene/BN hybrids given any target bandgap values. These structures are distinguished from the ones used for training and exhibit high diversity for a given bandgap. Moreover, they exhibit high fidelity, yielding bandgaps within ~10% MAE F of the desired bandgaps as validated by density functional theory (DFT) calculations. Analysis by the principle component analysis (PCA) and modified locally linear embedding (MLLE) reveals that the generator has successfully generated structures following the statistical distribution of the real structures. It implies the possibility of the RCGAN in recognizing physical rules hidden in the high-dimensional data. The novel strategy for designing regressional GAN architecture together with the successful application to inverse design of materials would inspire further exploration in research fields beyond materials.

36 MATERIALS SCIENCE↗
DOE OSTI2023

Theoretical Kinetics Predictions for Reactions on the NH 2 O Potential Energy Surface

Recent modeling studies of ammonia oxidation, which are motivated by the prospective role of ammonia as a zero-carbon fuel, have indicated significant discrepancies among the existing literature mechanisms. In this study, high-level theoretical kinetics predictions have been obtained for reactions on the NH 2 O potential energy surface, including the NH 2 + O, HNO + H, and NH + OH reactions. These reactions have previously been highlighted as important reactions in NH 3 oxidation with high sensitivity and high uncertainty. The potential energy surface is explored with coupled cluster calculations, including large basis sets and high-level corrections to yield high-accuracy (~0.2 kcal/mol 2σ uncertainty) estimates of the stationary point energies. Variational transition state theory is used to predict the microcanonical rate constants, which are then incorporated in master equation treatments of the temperature- and pressure-dependent kinetics. For radical–radical channels, the microcanonical rates are obtained from variable reaction coordinate transition state theory implementing directly evaluated multireference electronic energies. The analysis yields predictions for the total rate constants as well as the branching ratios. We find that the NO + H 2 channel contributes 10% of the total NH 2 + O flux at combustion temperatures, although this channel is not included in modern NH 3 oxidation mechanisms. Modeling is used to illustrate the ramifications of these rate predictions on the kinetics of NH 3 oxidation and NO x formation. Finally, the present results for NH 2 + O are important for predicting the chain branching and formation of NO in the oxidation of NH 3 and thermal DeNO x .

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗