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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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All sourcesNASA NTRSDOE OSTI
DOE OSTI2022

In-Situ Spatial Mapping of Hydrogen in Yttrium Hydrides at LANSCE

This report summarizes the neutron imaging techniques and experimental activities performed at the Los Alamos Neutron Science Center (LANSCE) with the main goal of measuring temperature driven hydrogen diffusion within bulk-yttrium hydride (YH x ) materials. The experimental results and technique developments reported herein support the US Department of Energy Office of Nuclear Energy’s (DOE-NE) Microreactor Program under Technology Maturation. In particular, the critical need to experimentally validate and verify hydrogen-diffusion models of metal hydrides used in hightemperature microreactor designs is addressed by means of high-spatial-resolution neutron imaging coupled with custom-built and application-specific furnaces. These capabilities were designed to apply large temperature gradients across centimeter-sized YH x pellets to simulate conditions faced in the microreactor environment. Neutron imaging, combined with in-situ sample heating, enables near real-time tracking of hydrogen diffusion in YH x on the sub-millimeter scale. In this report, an overview of neutron imaging methodology and technologies are given in the context of recent spatial measures of hydrogen concentrations in similar metals hydrides. Additionally, the commissioning and operation of a custom-built compact dual-zone furnace is given along with details on the first two in-situ heating measurements of YH x performed over the 2020 and 2021 LANSCE operation cycles. In parallel with in-situ heating measurements, neutron computed tomography scans were performed to visualize hydrogen aggregates in post-heated YH x samples. Analysis and results from these neutron imaging measurements are given along with outlooks and plans for improving future hydrogen diffusion measurements at LANSCE.

47 OTHER INSTRUMENTATION↗
DOE OSTI2023

Machine Learning Predictions of Simulated Self-Diffusion Coefficients for Bulk and Confined Pure Liquids

Diffusion properties of bulk fluids have been predicted using empirical expressions and machine learning (ML) models, suggesting that predictions of diffusion also should be possible for fluids in confined environments. The ability to quickly and accurately predict diffusion in porous materials would enable new discoveries and spur development in relevant technologies such as separations, catalysis, batteries, and subsurface applications. Here in this work, we apply artificial neural network (ANN) models to predict the simulated self-diffusion coefficients of real liquids in both bulk and pore environments. The training data sets were generated from molecular dynamics (MD) simulations of Lennard-Jones particles representing a diverse set of 14 molecules ranging from ammonia to dodecane over a range of liquid pressures and temperatures. Planar, cylindrical, and hexagonal pore models consisted of walls composed of carbon atoms. Our simple model for these liquids was primarily used to generate ANN training data, but the simulated self-diffusion coefficients of bulk liquids show excellent agreement with experimental diffusion coefficients. ANN models based on simple descriptors accurately reproduced the MD diffusion data for both bulk and confined liquids, including the trend of increased mobility in large pores relative to the corresponding bulk liquid.

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

A Transferable Force Field for Predicting Adsorption and Diffusion of Water in Cationic Zeolites with Coupled Cluster Accuracy

We present a transferable force field for water in proton-exchanged, alkali (Li, Na, K, Rb, and Cs) metal-exchanged, and alkaline-earth (Mg, Ca, Sr, and Ba) metal-exchanged zeolites. The fitting methodology is based on adsorbate–adsorbent interaction energies obtained from periodic density functional theory calculations and corrected using the coupled-cluster method applied to small model clusters. To ensure an accurate prediction of both adsorption and diffusion properties of water, sets of configurations that sample both adsorption sites and intracrystalline hopping transition states were used in the fitting. The quality of the force field is assessed for a wide range of zeolites with different topologies and chemical compositions, demonstrating good agreement between theoretical predictions and experimental measurements of water adsorption and diffusion.

Adsorption↗
DOE OSTI2021

Designing a Zn–Ag Catalyst Matrix and Electrolyzer System for CO 2 Conversion to CO and Beyond

CO 2 emissions can be transformed into high-added-value commodities through CO 2 electrocatalysis; however, efficient low-cost electrocatalysts are needed for global scale-up. Inspired by other emerging technologies, the authors report the development of a gas diffusion electrode containing highly dispersed Ag sites in a low-cost Zn matrix. Here, this catalyst shows unprecedented Ag mass activity for CO production: –614 mA cm –2 at 0.17 mg of Ag. Subsequent electrolyte engineering demonstrates that halide anions can further improve stability and activity of the Zn–Ag catalyst, outperforming pure Ag and Au. Membrane electrode assemblies are constructed and coupled to a microbial process that converts the CO to acetate and ethanol. Combined, these concepts present pathways to design catalysts and systems for CO 2 conversion toward sought-after products.

36 MATERIALS SCIENCE↗
DOE OSTI2025

Effect of convective transport in edge and scrape-off-layer plasmas of ADITYA-U tokamak

The 2-D edge plasma fluid transport code, UEDGE has been used to simulate the edge and scrape-off layer regions of circular limiter plasmas of ADITYA-U. The computational mesh defining the limiter geometry of ADITYA-U has been introduced in the UEDGE code through an in-house developed routine. It has successfully been integrated with the UEDGE code and the measured radial profile of electron density (n e ) in the edge region is modelled using the simulations. The plasma discharge for the present study has plasma current I P ~ 130 kA, duration ~ 150 ms, chord-averaged electron density ~ 1.3 x 10 19 m -3 , and central electron temperature ~ 270 eV has been observed. The maximum toroidal magnetic field is ~ 1.2 T. It has been found that an inward convective velocity, v conv ~ 1.5 m/s is required in addition to the constant perpendicular diffusion coefficient, D ⟂ ~ 0.2 m 2 /s to adequately match the measured n e profile in typical discharges of ADITYA-U. The value of D ⟂ ~ 0.2 m 2 /s is found to lie between the estimated neoclassical diffusivity and Bohm diffusivity.

Physics - Plasma physics↗
DOE OSTI2024

Demonstration of event position reconstruction based on diffusion in the NEXT-white detector

Noble element time projection chambers are a leading technology for rare event detection in physics, such as for dark matter and neutrinoless double beta decay searches. Time projection chambers typically assign event position in the drift direction using the relative timing of prompt scintillation and delayed charge collection signals, allowing for reconstruction of an absolute position in the drift direction. In this paper, alternate methods for assigning event drift dis tance via quantification of electron diffusion in a pure high pressure xenon gas time projection chamber are explored. Data from the NEXT-White detector demonstrate the ability to achieve good position assignment accuracy for both high and low-energy events. Using point-like energy deposits from 83m Kr calibration electron captures (E ~ 45 keV), the position of origin of low-energy events is determined to 2 cm precision with bias < 1 mm. A convolutional neural network approach is then used to quantify diffusion for longer tracks (E ≥ 1.5 MeV), from radiogenic electrons, yielding a precision of 3 cm on the event barycenter. The precision achieved with these methods indicates the feasibility energy calibrations of better than 1% FWHM at Q ββ in pure xenon, as well as the potential for event fiducialization in large future detectors using an alternate method that does not rely on primary scintillation.

46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND ↗
DOE OSTI2022

Versatile Heat Transfer Module

Development of micro-reactors that can be easily transported to remote areas for civilian or military applications is an immediate focus of U.S. nuclear industry and therefore is being pursued by multiple reactor vendors (e.g. HolosGen LLC, Westinghouse, Ultra Safe Nuclear Corporation, URENCO, etc.) and actively supported by DOE-NE, DOE-ARPA-E and by the Department Of Defense (DoD). Very-high-temperature gas-cooled (VHTR) and heat pipe cooled reactor technologies are regarded as promising concepts for microreactors applications since they enable compact power conversion systems and display beneficial inherent safety features. Development of microreactors with enhanced performance must be supported by innovations in nuclear technologies. Several technologies being developed by the microreactor industry are being revisited with material technologies developed at the Argonne National Laboratory (ANL). Innovative high-temperature compatible solutions are developed to enable advanced thermal-spectrum microreactor designs featuring improved neutronics performance at high-temperature operating conditions to enable high thermal efficiency and economic competitiveness. This report focuses on the Versatile Heat Transfer Module (VHTM) technology that is developed at ANL for heat-transfer in thermal-spectrum microreactors. This technology is built on a liquid alkali metal heat pipe concept while leveraging advanced nuclear material technologies for high-temperature applications at reduced neutronic penalty with potential improvement in inherent safety behavior. The VHTM can be applied to design high-temperature thermal-spectrum microreactor systems with reduced size, weight, uranium enrichment, or increased lifetime. The INFINIT technology deriving from the VHTM enables design of a highly compact long-life microreactor concept for fission surface power (FSP). Demonstration of multiphysics modeling capability was also completed to showcase the added inherent safety benefits obtained with this concept. The VHTM technology remain at an early development stage with low Technology Readiness Level and Manufacturing Readiness Level. The current demonstration of these technologies has focused on manufacturing capability, on performance of the hydrogen diffusion barrier under thermal cycling, while radiation tolerance demonstration is planned. The next step of the demonstration plan will be the assembly of miniature VHTM for high-temperature testing, which can be used as a prototype for future pilot scale demonstration. Such research is critical to pursue development of these technologies that have the clear potential to significantly improve the performance of currently investigated micro-reactors systems.

22 GENERAL STUDIES OF NUCLEAR REACTORS↗
DOE OSTI2023

Explainable machine learning for hydrogen diffusion in metals and random binary alloys

Hydrogen diffusion in metals and alloys plays an important role in the discovery of new materials for fuel cell and energy storage technology. While analytic models use hand-selected features that have clear physical ties to hydrogen diffusion, they often lack accuracy when making quantitative predictions. Machine learning models are capable of making accurate predictions, but their inner workings are obscured, rendering it unclear which physical features are truly important. To develop interpretable machine learning models to predict the activation energies of hydrogen diffusion in metals and random binary alloys, we create a database for physical and chemical properties of the species and use it to fit six machine learning models. Our models achieve root-mean-squared errors between 98–119 meV on the testing data and accurately predict that elemental Ru has a large activation energy, while elemental Cr and Fe have small activation energies. By analyzing the feature importances of these fitted models, we identify relevant physical properties for predicting hydrogen diffusivity. While metrics for measuring the individual feature importances for machine learning models exist, correlations between the features lead to disagreement between models and limit the conclusions that can be drawn. Instead grouped feature importance, formed by combining the features via their correlations, agree across the six models and reveal that the two groups containing the packing factor and electronic specific heat are particularly significant for predicting hydrogen diffusion in metals and random binary alloys. In conclusion, this framework allows us to interpret machine learning models and enables rapid screening of new materials with the desired rates of hydrogen diffusion.

36 MATERIALS SCIENCE↗
DOE OSTI2022

Minimum stabilizing energy release for mixing processes

Diffusive operations, which mix the populations of different elements of phase space, can irreversibly transform a given initial state into any of a spectrum of different states from which no further energy can be extracted through diffusive operations. We call these ground states. Here, the lower bound of accessible ground-state energies represents the maximal possible release of energy. This lower bound, sometimes called the diffusively accessible free energy, is of interest in theories of instabilities and wave-particle interactions. On the other hand, the upper bound of accessible ground-state energies has escaped identification as a problem of interest. Yet, as demonstrated here, in the case of a continuous system, it is precisely this upper bound that corresponds to the paradigmatic “quasilinear plateau” ground state of the bump-on-tail distribution. Although for general discrete systems the complexity of calculating the upper bound grows rapidly with the number of states, using techniques adapted from treatments of the lower bound, the upper bound can in fact be computed directly for the three-state discrete system.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗
DOE OSTI2022

A reaction mechanism for plasma electrolysis of AgNO 3 forming silver nanoclusters and nanoparticles

In plasma-driven solution electrolysis (PDSE), gas-phase plasma-produced species interact with an electrolytic solution to produce, for example, nanoparticles. An atmospheric pressure plasma jet (APPJ) directed onto a liquid solution containing a metallic salt will promote reduction of metallic ions in solution, generating metallic clusters that nucleate to form nanoparticles. In this article, results from a computational investigation are discussed of a PDSE process in which a radio-frequency APPJ sustained in helium impinges on a silver nitrate solution, resulting in growth of silver nanoparticles. A reaction mechanism was developed and implemented in a global plasma chemistry model to predict nanoparticle growth. To develop the reaction mechanism, density functional theory was used to generate probable silver growth pathways up to Ag 9 . Neutral clusters larger than Ag 9 were classified as nanoparticles. Kinetic reaction rate coefficients for thermodynamically favorable growth pathways were estimated based on an existing, empirically determined base reaction mechanism for smaller Ag particle interactions. These rates were used in conjunction with diffusion-controlled reaction rate coefficients that were calculated for other Ag species. The role of anions in reduction of Agn ions in forming nanoparticles is also discussed. Oxygen containing impurities or admixtures to the helium, air entrainment into the APPJ, and dissociation of saturated water vapor above the solution can produce additional reactive oxygen species in solution, resulting in the production of anions and O 2 – in particular. For a given molarity, delivering a sufficient fluence of reducing species will produce similar nanoparticle densities and sizes for all applied power levels. In conclusion, comparisons are made to alternate models for nanoparticle formation, including charged nanoparticles and use of direct current plasmas.

36 MATERIALS SCIENCE↗
DOE OSTI2020

Round 3 Update of Stress Testing Results for Organic Light-Emitting Diode Panels and Luminaires

Organic light-emitting diode (OLED) sources are a potential solid-state lighting technology for use in indoor lighting applications. Some of the advantages offered by OLEDs include thin profiles, low glare, diffuse lighting, and unique form factors. The U.S. Department of Energy (DOE) has released six technical reports focused on OLED technologies to provide information and analysis to the lighting industry. These reports include the evaluation of two different field deployment sites, a market analysis, a tear-down analysis, and two independent assessments of the performance of select commercially available OLED products. This report builds on earlier DOE efforts with OLED technology by updating information about previously benchmarked commercial OLED products. Specifically, this report provides updated findings about the robustness and reliability of commercial OLED products.

42 ENGINEERING↗
DOE OSTI2023

Reaction–Diffusion Coupling Facilitates the Sequential Precipitation of Metal Ions from Battery Feedstock Solutions

Here, the development of new technologies for chemical separations is urgently needed to meet the surging demand for critical materials that has strained resources and caused environmental challenges. Inspired by the classic Liesegang experiment, we demonstrated the separation of critical metal ions based on the coupling of ion diffusion and precipitation kinetics. For this purpose, a model feedstock solution simulating dissolved battery electrodes was placed on top of a hydrogel loaded with a precipitating agent, namely sodium hydroxide. As the lithium, manganese, cobalt, and nickel ions diffused into the gel, a gradient of precipitates formed along the length of the reactor. Elemental analysis of the spatially distributed precipitates showed the enrichment of nickel near the gel-solution interface, followed by the formation of an almost pure (>96%) manganese product further along the reactor. Optimization experiments revealed that a sodium hydroxide concentration of 10 mM and a gel/solution volume ratio of 2:1 favored efficient separations. The robustness of the method was demonstrated in four out of five feedstock compositions of typically used battery cathodes. Our proof-of-concept experiments present a paradigm for critical materials separations that does not require specialty chemicals, binding agents, membranes, or toxic solvents.

25 ENERGY STORAGE↗
DOE OSTI2025

REBOUND: Reverse Engineering Bidirectional Outflow Under Non-Equilibrium Diffusion

Rare-earth elements (REEs) are essential for electronics, renewable energy, and defense technologies. However, the current supply of REEs relies on mining concentrated in a few countries and energy-intensive separations. DOE’s Basic Energy Sciences (BES) program has launched a grand challenge which aims to ensure a sustainable supply of critical REEs by developing innovative and environmentally friendly separation methods. As an alternative to costly and harmful traditional methods, the Non-Equilibrium Transport Driven Separations (NETS) initiative has created a microfluidic Y-channel co-flow method that applies external fields to exploit magneto- and electrohydrodynamic effects for separating dilute REE ions from complex feedstocks. Computational fluid dynamics (CFD) studies have identified a few operating conditions with promising ion selectivity and separation efficiency. However, challenges remain regarding Y-channel versatility across feedstocks and accurate incorporation of physical phenomena into CFD models. In this work, we develop a multi-fidelity modelling approach which integrates experimental results with CFD simulation to build a surrogate model for the dependence of separation efficiency to variation of design parameters. The surrogate model enables a reinforcement learning (RL) method to adaptively launch CFD and experimental runs, improving model fidelity around optimal Y-channel parameters.

36 MATERIALS SCIENCE↗
DOE OSTI2020

Improving stability of ALD ZrN thin film coatings over U-Mo dispersion fuel

Atomic layer deposition (ALD) of ZrN is a candidate technology for coating U-Mo dispersion fuel as a diffusion barrier coating. During the early development stages of the coating the ZrN deposition showed to be mechanically unstable and ultimately resulted in spalling. Based on experimental outcomes it was found that instabillity can be eliminated through the introduction of a thin amorphous (a) Al 2 O 3 interlayer coating deposited in between the ~1-μm thick ZrN and the U-Mo. substrate. To elucidate these findings simulations were performed with density functional theory (DFT) to measure work of adhesions at different interfaces while finite element modeling (FEM) was performed to measure the residual stress distribution. DFT indicated that the ZrN coating deposited over (a)-Al 2 O 3 is ~2.5 times stronger when compared to direct deposition over U or UO 2 substrate. While calculations from FEM recognized; (1) large stress concentrations can originate from the irregularly distributed native surface oxides (UO 2 ) over U-Mo and (2) stress concentrations can be reduced if those surface UO 2 can be modified to a uniform layer (achieved after application of a 8 nm (a)-Al 2 O 3 interlayer). These results provided explanations and confirmed the role played by interlayer in eliminating the original mechanical instability of the ZrN.

11 NUCLEAR FUEL CYCLE AND FUEL MATERIALS↗
DOE OSTI2020

Noise from undetected sources in Dark Energy Survey images

ABSTRACT For ground-based optical imaging with current CCD technology, the Poisson fluctuations in source and sky background photon arrivals dominate the noise budget and are readily estimated. Another component of noise, however, is the signal from the undetected population of stars and galaxies. Using injection of artifical galaxies into images, we demonstrate that the measured variance of galaxy moments (used for weak gravitational lensing measurements) in Dark Energy Survey (DES) images is significantly in excess of the Poisson predictions, by up to 30 per cent, and that the background sky levels are overestimated by current software. By cross-correlating distinct images of ‘empty’ sky regions, we establish that there is a significant image noise contribution from undetected static sources (US), which, on average, are mildly resolved at DES resolution. Treating these US as a stationary noise source, we compute a correction to the moment covariance matrix expected from Poisson noise. The corrected covariance matrix matches the moment variances measured on the injected DES images to within 5 per cent. Thus, we have an empirical method to statistically account for US in weak lensing measurements, rather than requiring extremely deep sky simulations. We also find that local sky determinations can remove most of the bias in flux measurements, at a small penalty in additional, but quantifiable, noise.

46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND ↗
DOE OSTI2022

Low-temperature electroplating of zirconium: Ionic mixture methods

Non-aqueous ionic mixture electroplating allows for room-temperature or near-room-temperature applications of thin metal films, typically metals for which aqueous solvents cannot be used. While there are many methods of plating zirconium, each has disadvantages that ionic liquid plating could correct. However, ionic liquid plating requires further research and development, as a new class of solvents were only developed a few years ago. Current ionic liquids cost several thousand dollars per liter, yet produce films with less than 90% surface coverage as well as deposition of salt species that enable interdiffusion through the Zr. Film coverage needs to be increased while maintaining approximate room temperatures and lower solvent costs. Current ionic liquid technology has suffered from poor surface wetting and limited Zr diffusivity leading to dendrite formation which inhibits total surface coverage. Zr electroplating can be improved by application of room-temperature ionic mixtures as the plating medium. We have successfully demonstrated the capability to plate high-quality metallic zirconium layers using deep eutectic solvents (DES) and DES-ionic liquid (IL) mixtures. Plating thicknesses of up to 6 microns have been achieved, and the coatings are conformal to the substrate and not dendritic, with low contamination from the plating solution. Our current method uses the DES ethaline (a 1:2 ratio of choline chloride and ethylene glycol) to complex zirconium (IV) ions (present via the addition of ZrF4). We pulse plate the Zr from a bath with an overabundance of LiF, in accordance with prior literature. We have also had success in mixing the DES with ILs, such as Triethylsulfonium bis(trifluoromethylsulfonyl)imide, 1-Butyl-3-methylpyridinium bis(trifluormethylsulfonyl)imide, Methyl-trioctylammonium bis(trifluoromethylsulfonyl)imide, and Diethylmethyl(2-methoxyethyl)ammonium bis(trifluoromethylsulfonyl)imide.

36 MATERIALS SCIENCE↗
DOE OSTI2024

The ab initio amorphous materials database: Empowering machine learning to decode diffusivity

Amorphous materials exhibit unique properties that make them suitable for various applications in science and technology, ranging from optical and electronic devices and solid-state batteries to protective coatings. However, data-driven ex- ploration and design of amorphous materials is hampered by the absence of a com- prehensive database covering a broad chemical space. In this work, we present the largest computed amorphous materials database to date, generated from sys- tematic and accurate ab initio molecular dynamics (AIMD) calculations. We also show how the database can be used in simple machine-learning models to connect properties to composition and structure, here specifically targeting ionic conductiv- ity. These models predict the Li-ion diffusivity with speed and accuracy, offering a cost-effective alternative to expensive density functional theory (DFT) calculations. Furthermore, the process of computational quenching amorphous materials provides a unique sampling of out-of-equilibrium structures, energies, and force landscape, and we anticipate that the corresponding trajectories will inform future work in uni- versal machine learning potentials, impacting design beyond that of non-crystalline materials.

36 MATERIALS SCIENCE↗
DOE OSTI2023

Microstructure characterization of electric field assisted sintering (EFAS) sintered metallic and ceramic materials using local thermal diffusivity measurement

Electric Field Assisted Sintering (EFAS, also referred to as spark plasma sintering) is a powerful technology for the consolidation of powder materials. The high heating rate during the sintering process is critical for minimizing energy consumption, but it can also cause microstructure heterogeneities in sintered parts, such as spatially varied porosity. The examination of localized porosity usually requires the use of a scanning electron microscope with a carefully prepared surface. In this paper, photothermal radiometry is used to measure local thermal diffusivity and extract localized porosity of EFAS-sintered parts by using a percolation-threshold model. Applying this approach, we identified the radial position-dependent porosity variation in EFAS parts, which is likely formed due to the large temperature gradient during the sintering process. This approach has a unique advantage because it can measure samples with minimal or no surface preparation, enabling the possibility of in situ characterization in EFAS with proper system modification. Necessary modifications on the measurement approach for EFAS deployment and in situ characterization are also discussed.

36 MATERIALS SCIENCE↗