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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 235 records · Page 13
All sourcesNASA NTRSDOE OSTI
DOE OSTI2021

Tardigrade (NA-22 Quarterly Report)

Tardigrade is an effort to create tunable thermal expansion materials for use in a ruggedized lens housing. This requires new computational algorithms to model and predict thermal performance of meta-materials composed of multiple structured base materials and void-space, along with matching and developing 3D print technology to demonstrate the housing. The project is 24 months into the 36-month plan. We are continuing effort on 3 main technical thrusts: material design algorithm development (transitioning into full 3D design), multi-material additive manufacturing process exploration, and sensor/optics design and engineering. In the last quarter, we have focused on developing the code for 3D Topology Optimization, building data sets for 2D autoencoder training, and finishing lens housing engineering for the microbolometer based test sensor. We are exploring 2 potential multi-material AM technologies, a polymer-based technique in development at LLNL and a metal SLS system produced by Aconity, called the AeroSint deposition head.

36 MATERIALS SCIENCE↗
DOE OSTI2022

Tardigrade (Quarterly Report Q1 FY2022)

Tardigrade is an effort to create tunable thermal expansion materials for use in a ruggedized lens housing. This requires new computational algorithms to model and predict thermal performance of meta-materials composed of multiple structured base materials and void-space, along with matching and developing 3D print technology to demonstrate the housing. The project is 27 months into the 36-month plan. We are continuing effort on 3 main technical thrusts: material design algorithm development (transitioning into full 3D design), multi-material additive manufacturing process exploration, and sensor/optics design and engineering. In the last quarter, we have focused on developing the code for 3D Topology Optimization, building data sets for 2D autoencoder training, and finishing lens housing engineering for the microbolometer based test sensor. We are exploring 2 potential multi-material AM technologies, a polymer-based technique in development at LLNL and a metal SLS system produced by Aconity, called the AeroSint deposition head.

36 MATERIALS SCIENCE↗
DOE OSTI2022

Neptunium mononitride as a target material for Pu-238 production

Deep space exploration requires specialized sources for both thermal and power applications. Radioactive decay heat of plutonium-238 (238Pu) provides these sources in the form of radioisotope thermoelectric generators (RTGs). The 238 Pu is produced via neutron capture reaction involving neptunium-237 ( 237 Np) target material. Continual optimization of 237 Np target materials and evaluation of potential alternative targets for production of 238 Pu RTGs are advantageous for meeting ongoing space power system resource requirements. Current production of 238 Pu for RTGs for the United States space program utilizes neptunium dioxide ( 237 NpO 2 ) targets; however, the use of neptunium mononitride ( 237 NpN) presents an opportunity to increase the mass of 237 Np per target compared to the dioxide form, as well as increase the thermal conductivity of the target. To assess the viability of a 237 NpN target material, the material chemistry must be thoroughly evaluated, including synthesis methods and dissolution and reprocessing schemes. This review presents a summary of synthesis pathways for 237 NpN based on published literature on actinide mononitrides. Specific literature on 237 NpN is limited, necessitating evaluation of other actinide systems to gather parallels. This suggests a need for additional experimental studies on 237 NpN. A particular limitation in the existing literature is a lack of information on the differences in material characteristics, such as morphology, particle size, and trace chemical impurities, as a function of synthesis method. These parameters may affect subsequent reactor performance or dissolution of irradiated targets. The evaluation of existing literature is presented with a focus on the efficacy of 237 NpN targets for 238 Pu production.

38 RADIATION CHEMISTRY, RADIOCHEMISTRY, AND NUCLEA↗
DOE OSTI2020

Q4FY20 Quarterly Report. LL20-AM-sensor

Tardigrade is an effort to create tunable thermal expansion materials for use in a ruggedized lens housing. This requires new computational algorithms to model and predict thermal performance of meta-materials composed of multiple structured base materials and void-space, along with matching and developing 3D print technology to demonstrate the housing. Project is 13 months into the 30 month on complete project plan. We are continuing effort on 3 main technical thrusts: algorithm development, multi-material additive manufacturing process exploration, and sensor/optics design and engineering. In FY20 we underspent the initial budget, partly due to work restrictions from Covid-19 pandemic, but are working to make up lost time

42 ENGINEERING↗
DOE OSTI2025

Fluoropolymer Composites from Partially Perfluoroalkylated Waste Polyethylene

Chemically modified plastics have emerged as practical solutions to plastic waste increases. Here, the inherent novelty of decorating polymer chains with chemical functionality results in distinct properties that expand the available application space. Nevertheless, developing designer materials for specific applications beyond compatibilization or mild property enhancement is difficult due to the synergistic effects of both the polar functionality imparted and the parent materials' intrinsic properties. By incorporating perfluoro-alkyl side-chains onto the backbone of dehydrogenated waste HDPE, unique surface properties intermediate between polytetrafluoroethylene (PTFE, the model fluoropolymer) and HDPE become apparent, while the overall material mechanical and thermal properties result in more LLDPE-like materials. This is demonstrated through moderate decreases in the surface free energy of the perfluoroalkylated polyolefin surface (increase in H 2 O contact angle of ~ 6°) and increased ordering under shear when blended with PTFE nanoparticles where the crossover point occurred at higher strains. Critically, perfluoroalkylated HDPE possesses improved rheological modification properties at elevated temperatures with PTFE nanoparticles, resulting in more thermally robust and stable composite materials.

fluoropolymer↗
DOE OSTI2022

Predicting path-dependent diffusion barrier spectra in vast compositional space of multi-principal element alloys via convolutional neural networks

The emergent multi-principal element alloys (MPEAs) provide a vast compositional space to search for novel materials for technological advances. How to screen promising compositions from such an ample design space for targeted properties is a grand challenge. In this report, we demonstrate the state-of-the-art deep learning technology—convolutional neural network (CNN)—in predicting path-dependent vacancy migration energy barrier spectra, which are critical to diffusion behavior and many high-temperature properties, in the hyperdimensional composition space of MPEAs. The developed CNN model, fully capturing local chemical features surrounding each vacancy, accurately and efficiently predicts migration energy barrier of MPEAs with different degrees of chemical short-range order and at any unseen compositions. By varying the size of the local region encapsulating vacancy in the CNN model, we reveal that the length scale influencing vacancy migration is surprisingly extensive, up to its six nearest neighboring shells. The efforts of the CNN model make it promising for developing a database of diffusion barriers for various MPEA systems, which would have profound implications for accelerating alloy screening and discovering new compositions with desirable properties.

36 MATERIALS SCIENCE↗
DOE OSTI2021

Detecting Sub-Micron Space Weathering Effects in Lunar Grains With Synchrotron Infrared Nanospectroscopy

Space weathering processes induce changes to the physical, chemical, and optical properties of space-exposed soil grains. For the Moon, space weathering causes reddening, darkening, and diminished contrast in reflectance spectra over visible and near-infrared wavelengths. The physical and chemical changes responsible for these optical effects occur on scales below the diffraction limit of traditional far-field spectroscopic techniques. Recently developed super-resolution spectroscopic techniques provide an opportunity to understand better the optical effects of space weathering on the sub-micrometer length scale. This paper uses synchrotron infrared nanospectroscopy to examine depth-profile samples from two mature lunar soils in the mid-infrared, 1,500 –700 cm -1 (6.7–14.3 μm). Overall, our findings are broadly consistent with prior bulk observations and theoretical models of space weathered spectra of lunar materials. These results provide a direct spatial link between the physical/chemical changes in space-exposed grain surfaces and spectral changes of space weathered bodies.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2025

Review of radiation-induced defects in GaAs

Radiation-resilient optoelectronic materials are highly desired in space and nuclear applications, and understanding the relevant defects and electronic processes in those materials is crucial for both current and next-generation applications. GaAs is a prototypical semiconductor that serves as a foundational system for describing and understanding optoelectronic devices. In this review, both experimental studies and molecular dynamics (MD) simulations of irradiated GaAs are reviewed, with particular emphasis on the deep-level transient spectroscopy, irradiation-induced amorphization zones, and MD predictions of damage structures. The MD results are also compared to predictions of the non-ionized energy-loss model. Recent theoretical studies, in particular, density functional theory based calculations on the simple intrinsic defects and defect clusters in GaAs, are also reviewed. These defects have an important role in dictating the evolution of GaAs in radiation damage environments, as they impact the coupled dynamics of charge carriers. Finally, possible gaps and challenges toward the general understanding of defect evolution in GaAs are discussed.

36 MATERIALS SCIENCE↗
DOE OSTI2023

Space Launch Authorities

This report identifies current best understanding of federal agencies that are responsible for the safe transportation and handling of nuclear materials during various phases of space launch activities and how they interact. It explores the following questions: (1) Which federal agencies have roles, responsibilities, and statutory authorities related to the launch, orbit, and reentry of nuclear materials and components? (2) What relevant current/recent activities are those federal agencies involved in?

98 NUCLEAR DISARMAMENT, SAFEGUARDS, AND PHYSICAL P↗
DOE OSTI2025

Machine learning accelerated prediction of Ce-based ternary compounds involving antagonistic pairs

The discovery of novel quantum materials within ternary phase spaces containing antagonistic pairs such as Fe with Bi, Pb, In, and Ag, presents significant challenges yet holds great potential. In this work, we investigate the stabilization of these immiscible pairs through the integration of Cerium (Ce), an abundant rare-earth and cost-effective element. By employing a machine learning (ML)-guided framework, particularly crystal graph convolutional neural networks (CGCNN), combined with first-principles calculations, we efficiently explore the composition/structure space and predict 9 stable and 37 metastable Ce-Fe-X (X=Bi, Pb, In, and Ag) ternary compounds. Our findings include the identification of multiple new stable and metastable phases, which are evaluated for their structural and energetic properties. These discoveries not only contribute to the advancement of quantum materials but also offer viable alternatives to critical rare earth elements, underscoring the importance of Ce-based intermetallic compounds in technological applications.

36 MATERIALS SCIENCE↗
DOE OSTI2020

Ultrahigh power and energy density in partially ordered lithium-ion cathode materials

There is an intensive search for high-performance cathode materials for rechargeable batteries. In this work the authors report that oxyfluorides with partial spinel-like cation order, made from earth-abundant elements, display both exceptionally high energy and power. The rapid market growth of rechargeable batteries requires electrode materials that combine high power and energy and are made from earth-abundant elements. In this paper we show that combining a partial spinel-like cation order and substantial lithium excess enables both dense and fast energy storage. Cation overstoichiometry and the resulting partial order is used to eliminate the phase transitions typical of ordered spinels and enable a larger practical capacity, while lithium excess is synergistically used with fluorine substitution to create a high lithium mobility. With this strategy, we achieved specific energies greater than 1,100 Wh kg -1 and discharge rates up to 20 A g -1 . Remarkably, the cathode materials thus obtained from inexpensive manganese present a rare case wherein an excellent rate capability coexists with a reversible oxygen redox activity. Our work shows the potential for designing cathode materials in the vast space between fully ordered and disordered compounds.

25 ENERGY STORAGE↗
DOE OSTI2025

Investigation of High-Temperature Compatibility of Select Oxides with Yttrium Hydride for Micro and Space Reactor Applications

Yttrium hydride is a promising material for a high-temperature neutron moderator in advanced micro and space reactors due to its high hydrogen density and relative thermal stability compared to other metal hydrides. However, yttrium hydride desorbs hydrogen rapidly at temperatures above 800°C, which is below the operational temperature range of some reactor designs. A hydrogen barrier coating of oxide on the hydride surface may inhibit hydrogen loss at 800°C and beyond, but the high-temperature compatibility between yttrium hydride and many oxides is currently unknown. The high-temperature compatibility of Al 2 O 3 , MgO, and Y 2 O 3 with YH 1.92 was examined by subjecting mixed oxide–hydride pellets to a 1200°C heat treatment then using a combination of diffractometry, microscopy, and spectroscopy to determine changes in the pellet composition as a result. Yttrium scavenged oxygen from both Al 2 O 3 and MgO to form Y 2 O 3 , resulting in significant loss of YH 1.92 . Yttrium reacted with reduced aluminum to form YAl 2 and several other compounds. Reduced magnesium volatilized above 1091°C and vacated the pellet. Only Y 2 O 3 did not appreciably react with YH 1.92 . Of the three oxides tested, only Y 2 O 3 was compatible with YH 1.92 at 1200°C based on the experimental criteria.

compatibility↗
DOE OSTI2025

Multiscale and Machine Learning Modeling for Process-informed Microstructure Prediction in Additively Manufactured Materials using MALAMUTE

The Advanced Materials and Manufacturing Technologies (AMMT) program under the Department of Energy Office of Nuclear Energy aims to develop and qualify additively manufactured materials for nuclear applications. One key challenge to this is the microstructural variability observed in the additively manufactured products and their impact on the properties and performance of the material in extreme environments. AMMT is using a combination of high-throughput experimental and modeling techniques to accelerate qualification. Conventionally, in-situ and ex-situ characterizations and testing are performed to correlate different aspects of the additive manufacturing process to the final product and its performance. However, adopting a trial-and-error approach to experimentally evaluate the vast range of process parameters required to capture microstructural variability is cost-prohibitive. Modeling and simulation provide a comparatively inexpensive way to understand and correlate the microstructural evolution to the processing conditions. The modeling and simulation work-packages within the AMMT program aims to use physics-based and machine learning models to develop a digital twin for additive manufacturing that can correlate the process conditions to the final product and establish a process-structure-property-performance (PSPP) correlation. The melting and subsequent solidification that occurs during the additive process is a complex phenomenon that requires multiscale multiphysics analysis. This work package focuses on understanding the role of process variabilities on the unique microstructural characteristics of additively manufactured materials. Microstructural features at the subgrain level, such as compositional micro-heterogeneity and dislocation cells, are of particular interest here since they can influence the creep properties and radiation performance. Idaho National Laboratory’s Multiphysics Object-Oriented Simulation Environment (MOOSE), specifically the MOOSE Application Library for Advanced Manufacturing UTilitiEs (MALAMUTE) software, provides an ideal platform for developing the multiphysics multiscale model to explore the intricacies of the microstructural evolution during the AM processes within a single framework. Furthermore, given that such full-fidelity simulations can be computationally intensive, reduced order models are necessary to explore the PSPP space for additively manufactured materials in an efficient, reliable, and cost-effective way. This work focuses on capturing the microstructural variabilities at the subgrain level that are often missing in the part-scale models. In fiscal year 2025, we significantly advanced upon our work in the last fiscal year, in terms of the predictive capabilities of the physics-based and ML models, by adding the capabilities to capture subgrain-level micro-segregation during solidification using phase-field model and to predict the time-dependent dynamics of the AM process through the MOGPAR model. The alloy solidification model in MOOSE incorporates the thermodynamic properties and free energy relevant to 316 stainless steel. The model demonstrates the Cr and Ni segregation that occurs during solidification, including that the rate of solidification. The microstructural evolution model is connected to the process conditions via the surrogate model developed in this work. This enables predictions of the final microstructure in conjunctions with the manufacturing process. This work supports AMMT's rapid qualification goals by laying the foundation for an efficient and cost-effective model establishing the PSPP correlation for AM. The generated microstructures and predicted micro-segregation can be used by other work packages under AMMT to evaluate the properties and environmental response of the material at the mesoscale. Thus, this work helps to identify the key microstructural features at the subgrain level that are significant in property and performance predictions of additively manufactured components. This work will also provide inputs to the large-scale process variability models to reevaluate and validate assumptions and simplifications made in the part-scale models. Furthermore, through active learning this work can help identify the data need from both modeling and experimental sides for development of a robust digital twin for additive manufacturing and accelerate the AMMT's qualification efforts.

36 - MATERIALS SCIENCE↗
DOE OSTI2026

First High-Throughput Evaluation of Dark Matter Detector Materials

In this work we perform the first high-throughput search and evaluation of materials that can serve as excellent low-mass dark matter detectors. Using properties of close to 1000 materials from the Materials Project database, we project the sensitivity in dark matter parameter space for experiments constructed from each material, including both absorption and scattering processes between dark matter and electrons. Using the anisotropic materials in the dataset, we further compute the level of daily modulation in interaction rates and the resulting directional sensitivities, highlighting materials with prospects to detect the dark matter wind. Our methods provide the basic tools for the data-driven design of dark matter detectors, and our findings lay the groundwork for the next generation of highly optimized direct searches for dark matter as light as the keV scale. This represents a major step in the application of results from condensed matter physics to dark matter search design.

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗
DOE OSTI2024

Sample Testing Basket: Design and Capabilities Report

SFR designs all require integral components that can withstand prolonged submersion in the liquid sodium primary coolant as well as in the gas space of the reactor vessel. Materials are often desired to withstand these conditions for the lifetime of the reactor. The Sample Testing Basket (STB) was designed and fabricated to meet the anticipated need for material exposure testing in prototypic reactor coolant environments; in the liquid sodium primary coolant and the sodium-vapor-laden gas space. Developed at Argonne National Laboratory (ANL), the STB is a piece of equipment designed for use in the Mechanisms Engineering Test Loop (METL) to allow for sodium exposure testing of materials within the METL facility.

21 SPECIFIC NUCLEAR REACTORS AND ASSOCIATED PLANTS↗
DOE OSTI2021

First-principles equation of state database for warm dense matter computation

We put together a first-principles equation of state (FPEOS) database for matter at extreme conditions by combining results from path integral Monte Carlo and density functional molecular dynamics simulations of the elements H, He, B, C, N, O, Ne, Na, Mg, Al, and Si as well as the compounds LiF , B 4 C , BN , CH 4 , CH 2 , C 2 H 3 , CH , C 2 H , MgO , and MgSiO 3 . For all these materials, we provide the pressure and internal energy over a density-temperature range from ~ 0.5 to 50 g cm - 3 and from ~ 10 4 to 10 9 K, which are based on ~ 5000 different first-principles simulations. We compute isobars, adiabats, and shock Hugoniot curves in the regime of L - and K -shell ionization. Invoking the linear mixing approximation, we study the properties of mixtures at high density and temperature. Furthermore, we derive the Hugoniot curves for water and alumina as well as for carbon-oxygen, helium-neon, and CH-silicon mixtures. We predict the maximal shock compression ratios of H 2 O , H 2 O 2 , Al 2 O 3 , CO , and CO 2 to be 4.61, 4.64, 4.64, 4.89, and 4.83, respectively. Finally we use the FPEOS database to determine the points of maximum shock compression for all available binary mixtures. We identify mixtures that reach higher shock compression ratios than their end members. We discuss trends common to all mixtures in pressure-temperature and particle-shock velocity spaces. In the Supplemental Material, we provide all FPEOS tables as well as computer codes for interpolation, Hugoniot calculations, and plots of various thermodynamic functions.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗