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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 469 records · Page 26
All sourcesNASA NTRSDOE OSTI
DOE OSTI2020

Corrosion Behavior of Developmental Iron Phosphate Waste Forms: FY20 Status Report

Corrosion tests were performed to assess the dissolution behaviors of developmental iron phosphate materials that are being evaluated as an alternative to the glass-bonded sodalite ceramic waste form to immobilize salt wastes from electrochemical separation operations. Production of the iron phosphate waste form results in dehalogenation of the waste salt that increases potential waste loading. Ferric oxide is added to the waste form to increase the chemical durability. Tests conducted to optimize the amount of ferric oxide used to make the waste form are summarized in this report. Specimens prepared from materials made with between 17 and 34 mole % ferric oxide were subjected to two test methods to assess the effects of the iron content on the relative durability and retention of immobilized salt cations including cesium, strontium, and neodymium. Modified ASTM C1308 test results show the intrinsic durability was significantly higher for materials made with 27 mol % ferric oxide. Further additions generated iron-rich inclusion phases and did not improve durability. Modified ASTM C1285 test results show that solution feedback attenuates the dissolution rate similar to what is observed for borosilicate glasses within seven days. Analyses of test specimens after 42 days show no evidence that surface layers formed. Combined with previous studies addressing the salt loading, a composition region of the ammonium (di)hydrogen phosphate-waste salt-ferric oxide ternary has been defined for durable waste form compositions. Materials with that composition can be used to determine optimal processing conditions, assess long-term performance, and parameterize a degradation model.

Stariha, S. A.↗
DOE OSTI2020

Extension of OpenMC for Fixed Source Transmutation Calculations

This report documents work performed under a Strategic Partnership Project between Argonne National Laboratory (ANL) and the United Kingdom Atomic Energy Authority (UKAEA). The overall goal of this project is to extend OpenMC [1], a community-developed Monte Carlo particle transport code, to be able to perform fixed-source transmutation calculations. In fission and fusion reactors, the high flux of energetic neutrons causes materials within the reactor to “transmute,” or undergo a nuclear reaction that results in the addition/removal of neutrons and protons from the nucleus of an atom. If subjected to these reactions for long enough, the overall composition and physical properties of the material itself begin to change as a result of transmutation. Such a feature is vital for predicting the decrease in tritium production rate within a breeder blanket during the lifetime of a fusion reactor. OpenMC is capable of simulating neutron transport in fission/fusion systems, thereby allowing it to estimate the flux that causes transmutation. It is also capable of solving the transmutation equations, which determine how the composition of a material changes over time due to neutron irradiation and radioactive decay. However, solving the transmutation equations was previously only possible when the source of neutrons came from a fission system. In a fusion system, the source of neutrons is typically determined by a separate code and then given as an input to the particle transport simulation. This is known as a fixed source calculation. Through this project, we have extended OpenMC to solve the transmutation equations for a fixed source calculation. Evaluating the change in material compositions due to transmutation and its effect on physical properties is of key importance to a range of engineering analyses for fission and fusion systems. For example, in a fusion reactor, estimating the dose rate at different physical locations resulting from irradiated materials in the reactor allows designers to ensure that workers are not exposed to doses beyond applicable regulations. In order to properly dispose of irradiated materials, designers also need to estimate the radiotoxicity, which again relies on knowledge of the material composition at some future time. The specific tasks for this project that were agreed to between ANL and UKAEA were as follows: 1. Make changes and additions in the openmc.deplete and related modules in OpenMC to support transmutation calculations following a fixed source transport simulation. 2. Make necessary changes to OpenMC to model transmutation due to an arbitrary set of reactions needed for fusion applications. Use this new capability to generate a depletion chain file based on the TENDL nuclear data library. 3. Improve the openmc.deplete module in OpenMC to keep track of gases produced as a by-product of nuclear reactions during transmutation calculations. 4. Validate the new capabilities by carrying out fixed-source transmutation calculations on a suitable benchmark problem using OpenMC and a comparable Monte Carlo neutron transport code.

73 NUCLEAR PHYSICS AND RADIATION PHYSICS↗
DOE OSTI2022

Biocompatibility of NbTaTiVZr with Surface Modifications for Osteoblasts

Here, we report a potential biomedical material, NbTaTiVZr, and the impact of surface roughness on the osteoblast culture and later behavior based on in vitro tests of preosteoblasts. Cell activities such as adhesion, viability, and typical protein activity on NbTaTiVZr showed comparable results with that of commercially pure Ti (CP-Ti). In addition, NbTaTiVZr with a smooth surface exhibits better cell adhesion, viability, and typical protein activity which shows that surface modification can improve the biocompatibility of NbTaTiVZr. This supports the biological evidence and shows that NbTaTiVZr can potentially be evaluated as a biomedical material for clinical use.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2026

Toward Sustainable Lithium Recovery: A Universal Hydrothermal Approach for Lithium Extraction

The rapid growth of lithium-ion battery (LIB) deployment presents critical challenges in sustainable end-of-life management and raw material recovery. Conventional pyrometallurgical and hydrometallurgical methods suffer from high energy demand, lithium loss, and complex wastewater treatment. This study established a universal, highly efficient, and sustainable hydrothermal route for lithium extraction and material recovery from various spent lithium-ion battery cathodes using 1,2,4,5-benzenetetracarboxylic acid (BTCA). The optimized process achieved over 99% lithium leaching efficiency for lithium iron phosphate (LFP) and LiNi x Mn y Co 1–x–y O 2 (NMC), with transition metal coleaching below 1%. It was also broadly applicable to lithium manganese oxide, lithium cobalt oxide, and black mass, achieving 98.5%, 98.95%, and 94.06% leaching efficiencies, respectively. The extracted lithium was directly converted into battery-grade lithium sources, while transition metals were recovered as oxides. Unreacted BTCA was efficiently regenerated and reused without degradation. Electrochemical evaluation confirmed that cathode materials synthesized with recovered lithium exhibit comparable performance to commercial products. Compared to conventional hydrometallurgy, the BTCA-based process increased revenue by over 40% and reduced greenhouse gas emissions by up to 39%. This closed-loop, chemistry-agnostic strategy offered a scalable and economically viable solution for industrial LIB recycling, enabling resource circularity and reducing dependency on primary critical materials.

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

Mechanical Properties of Additively Manufactured 316L Stainless Steel Before and After Neutron Irradiation (FY21)

This report presents the materials property data of additively manufactured (AM) 316L stainless steel (SS) accumulated for the assessment of core materials in the Transformational Challenge Reactor (TCR) program. The TCR manufacturing approach includes using the laser powder bed fusion (LPBF) method for metallic (316L and Inconel 718) components. To assess the mechanical performance of printed components in reactor-relevant conditions and build a property database for the AM materials, mechanical tests and evaluations were performed before and after neutron irradiation. Miniature tensile specimens were irradiated in the High Flux Isotope Reactor to 0.2, 2, 8, and 10 dpa at target temperatures of 300 and 600°C. Postirradiation evaluation for the 0.2 and 2 dpa specimens was performed during FY20 and FY21, and the results are presented and discussed in this document.

22 GENERAL STUDIES OF NUCLEAR REACTORS↗
DOE OSTI2025

Assessing the Impact of Large Removable Beryllium Reflector Experiments on HFIR Performance and Safety Metrics

This study investigated the effect of various removable beryllium (RB) experiment configurations on High Flux Isotope Reactor (HFIR) metrics to address increasing interest in these facilities for materials and fuels irradiation research. The RB reflector contains eight large and four small irradiation experiment facilities that offer excellent neutron flux conditions to perform fission and fusion reactor materials and fuels irradiation research. This work aims to outline acceptable RB configurations based on safety, performance, and programmatic metrics. This study assessed experiment effects on reactivity, cycle length, fission rate density distributions, and neutron flux distributions using the Shift, HFIRCON, and SCALE ORIGEN codes. Initial evaluations focused on generic experiment materials including aluminum plugs, stainless steel plugs, molybdenum plugs, and aluminum plugs with gadolinium shields. Subsequent analyses of MiniFuel experiments were performed to evaluate a heterogenous experiment, consisting of a more complex geometry and bearing several materials, and to test the correlations developed with the generic materials on a real experiment. Furthermore, the effects of neutron poison concentrations in the standard beryllium plugs were evaluated. When assuming a reference RB configuration with eight large fresh beryllium plugs, perturbed configurations with three aluminum plugs, one stainless steel plug, one molybdenum plug, one gadolinium-shielded aluminum plug, and one MiniFuel experiment resulted in a cycle length reduction of less than 1.3 days, the current threshold before requiring additional approvals. Configurations with five aluminum plugs, one stainless steel plug, one molybdenum plug, one gadolinium-shielded aluminum plug, and two MiniFuel experiments meet the 1.3 day limit if irradiated beryllium plugs are considered. Fuel element fission rate density distributions remained within safety limits, with maximum local increases under 9%. Neutron flux calculations revealed large thermal flux depressions inside and around the perturbed RB facilities, while epithermal and fast neutron fluxes increased because of reduced neutron moderation by the perturbed materials. These findings provide valuable guidance for optimizing RB configurations to balance safety and performance at HFIR.

21 SPECIFIC NUCLEAR REACTORS AND ASSOCIATED PLANTS↗
DOE OSTI2022

Radiation hard Ga 2 O 3 detectors for high energy physics

In this project, we explore the use of ultra-wide bandgap Ga 2 O 3 materials for fabricating next-generation radiation hard solid-state detectors for high energy physics (HEP) applications. As an emerging semiconductor, Ga 2 O 3 has ultra-wide bandgap (4.5-4.9 eV), high breakdown electric field (8 MV/cm) and much lower production cost compared with radiation hard diamond detectors, all of which make Ga 2 O 3 a great candidate material working in harsh radiation environment of future HEP experiments. The recent breakthrough of growth technologies of Ga 2 O 3 significantly improves the availability of large area single crystalline Ga 2 O 3 . We focus on an early proof-of-principle demonstration of Ga 2 O 3 detectors and conduct comprehensive material and detector characterization to evaluate the potential of the emerging Ga 2 O 3 as a new radiation-hard detector material. Our endeavors directly support the instrumentation development and update need of HEP experiments and fits very well into the DOE HEP “Detector R&D” research subprogram.

36 MATERIALS SCIENCE↗
DOE OSTI2023

Development of Metal-Free Photocatalysts

Solar fuels show great promise as clean, sustainable energy sources; however, established technologies are still plagued by high price, toxicity concerns, or low efficiencies. There is a critical need for inexpensive, benign materials that can effectively harness the sun’s energy. The overarching objective of this project is to evaluate the compatibility of novel material combinations for use as metal-free, heterojunction photocatalysts. Our central hypothesis is that by tuning the electronic structure of individual components in hybrid composites, via selective chemical modifications and physical stimuli to the interface, we can improve the photocatalytic properties of the overall assembly. We will determine factors that affect energy band gaps and band edge positions in isolated photocatalyst systems as well as explore routes to modulate heterojunction band alignments in composite assemblies. Our results will predict accessible pathways for charge carriers in new composite photocatalysts, facilitating access to more of the solar spectrum via inexpensive, environmentally-friendly material combinations.

14 SOLAR ENERGY↗
DOE OSTI2024

Defect Thermodynamics and Transport Properties of Perovskite and Fluorite Materials for Solid-Oxide and Proton Conducting Oxide Cells Evaluated Based on Density Functional Theory Modeling

Density functional theory based defect thermodynamic modeling was performed to determine the effect of humidity and H2/O2 gas pressure on various defect chemistry and transport properties of perovskite and fluorite oxides for solid-oxide and proton-conducting-oxide cell applications, with inclusion of the electronic-conducting oxides (as electrodes) and insulating oxides (as electrolytes). Automatic defect generation workflow and first-principles charged defect analysis were implemented on NETL Joule supercomputer for modeling defect equilibria and transport properties of insulating oxides as electrolytes in SOCs and proton-conducting ceramic cells. A GNU Octave defect model subroutines were developed to facilitate defect modeling of electronic conducting oxides in a wide range of operating conditions guided by modeling and experiments. The model includes the hydride defect formation reaction under reducing conditions and allows to incorporate nonstoichiometry effects on the defect thermodynamic parameters. The developed model serves as a platform to facilitate fundamental understanding of the defect thermodynamics in SOC oxide materials and can be used as a novel tool in computational materials screening for SOC and other energy applications.

Lee, Yueh-Lin↗
DOE OSTI2022

The effect of micro-encapsulation on thermal characteristics of metallic phase change materials

Micro-encapsulated phase change material with metallic core can work under high temperature for energy storage purpose, making it an appealing candidate for renewable energy technologies, such as thermal energy storage for concentrating solar power plants. Here, the objective of this work is to study the impact of encapsulation on the thermal performance of the micro-encapsulated phase change materials under various operating conditions. To evaluate the thermal performance of the micro-encapsulated phase change material particles, the governing equation of transient heat conduction with phase change in spheres with composite walls is solved numerically. The impact of encapsulation shell on the convective heat transfer rate, total energy absorption, latent heat ratio, energy density, and duration of phase change is presented and analyzed under different operating conditions. Three different core materials including tin, aluminum and copper with distinct Stephan numbers are investigated. In addition to the encapsulation shell effect, this model can be used to study micro-encapsulated phase change materials with different material compositions, core to shell ratios, and it can be used to analyze the impact of air gaps in between materials. Understanding the thermal performance at particle level is essential, and the results of this study could potentially be used as input for thermal energy storage system level analysis, such as the charging and discharging processes.

42 ENGINEERING↗
DOE OSTI2024

Structure-aware graph neural network based deep transfer learning framework for enhanced predictive analytics on diverse materials datasets

Abstract Modern data mining methods have demonstrated effectiveness in comprehending and predicting materials properties. An essential component in the process of materials discovery is to know which material(s) will possess desirable properties. For many materials properties, performing experiments and density functional theory computations are costly and time-consuming. Hence, it is challenging to build accurate predictive models for such properties using conventional data mining methods due to the small amount of available data. Here we present a framework for materials property prediction tasks using structure information that leverages graph neural network-based architecture along with deep-transfer-learning techniques to drastically improve the model’s predictive ability on diverse materials (3D/2D, inorganic/organic, computational/experimental) data. We evaluated the proposed framework in cross-property and cross-materials class scenarios using 115 datasets to find that transfer learning models outperform the models trained from scratch in 104 cases, i.e., ≈90%, with additional benefits in performance for extrapolation problems. We believe the proposed framework can be widely useful in accelerating materials discovery in materials science.

Chemistry↗
DOE OSTI2023

Parametric Study of 239 Pu and 235 U Mass Equivalency in Spherical Geometries

In this technical document, parametric studies were performed to determine if a bounding mass equivalency can be used to simplify multi-material models in MCNP6 for criticality evaluations. The studies explored the simplification of MCNP6 models by substituting materials, particularly 239 Pu for 235 U. The initial hypothesis for the material simplification is based on the subcritical mass limits from the ANSI/ANS- 8.1 standard which states that for water reflected metal spheres the limits are 20.1 kg 235 U and 5.0 kg 239 Pu. Using those masses, it was hypothesized that a 20.1 kg sphere of 235 U and a 5.0 kg sphere of 239 Pu could be considered to have the same multiplication. Given that equivalency, 4kg 235 U could be modeled by 1kg 239 Pu (i.e., a mass ratio of 4:1). The parametric studies compare the materials 239 Pu and 235 U independently and in multi-material spherical models. Critical mass, critical thickness, critical mass ratio and critical thickness ratio values are presented as functions of inner radius. keff values are presented as a function of inner radius at different mass ratios and total masses. Results indicate that the conservative bounding mass equivalency is one. That is, if substituting 239 Pu for 235 U it should be done on a gram for gram basis.

11 NUCLEAR FUEL CYCLE AND FUEL MATERIALS↗
DOE OSTI2025

Interpretation of Ion Irradiation and Neutron Irradiation Damage in Additively Manufactured 316 Stainless Steel using Multiscale Modeling

The accelerated adoption of nuclear energy necessitates advanced manufacturing technologies, such as additive manufacturing, to meet heightened supply chain requirements and support innovative reactor technologies. Due to the unique microstructural characteristics of additively manufactured materials under distinct solidification conditions, comprehensive evaluation of their performance in reactor environments is essential. The Advanced Materials and Manufacturing Technologies program under the Department of Energy's Office of Nuclear Energy focuses on understanding the irradiation performance and damage evolution of laser powder bed fusion 316 stainless steel, with an emphasis on integrating ion and neutron irradiation data to accelerate the development and qualification of materials for advanced nuclear reactor applications. While ion irradiation is a cost- and time-effective method, modeling and simulation are required to interpret the data for the broader range of irradiation conditions encountered in advanced reactors. In fiscal year 2025, integrated multiscale modeling and simulations were conducted to assess irradiation damage in additively manufactured 316 stainless steel. Key outcomes include predictions of chromium enrichment at grain boundaries, nickel enrichment at dislocation cell walls and void surfaces, and heterogeneous void evolution under ion and neutron irradiation conditions. Cluster dynamics simulations revealed the coarsening of voids at high irradiation temperatures and the suppression of void growth by high network dislocation density, while also demonstrating significant growth and coarsening of voids and self-interstitial atom loops at low dose rates. Machine learning-accelerated atomistic simulations highlighted the impact of the local environment and chromium concentration on vacancy diffusivity, providing key insights on the influence of composition on void swelling and radiation-induced segregation. Additionally, molecular dynamics simulations demonstrated the presence of defect production bias and a significant effect of carbon content on defect cluster behavior. These combined efforts aim to predict the performance of additively manufactured materials under various reactor conditions, supporting their qualification for nuclear reactor applications by interpreting ion irradiation data. This report underscores the potential of integrated multiscale modeling to analyze ion irradiation data in the effort to accelerate the qualification of additively manufactured materials for nuclear reactor components.

316 stainless steel↗
DOE OSTI2024

Assessment of Machine Learning for Ultrasonic Nondestructive Evaluation of Alkali–Silica Reaction in Concrete

Alkali–silica reaction (ASR) is a type of material degradation in concrete structures that leads to concrete cracking and rebar corrosion, thereby reducing the material’s structural integrity and the overall structure’s lifetime and raising safety concerns. Ultrasonic nondestructive evaluation (NDE) has been proven to be a valuable technique for assessing concrete properties and monitoring ASR progression in concrete. However, the deployment and analysis of ultrasonic NDE and its data requires specialized expertise, often relying on the engineer’s subjective interpretation. With the surge in computational power, artificial intelligence (AI) and machine learning (ML) algorithms have become popular in automating NDE data analysis. Various industrial sectors are increasingly adopting ML algorithms for NDE data analysis with a growing emphasis on AI–assisted automation. Regulatory agencies are also preparing for this technological shift, anticipating corresponding revisions in standards. Thus, there is an urgent need to identify the capabilities and limitations of current ML technologies for the evaluation of concrete material properties and damage status. Furthermore, the effects of various factors on ML model performance must be thoroughly investigated. The study summarized herein evaluated the effectiveness of two ML models (i.e., support vector regression (SVR) and deep neural network (DNN)) in predicting concrete material damage induced by ASR based on the long-term ultrasonic monitoring data. Four distinct concrete specimens were cast with artificially induced ASR, and over a period exceeding 500 days, ultrasonic signals and expansion data were continuously collected. For the SVR model, wave velocity and 12 other wave features were extracted from the ultrasonic signals, with 6 out of 13 features selected as input for the model. Different combinations of training and testing datasets were designed to explore factors influencing prediction performance, including the range of data within training and testing sets, in addition to various signal preprocessing methodologies. These findings suggest the importance of using a training dataset with a broader data range compared with testing datasets for improved model performance alongside consistent signal preprocessing across datasets.

36 MATERIALS SCIENCE↗
DOE OSTI2022

Dispersion interactions in proposed covalent superhydride superconductors

Recent developments in high temperature superconducting materials under high pressure have made numerical evaluation of the superconducting transition temperature (T c ) of predicted materials critically important as a means to identification. Existing methods of calculating T c often do not agree with each other or experiment, often due to the large number of complex factors that contribute to this property; among them is the neglect of dispersion interactions in commonly used density functionals. We evaluate the effect of including dispersion interactions on the predicted superconducting properties of two examples of the covalent superhydride class of very high-T c superconducting materials. In both cases, dispersion is found to have sizable effects, increasing the electron-phonon coupling as compared to the reference case of elemental niobium. A detailed investigation traces the origin of this effect in a 270 GPa $\mathrm {R3m CSH_7}$ structure to structural distortions driven by long-range electron-phonon interactions rather than novel bonding networks.

36 MATERIALS SCIENCE↗
DOE OSTI2024

Materials Assessment for FLiBe Fusion Blankets

This evaluation was prepared at the request of DOE-FES and expands on an earlier evaluation prepared by Wilson, Wiffen and Keiser in 2015 (attached as Appendices A and B). Additional useful information for materials in fission molten salt reactor systems that use FLiBe can be found in a prior NRC report [Busby 2019]. The purpose of this report is to review the considerations that will determine the fusion blanket structural materials that will be suitable for use in a FLiBe breeder-coolant fusion power plant (FPP). Identifying a fusion-relevant structural material that is sufficiently compatible with FLiBe is a key issue to be addressed. The constraints and trade-offs made in structural materials selection include the usual power generation requirements such as mechanical properties, fabricability and durability, which assumes reasonable compatibility with the working fluid. For an FPP, additional constraints include neutron economy (breeding ratio), resistance to irradiation damage and low activation (i.e. waste disposal) characteristics.

36 MATERIALS SCIENCE↗
DOE OSTI2021

Production of Uranium from Seawater Using a Novel Polymer Adsorbent – Process Development and Cost Analysis (Final Scientific/Technical Report SBIR Phase II)

Research in developing techniques for extracting uranium from seawater is of considerable current interest. One reason which drives scientists to develop techniques of sequestering uranium from ocean is the prediction that the land-based uranium reserves would be depleted by the end of this century based on the current production rate. Uranium exists in seawater at a very low concentration (about 3 ppb) and as highly stable uranyl tris-carbonato complexes, primarily in the form Ca 2 [UO 2 (CO 3 ) 3 ]. Because of the enormous volume of seawater, the total amount of uranium in ocean is estimated to be a thousand times greater than the land-based uranium resources. As early as 1964, the idea of extracting uranium from seawater was discussed by Davies et al. in a Nature paper. In the past decades, many different materials were tested to evaluate their ability for sequestering uranium from seawater. Among them, amidoxime and carboxylate containing polymer fiber adsorbents appear most promising because of their high uranium adsorption capacity and stability in seawater. The carboxylate groups are necessary to make the polymer adsorbent hydrophilic whereas the amidoxime groups provide strong coordination sites for uranyl ions. Moreover, according to theoretical analysis, the adsorbability of uranium may involve synergistic effects of both amidoxime and carboxyl groups in the fiber adsorbent.

11 NUCLEAR FUEL CYCLE AND FUEL MATERIALS↗
DOE OSTI2020

Chemical trends of deep levels in van der Waals semiconductors

Abstract Properties of semiconductors are largely defined by crystal imperfections including native defects. Van der Waals (vdW) semiconductors, a newly emerged class of materials, are no exception: defects exist even in the purest materials and strongly affect their electrical, optical, magnetic, catalytic and sensing properties. However, unlike conventional semiconductors where energy levels of defects are well documented, they are experimentally unknown in even the best studied vdW semiconductors, impeding the understanding and utilization of these materials. Here, we directly evaluate deep levels and their chemical trends in the bandgap of MoS 2 , WS 2 and their alloys by transient spectroscopic study. One of the deep levels is found to follow the conduction band minimum of each host, attributed to the native sulfur vacancy. A switchable, DX center - like deep level has also been identified, whose energy lines up instead on a fixed level across different hosts, explaining a persistent photoconductivity above 400 K.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗