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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 55 records · Page 3
All sourcesNASA NTRSDOE OSTI
DOE OSTI2024

Modeling the Effect of Material Properties on Liquid-Alkaline Water Electrolysis

Liquid-alkaline water electrolyzers (LAWEs) use electricity to drive the conversion of water to H 2 and O 2 gas. These devices benefit from the use of low-cost nickel electrodes and metal-oxide separators, but suffer from lower current densities and higher cell voltages than proton-exchange-membrane water electrolyzers. Identifying the inefficiencies that result in this poor performance is key to mitigating losses and optimizing LAWEs. Here, we report an experimentally-validated 1-D continuum model of a LAWE that elucidates the gradients within the cell, simulates H 2 crossover, and projects the energy improvements made possible by modulating the properties of the electrodes and separator. The model captures the Nernstian polarization losses and the distribution of gas- and liquid-phases within the electrodes, enabling quantification of energy losses associated with kinetic, ohmic, and bubble-induced (mass-transport) resistances. Simulations demonstrate that LAWE can achieve energy intensities of 50 kWh kg −1 of H 2 at 1 A cm −2 using improved electrode and separator properties.

08 HYDROGEN↗
DOE OSTI2025

A Machine Learning Framework for Modeling Ensemble Properties of Atomically Disordered Materials

Atomic disorder can strongly influence material properties such as charge transport, optical response, and catalytic activity. However, efficiently modeling these disorder effects remains challenging for first-principles methods due to the cost of sampling large configurational spaces and computing complex physical quantities. Recent advances of machine learning techniques, particularly graph neural networks (GNNs), has enabled the efficient and accurate predictions of complex material properties, offering promising tools for studying disordered systems. In this work, we present a general machine-learning-assisted computational framework that integrates equivariant GNNs with Monte Carlo simulations to compute the thermodynamic and ensemble-averaged functional properties of disordered materials. Using the surface-termination-disordered MXene monolayer Ti 3 C 2 T 2–x as a representative system, we find that electrical conductivity exhibits an emergent peak near the order–disorder phase transition temperature due to the interplay between electron scattering and doping. In contrast, optical conductivity remains largely insensitive to local atomic disorder and reflects the global surface chemical composition. These results highlight the role of atomic disorder in affecting material properties and demonstrate the potential of our approach for statistically modeling disorder effects in a wide range of materials such as high-entropy alloys and spin liquids.

MXene↗
DOE OSTI2025

High-Temperature Gas Sensor Materials with Properties Predicted via First-Principles Calculations with Machine Learning Modeling and Experimental Corroboration

Understanding the temperature dependence of functional properties of sensing materials is vital for their applications in combustion environments. The electron-phonon coupling that derives the electronic structure change with temperatures is a key property of interest as it affects other sensing responses. Herein, we first assess the temperature dependence of band gap renormalization in sensing materials by employing Allen-Heine-Cardona (AHC) theory with density functional theory (DFT) simulations corroborated with experimental observation. As the AHC calculations are impractical for high-throughput screening of materials, we employ data-driven Gaussian process regression to predict the parameters employed in the O’Donnell empirical model from a set of physical features. To mitigate the reliability issues arising from the small size of the dataset, we apply a Bayesian technique to improve the generalizability of the data-driven models as well as to quantify the uncertainty associated with theoretical predictions. These models capture well the overall trend of the O’Donnell parameters with respect to a reduced feature set obtained by transforming the available physical features. Quantifying the associated uncertainty helps us understand the reliability of the predictions and, therefore, the variation of bandgap as a function of temperature for other novel materials. The predicted candidates from machine learning models are further validated by experiments and DFT calculations.

bandgap renormalization↗
DOE OSTI2025

Particle dynamics in the spatial afterglow of flowing dusty plasmas

Final technical report for project Department of Energy - DE-SC0022242. The overarching hypothesis of this project was that the particle dynamics in the afterglow of dusty plasmas are determined by the interplay of the transient plasma properties and the material properties of dust particles. By combining experiments and modeling in an approach fundamentally different from prior research, this project investigated correlations between material properties and basic dust charging and decharging mechanisms.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗
DOE OSTI2025

Active learning path-dependent properties using a cloud-based materials acceleration platform

Solid state materials are central to many modern technologies in which a given material may be exposed to a variety of environments. The material properties often vary with the sequence of environments in an irreversible manner, resulting in a quintessential path-dependency in experimental observables. While sequential learning techniques have been effectively deployed for accelerating learning of state properties of materials, they often use a consistent environment path in all experiments. To elevate such techniques for making optimal decisions in experimental investigations of path-dependent properties, we introduce an iterated expected information gain acquisition function that optimizes over entire experimental trajectories. This approach is implemented within a cloud-based Materials Acceleration Platform architecture utilizing an event-driven stateful broker coupled with remote HELAO (Hierarchical Experimental Laboratory Automation and Orchestration) instances and an AI science manager. The platform's efficacy was demonstrated through a case study optimizing multi-step spectro-electrochemical experiments to identify optically stable potential windows in (Co–Ni–Sb)O z metal oxides. The system successfully integrated AI-driven experiment design, remote laboratory automation, and cloud-based data infrastructure, validating the platform's capability for managing complex, adaptive, path-dependent workflows in materials discovery.

Guevarra, Dan [California Institute of Technology ↗
DOE OSTI2026

Connect microstructure evolution to property degradation with validated simulation

Reliable material performance is required for plasma-facing material (PFM) candidates. Previous research has shown that plasma and neutron radiation exposure induces microstructural changes in PFMs; changes in thermal and electrical conductivities and in material hardening and embrittlement were also observed after neutron irradiation. These material property changes will negatively impact the performance of the PFMs in a fusion reactor. Despite the well-known connection between material microstructure, properties, and performance, there is a need for validated modeling capabilities connecting PFM property degradation with microstructural evolution under fusion-relevant conditions. We are developing a simulation capability to couple plasma-induced microstructural evolution to material property degradation. Our approach relies on deliberate mapping between individual simulation models and experimental characterization for validation. The open-source Multiphysics Object-Oriented Simulation Environment (MOOSE) software was used for this simulation capability development. A MOOSE phase-field model was coupled with the cluster dynamics code, Xolotl, to predict microstructural evolution. Microstructure characterization techniques, including scanning electron microscopy (SEM), transmission electron microscopy (TEM), and laser scanning confocal microscopy (LSCM) are used to validate these microstructural evolution simulations. Calculation of thermal and electrical conductivities with first principles simulations was performed for bulk material and for grain boundaries; these results are used within MOOSE models to calculate effective thermal and electrical conductivities as a function of grain characteristics. Thermoreflectance and four-probe techniques were employed to measure the thermal and electrical conductivities, respectively. A MOOSE crystal plasticity model was adapted to predict microstructure-sensitive deformation behavior, and X-ray diffraction (XRD) was used to collect bulk dislocation density data for validation. After individual simulation validation, these models are coupled to predict material property changes resulting from plasma exposure. We focused here on an experimental design to emphasize the separate effects of moderate thermal loads and plasma exposure using tungsten. Annealing of tungsten was performed under a protective environment for temperatures ranging from 500$^o$C to 1500$^o$C. The plasma exposure was completed in the Tritium Plasma Experiment at Idaho National Laboratory under a deuterium flux of 1e22 $\frac{D}{m^2s}$. This incremental approach is employed to build confidence in the modeling capability: separate-effects tests ensure that the models capture key mechanisms from single environmental conditions before predicting PFM property degradation under combined loads. We will show our early results from coupling these simulation models to predict PFM property changes from microstructural evolution. Comparisons of the simulation results with preliminary validation data will be discussed.

36 - MATERIALS SCIENCE↗
DOE OSTI2025

Study of the Scintillating Properties of Materials Based on Self- Assembling Quantum Dots Embedded into a Semiconductor Bulk

The goal of the project is to investigate scintillating properties of materials based on self-assembled InAs QDs embedded into GaAs bulk. According to theoretical predictions, such materials can have scintillating properties and low self-absorption. Compared to scintillators based on inorganic scintillating crystals, QD-based scintillators could have significantly higher light yield and shorter emission times. We intend to check the theoretical predictions experimentally, measure scintillation parameters and their dependence on the QD-based materials and learn how the properties could be optimized for practical applications.

36 MATERIALS SCIENCE↗
DOE OSTI2024

Targeted materials discovery using Bayesian algorithm execution

Rapid discovery and synthesis of future materials requires intelligent data acquisition strategies to navigate large design spaces. A popular strategy is Bayesian optimization, which aims to find candidates that maximize material properties; however, materials design often requires finding specific subsets of the design space which meet more complex or specialized goals. We present a framework that captures experimental goals through straightforward user-defined filtering algorithms. These algorithms are automatically translated into one of three intelligent, parameter-free, sequential data collection strategies (SwitchBAX, InfoBAX, and MeanBAX), bypassing the time-consuming and difficult process of task-specific acquisition function design. Our framework is tailored for typical discrete search spaces involving multiple measured physical properties and short time-horizon decision making. We demonstrate this approach on datasets for TiO 2 nanoparticle synthesis and magnetic materials characterization, and show that our methods are significantly more efficient than state-of-the-art approaches. Overall, our framework provides a practical solution for navigating the complexities of materials design, and helps lay groundwork for the accelerated development of advanced materials.

42 ENGINEERING↗
DOE OSTI2024

Area-based composition predictions of materials fabricated using simultaneous wire-powder-directed energy deposition

Functionally graded materials are an emergent method for designing components with programmable site-specific material properties. These materials are typically fabricated using metal additive manufacturing tools by simultaneously feeding multiple wire and/or powder feedstocks at various rates to achieve spatial composition change. The wire-powder-directed energy deposition (WP-DED) technique is of particular interest for many functionally graded material applications by balancing the low raw materials cost of wire with the high resolution of powder. However, feeding wire and powder are inherently different processes since all extruded wire enters the melt pool, while much of the blown powder is scattered, which makes determining the composition of the build challenging. In this study, we devise a simple area-based measurement method for estimating the composition of WP-DED structures. WP-DED single beads are printed using 309L stainless steel wire and commercially pure Fe powder at five wire feed rates (0.5, 0.75, 1.00, 1.25, 1.50 mm/mm) and five powder feed rates (2, 4, 6, 8, 10 rpm). Characteristic defects including interface gaps and macrosegregation (lack of mixing) tendencies are examined. High powder feed rates (8, 10 rpm) result in interface gaps at all wire feed rates, but smooth deposition and complete mixing is achieved at low powder feed rates, particularly with lower wire feed rates as well. The area-based composition measurement method is within ±20% of energy dispersive x-ray spectroscopy measurements for all samples, showing its effectiveness as a rapid composition estimate for WP-DED materials development.

36 MATERIALS SCIENCE↗
DOE OSTI2026

Irradiation Testing of Additively Manufactured Materials for LWR Applications: Alloy 718 and 316L Stainless Steel

The objective of this NSUF Project is to assess the changes in irradiated additively manufactured (AM) material properties as compared to non-irradiated material. Type 316L stainless steel and Alloy 718 samples were produced using Direct Metal Laser Melting (DMLM) fabrication. Materials produced from this fabrication method have several potential applications within the nuclear industry as reactor internal repair parts, fuel debris resistant filters, or fuel spacers within existing light water reactors (LWRs). AM materials have been shown to achieve equivalent mechanical behavior in simulated reactor environments as compared to wrought materials, but have significantly more flexibility when it comes to unique design features. The increased component design flexibility makes these AM materials an attractive choice for both current LWR applications as well as for small modular reactor (SMR) designs. Prior to use of these materials in reactor fleet operation, the industry as a whole must evaluate the effects of irradiation on their material properties. Standard 0.4 inch thick Compact Tension specimens and SSJ3 type tensile bars were neutron irradiated at the Advanced Test Reactor to ~1 dpa for the purpose of performing a variety of mechanical tests in a range of simulated environments applicable to LWRs. For the ductile austenitic Type 316L stainless steel, the irradiated data will be used to confirm that the AM process produces materials with properties that are equivalent to wrought materials under testing conditions applicable to LWR operation. Transmission electron microscopy analysis was also performed in order to understand microstructural and microchemical changes induced in each material in response to neutron irradiation. If possible, data collected from these AM 316L samples will be used to remove fluence limits from specifications of ASME code cases for this alloy, which will give vendors much more flexibility in building future components.

36 - MATERIALS SCIENCE↗
DOE OSTI2025

Paper or Plastic? Multiscale Material Handling Properties of Two Model Municipal Solid Waste Streams

Purpose: Municipal Solid Waste (MSW) is a potentially valuable sustainable feedstock for fuel and chemical production due to its carbon-rich content and low cost. This study aims to assess the material handling properties of paperand plastic-rich MSW feedstocks to mitigate equipment failure and processing downtime. Methods: The material handling properties of crumbled MSW feedstocks were measured using apowder rheometer with mass flow hopper calculations to assess handling performance. Inverse gas chromatography was use to measure the surface energy differences between feedstocks. Electron microscopy and Raman spectroscopy was used to evaluate microscale features that may contribute to material handling differences. Results: Plastic and paper rich feedstocks crumbled to a nominal 2 mm particle size were observed to have similar flow and handling characteristics with reasonable hopper outlets. 2 mm plastic rich crumbles, with their higher bulk density, exhibited superior flow performance. By contrast, 4 mm material required significantly larger hopper outlets, indicating poor flowability. Paper rich and 4 mm plastic rich samples displayed broad particle size distributions, which contributed to particle interlocking, jamming, and other flow issues. Electron microscopy revealed that plastic rich samples were significantly smoother, enhancing their flowability compared to the rougher, paper rich materials. Conclusions: This study establishes critical material handling baselines for processing MSW as a viable feedstock for fuel and chemical production. The findings highlight the importance of optimizing particle size and feedstock composition to improve flowability and handling performance.

09 BIOMASS FUELS↗