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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 OSTI2026

Alpha-Imaging Detector System Development for Large Area Monitoring

Effective management and disposal of legacy nuclear waste are essential for ensuring safe work environments and minimizing environmental impacts. Monitoring airborne actinide contamination is particularly critical due to the high internal dose potential of alpha-emitting radionuclides. Traditional continuous air monitoring systems (CAMs) used in the industry are limited in the volume of air they can sample, potentially leading to inaccurate radiation detection over large areas. For example, in 2018, elevated levels of airborne Plutonium-239 were detected beyond the controlled areas of the Hanford Plutonium Finishing Plant, highlighting the potential risks to both plant workers and nearby residents. To address these challenges, high-efficiency particulate absorbing (HEPA) air purifiers can enhance air flow by up to 1.5 orders of magnitude, thereby increasing monitoring efficiency and providing a cost-effective solution for large-area surveillance. To quantify the activities of alpha-emitting radionuclides on HEPA filters, the Savannah River National Laboratory is developing an advanced alpha-imaging detection system. This system includes scintillating materials combined with a digital scientific camera. A significant concern in operating such a large-area airborne monitoring system is the handling of HEPA filters, which may be contaminated with radioactive particles. To mitigate these hazards, it is crucial to ensure that any contamination is securely fixed onto the filters. Efforts have been made to optimize the sensitivity of scintillator-epoxy composites and apply them to HEPA filters. These materials were characterized using fluoroscence spectroscopy. These techniques confirmed the purity of the raw materials, the dispersion of scintillators in the epoxy matrix, and the stability of their optical and structural properties post-modification. The optimal scintillator-epoxy composite was selected for use on alpha-spiked HEPA filters to evaluate the efficiency of the sprayer. HEPA filters, embedded with alpha particles collected by an air purifier deployed in an airborne radiation area, have been tested to assess detection efficiency. Future work will focus on employing multiple imaging sensors simultaneously to enhance sensitivity across different regions of the HEPA filter.

Pham, Phuong [Savannah River National Laboratory (↗
DOE OSTI2025

Advances in relaxation and memory effects of magnetic nanoparticles for biomedical applications

Functionalized magnetic nanoparticles are pivotal in magnetic resonance imaging, computed tomography, controlled drug delivery, and hyperthermia treatments due to their exceptional magnetic relaxation and functional properties. The magnetic core composition and structure significantly affects the complex magnetic properties of these nanoparticles necessitating a thorough examination of magnetism fundamentals related to these systems. One important aspect is the ability of magnetic nanoparticles to retain previous magnetic state configurations known as memory effect, primarily governed by domain structure and magnetic anisotropy. Despite its relevance to advanced applications, comprehensive studies on magnetic relaxation and memory effects remain limited. Here, the present review aims to bridge this gap by investigating relaxation mechanisms, synthesis strategies, and applications, fostering further innovation. It investigates the memory effects and their dependence on particle composition and morphology along with key synthesis techniques for large-scale production in industrial adoption. Structured into focused sections on magnetic properties and their influence on biomedical and technological applications, this review provides essential insights into memory effects, magneto-relaxation mechanisms, influencing factors, and both experimental and theoretical methodologies. It also delves into computational modelling and AI-driven design, which are revolutionizing the prediction, discovery, and optimization of materials with tailored properties.

36 MATERIALS SCIENCE↗
DOE OSTI2012

Process-Dependent Properties in Colloidally Synthesized “Giant” Core/Shell Nanocrystal Quantum Dots

Due to their characteristic bright and stable photoluminescence, semiconductor nanocrystal quantum dots (NQDs) have attracted much interest as efficient light emitters for applications from single-particle tracking to solid-state lighting. Despite their numerous enabling traits, however, NQD optical properties are frustratingly sensitive to their chemical environment, exhibit fluorescence intermittency ('blinking'), and are susceptible to Auger recombination, an efficient nonradiative decay process. Previously, we showed for the first time that colloidal CdSe/CdS core/shell nanocrystal quantum dots (NQDs) comprising ultrathick shells (number of shell monolayers, n, > 10) grown by protracted successive ionic layer adsorption and reaction (SILAR) leads to remarkable photostability and significantly suppressed blinking behavior as a function of increasing shell thickness. We have also shown that these so-called 'giant' NQDs (g-NQDs) afford nearly complete suppression of non-radiative Auger recombination, revealed in our studies as long biexciton lifetimes and efficient multiexciton emission. The unique behavior of this core/shell system prompted us to assess correlations between specific physicochemical properties - beyond shell thickness - and functionality. Here, we demonstrate the ability of particle shape/faceting, crystalline phase, and core size to determine ensemble and single-particle optical properties (quantum yield/brightness, blinking, radiative lifetimes). Significantly, we show how reaction process parameters (surface-stabilizing ligands, ligand:NQD ratio, choice of 'inert' solvent, and modifications to the SILAR method itself) can be tuned to modify these function-dictating NQD physical properties, ultimately leading to an optimized synthetic approach that results in the complete suppression of blinking. We find that the resulting 'guiding principles' can be applied to other NQD compositions, allowing us to achieve non-blinking behavior in the near-infrared. Lastly, in addition to realizing novel light-emission properties by refining nanoscale architectures at the single-NQD level, we also investigate collective properties by assembling our core/shell NQDs into larger scale arrays.

36 MATERIALS SCIENCE↗
DOE OSTI2017

MOOSE Framework Speed and Memory Optimizations

The Multiphysics Object Oriented Simulation Environment (MOOSE) framework has been developed to solve a variety of complex simulations ranging from groundwater flows to full reactor simulations. MOOSE excels at solving fully-implicit, fully-coupled, highly nonlinear models using the Jacobian Free Newton-Krylov (JFNK) method relying on years of expertise in several dependent packages including PETSc, libMesh, Hypre and many others. The modular design of MOOSE enables the rapid development of new physics models that can be coupled with relative ease within the framework for building up simulations of different levels of complexity. With the ongoing adoption of MOOSE to new application areas along with streamlining of existing simulations, the priorities must be adjusted to include code optimizations. This includes both execution speed and the memory footprint of MOOSE-based simulations. These two categories can be further broken down into ''micro-optimizations'' or source code optimizations and algorithmic optimizations where source code is restructured to accomplish the same tasks through a more efficient means. However, before any code optimizations can occur, one must have robust way of measuring these critical simulation properties to identify the best candidates for optimizations along with having a way to identify the effectiveness of proposed optimizations to the framework. This report discusses the preliminary work towards the implementation of a robust execution baseline of the framework and derivative applications under a variety of different simulations. In addition, several potential optimization target areas in both the MOOSE framework and dependent libraries have been identified. An implementation strategy and potential benefits are discussed.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2019

Building a Consistent and Reproducible Database for Adsorption Evaluation in Covalent–Organic Frameworks

We present a workflow that traces the path from the bulk structure of a crystalline material to assessing its performance in carbon capture from coal's postcombustion flue gases. This workflow is applied to a database of 324 covalent-organic frameworks (COFs) reported in the literature, to characterize their CO 2 adsorption properties using the following steps: (1) optimization of the crystal structure (atomic positions and unit cell) using density functional theory, (2) fitting atomic point charges based on the electron density, (3) characterizing the pore geometry of the structures before and after optimization, (4) computing carbon dioxide and nitrogen isotherms using grand canonical Monte Carlo simulations with an empirical interaction potential, and finally, (5) assessing the CO 2 parasitic energy via process modeling. The full workflow has been encoded in the Automated Interactive Infrastructure and Database for Computational Science (AiiDA). Both the workflow and the automatically generated provenance graph of our calculations are made available on the Materials Cloud, allowing peers to inspect every input parameter and result along the workflow, download structures and files at intermediate stages, and start their research right from where this work has left off. In particular, our set of CURATED (Clean, Uniform, and Refined with Automatic Tracking from Experimental Database) COFs, having optimized geometry and high-quality DFT-derived point charges, are available for further investigations of gas adsorption properties. We plan to update the database as new COFs are being reported.

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

Directed energy deposition of radially functionally graded nickel based superalloys IN625 and IN738: Microstructure and mechanical properties

This study investigates the microstructure and mechanical properties of radially functionally graded (RFG) materials fabricated from nickel-based superalloys IN625 and IN738 using directed energy deposition (DED). RFG cylindrical deposits with radial gradients of IN625-IN738 and IN738-IN625 were successfully fabricated and confirmed to be crack-free through tailored composition grading, scan strategy and process parameters that minimized thermal gradients and solidification velocities. Microstructural analysis revealed distinct variations across the gradient, characterized by differences in dendrite arm spacing, microsegregation, and the formation of distinct γ' precipitates and carbide/boride phases (MC, M 6 C, M 2 C, and MB 2 ) in as-deposited (AD) conditions, which evolved into M 23 C 6 and M 2 B upon subsequent heat treatment (HT). EBSD showed predominant columnar grains with a [100] texture, while microhardness profiles demonstrated consistent variation across the gradients. Mechanical testing highlighted the synergy of the graded structure, combining IN738's strength with IN625's ductility, resulting in enhanced properties such as a yield strength of 1131 MPa, ultimate tensile strength of 1382 MPa, and elongation of 12 % for HT IN625-IN738 grading. Numerical simulations employing CALPHAD thermodynamic calculations and finite element thermal modeling are used to establish phase evolution and microstructural feature correlation with thermal profiles, aiding in the optimization of process-structure-property relationship.

Additive manufacturing↗
DOE OSTI2025

Post-build stress-relief optimization for laser powder bed fusion 316H stainless steel

Nuclear energy remains a critical component of a diversified and efficient energy portfolio, offering reliable, high-capacity, and low-carbon power. However, in the U.S., aging infrastructure and the slow qualification and deployment of advanced materials and manufacturing techniques hinder progress in next-generation reactor technologies. This study explores the application of laser powder bed fusion (LPBF) additive manufacturing for stainless steel 316H, with a focus on optimizing post-build heat treatments to enhance material properties for high-temperature nuclear applications. The research targets the optimization of stress-relief temperatures to alleviate postbuild residual stresses, ensuring improvements in the microstructural corelated properties. A series of microstructural and mechanical evaluations were performed on LPBF-printed SS-316H samples which were subjected to annealing at temperatures varying between 650 °C and 850 °C. X-ray diffraction, scanning electron microscopy, and transmission electron microscopy analyses revealed that increasing the heattreatment temperature accelerated dislocation recovery. Vickers microhardness measurements showed an initial reduction in values, followed by stabilization over extended durations at all the temperatures. While higher temperatures facilitated faster recovery, they also promoted carbide precipitation along grain and solidification cell boundaries, narrowing the safe processing window. In contrast, heat treatment at 650°C preserved the cellular substructure and enabled controlled carbide precipitation over time. In conclusion, these findings highlight the importance of time–temperature optimization and suggest that 650°C for up to 2 h provides the most favorable balance between recovery and carbide control for a stress-relief treatment.

316 stainless steel↗
DOE OSTI2015

Structural properties of small rhodium clusters

We report a systematic study of the structural properties of rhodium clusters at the atomistic level. A novel global-minimum search algorithm, known as parallel tempering multicanonical basin hopping plus genetic algorithm (PTMBHGA), is used to obtain the geometrical structures with lowest minima at the semi-empirical level where Gupta potential is used to describe the atomic interaction among the rhodium atoms. These structures are then re-optimized at the density functional theory (DFT) level with exchange-correlation energy approximated by Perdew-Burke-Ernzerhof (PBE) generalized gradient approximation (GGA). The structures are optimized for different spin multiplicities. The ones with lowest energies will be taken as ground-state structures. In most cases, we observe only minor changes in the geometry and bond length of the clusters as a result of DFT-level re-optimization. Only in some limited cases, the initial geometries obtained from the PTMBHGA are modified by the re-optimization. The variation of structural properties, such as ground-state geometry, symmetry and binding energy, with respect to the cluster size is studied and agreed well with other results available in the literature.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗
DOE OSTI2020

Influence of Vegetation Canopy Architecture on Dynamics of Spatiotemporal Light Distribution and its Effect on Land-Biosphere-Atmosphere Fluxes of Energy, Carbon, and Water

The dynamic spatiotemporal distribution of light and micro climatic variables (wind speed, atmospheric CO 2 concentration, humidity, pressure, and air temperature) within the plant canopy as determined by the canopy architecture play a critical role in land-biosphere-atmosphere (LBA) photosynthetic carbon uptake, transpiratory water loss and energy partitioning. Vegetation models that include effects of sun and shade flecks, leaf temperature and energy balance partitioning between sensible and latent heat fluxes, and reduced turbulent mixing in dense vegetation canopies can accurately predict canopy performance, particularly its shaded leaves, which can constitute 80% of leaf area and contribute up to 50% of carbon update and 65% of water loss. However, current vegetation models with their bulk characterization of shaded leaves poorly represent the complex three dimensional (3D) canopy architecture are insufficient to capture the spatiotemporal dynamics of shaded leaves. Here, we propose to employ an x-ray computed tomography (XCT) imaging technique in combination with an automated, high throughput, image processing pipeline (3DArch, to be developed) to obtain an explicit 3D triangulated surface mesh of a plant canopy (Fig 1). 3DArch would provide a much needed technological jump in 3D LBA modeling where computer generated artificial 3D models suffer from lack of high resolution detailed canopy architecture parameters. The 3D canopy surface mesh output from 3DArch is coupled with an explicit 3D forward ray tracing model (fastTracer) to obtain a high resolution representation of the dynamic spatiotemporal light environment within the canopy over the course of a day (Fig 1). The results from the light attenuation model are then coupled with a process based multi-scale bio-physical canopy and leaf model of photosynthesis (PlantGro) to get fluxes of carbon, water and energy at the leaf and canopy scale, in addition to estimating whole plant carbon uptake, and water loss. Our proposed model framework would be the first of its kind to provide a breakthrough capability in the field of process-based plant modeling that bridges the key technological gap in shaded leaf modeling through explicit representation of 3D canopy architecture. Computer generated artificial 3D model canopy simulations on grasses show that the canopy light distribution does not follow an exponential behavior as assumed in most 1D vegetation models, but rather exhibits a heterogeneous behavior, with a small but significant portion of interior canopy leaves experiencing peak light levels due to sun flecks while the rest of the canopy receives substantially lower light levels. These results indicate a bias in traditional models that overestimates shaded leaf contribution towards whole canopy photosynthesis by up to 15% and underestimate transpiration by up to 12% with an associated increase in sensible heat fluxes since photosynthesis, transpiration and energy partitioning are tightly coupled processes. However, these test results need to be validated using the 3D architecture obtained from a real plant imaged in 3D using 3DArch, rather than an artificial model plant. Our proposed model framework would allow us to identify inefficiencies in canopy light distribution and prescribe optimal canopy architectures (leaf optical properties, leaf area, leaf angles, row spacing, planting density, etc.) to traditional and genetic engineering based crop breeders, for maximizing target functions such as light use efficiency, water use efficiency, drought tolerance, crop yield, crop resilience etc., with important applications in biofuel crops such as sorghum, and switchgrass. Our model can also be applied for designing optimal canopy architecture that increase surface albedo as a mitigation strategy for global warming and projections of crop yield for future food and water security under climate change.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2024

Growth of High-Purity CsPbBr 3 Crystals for Enhanced Gamma-Ray Detection

High-quality CsPbBr 3 crystals hold significant potential for gamma-ray detection due to their remarkable optoelectronic properties. This study details an optimized production process using the Bridgman method to achieve highly pure CsPbBr 3 crystals. By implementing rigorous synthesis and purification techniques, we successfully reduced the total impurity levels to 9 ppm, as confirmed by glow discharge mass spectroscopy (GDMS). The resulting CsPbBr 3 crystals demonstrate exceptional performance, including high transparency, intense photoemission, and prolonged photoluminescence decay times. These properties facilitate superior gamma-ray detection with an energy resolution of 1.4% for the 137 Cs 662 keV gamma-rays, comparable to commercial Cd 1-x Zn x Te (CZT) detectors. Our findings underscore the critical relationship between material purity and detector performance, highlighting the potential of CsPbBr 3 as a cost-effective alternative in radiation detection applications. Further studies on defect origins and electronic states are necessary to fully leverage the capabilities of CsPbBr 3 crystals in practical high-energy radiation detection systems.

Bridgman↗
DOE OSTI2024

Dielectric and magnetic properties of microwave-absorbing FeAl x O y catalysts fabricated via solution combustion synthesis

Iron-based alumina (FeAl x O y ) nanocomposites are microwave-absorbers and catalysts, which makes them promising for emerging microwave-assisted thermocatalytic technologies. Solution combustion synthesis (SCS) has been used to synthesize FeAl x O y powders, and prior work has demonstrated that adjusting SCS parameters significantly changes phase composition and specific surface area of the products. However, it is unclear how synthesis parameters affect their microwave-absorbing properties, which are essential for optimizing microwave-assisted technologies. To address this challenge, in the present work, twelve different FeAl x O y products were synthesized at different combinations of the SCS parameters such as two fuels (citric acid and glycine), two heating modes (hotplate and muffle furnace), and three Fe:Al molar ratios (2:1, 1:1, 1:2). Dielectric and magnetic properties of the products were characterized using a network analyzer and a vibrating sample magnetometer. Based on the measured permittivity and permeability, penetration depth and reflection loss were calculated as a function of frequency and bed thickness. The products were heated by microwaves at 2.45 GHz and then examined with X-ray diffraction (XRD) analysis. For all products, the magnetic saturation was lower than for bulk iron oxides because of the small crystallite size and aluminum substitution. The use of glycine induced high dielectric losses and enabled fast microwave-heating rates compared to citric acid. Higher Fe:Al ratio also led to higher dielectric and magnetic losses. With glycine fuel, SCS in a furnace induced larger penetration depth and lower microwave absorption than SCS on a hotplate. The minimization of reflected power was more sensitive to the thickness of the product bed than to the frequency of the electromagnetic field. Post-heating XRD analysis revealed different phase transformations in the FeAl x O y powders depending on the SCS parameters. As a result, an FeAl x O y material, synthesized via incipient wetness impregnation, lacked magnetic losses and did not heat well as compared to the SCS products.

Combustion synthesis↗
DOE OSTI2020

The potential of quantum annealing for rapid solution structure identification

Abstract The recent emergence of novel computational devices, such as quantum computers, coherent Ising machines, and digital annealers presents new opportunities for hardware-accelerated hybrid optimization algorithms. Unfortunately, demonstrations of unquestionable performance gains leveraging novel hardware platforms have faced significant obstacles. One key challenge is understanding the algorithmic properties that distinguish such devices from established optimization approaches. Through the careful design of contrived optimization tasks, this work provides new insights into the computation properties of quantum annealing and suggests that this model has the potential to quickly identify the structure of high-quality solutions. A meticulous comparison to a variety of algorithms spanning both complete and local search suggests that quantum annealing’s performance on the proposed optimization tasks is distinct. This result provides new insights into the time scales and types of optimization problems where quantum annealing has the potential to provide notable performance gains over established optimization algorithms and suggests the development of hybrid algorithms that combine the best features of quantum annealing and state-of-the-art classical approaches.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2014

Multicycle rapid thermal annealing optimization of Mg-implanted GaN: Evolution of surface, optical, and structural properties

The first step of a multi-cycle rapid thermal annealing process was systematically studied. The surface, structure, and optical properties of Mg implanted GaN thin films annealed at temperatures ranging from 900 to 1200 °C were investigated by Raman spectroscopy, photoluminescence, UV-visible spectroscopy, atomic force microscopy, and Nomarski microscopy. The GaN thin films are capped with two layers of in-situ metal organic chemical vapor deposition -grown AlN and annealed in 24 bar of N{sub 2} overpressure to avoid GaN decomposition. The crystal quality of the GaN improves with increasing annealing temperature as confirmed by UV-visible spectroscopy and the full widths at half maximums of the E{sub 2} and A{sub 1} (LO) Raman modes. The crystal quality of films annealed above 1100 °C exceeds the quality of the as-grown films. At 1200 °C, Mg is optically activated, which is determined by photoluminescence measurements. However, at 1200 °C, the GaN begins to decompose as evidenced by pit formation on the surface of the samples. Therefore, it was determined that the optimal temperature for the first step in a multi-cycle rapid thermal anneal process should be conducted at 1150 °C due to crystal quality and surface morphology considerations.

36 MATERIALS SCIENCE↗
DOE OSTI2017

Controlling Metastable Native Point-Defect Populations in Cu(In,Ga)Se 2 and Cu 2 ZnSnSe 4 Materials and Solar Cells through Voltage-Bias Annealing

Our contribution describes the influence of low-temperature annealing with and without applied voltage bias on thin-film Cu 2 ZnSnSe 4 (CZTSe), Cu(In,Ga)Se 2 (CIGS), and CdS material properties and solar cell performance. In order to quantify the effects of cation disorder on CZTSe device performance, completed devices were annealed under open-circuit conditions at various temperatures from 110 degrees C to 215 degrees C and subsequently quenched. Measurements on these devices document systematic, reversible changes in solar-cell performance consistent with a reduction in CZTSe band tails at lower annealing temperatures. CIGS and CZTSe solar cells were also annealed at various temperatures (200 degrees C for CIGS and 110 degrees C-215 degrees C for CZTSe) and subsequently quenched with continuously applied voltage bias to explore the effects of non-equilibrium annealing conditions. For both absorbers, large reversible changes in device characteristics correlated with the magnitude and sign of the applied voltage bias were observed. For CZTSe devices, the voltage-bias annealing (VBA) produced reversible changes in open-circuit voltage (VOC) from 289 meV to 446 meV. For CIGS solar cells, even larger changes were observed in device performance: photovoltaic (PV) conversion efficiency of the CIGS device varied from below 3% to above 15%, with corresponding changes in CIGS hole density of about three orders of magnitude. Findings from these VBA experiments are interpreted in terms of changes to the metastable point-defect populations that control key properties in the absorber layers, and in the CdS buffer layer. Computational device modeling was performed to assess the impacts of cation disorder on the CZTSe VOC deficit, and to elucidate the effects of VBA treatments on metastable point defect populations in CZTSe, CIGS, and CdS. Our results indicate that band tails impose important limitations on CZTSe device performance. Device modeling results also indicate that non-equilibrium processing conditions including the effects of voltage bias can dramatically alter point-defect-mediated opto-electronic properties of semiconductors. Implications for optimization of PV materials and connections to long-term stability of PV devices are discussed.

14 SOLAR ENERGY↗
DOE OSTI2023

GAHLS: an optimized graph analytics based high level synthesis framework

The urgent need for low latency, high-compute and low power on-board intelligence in autonomous systems, cyber-physical systems, robotics, edge computing, evolvable computing, and complex data science calls for determining the optimal amount and type of specialized hardware together with reconfigurability capabilities. With these goals in mind, we propose a novel comprehensive graph analytics based high level synthesis (GAHLS) framework that efficiently analyzes complex high level programs through a combined compiler-based approach and graph theoretic optimization and synthesizes them into message passing domain-specific accelerators. This GAHLS framework first constructs a compiler-assisted dependency graph (CaDG) from low level virtual machine (LLVM) intermediate representation (IR) of high level programs and converts it into a hardware friendly description representation. Next, the GAHLS framework performs a memory design space exploration while account for the identified computational properties from the CaDG and optimizing the system performance for higher bandwidth. The GAHLS framework also performs a robust optimization to identify the CaDG subgraphs with similar computational structures and aggregate them into intelligent processing clusters in order to optimize the usage of underlying hardware resources. Finally, the GAHLS framework synthesizes this compressed specialized CaDG into processing elements while optimizing the system performance and area metrics. Evaluations of the GAHLS framework on several real-life applications (e.g., deep learning, brain machine interfaces) demonstrate that it provides 14.27× performance improvements compared to state-of-the-art approaches such as LegUp 6.2.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2016

A QUANTITATIVE TEST OF THE NO-HAIR THEOREM WITH Sgr A* USING STARS, PULSARS, AND THE EVENT HORIZON TELESCOPE

The black hole in the center of the Milky Way, Sgr A*, has the largest mass-to-distance ratio among all known black holes in the universe. This property makes Sgr A* the optimal target for testing the gravitational no-hair theorem. In the near future, major developments in instrumentation will provide the tools for high-precision studies of its spacetime via observations of relativistic effects in stellar orbits, in the timing of pulsars, and in horizon-scale images of its accretion flow. We explore here the prospect of measuring the properties of the black hole spacetime using all of these three types of observations. We show that the correlated uncertainties in the measurements of the black hole spin and quadrupole moment using the orbits of stars and pulsars are nearly orthogonal to those obtained from measuring the shape and size of the shadow the black hole casts on the surrounding emission. Combining these three types of observations will therefore allow us to assess and quantify systematic biases and uncertainties in each measurement and lead to a highly accurate, quantitative test of the gravitational no-hair theorem.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2024

Robust Dissimilar Metal Friction Welded Spool for Enhanced Capability for Steam Power Components (Final Program Report)

This project successfully demonstrated the feasibility and advantages of producing durable, friction-welded dissimilar-metal spools, both with and without transition pieces, as well as the use of advanced oxidation-protective coatings for steam power applications. The work encompassed the optimization and full-scale production of NFA tubing, overcoming manufacturing and processing challenges to achieve enhanced strength, creep, and fatigue properties validated by extensive microstructural characterization. Optimized welding techniques were developed and refined for joining dissimilar materials, resulting in defect-free welds optimized for both low- and high-pressure boiler circuits. Systematic evaluation of welding parameters, coupled with heat treatments, ensured reliable performance and minimized weld defects such as banding and cracking. Comprehensive mechanical and non-destructive testing, including tensile, bend, fatigue, and CT scanning, confirmed the superior integrity and durability of the friction-welded joints under representative service conditions. To address oxidation-related degradation, a range of protective alloy coatings—such as IN625, APMT, Duplex 2507, and FeCrAlY—were selected and evaluated for their ability to counteract oxide notch formation at dissimilar metal interfaces. These coatings demonstrated improved oxidation resistance and long-term stability under thermal cycling, thereby extending weld service life. Thermal cyclic testing and long-term studies captured the phase stability and diffusion behavior of coated and uncoated configurations. Validated models for low-cycle fatigue (LCF), creep, and thermal mechanical fatigue (TMF) were developed to predict component life and failure modes. These models confirmed that friction-welded configurations exhibited significantly enhanced performance compared to conventional gas-tungsten arc welding (GTAW), with up to 7× improvement in creep resistance and at least 2× enhancement in fatigue properties. The introduction of a tailored transition piece further reduced thermally induced strains, enhancing fatigue life. The results demonstrate a robust pathway for retrofitting existing steam fleet components and upgrading future equipment to deliver substantially improved cold-start cycling behavior and reduction of unplanned outages due to premature weld failures.

11 NUCLEAR FUEL CYCLE AND FUEL MATERIALS↗
DOE OSTI2024

Implementation of an extensible property modeling framework in ESPEI with applications to molar volume and elastic stiffness models

Property models are becoming more widely adopted by commercial Calphad databases, but they are not nearly as common in non-commercial or traditional academic Calphad databases. A primary driver is that user-friendly Calphad modeling tools that support property models are not widely available. Here we present new property modeling capabilities that have been implemented in ESPEI (the Extensible, Self-optimizing Phase Equilibrium Infrastructure). These capabilities include both generating property model parameters from data and improvements to the algorithmic selection of the most appropriate model from a series of candidates. Additionally, two illustrative examples are given that use ESPEI to fit different property models. First, we generate molar volume model parameters for Group IV, V, and VI refractory BCC alloys based on the model by Lu et al. (2005). Second, we demonstrate the extensibility of ESPEI’s property modeling capabilities by implementing a custom PyCalphad model for BCC elastic stiffness parameters to generate and compare parameters to the ones assessed by Marker et al. (2018) using the same data. Property models generated by ESPEI can be used in PyCalphad or further optimized with uncertainty quantification using ESPEI.

36 MATERIALS SCIENCE↗