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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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601 records · Page 26
All sourcesNASA NTRSDOE OSTI
DOE OSTI2024

A Novel Approach to Investigate Thermal Protection Systems Materials

The Koo Research Group (KRG) at The University of Texas at Austin (UT) and KAI has specialized in “Ablation Research” for more than fifteen years. Recently, the group has developed several incredibly unique capabilities that can advance “Thermal Protection Systems (TPS) Materials Research & Development” using an integrated experimental and numerical approach. The paper aims to introduce the methodology KRG has developed to solve this challenging problem. It will discuss how the KRG develops “Process-Properties-Performance” relationships of novel TPS materials in a systematical approach using (a) processing and fabrication, (b) thermal characterization of properties, (c) aerothermal testing, (d) microstructures characterization and analysis, and (e) numerical modeling. Progress and challenges of this research will also be discussed.

Engineering↗
DOE OSTI2026

Explainable machine learning reveals that local structural motifs encode the thermodynamic state across the CuZr metallic glass-forming range

Metallic glasses derive their properties from the statistics of local atomic motifs rather than from long-range order, yet a quantitative, chemistry-specific link between motif populations and the underlying glassy state has remained elusive. In this work we combine large-scale molecular dynamics, Voronoi tessellation, deep neural networks, and SHapley Additive exPlanations (SHAP) to identify which local structural motifs define the glassy state of Cu—Zr metallic glasses. A dataset of 17,180 atomistic configurations spanning ten compositions (Cu 20 Zr 80 –Cu 80 Zr 20 ) and four quench rates (10 9 –10 12 K/s) is used to train a feed-forward neural network that regresses temperature across the 50–2000 K liquid–supercooled–glass range, achieving a mean absolute error of 19.89 K and R 2 = 0.9974, confirming that the local structural state is faithfully encoded in motif-level structure. SHAP analysis then reveals that a tightly coupled near-icosahedral family of motifs (coordination numbers (CN) 11–13, including the full icosahedron 001200 and its single-atom-perturbation sibling 10930) collectively encodes the thermodynamic state of the system across the full glass-forming range. The CN = 11–13 ordered members carry negative SHAP values at high populations, tracking the most deeply-quenched configurations, while 10930 shows the reversed signature consistent with its role as a soft-spot host whose population shrinks as the icosahedral network deepens. The analysis demonstrates that explainable machine learning can isolate the minimal motif vocabulary defining the glassy state and recovers the near-icosahedral building blocks previously identified by data-driven analyses of Cu—Zr. The approach provides a general, chemistry-specific route for characterizing the structural state of disordered materials.

36 MATERIALS SCIENCE↗
DOE OSTI2023

The brighter-fatter effect in the JWST MIRI Si:As IBC detectors: I. Observations, impact on science, and modeling

Context.The Mid-Infrared Instrument (MIRI) on board theJames WebbSpace Telescope (JWST) uses three Si:As impurity band conduction (IBC) detector arrays. The output voltage level of each MIRI detector pixel is digitally recorded by sampling up the ramp. For uniform or low-contrast illumination, the pixel ramps become nonlinear in a predictable way, but in areas of high contrast, the nonlinearity curve becomes much more complex. The origin of the effect is poorly understood and currently not calibrated out of the data. Aims.We provide observational evidence of the brighter-fatter effect (BFE) in MIRI conventional and high-contrast coronagraphic imaging, low-resolution spectroscopy, and medium-resolution spectroscopy data, and we investigate the physical mechanism that gives rise to the effect on the MIRI detector pixel raw voltage integration ramps. Methods.We used public data from the JWST/MIRI commissioning and Cycle 1 phase. We also developed a numerical electrostatic model of the MIRI detectors using a modified version of the publicPoisson_CCDcode. Results.We find that the physical mechanism behind the BFE manifesting in MIRI data is fundamentally different to that of charge-coupled devices and photodiode arrays such as the Hawaii-XRG near-infrared detectors used by the NIRISS, NIRCam, and NIRSpec instruments on board JWST. Observationally, the BFE makes the JWST MIRI data yield 10–25% larger point sources and spectral line profiles as a function of the relative level of de-biasing of neighboring detector pixels. This broadening impacts the MIRI absolute flux calibration, time-series observations of faint companions, and point spread function modeling and subtraction. We also find that the intra-pixel 2D profile of the shrinking Si:As IBC detector depletion region directly impacts the accuracy of the pixel ramp nonlinearity calibration model.

Astronomy & Astrophysics↗
DOE OSTI2026

Effect of spacer grids on high-burnup fuel fragmentation, relocation, and dispersal

Increasing the fuel burnup limit in light-water reactors to improve fuel cycle economics requires a strong technical foundation. Experimental observations from the Halden and Studsvik programs have revealed severe fuel fragmentation during loss-of-coolant accident (LOCA) conditions, highlighting the need for additional technical evaluation. Consequently, further LOCA test data are needed to complement existing findings and improve the understanding of fuel fragmentation, relocation, and dispersal (FFRD) behavior. Oak Ridge National Laboratory’s Severe Accident Test Station has played a significant role in advancing the understanding of high-burnup fuel fragmentation, relocation, and dispersal phenomena. One remaining gap in the available experimental database is the effect of fuel assembly structural features on cladding deformation behavior during a LOCA, and more specifically, their impact on the fuel’s ability to fragment, relocate, and disperse. Recent analyses using the BISON fuel performance code suggest that cladding deformation near grid spacers will remain below the 3% threshold that has been reported in the NRC Research Information Letter, indicating that the cladding could remain mechanically constrained during the LOCA event. This paper builds upon the BISON analyses to design and conduct a series of out-of-cell tests aimed at further evaluating cladding deformation in and around grid spacers. In addition, these tests were used to assess local cladding temperature conditions and compare them against analytical predictions in order to better replicate expected in-reactor behavior. Finally, an in-cell high-burnup LOCA test was designed and performed to evaluate the effects of a grid spacer, or cladding restraint, on fuel fragmentation, relocation, and dispersal susceptibility. The high-burnup test results differed from those of historical LOCA experiments, with a recorded rupture temperature of 861°C. Two ballooned regions and corresponding rupture openings were observed, with rupture widths of approximately 0.64 mm for both ruptures and rupture lengths of 4.8 mm and 5.6 mm, respectively.

Capps, Nathan [ORNL]↗
DOE OSTI2026

Field Validation of a Grid-Interactive Efficient Building Software Solution

The U.S. General Services Administration's (GSA's) Green Proving Ground (GPG) program, in partnership with the National Laboratory of the Rockies (NLR), completed a field study of a Grid-Interactive Efficient Buildings (GEB) software solution. The study focused on a single testbed facility to test the GEB functionality of the software solution, along with other features. The testbed facility - a courthouse - is a common building type in GSA's vast building portfolio, offering potentially impactful findings on a scalable level. The study evaluated Prescriptive Data's technology, Nantum OS, a connected building operating system ("GEB Solution") which aggregates multiple sources of previously siloed building data and combines that data with external sources, such as weather information or utility signals, into a single integrated platform. A GEB Solution is a type of Energy Management Information System (EMIS). EMIS is defined as a system of devices, data services, and software applications that communicates with any building system or third-party data source to aggregate and transform data into new capabilities to aid in the optimization of energy use at the building, campus, or agency level. This specific GEB Solution is an EMIS with ASO, automated system optimization, offering supervisory control of certain aspects of the Building Automation System (BAS). Multiple features were evaluated including, but not limited to, Continuous Demand Management to avoid setting new monthly kilowatt (kW) peaks, energy efficiency for reduction of kilowatt hours (kWh) and natural gas consumption, and automated demand response (ADR) for purposes of lowering demand during a utility called Demand Response (DR) event. The testbed facility was the Foley Federal Building and US Courthouse ("Foley Federal Building") located in Las Vegas, NV. This is a 209,496 sq. ft. building constructed in the 1960s with major renovations in 2004. The facility was a good candidate due to the large prevalence of office and courthouse spaces in the GSA portfolio of buildings. It also has many features which allow integration into and control of the building and a strong facilities team to assist with the study. Quantitative and qualitative performance objectives were developed using GSA's GPG GEB project template along with input from the vendor and building facility staff; these are outlined in Table 1. The quantitative performance objectives focused on continuous demand management, energy efficiency, and automated demand response. The qualitative performance objectives focused on the ease of installation and commissioning as well as the operability of the GEB solution. Other performance metrics that are reported on include carbon reduction, cost effectiveness, and occupant acceptance.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗
DOE OSTI2024

RMCProfile7 : reverse Monte Carlo for multiphase systems

This work introduces a completely rewritten version of the programRMCProfile(version 7), big-box, reverse Monte Carlo modelling software for analysis of total scattering data. The major new feature ofRMCProfile7is the ability to refine multiple phases simultaneously, which is relevant for many current research areas such as energy materials, catalysis and engineering. Other new features include improved support for molecular potentials and rigid-body refinements, as well as multiple different data sets. An empirical resolution correction and calculation of the pair distribution function as a back-Fourier transform are now also available.RMCProfile7is freely available for download at https://rmcprofile.ornl.gov/.

Chemistry↗
DOE OSTI2025

Revealing the evolution of order in materials microstructures using multi-modal computer vision

The development of high-performance materials for microelectronics, energy storage, and extreme environments depends on our ability to describe and direct property-defining microstructural order. Our present understanding is typically derived from laborious manual analysis of imaging and spectroscopy data, which is difficult to scale, challenging to reproduce, and lacks the ability to reveal latent associations needed for mechanistic models. Here, we demonstrate a multi-modal machine learning (ML) approach to describe order from electron microscopy analysis of the complex oxide La 1−x Sr x FeO 3 . We construct a hybrid pipeline based on fully and semi-supervised classification, allowing us to evaluate both the characteristics of each data modality and the value each modality adds to the ensemble. We observe distinct differences in the performance of uni- and multi-modal models, from which we draw general lessons in describing crystal order using computer vision.

36 MATERIALS SCIENCE↗
DOE OSTI2026

Predicting Band-Gap of Inorganic Materials Using Neuromorphic Graph Learning

Predicting properties of inorganic materials is a heavily researched topic, with several new prediction approaches emerging as competitors. One such competitor is graph neural networks, which leverage the structure of the graph to aid in the prediction process. In this work, we propose integration of neuromorphic computation into the graph neural network pipeline. We call this approach Neuromorphic Graph Learning (NGL). We utilize the NGL approach to leverage evolutionary algorithms and a novel Spike Pipeline for Raster Analysis (SPIRE) for the prediction of band gap in inorganic materials.

Mulet, Ian [University of Tennessee (UT)]↗
DOE OSTI2026

SENTRA: A Modular Computational Graph Framework for Critical Mineral and Materials Supply Chains: Part I: Network Construction Latent-Quantity Estimation, and Temporal Graph Forecasting

Global supply chains for critical minerals and materials are complex, evolving networks of countries, products, production stages, and trade relationships. Existing analytical approaches are limited by fragmented data and static network representations that do not capture the dynamic production dependencies linking raw materials, intermediate products, and final goods across multiple countries. Trade and production statistics provide only a partial view of domestic production, inventories, and material flows, making it difficult to identify indirect sourcing pathways, hidden dependencies, and embedded foreign exposures. This paper introduces the Supply Chain Exposure Network Tracking and Risk Assessment (SENTRA) framework, a modular graph-based computational framework for constructing, analyzing, and forecasting dynamic supply chain networks. As the first paper in a three-part methodological series, it establishes the computational foundation of SENTRA by constructing a temporal attributed multi-relational graph whose nodes represent product–country pairs and whose edges encode observed trade and within-country value-chain relationships. Statistical estimation and constrained optimization recover latent production, final demand, and product input dependency coefficients while enforcing economic accounting constraints. Graph-derived exposure measures quantify direct, transshipment, value-chain, and multi-hop supply chain dependencies independently of the forecasting model. A temporal graph forecasting architecture based on a relational graph neural network then forecasts the evolution of the graph under mass-balance constraints with distribution-free conformal uncertainty quantification. Validation on the global aluminum supply chain shows that the learned graph representations recover economically meaningful supply chain structure, accurately forecast out-of-sample trade relationships, and produce well-calibrated prediction intervals. Subsequent papers apply this computational foundation to exposure assessment, disruption analysis, and scenario-based policy analysis, and extend the framework to multimaterial supply chain modeling and decision support.

36 MATERIALS SCIENCE↗
DOE OSTI2024

Chemical recycling of mixed textile waste

Globally, less than 0.5% of postconsumer textile waste is recycled, with the majority incinerated or ending up in landfills. Most postconsumer textiles are mixed fibers, complicating mechanical recycling due to material blends and contaminants. Here, we demonstrate the chemical conversion of postconsumer mixed textile waste using microwave-assisted glycolysis over a ZnO catalyst followed by solvent dissolution. This approach electrifies the process heat while allowing rapid depolymerization of polyester and spandex to their monomers in 15 minutes. A simple solvent dissolution enables the separation of cotton and nylon. We assess the quality of all components through extensive material characterization, discuss their potential for sustainable recycling, and provide a techno-economic analysis of the economic feasibility of the process.

Science & Technology - Other Topics↗
DOE OSTI2026

Enhanced Preparation for Intelligent Cybermanufacturing Systems (EPICS)

Opportunities exist for realizing transformative advances in productivity and reductions in energy footprint through ubiquitous sensing in manufacturing environments. Enhanced Preparation for Intelligent Cybermanufacturing Systems (EPICS) is a 21-month (4 academic semesters, plus one summer) experience for graduate students that focuses on scaling the knowledge, understanding and leadership skills in the cyber manufacturing area. Masters students (8/year, 32 total) complete 2-year projects on industrially-driven project topics, rotating to internships in summer semester to work on scoping and implementation at project partners. Students complete academic training in embedded systems, process modeling, data science, and cloud-based systems design. Their projects are targeted toward sensor retrofit, process monitoring, root cause analysis, and sensor fusion.

Advanced Manufacturing↗
DOE OSTI2026

Structured illumination for surface-resolved grazing-incidence X-ray scattering

Grazing-incidence (GI) scattering techniques are widely used to characterize thin films, offering high surface sensitivity and insight into morphology and structure. However, these approaches typically provide statistical averaged information due to elongated footprint or limited spatial resolution due to beam size. Here we introduce a method that combines structured illumination with GI X-ray scattering and leverages our computational imaging approach to resolve local structural details. We demonstrate that our method captures local features of an organic semiconductor thin film without the need for sample rotation as in tomography. The method expands GI techniques from statistical averaging to high-resolution imaging, thereby providing the capability for detailed analysis of local material properties, such as domain shape, orientation and polymorphism, which are critical for advancing material design towards more efficient and tailored materials.

Gursoy, Doga↗
DOE OSTI2026

Identifying Suitable Front Contacts for High‐Efficiency Cd(Se,Te) Solar Cells on Space‐Qualified Cover Glass

Deployment of photovoltaics in space requires devices that combine high-efficiency, low areal mass, and resilience to harsh environments. Historically, high-efficiency multijunction III–V materials have dominated space power systems; however, their high cost and limited manufacturing throughput motivate the exploration of scalable alternatives. While CdTe-based thin-film photovoltaics offer an attractive option, their performance on non-conventional substrates can suffer from front contact instability under higher-temperature processing. Here, the role of front contact chemistry in limiting cell performance is investigated using CdTe-based devices fabricated on 150 μm thick Ceria-doped space-qualified 0214 Corning glass. A matrix of four transparent conducting oxides (TCOs: CTO, AZO, ITO, IZO) combined with two n-type emitters (MZO, IGO) reveals chemical stability at the front interface—rather than absorber composition alone—governs recombination losses, voltage deficits, and device reproducibility. Chemically stable front contact combinations suppress elemental diffusion and interfacial degradation, resulting in significantly improved carrier lifetimes and junction quality. These insights are validated through record-certified Cd(Se,Te) cell efficiencies of 18.4% under AM1.5G and 16.2% under AM0 illumination on ultra-thin glass. Beyond CdTe, this work provides a general framework for the rational selection of TCO/emitter interfaces in superstrate thin-film photovoltaics, including emerging technologies like metal halide perovskites, while enabling high-efficiency, lightweight photovoltaics for space applications.

14 SOLAR ENERGY↗
DOE OSTI2025

Probing the role of local tunnel variations in early-stage lithiation of α-MnO₂ nanowires via in situ TEM

Understanding lithium-ion transport in tunnel-structured manganese oxides is essential for designing high-performance lithium-ion battery electrode materials. Here, we elucidate the early-stage lithiation mechanism of potassium-stabilized α-MnO 2 nanowires using in situ transmission electron microscopy (TEM) coupled with electron energy-loss spectroscopy (EELS), high-resolution TEM (HRTEM), and geometric phase analysis (GPA). Real-time TEM imaging reveals clear volume expansion at the reaction front, while EELS analysis uncovers lithium-ion diffusion far beyond this region, where no visible expansion is observed, indicating fast, defect-assisted transport. GPA and HRTEM analyses show that localized tensile and compressive strain fields, originating from pre-existing local tunnel structural variations, persist after lithiation. The tensile-strained regions enable lithium-ion insertion with minimal lattice distortion, offering additional free volume that facilitates rapid lithium-ion accommodation ahead of the structural transformation. Our results demonstrate a local tunnel variation-mediated fast diffusion pathway that precedes bulk reaction, underscoring the critical role of local strain in enabling early-stage lithium transport. Given the structural versatility of MnO 2 and its ability to accommodate diverse atomic arrangements beyond the well-known tunnel phases (β-, γ-, δ-, λ-, R-phases), our findings highlight the importance of understanding and engineering local structural environments. This work provides fundamental insights into the interplay between defects, strain, and ion dynamics, and presents defect engineering as a promising approach to enhance both rate performance and structural stability in manganese-based cathodes.

75 CONDENSED MATTER PHYSICS, SUPERCONDUCTIVITY AND↗
DOE OSTI2025

Machine Learning for Predicting Multipactor Susceptibility in Planar RF Structures

Multipactor discharge is a persistent challenge in high-power microwave (HPM) and accelerator systems, where secondary electron avalanches can cause heating, vacuum degradation, and failure. This work presents the first supervised machine learning (ML) framework for multipactor prediction, trained on high-fidelity 3D Particle-in-Cell (PIC) simulation data in planar geometries. The model maps operational, geometric, and material-dependent secondary electron yield (SEY) parameters to the time-averaged electron growth rate, enabling rapid reconstruction of susceptibility charts. Among the models evaluated, tree-based ensemble methods such as Random Forest and Extra Trees demonstrate superior generalization to unseen materials compared to neural networks such as multilayer perceptron (MLP). Performance metrics, including Intersection over Union (IoU), Structural Similarity Index Measure (SSIM), and Pearson correlation, show close agreement with simulation benchmarks. Principal Component Analysis attributes generalization limits to material feature-space disjointedness.

43 PARTICLE ACCELERATORS↗
DOE OSTI2025

Topological prethermal strong zero modes on superconducting processors

Abstract Symmetry-protected topological phases 1–4 cannot be described by any local order parameter and are beyond the conventional symmetry-breaking model 5 . They are characterized by topological boundary modes that remain stable under symmetry respecting perturbations 1–4,6–8 . In clean, gapped systems without disorder, the stability of these edge modes is restricted to the zero-temperature manifold; at finite temperatures, interactions with mobile thermal excitations lead to their decay 9–11 . Here we report the observation of a distinct type of topological edge mode 12–14 , which is protected by emergent symmetries and persists across the entire spectrum, in an array of 100 programmable superconducting qubits. Through digital quantum simulation of a one-dimensional disorder-free stabilizer Hamiltonian, we observe robust long-lived topological edge modes over up to 30 cycles for a wide range of initial states. We show that the interaction between these edge modes and bulk excitations can be suppressed by dimerizing the stabilizer strength, leading to an emergent U(1) × U(1) symmetry in the prethermal regime of the system. Furthermore, we exploit these topological edge modes as logical qubits and prepare a logical Bell state, which exhibits persistent coherence, despite the system being disorder-free and at finite temperature. Our results establish a viable digital simulation approach 15–18 to experimentally study topological matter at finite temperature and demonstrate a potential route to construct long-lived, robust boundary qubits in disorder-free systems.

Science & Technology - Other Topics↗
DOE OSTI2026

Solar Heat for Industrial Processes: Integration with Chemical Reactors

The integration of solar thermal systems with chemical reactors has been proposed as part of a larger effort to develop and deploy solar heat for industrial processes (SHIP) technologies. A strong motivation for SHIP processes and technologies is the potential for high thermal efficiency coupled with low-cost thermal energy storage (TES) which can enable commercial deployment of such systems. While there are different ways to categorize SHIP technologies, one important such distinction is between directly irradiated systems and indirect off-sun process driven by a SHIP system. While directly irradiated systems can provide high thermal efficiencies and high fluxes, they usually require complex engineering solutions due to the need for redesigning the established processes and unit operations. In most cases, it is also more challenging to couple such a process to a TES system, losing some of the benefits of SHIP. On the other hand, using a SHIP system to drive an industrial process off-sun can allow better integration with existing process chains, easier TES capabilities, and potential for more applications fitting a specific SHIP technology. However, the integration of SHIP systems with the industrial processes is not fully explored in detail, especially in the case of high-temperature processes such as reforming, cracking, cement manufacturing, and iron/steelmaking. Many of these systems require heating fluxes of >50 kW/m^2, supplied via combustion of hydrocarbons in a fire box and benefitting from radiative heat transfer between the flue gases and the reaction zones. As such, using SHIP systems for such processes is more complex than providing the same thermal input in the form of a heat transfer medium (HTM) entering the reactor, kiln, or furnace. Moreover, in case convective heat transfer using SHIP is envisioned, for example using supercritical CO2 as the HTM from a particle receiver, the thermal integration might be more challenging than initially envisioned: lower heat transfer coefficients and limited approach temperature might require large flow rates, causing a mismatch between the process thermal requirements and the thermal capacity of the SHIP system. In addition, even if the heat exchange between SHIP and reactor is effective, there is still a cold leg HTM at the reaction temperature or slightly below it. Chemical plants usually include a set of heat exchangers, heat recovery steam generators, and even power generation units - in a tightly integrated design - to recover the flue gases which are eventually vented. With SHIP systems mostly operating on a closed HTM loop, bottoming the cold leg is crucial. In this talk, we will present different modeling results for a variety of syngas production reactions, using catalytic and chemical looping processes, and discuss some of the challenges and design considerations for off-sun chemical reactors using SHIP systems.

14 SOLAR ENERGY↗
DOE OSTI2024

Temperature-dependent spin dynamics in Cr 2 O 3

Cr 2 ⁢O 3 emerges as a prominent candidate material for spintronics and magnetoelectronic applications. However, a comprehensive understanding of its temperature-dependent spin dynamics remains elusive, impeding the engineering of novel spintronics based on this material. Here, we delve into this through a combination of inelastic neutron scattering experiments and atomistic simulations. Our results unveiled the emergence of paramagnons above and below 𝑇 𝑁 . We demonstrated a significant softening of linear magnons upon heating in the antiferromagnetic state. Further analysis revealed that this softening primarily originated from four-magnon interactions, while thermal expansion played a minor role.

antiferromagnetism↗