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Search indexed NASA NTRS and DOE OSTI research on propulsion, heat transfer, battery materials and energy systems. Follow report and document links to the original sources.

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At least 235 records · Page 13
All sourcesNASA NTRSDOE OSTI
DOE OSTI2025

3D printing of architected sulfur cathodes with dual-site atomic catalysts for accelerated polysulfide kinetics and Li-ion transport in high areal-loading lithium–sulfur batteries

The practical deployment of lithium–sulfur batteries (LSBs) is hindered by fundamental limitations in conventional slurry-cast cathodes, including poor sulfur utilization, sluggish ion transport, and low areal capacity, particularly in thick electrodes required for high energy density. To address these challenges, we present direct ink writing (DIW) as an additive manufacturing strategy to fabricate advanced current-collector-free, 3D-printed sulfur cathodes (3DP S@CoNi-DSACs/NC) with hierarchically porous architectures that enhance lithium-ion diffusion, promote electrolyte penetration, and reduce interfacial resistance. The synergistic effects of Co/Ni dual-atom sites accelerate redox kinetics and mitigate polysulfide shuttling. As a result, the optimized 3DP cathode with a sulfur loading of 5.4 mg cm −2 demonstrated excellent rate capability, delivering a high reversible capacity of 1041.4 mAh g −1 at 1C with 85.5% capacity retention after 1000 cycles, significantly outperforming its cast counterpart. Remarkably, even at a higher sulfur loading of 8.1 mg cm −2 , the 3DP cathode maintains outstanding performance, achieving a discharge capacity of 1538.4 mAh g −1 and an areal capacity of 12.5 mAh cm −2 at 0.1C. This study not only demonstrates the functional integration of catalytically active materials into 3D printable sulfur cathode architectures but also offers a scalable and transformative platform for building high-performance LSBs beyond conventional electrode manufacturing methods.

3D electrode↗
DOE OSTI2026

Phase-field model of freeze casting

Directional solidification of water-based solutions has emerged as a versatile technique for templating hierarchical porous materials. However, the underlying mechanisms of pattern formation remain incompletely understood. In this work, we present a detailed derivation and analysis of a quantitative phase-field model for simulating this nonequilibrium process. The phase-field model extends the thin-interface formulation of dilute binary alloy solidification with antitrapping to incorporate the highly anisotropic energetic and kinetic properties of the partially faceted ice-water interface. This interface is faceted in the basal plane normal to the ⟨0001⟩ directions and atomically rough in other directions within the basal plane. On the basal plane, the model reproduces a linear or nonlinear relationship between the interface growth rate and the kinetic undercooling that can be linked to experimental measurements. In both cases, spontaneous parity breaking of the solidification front is observed when the preferred growth direction is aligned with the temperature gradient. This phenomenon leads to the formation of partially faceted ice lamellae that drift laterally in one of the ⟨0001⟩ directions. Here, we demonstrate that the drifting velocity of the ice lamellae is controlled by the kinetics on the basal plane and converges as the thickness of the diffuse solid-liquid interface decreases. Furthermore, we examine the effect of the form of the kinetic anisotropy, which is chosen here such that the inverse of the kinetic coefficient varies linearly from a finite value in the ⟨0001⟩ directions to zero in all other directions within the basal plane, consistent with the assumption that the interface grows in local thermodynamic equilibrium in this plane. Our results indicate that the drifting velocity of ice lamellae is not affected by the slope of this linear relation, and the radius and undercooling at the tip of an ice lamella converge at relatively small slope values. Consequently, the phase-field simulations remain quantitative with computationally tractable choices of both the interface thickness and the slope assumed in the form of the kinetic anisotropy.

Materials science↗
DOE OSTI2025

Deep Learning-based Surrogate Model for Efficient Reservoir Simulation in Large-scale Geological Carbon Storage: Application in IBDP Dataset

This project introduces an advanced deep learning (DL)-based surrogate modeling approach to enhance the efficiency and accuracy of large-scale geological carbon storage (GCS) simulations. Using the Illinois Basin Decatur Project (IBDP) dataset as training data, the study employs a residual U-Net architecture to predict critical state variables such as pressure and CO₂ saturation, as well as CO₂ plume migration. By incorporating key geological parameters (e.g., porosity, permeability, and rock facies) and physics-informed inputs like the diffusive time of flight and time step, the DL model effectively reduces computational complexity while maintaining robust physical constraints. Compared to traditional simulators like Eclipse, the DL model achieves remarkable accuracy, with a root mean square error (RMSE) of 1.57 psi for pressure and 0.007 for saturation, and dramatically reduces computational time from hours to just 69.9 seconds for 50-step simulations. These results demonstrate the potential of innovative DL methodologies to improve the predictivity and operational efficiency of GCS simulations, providing a reliable foundation for decision-making in CCS operations. Supported by the SMART initiative, this project underscores the success of leveraging computational innovations to advance CCS technologies.

advanced deep learning↗
DOE OSTI2025

Diffusion Behavior of Oversized Fission Products in bcc Fe Cladding: A First-Principles Study

Fuel-Cladding Chemical Interaction (FCCI) poses significant challenges in nuclear reactors, where fission products from nuclear fuel interact with Fe-based cladding materials, potentially compromising their structural integrity. This study investigates the diffusion behavior of oversized fission products, Pr, Nd, Ce, and La, within bcc Fe cladding using density functional theory (DFT), nudged elastic band (NEB) method, and self-consistent mean field (SCMF) theory. Our results reveal significant long-range vacancy binding energies, particularly up to the 6th nearest neighbor, with La exhibiting the strongest binding affinity, followed by Nd, Ce, and Pr. The NEB calculations indicate significant high barriers for the dissociation of 1nn vacancy-solute pairs for all fission products. The tracer diffusion coefficients of these fission products was derived in Arrhenius form. The significant trapping effect of vacancies by a very dilute amount of fission products reduces vacancy mobility, leading to an oversaturation of point defects, void nucleation, and swelling. These are critical issues for irradiated cladding materials. The tracer diffusion coefficients indicate that Nd diffuses the fastest, followed by La, Ce, and Pr. This study provides essential insights for developing advanced cladding materials and design strategies to mitigate FCCI, ultimately enhancing nuclear reactor safety and performance.

11 NUCLEAR FUEL CYCLE AND FUEL MATERIALS↗
DOE OSTI2024

In Vitro Antimicrobial Activity of Volatile Compounds from the Lichen Pseudevernia furfuracea (L.) Zopf. Against Multidrug-Resistant Bacteria and Fish Pathogens

Lichens are symbiotic organisms with unique secondary metabolism. Various metabolites from lichens have shown antimicrobial activity. Nevertheless, very few studies have investigated the antimicrobial potential of the volatile compounds they produce. This study investigates the chemical composition and antimicrobial properties of volatile compounds from Pseudevernia furfuracea collected in two regions of Morocco. Hydrodistillation was used to obtain volatile compounds from samples collected in the High Atlas and Middle Atlas. Gas chromatography–mass spectrometry (GC-MS) analysis identified phenolic cyclic compounds as the primary constituents, with atraric acid and chloroatranol being the most abundant. Additionally, eight compounds were detected in lichens for the first time. The antimicrobial activity of these compounds was assessed using disc diffusion and broth microdilution methods. Both samples demonstrated significant antimicrobial effects against multidrug-resistant human bacteria, reference microorganisms, fish pathogens, and Candida albicans, with minimum inhibitory concentrations (MICs) ranging from 1000 µg/mL to 31.25 µg/mL. This study provides the first report on the volatile compounds from Pseudevernia furfuracea and their antimicrobial effects, particularly against fish pathogens, suggesting their potential as novel antimicrobial agents for human and veterinary use. Further research is warranted to explore these findings in more detail.

Essadki, Yasser (ORCID:0009000648460075)↗
DOE OSTI2026

Elucidating Lithium Transport Mechanisms in Disordered LiF from Machine-Learning Molecular Dynamics Simulations

Lithium fluoride (LiF) is a ubiquitous component of solid- and cathode–electrolyte interphases, yet its functional role remains unclear under the structural and chemical heterogeneity typical of cycling batteries. Here, we systematically quantify how structural disorder, off-stoichiometry, and strain govern Li-ion transport in LiF. Using a machine-learning potential to enable extensive molecular-dynamics sampling, we compare crystalline and amorphous LiF, Li 0.95 F, and LiF 0.95 , and evaluate the impact of small homogeneous deformations. Defect-free crystalline LiF is effectively ion-blocking at 300−500 K, whereas amorphization generates free-volume–assisted percolation pathways that facilitates Li-ion diffusion. At elevated temperatures, thermodynamically driven crystallization disrupts these pathways, leading to non-Arrhenius behavior. In crystalline phases, Li deficiency activates vacancy-mediated diffusion, while in amorphous LiF, transport is governed primarily by network connectivity. Strain is found to have only a marginal effect on Li mobility in both crystalline and amorphous structures.

Batteries↗
DOE OSTI2025

Multireference diffusion Monte Carlo reaches 2D materials

Abstract Quantum confinement in 2D materials strongly enhances electronic correlation effects. Therefore, predicting the properties of these unique materials, with both a high level of accuracy and computational efficiency, without relying on adjustable parameters or functionals, remains an outstanding theoretical challenge. The majority of theoretical studies are based on the approximations of density functional theory (DFT). The reliability of DFT predictions are heavily dependent on the choice of an approximated exchange-correlation functional. Here, we estimate the magnitude of impact of correlation on the total energy for the quintessential 2D material, graphene, by performing and comparing state-of-the-art selected CI and quantum Monte Carlo extrapolated calculations for a single unit cell at the$$\Gamma$$point. We demonstrate that Self-Healing Diffusion Monte Carlo (SHDMC) obtains a very compact, but high-quality wavefunction for this system that lacks the strong basis set dependence displayed by state of the art quantum chemistry methods. The SHDMC wavefunction is of higher quality compared to that obtained from sCI, in the same orbital basis, while being$$\sim$$ 1000 times smaller in terms of determinant count compared to sCI. We also demonstrate that extrapolating SHDMC results to the infinite determinant limit compares extremely well with complete basis set extrapolated sCI. Our work paves the way for future validation of SHDMC applied to challenging 2D materials.

Science & Technology - Other Topics↗
DOE OSTI2025

Mixing-Controlled Combustion of Ethanol Enabled by Prechamber Ignition (PC-MCC): A Preliminary Experimental Demonstration

This experimental study presents preliminary investigations of prechamber-enabled mixing-controlled combustion (PC-MCC) at −2 bar brake mean effective pressure (BMEP) and 2200 rpm with fuel-grade ethanol (E98). Experimental results are conducted on a prechamber retrofitted single-cylinder Caterpillar C9.3B test engine. First, a series of prechamber-only experiments were conducted with a motored engine to evaluate the salient combustion trends in response to relevant prechamber operating parameters. Under firing conditions, the prechamber operating strategy was assessed with respect to the impact on ignition assistance of direct-injected E98 and overall engine performance. The preliminary results indicate the jet-induced ignition process is robust and prompts diffusion combustion of E98 at diesel-like boundary conditions. Here, the effect of external exhaust gas recirculation (EGR) on the residual tolerance of the prechamber combustion process was also investigated and showed stable combustion in both the main chamber and prechamber up to 30% EGR. Experiments were also conducted with the stock diesel engine for baseline comparison. At matched combustion phasing, mixing-controlled combustion of ethanol enabled by prechamber ignition was able to achieve heightened gross thermal efficiency while simultaneously reducing NOx and practically eliminating smoke emissions relative to diesel combustion. In addition, the covariance of load and standard deviation of combustion phasing was diesel-like and less than 2% and 1 CAD, respectively.

PC-MCC↗
DOE OSTI2024

Intrinsic rotation modulation by diffusive neutral particles in tokamaks

Abstract The modulated transport model, a model kinetic ion transport equation for the pedestal and scrape-off layer (SOL), is generalized to self-consistently include effects of a single neutral particle species. The neutrals contribute additional transport terms, modifying the v ∥ -dependent orbit-averaged ion diffusivities of the original work and the resulting predicted intrinsic rotation of the ions. After making simplifying assumptions of the neutral transport, in particular taking the continuous transport limit via a short charge-exchange step expansion, we derive relatively simple analytic expressions that capture the diffusive neutral physics. Within the scope of the model’s validity, the neutral-driven intrinsic rotation can compete with the turbulence-driven intrinsic rotation. However, for physically motivated parameters, the neutral-driven intrinsic rotation appears negligible, either on a term-by-term basis or due to a strong cancellation between the neutral-driven momentum diffusion and pinch terms. It appears that a treatment containing finite charge-exchange steps is necessary to capture neutral transport of strong flow momentum into the confined region from the SOL.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗
DOE OSTI2026

Thermal cycling-driven microstructural changes of eutectic Al–Si phase change materials in SS304 containers revealed by multi-modal imaging

Aluminum-based Al–Si alloys are widely used as phase change materials (PCMs) in thermal energy storage (TES) systems owing to their high volumetric latent heat and superior thermal conductivity. However, their long-term reliability is limited by degradation processes that remain insufficiently understood. In this work, we employ a multimodal, correlative characterization framework to systematically resolve the degradation behavior of eutectic Al–Si PCMs in contact with SS304 containers under repeated thermal cycling. By integrating high-resolution electron microscopy, three-dimensional X-ray fluorescence (3D XRF) imaging, and differential scanning calorimetry (DSC), we directly link spatially resolved compositional and microstructural evolution to changes in thermophysical properties. The correlative analysis reveals that elemental leaching of Fe, Cr, and Ni from the stainless-steel container into the PCM drives the formation of intermetallic compounds (IMCs) both at the interface and within the bulk PCM, leading to pronounced compositional heterogeneity. These interfacial reactions and diffusion-induced transformations progressively destabilize the Al–Si eutectic, reducing the effective phase-transforming fraction. Consistent with these observations, DSC measurements show a decrease in melting temperature and latent heat of fusion with thermal cycling. These results underscore the critical influence of interfacial reactions and material compatibility on the stability, durability, and overall performance of Al–Si-based TES systems.

25 ENERGY STORAGE↗
DOE OSTI2024

Computational discovery of fast interstitial oxygen conductors

New highly oxygen-active materials may enhance many energy-related technologies by enabling efficient oxygen-ion transport at lower temperatures, for example, below ~400 °C. Interstitial oxygen conductors have the potential to realize such performance but have received far less attention than vacancy-mediated conductors. Here, in this study, we combine physically motivated structure and property descriptors, ab initio simulations and experiments to demonstrate an approach to discover new fast interstitial oxygen conductors. Multiple new families were found, which adopt completely different structures from known oxygen conductors. From these families, we synthesized and studied oxygen kinetics in La 4 Mn 5 Si 4 O 22+δ , a representative member of the perrierite/chevkinite family. We found that La 4 Mn 5 Si 4 O 22+δ has higher oxygen-ion conductivity than the widely used yttria-stabilized ZrO 2 , and among the highest surface oxygen exchange rates at the intermediate temperature of known materials. The fast oxygen kinetics is the result of simultaneously active interstitial and interstitialcy diffusion pathways. We propose that the essential features for forming an effective interstitial oxygen conductor are the availability of electrons and structural flexibility, enabling a sufficient accessible volume. This work provides a powerful approach for understanding and discovering new interstitial oxygen conductors.

36 MATERIALS SCIENCE↗
DOE OSTI2024

Oxygen vacancies in niobium pentoxide as a source of two-level system losses in superconducting niobium

We identify a major source of quantum decoherence in three-dimensional superconducting radio-frequency (SRF) resonators and two-dimensional transmon qubits composed of oxidized niobium: oxygen vacancies in the niobium pentoxide, which drive two-level system (TLS) losses. By probing the effect of sequential in situ vacuum-baking treatments on the rf performance of bulk Nb SRF resonators and on the oxide structure of a representative Nb sample using TOF SIMS, we find a nonmonotonic evolution of cavity quality factor Q 0 , which correlates with the interplay of Nb 2 O 5 vacancy generation and oxide-thickness reduction. We localize this effect to the oxide itself and present the insignificant role of diffused interstitial oxygen in the underlying Nb by regrowing the oxide via wet oxidation, which reveals a mitigation of aggravated TLS losses. We hypothesize that such vacancies in the pentoxide serve as magnetic impurities and are a source of TLS-driven rf loss.

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗
DOE OSTI2025

Oxygen vacancies in niobium pentoxide as a source of two-level system losses in superconducting niobium

Two-level systems (TLS) have long been a catch-all explanation for RF loss and quantum decoherence in superconducting devices. In our study, the first to directly link TLS losses to a specific physical mechanism, we demonstrate that oxygen vacancies in the naturally formed Nb₂O₅ on oxidized niobium are a major driver of such dissipation. We performed sequential in situ vacuum-baking treatments on niobium superconducting radio-frequency (SRF) cavities and used time-of-flight secondary ion mass spectrometry (TOF-SIMS) to reveal a nonmonotonic evolution in cavity quality factor (Q₀). This behavior correlates with the interplay of Nb₂O₅ vacancy generation and oxide-thickness dissolution. We localize this effect to the oxide itself and present the insignificant role of diffused interstitial oxygen in the underlying Nb by regrowing the oxide via wet oxidation, revealing a mitigation of aggravated TLS losses. We hypothesize that such vacancies in the pentoxide serve as magnetic impurities and are a source of TLS-driven rf loss. Although our measurements center on 3-D SRF cavities, the insights gained here have significant implications for mitigating decoherence in 2-D superconducting qubits.

Bafia, Daniel P. [Fermilab]↗
DOE OSTI2025

Oxygen Vacancies in Niobium Pentoxide as a Source of Two-Level System Losses in Superconducting Niobium

We identify a major source of quantum decoherence in three-dimensional superconducting radio-frequency (SRF) resonators and two-dimensional transmon qubits composed of oxidized niobium: oxygen vacancies in the niobium pentoxide, which drive two-level system (TLS) losses. By probing the effect of sequential in situ vacuum-baking treatments on the rf performance of bulk Nb SRF resonators and on the oxide structure of a representative Nb sample using TOF SIMS, we find a nonmonotonic evolution of cavity quality factor Q0, which correlates with the interplay of Nb2O5 vacancy generation and oxide-thickness reduction. We localize this effect to the oxide itself and present the insignificant role of diffused interstitial oxygen in the underlying Nb by regrowing the oxide via wet oxidation, which reveals a mitigation of aggravated TLS losses. We hypothesize that such vacancies in the pentoxide serve as magnetic impurities and are a source of TLS-driven rf loss.

Bafia, Daniel P. [Fermilab]↗
DOE OSTI2026

Improving unfolding and systematic uncertainty estimation using generative diffusion networks (Final Technical Report)

This final technical report summarizes the key accomplishments on the unfolding using diffusion model project, a DOE award received by PI Pierre-Hugues Beauchemin at Tufts University. This project main goal was to investigate the potential of diffusion models for unfolding experimental High Energy Physics data from detector effects while controlling systematics uncertainties. The project accomplished its goals by completing the following objectives: 1) Performing an object-by-object, event-by-event unfolding of various kinematic distributions reconstructed from detector data in HEP in a way that keeps correlations between unfolded observables while demonstrating competitive performance compared to standard algorithms used in the field; 2) Address the generalization problem by developing an unfolding algorithm capable to correctly infer the underlying distributions of observables and processes never seen before, while controlling the dominant theoretical uncertainties affecting the process, therefore increasing the effectiveness, the precision, and the applicability of the developed algorithm; 3) Understand the theoretical foundations between the developed algorithm so to extend it to applications beyond experimental HEP, for broader benefits to the society. This report provides an overview of the accomplishments related to each of these key objectives.

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗
DOE OSTI2025

Geospatial Diffusion for Land Cover Imperviousness Change Forecasting

Land-use and land-cover (LULC) has a significant effect on several Earth system processes. For example, impervious surfaces reduce infiltration and speed water flow, impacting regional hydrology and flood risk. While Earth System models have improved forecasting hydrologic and atmospheric processes at higher resolutions, the ability to forecast LULC change has lagged behind. In this paper, we propose a new paradigm exploiting Generative AI (GenAI) for land cover change forecasting by framing it as a data synthesis problem conditioned on historical and auxiliary data-sources. To demonstrate the feasibility of our methodology, we perform experiments where a diffusion model is trained for decadal forecasting of imperviousness change across the entire United States. We find that our model yields MAE lower than a no-change baseline for resolutions ≥ 0.7 X 0.7km2 on average, demonstrating its ability to capture and project accurate spatiotemporal patterns. Finally, we discuss future research to incorporate Earth's physical properties and enabling scenario simulations via driver variables.

Varshney, Debvrat [ORNL] (ORCID:0000000188981736)↗
DOE OSTI2024

Anionic Effects on Concentrated Aqueous Lithium Ion Dynamics

In this article, the structural dynamics, chemical reactivity, anisotropy, diffusivity, viscosity, and density were measured for concentrated lithium salt solutions, including lithium chloride (LiCl), lithium bromide (LiBr), lithium nitrite (LiNO 2 ), and lithium nitrate (LiNO 3 ), with methyl thiocyanate as an infrared vibrational probe molecule, using two-dimensional infrared spectroscopy (2D IR), nuclear magnetic resonance (NMR) spectroscopy, and viscometry. 2D IR, NMR, and viscosity results show that LiNO 2 exhibits longer correlation times, lower diffusivity, and nearly four times greater viscosity when compared to the other lithium salt solutions of the same concentration, suggesting that nitrite anions may strongly facilitate structure formation via strengthening water-ion interactions, directly impacting bulk solution properties at sufficiently high concentrations. Additionally, the LiNO 2 and LiNO 3 solutions show significantly reduced chemical reactivity with respect to lithium cations coordinating with the methyl thiocyanate when compared to the lithium halide salts.

2D IR↗
DOE OSTI2024

Moving beyond the Aerosol Climatology of WRF-Solar: A Case Study over the North China Plain

Numerical weather prediction (NWP), when accessible, is a crucial input to short-term solar power forecasting. WRF-Solar, the first NWP model specifically designed for solar energy applications, has shown promising predictive capability. Nevertheless, few attempts have been made to investigate its performance under high aerosol loading, which attenuates incoming radiation significantly. The North China Plain is a polluted region due to industrialization, which constitutes a proper testbed for such investigation. Here, in this paper, aerosol direct radiative effect (DRE) on three surface shortwave radiation components (i.e., global, beam, and diffuse) during five heavy pollution episodes is studied within the WRF-Solar framework. Results show that WRF-Solar overestimates instantaneous beam radiation up to 795.3 W m -2 when the aerosol DRE is not considered. Although such overestimation can be partially offset by an underestimation of the diffuse radiation of about 194.5 W m -2 , the overestimation of the global radiation still reaches 160.2 W m -2 . This undesirable bias can be reduced when WRF-Solar is powered by Copernicus Atmosphere Monitoring Service (CAMS) aerosol forecasts, which then translates to accuracy improvements in photovoltaic (PV) power forecasts. This work also compares the forecast performance of the CAMS-powered WRF-Solar with that of the European Centre for Medium-Range Weather Forecasts model. Under high aerosol loading conditions, the irradiance forecast accuracy generated by WRF-Solar increased by 53.2% and the PV power forecast accuracy increased by 6.8%.

54 ENVIRONMENTAL SCIENCES↗