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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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186 records · Page 4
All sourcesNASA NTRSDOE OSTI
DOE OSTI2026

Catalytic disproportionation on carbon superstructures enables long-life, high-loading Li–S batteries

Electrocatalysis has been widely explored as an effective strategy to accelerate polysulfide (PS) conversion and suppress the shuttle effect in lithium–sulfur (Li–S) batteries. However, the underlying mechanisms remain elusive, and electrocatalytic reactions are inactive during cell resting. In this work, we reveal and quantitatively analyze a previously unrecognized sulfur reduction route (SRR) driven by catalytic disproportionation at the carbon cathode surface—fundamentally distinct from conventional electrocatalysis. Unlike conventional stepwise pathways, this SRR enables high-order polysulfides (Sₓ²⁻, x = 5–8) to directly convert into S₈ and Li₂S₂, bypassing low-order intermediates. This sulfur-reduction shortcut is systematically elucidated through high-performance liquid chromatography, revealing the intrinsic catalytic contribution of carbon frameworks and the dynamic evolution of PS species. We demonstrate that carbon superstructures (CSS-0.5), assembled from nanosheet subunits with abundant N/O functionalities and interconnected charge-migration channels, synergistically promote this catalytic process. Benefiting from these features, CSS-0.5 delivers superior electrochemical performance under practical conditions, enabling high sulfur loading (6.0 mg cm⁻²) pouch cells with 80.5% capacity retention over 210 cycles. This study provides the first quantitative evidence of electrocatalytic disproportionation in Li–S batteries, offering mechanistic insights and design principles for advanced sulfur cathodes.

25 ENERGY STORAGE↗
DOE OSTI2026

Conditional diffusion machine-learning framework for mapping valence electron distribution from convergent beam electron diffraction

Quantitative convergent beam electron diffraction (CBED) enables determination of aspherical valence electron distributions through refinement of low-order structure factors, which are highly sensitive to chemical bonding and charge density variations. However, conventional quantitative CBED (QCBED) requires solving a highly nonlinear inverse problem with many coupled parameters, and computationally intensive dynamical diffraction calculations, making it time-consuming and difficult to apply to complex systems. More broadly, reconstructing charge density and orbital electron distribution from diffraction data has long been a central challenge in both x-ray and electron crystallography. Here, in this study, we introduce an artificial-intelligence (AI)-based framework that replaces traditional refinement with a data-driven inverse solver. Using a large synthetic CBED dataset generated by Bloch-wave simulations, we train a conditional diffusion model to directly infer crystal structural parameters and multipole density formalism parameters, and hence valence electron distributions, from CBED patterns alone. By learning from forward simulations across realistic parameter space, the model effectively solves the inverse problem. Compared with direct regression approaches, the diffusion-based framework provides posterior parameter distributions for rigorous uncertainty quantification while preserving quantitative fidelity and reducing analysis time by orders of magnitude. By eliminating the need for external single-crystal x-ray diffraction data and complex nonlinear refinement, this approach enables practical, high-throughput, and in situ quantitative CBED, enabling real-time mapping of valence electron distributions and their correlation with functional responses in quantum and energy materials.

75 CONDENSED MATTER PHYSICS, SUPERCONDUCTIVITY AND↗
DOE OSTI2024

Selective solid-state isolation of NMR circuit elements using back-to-back field effect transistors

Nuclear Magnetic Resonance (NMR) electronics that employ selective solid-state isolation of circuit elements can include solid-state switches, such as back-to-back Field Effect Transistor (FET) pairs, and isolated gate drive electronics adapted to operate the solid-state switches in order to selectively decouple induction coils from receive electronics. The solid-state switches can be placed in series to achieve higher standoff voltages, and can be configured for low on resistance and short switching times. The gate drive electronics can include electrical isolation components adapted to enhance standoff voltages and reduce electrical noise at the selectively isolated receive electronics.

Walsh, David O.↗
DOE OSTI2026

Theory of stimulated and spontaneous axion scattering.

We present a theory for nonlinear, resonant excitation of dynamical axions by counterpropagating electromagnetic waves in materials that break both 𝒫 and 𝒯 symmetries. We show that dynamical axions can mediate an exponential growth in the amplitude of the lower frequency (Stokes) beam. We also discuss spontaneous generation of a counterpropagating Stokes mode, enabled by resonant amplification of quantum and thermal fluctuations in the presence of a single pump laser. Remarkably, the amplification can be orders of magnitude larger than that obtained via stimulated Brillouin and Raman scattering processes, and can be modulated with the application of external magnetic fields, making stimulated axion scattering promising for optoelectronics applications.

Smith, M.↗
DOE OSTI2026

Metal–bicarbonate ion pairing in alkaline aqueous solutions from multilevel embedded correlated wavefunction theory and molecular dynamics

In this work, we examine ion-pairing mechanisms of bicarbonates in alkaline aqueous solutions with the divalent metal ions most abundantly present in seawater, namely, Ca 2+ and Mg 2+ . We employ a rare-event enhanced sampling approach within first-principles molecular dynamics to explore regions of phase space spanning solvent-shared to contact ion pairs. Second-order Møller–Plesset perturbation theory (MP2) corrections are subsequently applied in an embedding framework (EMB) to refine the electronic structure of stationary states and associated reaction barriers along the free-energy profiles while retaining the extended solvent effects at the density functional theory (DFT) level. Ca 2+ –HCO 3 − was previously hypothesized to exist in a solvent-shared ion pair (SSHIP) by DFT studies with an endergonic contact ion pair (CIP) formation; however, our EMB-MP2 refinement of the DFT ion-pairing pathways reveals that Ca 2+ and HCO 3 − form a virtually barrier-free CIP in alkaline solutions, with even more energetic ease than the widely studied Ca–CO 3 ion pair. We find qualitative agreement between DFT and EMB-MP2 for Mg 2+ —unlike Ca 2+ , Mg 2+ refuses to shed its strong hydration shell, thereby preferring a SSHIP state with a significant activation barrier to crossover to the CIP forms—a trait reminiscent of ion pairing in Mg–CO 3 and closely related to the kinetic limitations underlying the famous subject of the dolomite problem. Our study highlights the importance of improved electronic structure descriptions of liquids, modeled as a condensed phase of matter lacking in long-range crystalline order. It also strongly suggests that Ca 2+ –HCO 3 − CIPs are likely precursors involved in prenucleation of CaCO 3 mineral formation in seawater.

Sharma, Vidushi [Princeton Plasma Physics Laborato↗
DOE OSTI2026

Effect of Thermodynamic and Environmental Factors on Crystallization of DNA‐Origami Superlattices

The directed self‐assembly of nanoscale materials into ordered superlattices presents a powerful strategy for creating next‐generation materials with programmable mechanical, optical, and photonic properties. Deoxyribonucleic acid (DNA) origami has emerged as a versatile scaffold for encoding nanoscale geometry and guiding the crystallization of complex 3D architectures. However, a systematic understanding of the parameters that govern the efficiency and quality of superlattice formation remains limited. In this study, we utilize octahedral DNA nanoscale frames as a model system to investigate the relative influence of key factors, including buffer composition, ionic strength, frame concentration, and thermal annealing protocols, on the size, order, and reproducibility of the resulting superlattices. Our findings provide a quantitative framework to rationally optimize DNA‐based assembly pathways. Structural characterization via small‐angle x‐ray scattering (SAXS), scanning electron microscopy (SEM), and optical microscopy validates the quality and fidelity of the assembled lattices. Moreover, by templating these DNA frameworks into inorganic replicas, we establish general design principles that extend beyond biomolecular systems, providing a foundation for the synthesis of programmable materials in broader nanofabrication contexts.

77 NANOSCIENCE AND NANOTECHNOLOGY↗
DOE OSTI2024

Thermodynamically informed priors for uncertainty propagation in first-principles statistical mechanics

Here, this work demonstrates how first-principles statistical mechanics approaches within a Bayesian framework can quantify and propagate uncertainties to downstream thermodynamic calculations. To address the issue of Bayesian prior selection, knowledge of 0 K ground states in the material system of interest is incorporated into the prior. The effectiveness of this framework is shown by creating a phase diagram for the fcc zirconium nitride system, including confidence intervals on order-disorder transition temperatures.

Bayesian methods↗
DOE OSTI2008

Vehicle Technologies Program: Energy Storage R&D (2008 Annual Progress Report)

One of the primary objectives of the Energy Storage effort is the development of durable and affordable advanced batteries (and ultracapacitors) for use in a full range of vehicle applications, from start/stop to full-power HEVs, EVs, and PHEVs. The battery technology development activity spans three areas: system development of full battery systems; benchmark testing of emerging technologies in order to remain abreast of the latest industry developments; and Small Business Innovative Research (SBIR) to fund early-stage R&D for small businesses/entrepreneurs.

25 ENERGY STORAGE↗
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 OSTI2026

Scan‐Path‐ and Initial‐State‐Dependent Superdomain Switching in (111)‐Oriented PZT

Polarization switching in ferroelectric materials arises from the collective evolution of complex domain hierarchies, yet deterministic control over these processes remains challenging. Here, we investigate scan-path- and initial-state-dependent switching in epitaxial (111)-oriented PbZr 0.2 Ti 0.8 O 3 thin films using automated AFM-based writing combined with quantitative 3D piezoresponse force microscopy. We show that the scan trajectory acts as an experimentally accessible control parameter for superdomain formation. Box-in-box raster scans reproducibly stabilize ordered stripe superdomains with a reduced subset of symmetry-allowed variants, whereas spiral trajectories generate frustrated mixed-variant states with a broader distribution of final microstructures. Automated pulsing experiments further show that the local superdomain configuration at the nucleation site strongly influences the final written morphology. Phase-field modeling qualitatively reproduces the contrast between representative initial-state geometries and supports the role of compatibility constraints among competing ferroelastic pathways. These findings establish scan-path and initial-state engineering as practical handles to program ferroic order in hierarchical ferroelectric domain structures.

Vasudevan, Rama K. [Oak Ridge National Laboratory ↗
DOE OSTI2026

Disorder-induced spin-cluster magnetism in a doped kagome spin liquid candidate

The search for new quantum spin liquid materials relies on systems with strong frustration such as spins on an ideal kagome lattice. However, lattice imperfections can have substantial effects which are as yet not well understood. In recent work, the two-dimensional kagome system YCu 3 ⁢(OH) 6 ⁢[(Cl 𝑥 ⁢Br (1−𝑥) ) 3−𝑦 ⁢(OH) 𝑦 ] has emerged as a leading candidate hosting a Dirac spin liquid which appears to survive at least for 𝑥 < 0.4, associated with alternating-bond-hexagon (ABH) disorder. Here in magnetic samples with 𝑥 = 0.58, 𝑦 = 0.1 we report unusual in-plane ferromagnetic canting (FM) of the in-plane antiferromagnet (AFM), with an unusually wide regime of short-ranged order, and propose theoretical models to explain this behavior. First, we show that Kitaev-type exchanges naturally arise on the kagome lattice to second order in the known Dzyaloshinskii-Moriya exchanges, and that these interactions can produce the unusual in-plane FM canting from antichiral AFM. Second, we propose a phenomenological model of weakly FM-canted spin clusters to describe the short-ranged regime and analyze quantum fluctuations in an ABH toy model to show how ABH disorder can stabilize this regime. Here, the combination of experimental observation and theory suggests that kagome-Kitaev interactions and ABH disorder are necessary for describing the magnetic fluctuations in this family of materials, with potential implications for the proposed proximate spin liquid phase.

Seth, Arnab [Georgia Institute of Technology, Atla↗
DOE OSTI2026

Probabilistic Resource Adequacy Suite (PRAS) v0.8 Model Documentation

The Probabilistic Resource Adequacy Suite, or PRAS, is a software package for studying power system resource adequacy. It allows the user to simulate power system operations under a wide range of operating conditions, in order to study the system's risk of failing to meet demand due to a resource shortfall, and identify the time periods and regions in which that risk occurs. This reports documents version 0.8 of the tool.

29 ENERGY PLANNING, POLICY, AND ECONOMY↗
DOE OSTI2025

Large moiré superstructure of stacked incommensurate charge density waves

This article reports how two different charge density waves with slightly different wavevectors can exist in the same crystal and create moiré superstructure. Advances in heterostructure fabrication have opened new frontiers in moiré physics. Here we extend moiré engineering from artificially assembled thin flakes with mismatched lattice parameters to materials that host incommensurate orders, presenting a long-period moiré superlattice in a layered charge-density-wave compound, EuTe 4 . Using high-momentum-resolution X-ray diffraction, we found two coexisting incommensurate charge density waves with slightly mismatched in-plane wavevectors. The interaction between these two charge density waves leads to joint commensuration with the lattice and a moiré superstructure with a period of ~13.6 nm, offering key insights into the unique properties of EuTe4, such as the temperature-invariant incommensurate wavevectors and unconventional in-gap states. Owing to interlayer phase shifts, the moiré superstructure exhibits a clear thermal hysteresis, accounting for the large hysteresis in electrical resistivity and numerous metastable states. Our findings open new directions for moiré engineering based on incommensurate lattices and highlight the important role of interlayer ordering in stacked structures.

36 MATERIALS SCIENCE↗
DOE OSTI2025

Methodology of Atomic Force Microscopy Visualization of Electrode–Electrolyte Interfaces

Electrochemical atomic force microscopy (EC-AFM) provides unprecedented insights into the microstructure of electrode–electrolyte interfaces during electrochemical reactions. However, performing EC-AFM measurements has many challenges, for example, drift, contamination, and probe degradation. We present solutions to these experimental issues through electrochemical cell design and carefully chosen experimental parameters. The possibility that the probes can react with the interface during scanning, generating false-positive electrochemical dynamics, is discussed as an example of the challenge of high-fidelity EC-AFM measurement. Here, we demonstrate this effect in highly ordered pyrolytic graphite and show that we could use electrochemical control of the AFM probe to enable high-fidelity in situ AFM visualization of solid–liquid interfaces during electrochemical reactions.

Electrochemical cells↗
DOE OSTI2026

Assessing the Impact of a Forest Canopy on Near-Surface Wind Statistics

Representing the forest canopy in atmospheric numerical models should improve simulated winds within and above the canopy up to a few hundred meters above the ground. Here, in this study, we implement a forest canopy parameterization into the Weather Research and Forecasting (WRF) Model in a large-eddy simulation (LES) mode by applying drag forces across multiple layers within the canopy height. We use unique observations from the Lidar Experiments for Assessing Flow over Forests (LEAFF) field campaign at the Wind River Experimental Forest (WREF) in the U.S. Pacific Northwest to evaluate model performance. In a 2-day case study, the canopy parameterization improved wind predictions both within and above the canopy, particularly during the daytime and at finer grid resolution. Without it, winds were frequently overpredicted above the canopy. Similarly, derived quantities such as the wind shear index also yielded estimates closer to observations with the canopy parameterization implemented. These findings suggest that representing the canopy using drag forces alone can improve simulated mean winds up to 200 m above the surface. Furthermore, second-order statistical moments of wind were more sensitive to canopy density than first-order moments, especially during the daytime. This increased sensitivity and the improved daytime performance in wind speed—evidenced by the lowest bias from observations (3% compared to 20% over diurnal cycle)—imply that winds above the canopy layer are strongly influenced by how well turbulence above the canopy is modeled. The results of this study can serve as a foundation for parameterizing forest canopy effects in coarser weather forecast models.

Energy - Wind↗
DOE OSTI2025

Enhancement of Superconductivity in WP via Oxide-Assisted Chemical Vapor Transport

Tungsten monophosphide (WP) has been reported to superconduct below 0.8 K, and theoretical work has predicted an unconventional Cooper pairing mechanism. Here we present data for WP single crystals grown by means of chemical vapor transport (CVT) of WO3, P, and I2. In comparison to synthesis using WP powder as a starting material, this technique results in samples with substantially decreased low-temperature scattering and favors a more three-dimensional morphology. We also find that the resistive superconducting transitions in these samples begin above 1 K. Variation in Tc is often found in strongly correlated superconductors, and its presence in WP could be the result of influence from a competing order and/or a non-s-wave gap.

Chemistry↗
DOE OSTI2025

Interwoven magnetic kagome metal overcomes geometric frustration

Magnetic kagome materials provide a platform for exploring magneto-transport phenomena, symmetry breaking and charge ordering driven by the intricate interplay among electronic structure, topology and magnetism. Yet geometric frustration in conventional kagome magnets limits their tunability. Here we propose a design strategy for interweaving quasi-one-dimensional magnetic Tb zigzag chains with non-magnetic Ti-based kagome bilayers in TbTi 3 Bi 4 . Comprehensive spectroscopic analyses reveal coexisting elliptical-spiral magnetic and spin-density-wave orders accompanied by a large ~90 meV band-folding gap. The combined magnetic and electronic state leads to a giant anomalous Hall conductivity of 10 5 Ω −1 cm −1 , which exceeds that observed in frustrated kagome analogues. These results establish TbTi 3 Bi 4 as a model system of magnetic kagome metals with strong electron–magnetism interactions and underscore the necessity of interweaving designed magnetic and charge layers separately to achieve tunable transport properties. This design strategy will enable the discovery of emergent quantum states and next-generation electronic materials.

75 CONDENSED MATTER PHYSICS, SUPERCONDUCTIVITY AND↗
DOE OSTI2026

Model-based, in-situ, non-destructive qualification and certification of parts made by autonomous additive manufacturing

To address the significant productivity challenges associated with the qualification and certification (Q&C) tasks of additively manufactured (AM) parts, which have traditionally relied on rigorous post‐build inspection and testing, we propose an integrated framework that combines model‐based qualification and certification (MBQ&C) with autonomous additive manufacturing (AAM). MBQ&C employs high‐fidelity predictive models, developed within the Integrated Computational Materials Engineering (ICME) paradigm, to simulate process–structure–property–performance relationships for assessing a part’s fitness for use. Since predictive models are commonly machine learning (ML)-based or reduced-order surrogates of validated physics models, they run efficiently, enabling timely inference. In parallel, the self-driving AAM utilises ML-based adaptive, closed‐loop control strategies to avoid, mitigate, or repair defects and anomalies during fabrication, thereby increasing the likelihood of producing acceptable parts. A key feature of the combined AAM-MBQ&C framework is that predictive models explicitly incorporate defects or anomalies that persist after the build, using instance-specific data captured via in-situ sensing. This customisation enables a build‐specific assessment of fitness for use, rather than relying on nominal or generic parameters. Such individualised evaluation provides a robust basis for Q&C-related acceptance decisions relating to each build. Additionally, the rapid solution capabilities of ML or reduced-order models enable the determination of a part’s suitability for service shortly after build completion. As the framework matures, it has the potential to substantially reduce reliance on conventional point‐design approaches—such as time‐consuming post‐build computed tomography scanning and costly destructive testing. Thus, the AAM-MBQ&C framework represents a transformative, scalable strategy for quality assurance of AM components, as parts produced within a stable, validated, and certified envelope can be certified with reduced testing. Key benefits include: (1) significant gains in Q&C productivity through efficient, model-centric assessment; (2) performance-based classification of defects into critical and non-critical categories; (3) the ability to predict potential deviations in the performance of parts affected by real-time, adaptive process control interventions relative to those produced under a certified process, and (4) the enabling of virtual Q&C for service environments that are difficult, hazardous, or impractical to access or reproduce experimentally. Collectively, these capabilities strengthen the business case for AM, particularly for high‐consequence and mission‐critical applications. Finally, although this work focuses on powder-based AM, the proposed techniques could be extended to AM processes employing alternative feedstock forms.

Gunasegaram, Dayalan↗