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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

Numerical Investigation of Fluid Flow and Space Charge in Liquid Argon Time Projection Chamber (LArTPC) Detectors

Overview This project focused on developing a high-fidelity numerical framework to simulate the multiphysics environment within Liquid Argon Time Projection Chamber (LArTPC) detectors. The primary objective was to characterize the complex interplay between ion transport, background fluid dynamics, and electric field distortions—a critical factor for the calibration and sensitivity of next-generation High Energy Physics experiments, such as DUNE. Technical Achievements The research successfully yielded a hybrid numerical space-charge solver utilizing a Cell-Centered Finite Volume Method (FVM) for ion transport coupled with a Finite Element Method (FEM) for electric potential. Key accomplishments include: • Verification & Validation: The 3-D solver was rigorously verified against 1-D analytical solutions, demonstrating high numerical accuracy in predicting space-charge-induced field deviations. • Field Distortion Analysis: 3D simulations revealed that space charge effects introduce significant non-uniformities in the electric field. Critically, the research identified that background LAr flow velocities, when comparable to ion drift velocities, markedly exacerbate these distortions. • Technology Transfer: The resulting source code and comprehensive user manuals were successfully transferred to collaborators at Fermilab, providing a portable computational tool for the broader scientific community. Challenges and Future Directions While the space-charge solver achieved all performance metrics, the integrated fluid dynamics modeling encountered convergence challenges stemming from the extreme 200-fold disparity in length scales between the detector's 37 mm inlet pipes and the 8-meter global domain. To address this, the project has identified a clear technical pivot toward Hierarchical Geometric Adaptive Mesh Refinement (HG-AMR). By implementing an h-type refinement strategy with hanging nodes, future iterations of this solver will be capable of resolving localized high-gradient inlet flows without the prohibitive computational costs of regular grids. This advancement, combined with data-driven uncertainty quantification based on MicroBooNE-style calibration, will enable the precise modeling of detector responses in large-scale cryogenic environments where direct measurement remains difficult. Impact The computational tools developed under this award provide a foundation for enhancing the energy resolution and spatial reconstruction of noble liquid detectors. By bridging the gap between theoretical fluid dynamics and experimental field calibration, this work supports the DOE’s mission to advance the frontiers of neutrino physics and dark matter detection.

42 ENGINEERING↗
DOE OSTI2023

Hybrid Data‐Driven Discovery of High‐Performance Silver Selenide‐Based Thermoelectric Composites

Optimizing material compositions often enhances thermoelectric performances. However, the large selection of possible base elements and dopants results in a vast composition design space that is too large to systematically search using solely domain knowledge. To address this challenge, a hybrid data-driven strategy that integrates Bayesian optimization (BO) and Gaussian process regression (GPR) is proposed to optimize the composition of five elements (Ag, Se, S, Cu, and Te) in AgSe-based thermoelectric materials. Data is collected from the literature to provide prior knowledge for the initial GPR model, which is updated by actively collected experimental data during the iteration between BO and experiments. Within seven iterations, the optimized AgSe-based materials prepared using a simple high-throughput ink mixing and blade coating method deliver a high power factor of 2100 µW m −1 K −2 , which is a 75% improvement from the baseline composite (nominal composition of Ag 2 Se 1 ). In conclusion, the success of this study provides opportunities to generalize the demonstrated active machine learning technique to accelerate the development and optimization of a wide range of material systems with reduced experimental trials.

36 MATERIALS SCIENCE↗
DOE OSTI2024

Mechanochemically responsive polymer enables shockwave visualization

Abstract Understanding the physical and chemical response of materials to impulsive deformation is crucial for applications ranging from soft robotic locomotion to space exploration to seismology. However, investigating material properties at extreme strain rates remains challenging due to temporal and spatial resolution limitations. Combining high-strain-rate testing with mechanochemistry encodes the molecular-level deformation within the material itself, thus enabling the direct quantification of the material response. Here, we demonstrate a mechanophore-functionalized block copolymer that self-reports energy dissipation mechanisms, such as bond rupture and acoustic wave dissipation, in response to high-strain-rate impacts. A microprojectile accelerated towards the polymer permanently deforms the material at a shallow depth. At intersonic velocities, the polymer reports significant subsurface energy absorption due to shockwave attenuation, a mechanism traditionally considered negligible compared to plasticity and not well explored in polymers. The acoustic wave velocity of the material is directly recovered from the mechanochemically-activated subsurface volume recorded in the material, which is validated by simulations, theory, and acoustic measurements. This integration of mechanochemistry with microballistic testing enables characterization of high-strain-rate mechanical properties and elucidates important insights applicable to nanomaterials, particle-reinforced composites, and biocompatible polymers.

Science & Technology - Other Topics↗
DOE OSTI2023

Polycarbonate‐Based Solid‐Polymer Electrolytes for Solid‐State Sodium Batteries

Solid-polymer electrolytes comprised of polypropylene carbonate (PPC) and varied sodium bis(fluorosulfonyl)imide (NaFSI) salt concentrations are investigated for implementation as a conductive solid polymer electrolyte into solid-state cathode composites utilizing a sodium-layered oxide active material. The ionic conductivity generally increases with NaFSI salt content, reaching ≈1 mS cm −1 at 80 °C at the highest salt concentration (PPC:NaFSI = 0.5:1). Through an all-in-one slurry casting method, Na 2/3 Ni 1/3 Mn 2/3 O 2 cathode composites are fabricated in which the dispersed PPC electrolyte acts as the primary binder. Enabled by a bilayer polymer electrolyte system, cycling performance with the PPC cathode electrolyte is optimized with respect to salt concentration and anode material. The best cyclability is achieved with a moderate salt concentration electrolyte (PPC:NaFSI = 5:1), showcasing an initial capacity of 83 mA h g −1 with a remarkable 80% capacity retention after 150 cycles at C/5 rate and 60 °C. The superior performance of the lower salt concentration electrolyte is attributed to better electrochemical stability, as confirmed by linear sweep voltammetry and online electrochemical mass spectrometry measurements. In conclusion, these results underscore the potential of carbonate-based polymer electrolytes and the importance of balancing electrolyte conductivity and stability in cell design.

25 ENERGY STORAGE↗
DOE OSTI2026

Dark matter and baryon asymmetry from monopole-axion interactions

We introduce a novel mechanism where the kinetic energy of a rotating axion can be dissipated by the interactions with dark magnetic monopoles. This mechanism leads to a framework where the QCD axion and dark monopoles account for the dark matter density, and the observed baryon asymmetry is generated through the rotating QCD axion via axiogenesis. The monopoles acquire masses from a nonzero axion field, and they can transition between different quantized dyonic levels in the presence of a rotating axion field. The axion kinetic energy is dissipated by the transition, and thus the axion abundance is depleted to the observed dark matter abundance. We predict that the axion decay constant should be below 10 9 GeV to explain the observed dark matter and baryon densities.

Axions and ALPs↗
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

Complex orders and chirality in the classical Kitaev-Γ model

It is well recognized that the low-energy physics of many Kitaev materials is governed by two dominant energy scales, the Ising-type Kitaev coupling 𝐾 and the symmetric off-diagonal Γ coupling. An understanding of the interplay between these two scales is therefore the natural starting point toward a quantitative description that includes subdominant perturbations that are inevitably present in real materials. This study focuses on the classical 𝐾−Γ model on the honeycomb lattice, with a specific emphasis on the region 𝐾< 0 and Γ > 0 , which is the most relevant for the available materials and which remains enigmatic in both quantum and classical limits, despite much effort. We employ large-scale Monte Carlo simulations on specially designed finite-size clusters and unravel the presence of a complex multisublattice magnetic order in a wide region of the phase diagram, whose structure is characterized in detail. We show that this order can be quantified in terms of a coarse-grained scalar-chirality order, featuring a counterrotating modulation on the two spin sublattices. Here, we also provide a comparison to previous studies and discuss the impact of quantum fluctuations on the phase diagram.

75 CONDENSED MATTER PHYSICS, SUPERCONDUCTIVITY AND↗
DOE OSTI2026

A Chemist’s Guide to Solid-State Qubits: Diamond Color Centers as Embedded Vacancy-Centered Molecules

Diamond point defects or color centers are at the crux of solid-state quantum technology applications, from computing to sensing. Modeling molecular qubits has advanced by leaps and bounds mainly due to the advanced quantum mechanical (QM) tools in the arsenal of quantum chemists for modeling (isolated) molecular systems, which should be easily translatable for analogous systems in the solid state. Here, we simulate a series of diamond defects: a carbon vacancy (V C ) and second row substitutions that are adjacent to a V C or D C V C , with D = B, N, and O, to evaluate their magnetic and optical properties. We applied molecular orbital principles, an embedded cluster method, and a multiconfigurational QM theory to computationally characterize the above-mentioned defects for a multitude of spin, charge, and excited states. Our computational approach delivered high-quality QM insights that compare well with experiments. Furthermore, with this framework, we confirm the spin-triplets B C V C – and O C V C 0 to be analogous to N C V C – , with calculated bright transitions that respectively correspond to UV (3.80 eV) and blue (2.65 eV) light absorption and emission lifetimes (τ) of 2.3 and 5.6 ns, which are higher in energy and more short-lived (and thus brighter) than that for N C V C – : green (2.26 eV), τ = 9.5 ns.

Martirez, John Mark P. [Princeton Plasma Physics L↗
DOE OSTI2024

Learning Nonlinear Reduced Models from Data with Operator Inference

This review discusses Operator Inference, a nonintrusive reduced modeling approach that incorporates physical governing equations by defining a structured polynomial form for the reduced model, and then learns the corresponding reduced operators from simulated training data. The polynomial model form of Operator Inference is sufficiently expressive to cover a wide range of nonlinear dynamics found in fluid mechanics and other fields of science and engineering, while still providing efficient reduced model computations. The learning steps of Operator Inference are rooted in classical projection-based model reduction; thus, some of the rich theory of model reduction can be applied to models learned with Operator Inference. This connection to projection-based model reduction theory offers a pathway toward deriving error estimates and gaining insights to improve predictions. Furthermore, through formulations of Operator Inference that preserve Hamiltonian and other structures, important physical properties such as energy conservation can be guaranteed in the predictions of the reduced model beyond the training horizon. This review illustrates key computational steps of Operator Inference through a large-scale combustion example.

Mechanics↗
DOE OSTI2024

Actuating Liquid Crystals Rapidly and Reversibly by Using Chemical Catalysis

Abstract Microtubules and catalytic motor proteins underlie the microscale actuation of living materials, and they have been used in reconstituted systems to harness chemical energy to drive new states of organization of soft matter (e.g., liquid crystals (LCs)). Such materials, however, are fragile and challenging to translate to technological contexts. Rapid (sub‐second) and reversible changes in the orientations of LCs at room temperature using reactions between gaseous hydrogen and oxygen that are catalyzed by Pd/Au surfaces are reported. Surface chemical analysis and computational chemistry studies confirm that dissociative adsorption of H 2 on the Pd/Au films reduces preadsorbed O and generates 1 ML of adsorbed H, driving nitrile‐containing LCs from a perpendicular to a planar orientation. Subsequent exposure to O 2 leads to oxidation of the adsorbed H, reformation of adsorbed O on the Pd/Au surface, and a return of the LC to its initial orientation. The roles of surface composition and reaction kinetics in determining the LC dynamics are described along with a proof‐of‐concept demonstration of microactuation of beads. These results provide fresh ideas for utilizing chemical energy and catalysis to reversibly actuate functional LCs on the microscale.

Chemistry↗
DOE OSTI2024

Enhancement of the Physical and Mechanical Properties of Cellulose Nanofibril-Reinforced Lignocellulosic Foams for Packaging and Building Applications

Biobased foams have the potential to serve as eco-friendly alternatives to petroleum-based foams, provided they achieve comparable thermomechanical and physical properties. We propose a facile approach to fabricate eco-friendly cellulose nanofibril (CNF)-reinforced thermomechanical pulp (TMP) fiber-based foams via an oven-drying process with thermal conductivity as low as 0.036 W/(m·K) at a 34.4 kg/m3 density. Acrodur®, iron chloride (FeCl3), and cationic polyacrylamide (CPAM) were used to improve the foam properties. Acrodur® did not have any significant effect on the foamability and density of the foams. Mechanical, thermal, cushioning, and water absorption properties of the foams were dependent on the density and interactions of the additives with the fibers. Due to their high density, foams with CPAM and FeCl3 at a 1% additive dosage had significantly higher compressive properties at the expense of slightly higher thermal conductivity. There was slight increase in compressive properties with the addition of Acrodur®. All additives improved the water stability of the foams, rendering them stable even after 24 h of water absorption.

Chemistry↗
DOE OSTI2026

Vanishing dynamic strength measured in a transient high-pressure phase of tin

This work reports two independent experimental estimations of the dynamic strength of the body-centered tetragonal (bct) 𝛾 phase of tin, which occurs at pressures above about 9 GPa and transforms promptly back to the ambient 𝛽 phase upon pressure release. Measuring strength in such a transient high-pressure phase is challenging. One measurement used free-surface Richtmyer-Meshkov instabilities generated with gas gun impact. Another set of measurements used ramp-release loading in a pulsed-power facility. Strength estimations came from comparison to simulations using a comprehensive multiphase modeling framework that includes equation of state, shear moduli, and strength separately for each phase, and treats mixed-phase regions. Both experimental methods found the 𝛾-phase strength to be very low, within experimental uncertainty of zero. The existing literature on other materials, by contrast, almost universally reports higher strength in transient high-pressure phases compared with ambient phases. Recent Molecular-Dynamics simulations on tin in the literature showed almost zero deviatoric strength during a deformation-induced bct → bct transformation in which one of the 𝛾-phase 𝑎 axes “flips” to the 𝑐 axis. This reorientation currently provides the most plausible explanation for the low observed strength in 𝛾-phase tin.

36 MATERIALS SCIENCE↗
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 OSTI2023

Design and Synthesis of Cubic K 3−2 x Ba x SbSe 4 Solid Electrolytes for K–O 2 Batteries

Developing K-ion conducting solid-state electrolytes (SSEs) plays a critical role in the safe implementation of potassium batteries. In this work, a chalcogenide-based potassium ion SSE is reported, K 3 SbSe 4 , which adopts a trigonal structure at room temperature. Single-crystal structural analysis reveals a trigonal-to-cubic phase transition at the low temperature of 50 °C, which is the lowest among similar compounds and thus provides easy access to the cubic phase. The substitution of barium for potassium in K 3 SbSe 4 leads to the creation of potassium vacancies, expansion of lattice parameters, and a transformation from a trigonal phase to a cubic phase. As a result, the maximum conductivity of K 3−2x Ba x SbSe 4 reaches around 0.1 mS cm −1 at 40 °C for K 2.2 Ba 0.4 SbSe 4 , which is over two orders of magnitude higher than that of undoped K 3 SbSe 4 . This novel SSE is successfully employed in a K–O 2 battery operating at room temperature where a polymer-laminated K 2.2 Ba 0.4 SbSe 4 pellet serves as a separator between the oxygen cathode and the potassium metal anode. Effective protection of the K metal anode against corrosion caused by O 2 is demonstrated.

25 ENERGY STORAGE↗
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 OSTI2026

Spectroscopic Demarcation of Emergent Photons and Spinons in a Dipolar-Octupolar Quantum Spin Liquid

The identification of fractionalized excitations in quantum spin liquids (QSLs) remains a central challenge in condensed matter physics. In dipolar-octupolar (DO) pyrochlores, such as Ce 2⁢ Zr 2 ⁢O 7 , the candidate 𝜋-flux quantum spin ice (QSI) state is predicted to host both gapless emergent photons and a continuum of spinons. However, resolving these modes at zero field is complicated by their spectral overlap and the presence of nonmagnetic scattering near zero energy. Here, in this study, we report neutron scattering experiments on Ce 2 ⁢Zr 2 ⁢O 7 under a magnetic field along the [1, 1, 1] direction. In contrast to previous unpolarized studies at zero field that relied on high-temperature subtraction, we use a same-temperature high-field subtraction protocol to isolate the photon and spinon modes. Leveraging the selective coupling of the magnetic field to the dipolar degrees of freedom, we demonstrate the spectroscopic demarcation of these excitations. We observe that weak fields (≈ 0.15 T) suppress the low-energy photon weight while leaving the high-energy spinon continuum robust, albeit hardened. Our results, supported by gauge mean-field theory and exact diagonalization calculations, provide strong evidence for the 𝜋-flux QSI state and introduce a powerful field-tuning protocol for investigating DO-QSLs.

Gao, Bin [Rice University, Houston, TX (United Sta↗
DOE OSTI2024

Investigating two-dimensional adjoint QCD on the lattice

We present our investigations of SU(N) adjoint QCD in two dimensions with one Majorana fermion on the lattice. We determine the relevant parameter range for the simulations with Wilson fermions and present results for Polyakov loop, chiral condensate, and string tension. In the theory with massive fermions, all observables we checked show qualitative agreement between numerical lattice data and theory, while the massless limit is more subtle since chiral and non-invertible symmetry of the continuum theory are explicitly broken by lattice regularization. In thermal compactification, we observe N perturbative vacua for the holonomy potential at high-T with instanton events connecting them, and a unique vacuum at low-T. At finite-N, this is a cross-over and it turns to a phase transition at large-N thermodynamic limit. In circle compactification with periodic boundary conditions, we observe a unique center-symmetric minimum at any radius. In continuum, the instantons in the thermal case carry zero modes (for even N) and indeed, in the lattice simulations, we observe that chiral condensate is dominated by instanton centers, where zero modes are localized. We present lattice results on the issue of confinement vs. screening in the theory and comment on the roles of chiral symmetry and non-invertible symmetry.

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗
DOE OSTI2024

Resonant Metasurfaces with Van Der Waals Hyperbolic Nanoantennas and Extreme Light Confinement

This work reports on a metasurface based on optical nanoantennas made of van der Waals material hexagonal boron nitride. The optical nanoantenna made of hyperbolic material was shown to support strong localized resonant modes stemming from the propagating high-k waves in the hyperbolic material. An analytical approach was used to determine the mode profile and type of cuboid nanoantenna resonances. An electric quadrupolar mode was demonstrated to be associated with a resonant magnetic response of the nanoantenna, which resembles the induction of resonant magnetic modes in high-refractive-index nanoantennas. The analytical model accurately predicts the modes of cuboid nanoantennas due to the strong boundary reflections of the high-k waves, a capability that does not extend to plasmonic or high-refractive-index nanoantennas, where the imperfect reflection and leakage of the mode from the cavity complicate the analysis. In the reported metasurface, excitations of the multipolar resonant modes are accompanied by directional scattering and a decrease in the metasurface reflectance to zero, which is manifested as the resonant Kerker effect. Van der Waals nanoantennas are envisioned to support localized resonances and can become an important functional element of metasurfaces and transdimensional photonic components. By designing efficient subwavelength scatterers with high-quality-factor resonances, this work demonstrates that this type of nanoantenna made of naturally occurring hyperbolic material is a viable substitute for plasmonic and all-dielectric nanoantennas in developing ultra-compact photonic components.

Chemistry↗