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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 415 records · Page 23
All sourcesNASA NTRSDOE OSTI
DOE OSTI2026

Chirality transfer from chiral perovskite to molecular dopants via charge transfer states

Chiral perovskites are semiconductors with broken mirror symmetries. Their photo responses are often constrained in the UV range. In this work, we demonstrate that doping 2,3,5,6-Tetrafluoro-7,7,8,8-tetracyanoquinodimethane in the chiral perovskite matrix introduces a visible light absorption feature through the emerging charge-transfer electronic states. These charge-transfer states exhibits circular dichroism inherited from the chiral host, indicating effective chirality transfer from host to guest component via electronic coupling. Quantum-chemical modeling identifies a strong wave function overlap between an electron and a hole of the guest-host in a closely packed crystal configuration promoting the charge transfer state’s optical activity. We further integrate the doped chiral perovskite film into photodetectors and demonstrate a selective detection of circularly polarized light in both UV and visible regions. Our results suggest a universal approach of introducing visible photo absorption states to the chiral matrix to broaden the optical active range while enhancing the electrical conductivity.

36 MATERIALS SCIENCE↗
DOE OSTI2026

Particle‐Size‐Dependent Lithium‐Ion Transport in PEO/LLZO Composite Electrolytes

Lithium-metal batteries with solid electrolytes can deliver higher energy density and improved safety than conventional Li-ion batteries. Among solid electrolyte candidates, polymer/ceramic composite electrolytes are attractive because they combine polymer flexibility with the high ionic conductivity of ceramics. However, whether ceramic fillers synergistically reduce polarization losses in the polymer matrix remains unclear. A central unknown is the critical polymer/ceramic interfacial resistance (Rint,crit), below which adding ceramics lowers electrolyte overpotential. Here, we present the first macroscale model framework to quantify R int,crit for composite electrolytes based on polyethylene oxide (PEO) and Ta-doped Li 7 La 3 Zr 2 O 12 (LLZO). A 1D model for DC-polarization of tri-layer cells (PEO-LiTFSI/LLZO/PEO-LiTFSI) shows that LLZO surface functionalization reduces the PEO/LLZO interfacial resistance, consistent with electrochemical impedance measurements. Extending to a 2D composite model, we show notably that Rint,crit scales linearly with LLZO particle diameter and shifts toward experimentally accessible values (e.g., 28.8 Ωcm 2 ) as particle size increases. At fixed ceramic volume fraction, larger LLZO particles reduce the number of interfacial crossings, driving more current through the ceramic phase and lowering concentration polarization. In contrast, R int,crit is largely independent of ceramic volume fraction. These results demonstrate that ceramic filler-size engineering can enable synergistic, energy-efficient transport in polymer/ceramic composite electrolytes.

25 ENERGY STORAGE↗
DOE OSTI2026

Mitigation of polysulfide shuttle effect in Li-S batteries through catalytic disproportionation reaction

Polysulfides are poorly retained within porous cathodes and readily diffuse into the electrolyte over time, leading to the well-known shuttle effect that undermines the reversibility of Li-S batteries. Here, in this study, we demonstrate that catalytic disproportionation of polysulfides provides an effective pathway to suppress this process by rapidly converting dissolved species into solid sulfur and sulfides, thereby preventing their migration into the electrolyte. Fundamentally, the sluggish kinetics of sulfur redox reactions are responsible for the accumulation and redistribution of soluble polysulfides in the bulk electrolyte. By accelerating these kinetics, catalyzed disproportionation not only confines sulfur within the conductive cathode matrix but also promotes the homogeneous precipitation of Li₂S₂/Li₂S, which enhances electrochemical reversibility and cycling stability. Using nitrogen-doped carbon (NC800) as a model catalyst, we reveal its ability to drive a pseudo-16-electron reduction pathway, leading to a single dominant Li₂S product and uniform deposition within the porous framework. In contrast, a non-catalytic carbon (KB) yields multiple polysulfide intermediates and heterogeneous deposition. The mechanistic insights provided here highlight the pivotal role of catalytic disproportionation in reshaping sulfur redox pathways and offer a rational strategy for mitigating polysulfide shuttling in practical Li-S pouch cells.

25 ENERGY STORAGE↗
DOE OSTI2024

Random insights into the complexity of two-dimensional tensor network calculations

Projected entangled pair states (PEPS) offer memory-efficient representations of some quantum many-body states that obey an entanglement area law and are the basis for classical simulations of ground states in two-dimensional (2d) condensed matter systems. However, rigorous results show that exactly computing observables from a 2d PEPS state is generically a computationally hard problem. Yet approximation schemes for computing properties of 2d PEPS are regularly used, and empirically seen to succeed, for a large subclass of (“not too entangled”) condensed matter ground states. Adopting the philosophy of random matrix theory, in this work, we analyze the complexity of approximately contracting a 2d random PEPS by exploiting an analytic mapping to an effective replicated statistical mechanics model that permits a controlled analysis at a large bond dimension. Through this statistical-mechanics lens, we argue that (i) although approximately sampling wave-function amplitudes of random PEPS faces a computational-complexity phase transition above a critical bond dimension, and (ii) one can generically efficiently estimate the norm and correlation functions for any finite bond dimension. Furthermore, these results are supported numerically for various bond-dimension regimes. It is an important open question whether the above results for random PEPS apply more generally also to PEPS representing physically relevant ground states.

75 CONDENSED MATTER PHYSICS, SUPERCONDUCTIVITY AND↗
DOE OSTI2025

𝐵 → 𝜌⁢ℓ⁢$\bar{v}$ Resonance Form Factors from 𝐵→ 𝜋⁢𝜋⁢ℓ⁢$\bar{v}$ in Lattice QCD

The decay 𝐵 → 𝜌⁢ℓ⁢$\bar{v}$ is an attractive process for determining the magnitude of the smallest Cabibbo-Kobayashi-Maskawa matrix element, |𝑉 𝑢⁢𝑏 |, and can provide new insights into the origin of the long-standing exclusive-inclusive discrepancy in determinations of this standard-model parameter. This requires a nonperturbative QCD calculation of the 𝐵 → 𝜌 form factors 𝑉, 𝐴 0 , 𝐴 1 , and 𝐴 12 . The unstable nature of the 𝜌 resonance has prevented precise lattice QCD calculations of these form factors to date. Here, we present the first lattice QCD calculation of the 𝐵 → 𝜌 form factors in which the 𝜌 is treated properly as a resonance in 𝑃-wave 𝜋⁢𝜋 scattering. To this end, we use the Lellouch-Lüscher finite-volume formalism to compute the 𝐵 → 𝜋⁢𝜋 form factors as a function of both momentum transfer and 𝜋⁢𝜋 invariant mass, and then analytically continue to the 𝜌 resonance pole. This calculation is performed with 2 + 1 dynamical quark flavors at a pion mass of approximately 320 MeV, and demonstrates a clear path toward results at the physical point.

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗
DOE OSTI2024

Controlling cantilevered adaptive X-ray mirrors

Modeling the behavior of a prototype cantilevered X-ray adaptive mirror (held from one end) demonstrates its potential for use on high-performance X-ray beamlines. Similar adaptive mirrors are used on X-ray beamlines to compensate optical aberrations, control wavefronts and tune mirror focal distances at will. Controlled by 1D arrays of piezoceramic actuators, these glancing-incidence mirrors can provide nanometre-scale surface shape adjustment capabilities. However, significant engineering challenges remain for mounting them with low distortion and low environmental sensitivity. Finite-element analysis is used to predict the micron-scale full actuation surface shape from each channel and then linear modeling is applied to investigate the mirrors' ability to reach target profiles. Using either uniform or arbitrary spatial weighting, actuator voltages are optimized using a Moore–Penrose matrix inverse, or pseudoinverse, revealing a spatial dependence on the shape fitting with increasing fidelity farther from the mount.

47 OTHER INSTRUMENTATION↗
DOE OSTI2024

Development and demonstration of a BISON–Griffin modeling framework for the design of targeted TRISO transient experiments in the Transient Reactor Test Facility

Uranium oxycarbide (UCO)-bearing tri-structural isotropic (TRISO) particle fuels are expected to be used in numerous U.S. commercial reactor applications within the next decade. Here, in this work, we reviewed historical particle fuel transient experiments to identify gaps in TRISO fuel performance transient testing. A BISON–Griffin modeling framework was then developed to conduct preliminary TRISO transient analyses and begin to address these gaps. The framework was demonstrated using limiting-case transient conditions from a prototypic high-temperature gas-cooled reactor (HTGR). It was then applied to develop a matrix of experiments that could be performed in the Transient Reactor Test Facility (TREAT) to (1) evaluate UCO-fueled particle performance at moderate and high heat rates, (2) assess whether historical testing involving UO 2 -fueled particles is applicable to modern UCO-fueled particles, (3) deconvolute the impacts of temperature and heat rate on particle transient response, and (4) collect the data needed for fuel performance model validation and/or further development.

11 NUCLEAR FUEL CYCLE AND FUEL MATERIALS↗
DOE OSTI2026

3D TRISO particle-explicit compact meshing

The TRI-structural ISOtropic (TRISO) layered fuel particle is a robust nuclear fuel form offering enhanced safety and performance for advanced reactor concepts, including high-temperature gas-cooled reactors and other Generation IV designs. These poppy-seed-sized particles are embedded in a graphite matrix to form fuel elements that must withstand elevated temperatures and high burn-up levels. The heterogeneous nature of these fuel elements — comprising thousands of randomly distributed TRISO particles — produces complex stress fields and thermal gradients that one- and two-dimensional models cannot accurately capture. While three-dimensional modeling has improved predictions of dimensional changes, internal pressure buildup, and fission product transport under irradiation, current approaches rely on homogenized material properties that are known to have considerable divergence from experimental observations. This work presents a methodology for optimized random packing of TRISO fuel compacts and full three-dimensional mesh generation within the BISON fuel performance code, with each particle coating layer individually discretized. The resulting mesh was demonstrated through heat conduction simulations under representative in-reactor operating conditions, showing strong agreement with expected behavior. This capability enables detailed analysis of particle-to-particle interactions, matrix cracking mechanisms, and the statistical distribution of coating layer failures — all of which directly govern fuel performance and safety margins.

11 - NUCLEAR FUEL CYCLE AND FUEL MATERIALS↗
DOE OSTI2026

Machine Learning–Augmented Laser-Induced Breakdown Spectroscopy for Spectral Discrimination of Iron Oxalates

Enhanced characterization and phase identification of post-PUREX Pu Oxalates (PuOXA) are pivotal for nonproliferation and pre-detonation nuclear forensics. Despite significant advances in the characterization of PuO 2 samples, little is known about the impact of both the chemical structure and oxidation states of PuOXA (i.e., Pu(III) and Pu(IV)) have on optical emission signatures. Here, we demonstrate the analytical capabilities of laser-induced breakdown spectroscopy (LIBS) applied to Fe(II) and Fe(III) oxalate samples as surrogates for PuOXA, highlighting the discriminating features in the LIBS emission spectra arising from differences in the oxidation states within mixed FeOXA samples. We report the enhancement of spectral feature selection using Principal Component Analysis (PCA), which enables the analytical superiority of machine learning algorithms such as Linear Discriminant Analysis (LDA), Quadratic Discriminant Analysis (QDA), Partial Least Squares Regression (PLSR), Support Vector Regression (SVR), and Random Forest Regression (RFR) over conventional univariate techniques for phase discrimination and chemometric analysis. Cluster analysis revealed how both matrix effects and laser ablation influence cluster separability by introducing spectral artifacts that misdirect the maximization of variance. PCA-selected emission lines were used in the regression models, demonstrating that both univariate and multivariate linear regression models (i.e., PLSR and SVR) can achieve acceptable performance, with machine learning models outperforming conventional calibration regressions. Furthermore, the application of non-linearly activated PCA-selected emission lines illustrates how simplifying the data while retaining captured variance enables the use of less complex and more computationally efficient models. Furthermore, this is particularly evident in the underperformance of RFR, which suffers from increased computational costs and overfitting owing to its high complexity.

Oxalates↗
DOE OSTI2024

Aging heat treatment design for Haynes 282 made by wire-feed additive manufacturing using high-throughput experiments and interpretable machine learning

Wire-feed additive manufacturing (WFAM) produces superalloys with complex thermal cycles and unique microstructures, often requiring optimized heat treatments. To address this challenge, we present a hybrid approach that combines high-throughput experiments, precipitation simulation, and machine learning to design effective aging conditions for the WFAM Haynes 282 superalloy. Our results demonstrate that the γ’ radius is the critical microstructural feature for strengthening Haynes 282 during post-heat treatment compared with the matrix composition and γ’ volume fraction. New aging conditions at 770°C for 50 hours and 730°C for 200 hours were discovered based on the machine learning model and were applied to enhance yield strength, bringing it on par with the wrought counterpart. This approach has significant implications for future AM alloy production, enabling more efficient and effective heat treatment design to achieve desired properties.

CALPHAD↗
DOE OSTI2025

Quasiprobabilistic Readout Correction of Midcircuit Measurements for Adaptive Feedback via Measurement Randomized Compiling

Quantum measurements are a fundamental component of quantum computing. However, on present-day quantum computers, measurements can be more error prone than quantum gates and are susceptible to nonunital errors as well as nonlocal correlations due to measurement crosstalk. While readout errors can be mitigated in postprocessing, this is inefficient in the number of qubits due to a combinatorially large number of possible states that need to be characterized. In this work, we show that measurement errors can be tailored into a simple stochastic error model using randomized compiling, enabling the efficient mitigation of readout errors via quasiprobability distributions reconstructed from the measurement of a single preparation state in an exponentially large confusion matrix. We demonstrate the scalability and power of this approach by correcting readout errors without matrix inversion on a large number of different preparation states applied to a register of eight superconducting transmon qubits. Moreover, we show that this method can be extended to midcircuit measurements used for active feedback via quasiprobabilistic error cancellation, and we demonstrate the correction of measurement errors on an ancilla qubit used to detect and actively correct bit-flip errors on an entangled memory qubit. Our approach enables the correction of readout errors on large numbers of qubits and offers a strategy for correcting readout errors in adaptive circuits in which the results of midcircuit measurements are used to perform conditional operations on nonlocal qubits in real time.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗
DOE OSTI2026

Mechanical Behavior of Neutron Irradiated Refractory Multi-Principal Element Alloys Processed via Spark Plasma Sintering

The search for advanced materials capable of withstanding the extreme conditions of Generation IV reactors is a critical area in materials science research. These reactors operate under severe environments, including high temperatures, corrosion, stress, and irradiation damage. Consequently, there is a need for innovative alloy systems to ensure the reliability and longevity of proposed Generation IV reactor components. Refractory multi-principal-element alloys (RMPEA) have emerged as a promising candidate due to their exceptional properties. These alloys, characterized by their composition of multiple principal elements in near-equiatomic ratios, exhibit superior resistance to irradiation damage, reduced void swelling, enhanced microstructural stability, and minimal irradiation-induced hardening. While initial studies on RMPEAs have shown promising results, most research has been limited to thin films, nanocrystalline microstructures, and ion irradiation, which do not accurately represent the behavior of bulk materials. To address this gap, our research focused on the neutron irradiation of bulk RMPEAs. We aim to conduct comprehensive post-irradiation examinations (PIE) of RMPEAs irradiated at the Advanced Test Reactor at Idaho National Laboratory. The RMPEAs were synthesized using spark plasma sintering (SPS) with mechanically alloyed metallurgical powder. The RMPEA specimens are a MoNbTi alloy system with additions of -Zr, and -ZrV. Furthermore, PIE consisted of mechanical testing and advanced materials characterization. The mechanical testing consisted of sub-sized tensile testing, micro- and nano- indentation. Microstructural characterization included scanning electron microscopy and transmission electron microscopy. Mechanical testing coupled with advanced microscopy techniques provides insight into phase morphology and its effects on the mechanical properties of the RMPEA specimens. The results indicate that both pristine and irradiated RMPEA specimens exhibited brittle behavior during tensile testing, which can be attributed to their heterogeneous microstructure. The SPS manufacturing process did not include any post treatment, which resulted in a heterogeneous microstructure. Energy-dispersive X-ray spectroscopy revealed the presence of intermetallic such as laves phases within the microstructure. Specifically, Ti-rich precipitates were observed in the MoNbTi specimen, while Mo-rich precipitates were found in the MoNbTiZrV specimen. Nano-hardness testing of pristine samples showed that the laves phases exhibited higher hardness values compared to the matrix phase, suggesting that precipitate hardening is likely the dominant hardening mechanism in these specimens. The results from this work will be used to build a finite element model to predict mechanical behavior of future MPEA compositions. Thus, enabling for a streamlined approach to developing novel MPEAs for the nuclear industry.

36 - MATERIALS SCIENCE↗
DOE OSTI2026

Chirality reversal at finite magnetic impurity strength and local signatures of a topological phase transition

Here, we study the honeycomb lattice with a single magnetic impurity modeled by adding imaginary next-nearest-neighbor hopping 𝑖⁢ℎ on a single hexagon. This Haldane defect gives a topological mass term to the gapless Dirac cones and generates chirality. For a small density of defects, Neehus et al. [Phys. Rev. Lett. 135, 126604 (2025)] found that the system's chirality reverses at a critical ℎ 𝑐 ≈ 0.95 associated with an unexpected tricritical point of Dirac fermions at zero defect density. We investigate this zero-density limit by analyzing a single defect and computing two experimentally relevant measures of chirality: (1) orbital magnetization via local Chern marker, a bulk probe of all occupied states; and (2) electronic currents of low-energy states. Both probes show a chirality reversal at a critical ℎ 𝑐 ≈ 0.9–1.0. Motivated by this consistency, we propose a defect-scale toy model whose low-energy states reverse their chirality at ℎ$^{'}_{c}$ ≈ 0.87. Remarkably, the same pair of zero-energy bound states also generates the critical point ℎ 𝑐 in the full impurity projected T-matrix. Our results show how the chirality reversal produced by an impurity can be observed either in local probes or in the global topology, and suggest a possible role of the microscopic defect structure at the critical point.

Chern insulators↗
DOE OSTI2024

Enabling Parallel Performance and Portability of Solid Mechanics Simulations Across CPU and GPU Architectures

Efficiently simulating solid mechanics is vital across various engineering applications. As constitutive models grow more complex and simulations scale up in size, harnessing the capabilities of modern computer architectures has become essential for achieving timely results. This paper presents advancements in running parallel simulations of solid mechanics on multi-core CPUs and GPUs using a single-code implementation. This portability is made possible by the C++ matrix and array (MATAR) library, which interfaces with the C++ Kokkos library, enabling the selection of fine-grained parallelism backends (e.g., CUDA, HIP, OpenMP, pthreads, etc.) at compile time. MATAR simplifies the transition from Fortran to C++ and Kokkos, making it easier to modernize legacy solid mechanics codes. We applied this approach to modernize a suite of constitutive models and to demonstrate substantial performance improvements across different computer architectures. This paper includes comparative performance studies using multi-core CPUs along with AMD and NVIDIA GPUs. Results are presented using a hypoelastic–plastic model, a crystal plasticity model, and the viscoplastic self-consistent generalized material model (VPSC-GMM). The results underscore the potential of using the MATAR library and modern computer architectures to accelerate solid mechanics simulations.

Morgan, Nathaniel (ORCID:0000000276118449)↗
DOE OSTI2025

Greedy emulators for nuclear two-body scattering

Applications of reduced basis method emulators are increasing in low-energy nuclear physics because they enable fast and accurate sampling of high-fidelity calculations, enabling robust uncertainty quantification. Here, in this paper, we develop, implement, and test two model-driven emulators based on the (Petrov-)Galerkin projection using the prototypical test case of two-body scattering with the Minnesota potential and a more realistic local chiral potential. The high-fidelity scattering equations are solved with the matrix Numerov method, a reformulation of the popular Numerov recurrence relation for solving special second-order differential equations as a linear system of coupled equations. A novel error estimator based on reduced-space residuals is applied to an active learning approach (a greedy algorithm) to choosing training samples (“snapshots”) for the emulator and contrasted with a proper orthogonal decomposition (POD) approach. Both approaches allow for computationally efficient offline-online decompositions, but the greedy approach requires many fewer snapshot calculations. These developments set the groundwork for emulating scattering observables based on chiral nucleon-nucleon and three-nucleon interactions and optical models, where computational speed-ups are necessary for Bayesian uncertainty quantification. Our emulators and error estimators are widely applicable to linear systems.

Bayesian methods↗
DOE OSTI2025

Methodologies and strategies for detecting fiber orientation in polymeric fiber-reinforced composites

In this review paper different methods and techniques for representing fiber orientation in advanced polymer matrix composites are presented. A description of the effect of fiber orientation on processing, fabrication and mechanical properties of a composite material is presented. The paper discusses the mathematical modeling and modeling techniques for fiber structure representation in polymeric composites, with an emphasis on fiber orientation in a composite part. The paper is divided into two sections; namely—destructive techniques and non-destructive techniques. The capabilities and limitations of each technique with respect to fiber orientation detection and measurements are discussed.

composites↗
DOE OSTI2025

Unlocking soybean meal pectin recalcitrance using a multi-enzyme cocktail approach

Pectin is a complex plant heteropolysaccharide whose structure and function differ depending on its source. In animal feed, breaking down pectin is essential, as its presence increases feed viscosity and reduces nutrient absorption. Soybean meal, a protein-rich poultry feed ingredient, contains significant amounts of pectin, the structure of which remains unclear. Consequently, the enzyme activities required to degrade soybean meal pectin and how they interact are still open questions. In this study, we produced 15 recombinant fungal carbohydrate-active enzymes (CAZymes) identified from fungal secretomes acting on pectin. After observing that these enzymes were not active on soybean meal pectin when used alone, we developed a semi-miniaturized method to evaluate their effect as multi-activity cocktails. We designed and tested 12 enzyme pools, containing up to 15 different CAZymes, using several hydrolysis markers. Thanks to our multiactivity enzymatic approach combined with a Pearson correlation matrix, we identified 10 fungal CAZymes efficient on soybean meal pectin, 9 of which originate from Talaromyces versatilis. Based on enzyme specificity and linkage analysis, we propose a structural model for soybean meal pectin. Our findings underscore the importance of combining CAZymes to improve the degradation of agricultural co-products.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2024

Bayesian mixture model approach to quantifying the empirical nuclear saturation point

The equation of state (EOS) in the limit of infinite symmetric nuclear matter exhibits an equilibrium density, $n_0 \approx 0.16 \, \mathrm{fm}^{-3}$, at which the pressure vanishes and the energy per particle attains its minimum, $E_0 \approx -16 \, \mathrm{MeV}$. Although not directly measurable, the nuclear saturation point $(n_0,E_0)$ can be extrapolated by density functional theory (DFT), providing tight constraints for microscopic interactions derived from chiral effective field theory (EFT). However, when considering several DFT predictions for $(n_0,E_0)$ from Skyrme and Relativistic Mean Field (RMF) models together, a discrepancy between these model classes emerges at high confidence levels that each model prediction's uncertainty cannot explain. How can we leverage these DFT constraints to rigorously benchmark nuclear saturation properties of chiral interactions? To address this question, we present a Bayesian mixture model that combines multiple DFT predictions for $(n_0,E_0)$ using an efficient conjugate prior approach. The inferred posterior distribution for the saturation point's mean and covariance matrix follows a Normal-inverse-Wishart class, resulting in posterior predictives in the form of correlated, bivariate $t$-distributions. The DFT uncertainty reports are then used to mix these posteriors using an ordinary Monte Carlo approach. At the 95\% credibility level, we estimate $n_0 \approx 0.157 \pm 0.010 \, \mathrm{fm}^{-3}$ and $E_0 \approx -15.97 \pm 0.40 \, \mathrm{MeV}$ for the marginal (univariate) $t$-distributions. Combined with chiral EFT calculations of the pure neutron matter EOS, we obtain bivariate normal distributions for the nuclear symmetry energy and its slope parameter evaluated at $n_0$: $S_v \approx 32.0 \pm 1.1 \, \mathrm{MeV}$ and $L\approx 52.6\pm 8.1 \, \mathrm{MeV}$ (95\%), respectively. Furthermore, our Bayesian framework is publicly available, so practitioners can readily use and extend our results.

73 NUCLEAR PHYSICS AND RADIATION PHYSICS↗