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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 433 records · Page 24
All sourcesNASA NTRSDOE OSTI
DOE OSTI2025

Solvent-Free Melt-Processed Cathode Mitigates Li Anode Instability in Polymer-Based Solid-State Batteries

Solvent-free manufacturing of battery components is a promising alternative to traditional slurry processing for reducing the cost and environmental impact. In this work, we used twin-screw melt extrusion to fabricate a polymer-based high voltage composite cathode. The melt-processed cathode is dense (near zero porosity) and thick (65 μm) and has high active material loading (80 wt %). The active particles are distributed uniformly throughout the melt-processed cathode, unlike the traditional slurry-cast cathode, which exhibits inhomogeneous particle distribution. In the melt-processed cathode, polymer and carbon form separate phases, whereas in the slurry-cast cathode they blend into a single phase. Due to these structural differences, the melt-processed cathode shows smooth charge–discharge profiles, while the slurry-cast cathode shows noisy charging and soft-shorting behavior. In conclusion, this work highlights that twin-screw extrusion as a scalable, solvent-free manufacturing method is advantageous in producing uniform cathodes, which mitigates anode instability.

25 ENERGY STORAGE↗
DOE OSTI2025

Photoelectrochemical Imaging of Charge Separation between MoS 2 Triangles and Insulating SiO 2 Support

The role of the insulating support in photocatalysis is poorly understood. Using high-resolution photo-scanning electrochemical microscopy (photo-SECM), we observed significant spatial charge separation in few-layer-thick molybdenum disulfide (MoS 2 ) triangles attached to a SiO 2 substrate. Spatially resolved surface photovoltage (SPV) measurements revealed that photogenerated holes migrate from MoS 2 to the SiO 2 surface and travel laterally over distances exceeding 2 μm, driven by the builtin electric field of ~1.7 kV/cm. In thicker and less uniform flakes, the charge separation is dominated by internal driving forces within MoS 2 , without significant contribution from SiO 2 . These findings underscore the importance of insulator–semiconductor interactions for effective charge separation, suggesting a new strategy for optimizing photocatalytic systems.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2025

Predicting Flow in Fracture Networks With Quantum Algorithms

Uncertainty quantification plays a crucial role in the modeling of subsurface flow. For instance, uncertainties in the properties of geologic fracture networks significantly impact flow, requiring numerous simulations to accurately estimate quantities of interest. However, each simulation is computationally expensive because it requires solving a large linear system to capture features that involve both small and large fractures. An example is in percolation, where the interaction of many small fractures (which cumulatively can have a large surface area) with the rock matrix must be modeled precisely. Quantum computing is an emerging tool with the potential to address this issue. Quantum algorithms offer a significant speedup in solving linear systems, achieving efficiencies that are challenging to match with classical approaches. These classical approaches include direct solvers, such as LU decomposition, and iterative methods, notably preconditioned conjugate gradient, commonly used in subsurface modeling to solve large sparse systems. However, applying quantum algorithms to geologic fracture flow requires careful attention to algorithmic and problem-specific constraints to fully realize this quantum advantage. In this work we describe a quantum algorithm for generalized Monte Carlo applications with a quadratic speedup over the classical approaches which can be combined with the quantum speedup, currently under investigation, for solving quantum linear systems for subsurface flow. We show that for quantum algorithms the computational cost of estimating a quantity of interest for a statistical ensemble of networks is roughly the same as that of a single realization, essentially implying that one can get uncertainty quantification for free.

58 GEOSCIENCES↗
DOE OSTI2025

Understanding and tuning organocatalysts for versatile condensation polymer deconstruction

Plastics are widely used for their durability and versatility, but recycling remains a major challenge, especially for mixed or contaminated waste. Mechanical recycling works well for clean, single-polymer streams like PET but has limited efficiency for complex waste streams. Chemical recycling, particularly glycolysis, is often employed to selectively deconstruct condensation polymers under mild conditions. This study explores catalyst design for glycolysis using linear free energy (Hammett) analysis to evaluate how catalyst structure influences polymer deconstruction. Polycaprolactone (PCL) is used as a model polyester due to its solubility and low deconstruction temperature. Triazabicyclodecene (TBD) paired with benzoic acid derivatives depicts a clear linear trend in depolymerization rates with Hammett values. TBD with p-aminobenzoic acid (PABA) stands out for its catalytic efficiency, thermal stability, and scalability, along with PABA's commercial availability as vitamin B-10. The TBD : PABA catalyst not only effectively breaks down PCL but also enables sequential deconstruction of polycarbonate, PET, and Nylon in mixed waste streams. These results highlight the value of Hammett-guided catalyst design and establish TBD : PABA as a promising, scalable organocatalyst for mixed plastic recycling, enabling recovery of individual polymer building blocks from blended waste and offering a practical route toward circular plastics.

Zheng, Jackie [Univ. of Tennessee, Knoxville, TN (↗
DOE OSTI2026

Locating the missing large-scale emission in the jet of M87* with short EHT baselines

In very-long baseline interferometric arrays, nearly co-located stations probe the largest scales and typically cannot resolve the observed source. In the absence of a large-scale structure, closure phases constructed with these stations are zero and, since they are independent of station-based errors, they can be used to probe data issues. Here, we show how these trivial closure phases become nonzero with a brightness distribution on smaller scales than their short baseline would suggest. When applied to sources that are made up of a bright compact and large-scale diffuse component, the trivial closure phases directly measure the centroid relative to the compact source and higher-order image moments. We present a technique to measure these image moments with minimal model assumptions and validate it on synthetic Event Horizon Telescope (EHT) data. We then apply this technique to 2017 and 2018 EHT observations of M87* and find a weak preference for extended emission in the direction of the large-scale jet. We also apply it to 2021 EHT data and measure the source centroid about 1 mas northwest of the compact ring, which is consistent with the jet observed at lower frequencies.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2025

Effect of toolpath in large-format additive manufacturing with bio-derived composites

There has been growing interest in integrating bio-derived composites into Large-format additive manufacturing (LFAM) feedstocks to reduce the use of petroleum-derived materials and reduce the overall carbon footprint of LFAM. However, these materials present unique challenges during manufacturing due to their variability, which can lead to unintended deformations and failures attributable to suboptimal process conditions. While numerical modelling has been extensively employed to simulate numerous manufacturing processes, its application in LFAM with bio-derived composites remains limited. This study addresses this gap by systematically developing a numerical model to simulate the LFAM process using bio-based materials, specifically wood fiber-reinforced polylactic acid (PLA/WF). Experimental investigations were conducted to characterise the thermal and mechanical properties of additively manufactured PLA/WF specimens. Numerical simulations were performed to predict temperature profiles and deformations during LFAM. The effect of varying infill patterns, internal structures, and tool paths on the temperature distribution and deformation of printed parts was explored using the developed model. This article aims to advance the utilisation of bio-derived composites in LFAM systems and provide a comprehensive understanding of the LFAM process. The findings offer valuable insights for optimising process parameters and enhancing the performance of LFAM with bio-based composites.

Large-format additive manufacturing↗
DOE OSTI2025

Constraining gas motion and non-thermal pressure beyond the core of the Abell 2029 galaxy cluster with XRISM

We report on a detailed spectroscopic study of the gas dynamics and hydrostatic mass bias of the galaxy cluster Abell 2029, utilizing high-resolution observations from XRISM Resolve. Abell 2029, known for its cool core and relaxed X-ray morphology, provides an excellent opportunity to investigate the influence of gas motions beyond the central region. Expanding upon prior studies that revealed low turbulence and bulk motions within the core, our analysis covers regions out to the scale radius $R_{2500}$ (670 kpc) based on three radial pointings extending from the cluster center toward the northern side. We obtain accurate measurements of bulk and turbulent velocities along the line of sight. The results indicate that non-thermal pressure accounts for no more than 2% of the total pressure at all radii, with a gradual decrease outward. The observed radial trend differs from many numerical simulations, which often predict an increase in non-thermal pressure fraction at larger radii. These findings suggest that deviations from hydrostatic equilibrium are small, leading to a hydrostatic mass bias of around 2% across the observed area.

X-rays: galaxies: clusters↗
DOE OSTI2025

Thermal and kinematic properties of ejecta in SN1987A revealed by XRISM

We present an analysis of high-resolution spectra from the shock-heated plasmas in SN 1987A, based on an observation using the Resolve instrument onboard the X-Ray Imaging and Spectroscopy Mission (XRISM). The 1.7–10 keV Resolve spectra are accurately represented by a single-component, plane-parallel shock plasma model, with a temperature of $2.84_{-0.08}^{+0.09}$ keV and an ionization parameter of $2.64_{-0.45}^{+0.58}$ × $10^{11}\,\,{\rm s\,\, cm}^{-3}$. The Resolve spectra are also well reproduced by the 3D magneto-hydrodynamic simulation presented by Orlando et al. (2020, A&A, 636, A22) suggesting substantial contribution from the ejecta. The metal abundances obtained with Resolve align with the Large Magellanic Cloud value, indicating that the X-rays in 2024 originate from “non-metal-rich” shock-heated ejecta and the reverse shock has not reached the inner metal-rich region of ejecta. Doppler widths of the atomic lines from Si, S, and Fe correspond to velocities of 1500–1700 km s$^{-1}$, where the thermal broadening effects in this non-metal-rich plasma are negligible. Therefore, the line broadening seen in Resolve spectra is determined by the large bulk motion of ejecta. For reference, we determined a $90\%$ upper limit on non-thermal emission from a pulsar wind nebula at $4.3 \times 10^{-13}$ erg cm$^{-2}$ s$^{-1}$ in the 2–10 keV range, aligning with NuSTAR findings by Greco et al. (2022, ApJ, 931, 132). Additionally, we searched for the $^{44}$Sc K line feature and found a $1\sigma$ upper limit of $1.0 \times 10^{-6}$ photons cm$^{-2}$ s$^{-1}$, which translates to an initial $^{44}$Ti mass of approximately $2 \times 10^{-4}\, M_{\odot }$, consistent with previous X-ray to soft gamma-ray observations (Boggs et al. 2015, Science, 348, 670; Grebenev et al. 2012, Nature, 490, 373; Leising 2006, ApJ, 651, 1019).

ISM: supernova remnants↗
DOE OSTI2025

XRISM/Resolve view of Abell 2319: Turbulence, sloshing, and ICM dynamics

Here, we present results from XRISM/Resolve observations of the core of the galaxy cluster Abell 2319, focusing on its kinematic properties. The intracluster medium (ICM) exhibits temperatures of approximately 8 keV across the core, with a prominent cold front and a high-temperature region (⁠~11 keV) in the north-west. The average gas velocity in the 3' x 4' region around the brightest cluster galaxy (BCG) covered by two Resolve pointings is consistent with that of the BCG to within 40 km s -1 ⁠ and we found modest average velocity dispersion of 230–250 km s -1 ⁠. On the other hand, spatially resolved spectroscopy reveals interesting variations. A blueshift of up to ~230 km s -1 ⁠ is observed around the east edge of the cold front, where the gas with the lowest specific entropy is found. The region further south inside the cold front shows only a small velocity difference from the BCG; however, its velocity dispersion is enhanced to ~400 km s -1 ⁠, implying the development of turbulence. These characteristics indicate that we are observing sloshing motion with some inclination angle following BCG and that gas phases with different specific entropy participate in sloshing with their own velocities, as expected from simulations. No significant evidence for a high-redshift ICM component associated with the subcluster Abell 2319B was found in the region covered by the current Resolve pointings. These results highlight the importance of sloshing and turbulence in shaping the internal structure of Abell 2319. Further deep observations are necessary to better understand the mixing and turbulent processes within the cluster.

Astronomy and AstroPhysics↗
DOE OSTI2026

Gauging nexus between topological and fracton phases

Coupled layer constructions are a valuable tool for capturing the universal properties of certain interacting quantum phases of matter in terms of the simpler data that characterizes the underlying layers. In the study of fracton phases, the X-Cube model in 3+1D can be realized via such a construction by starting with a stack of 2+1D Toric Codes and turning on a coupling which condenses a composite "particle-string" object. In a recent work [Phys. Rev. B 112, 125124 (2025)], we have demonstrated that in fact, the particle-string can be viewed as a symmetry defect of a topological 1-form symmetry. In this paper, we study the result of gauging this symmetry in depth. We unveil a rich gauging web relating the X-Cube model to symmetry protected topological (SPT) phases protected by a mix of subsystem and higher-form symmetries, subsystem symmetry fractionalization in the 3+1D Toric Code, and non-trivial extensions of topological symmetries by subsystem symmetries. Here, our work emphasizes the importance of topological symmetries in non-topological, geometric phases of matter.

Anyons↗
DOE OSTI2025

Quantum entanglement correlations in double quark PDFs

Methods from Quantum Information Theory are used to scrutinize quantum correlations encoded in the two-quark density matrix over light-cone momentum fractions x 1 and x 2 . A nonperturbative three quark model light-cone wavefunction predicts significant nonclassical correlations associated with the “entanglement negativity” measure for asymmetric and small quark momentum fractions. We perform one step of QCD scale evolution of the entire density matrix, not just its diagonal (dPDF), by computing collinearly divergent corrections due to the emission of a gluon. Finally, we present first qualitative numerical results for single-step scale evolution of quantum entanglement correlations in double quark PDFs. At a higher Q 2 scale, the nonclassical correlations manifest in the dPDF for nearly symmetric momentum fractions.

Entanglement in field theory↗
DOE OSTI2024

Quantum embedding study of strain- and electric-field-induced Stark effects on the NV - center in diamond

The NV - color center in diamond has been demonstrated as a powerful nanosensor for quantum metrology due to the sensitivity of its optical and spin properties to external electric, magnetic, and strain fields. In view of these applications, we use quantum embedding to derive a many-body description of strain- and electric-field-induced Stark effects on the NV - center. Here, we quantify how strain longitudinal to the axis of NV - shifts the excited states in energy, while strain with a component transverse to the NV - axis splits the degeneracies of the 3 E and 1 E states. The largest effects are for the optically relevant 3 E manifold, which splits into E x and E y with transverse strain. From these responses we extract strain susceptibilities for the E x/y states within the quasilinear regime. Additionally, we study the many-body dipole matrix elements of the NV - and find a permanent dipole 1.6 D at zero strain, which is somewhat smaller than that obtained from recent density functional theory calculations. We also determine the transition dipole between the E x and E y and how it evolves with strain.

47 OTHER INSTRUMENTATION↗
DOE OSTI2025

Climate adaptation in Populus trichocarpa : key adaptive loci identified for stomata and leaf traits

We investigated adaptive genetic variation in Populus trichocarpa, a potential biofuel feedstock crop, to better understand how physiological traits may influence tolerance to water limitation. Our study focused on leaf and stomatal traits, given their roles in plant–water relations and adaptation. Using a diversity panel of over 1300 genotypes, we measured 14 leaf and stomatal traits under control (well-watered) and drought (water-limited) conditions. We conducted genome-wide association studies (GWAS), climate association analyses, and transcriptome (RNA-seq) profiling to identify genetic loci associated with phenotypic variation and adaptation. Stomatal traits, including size and density, were correlated with the climate of origin, with genotypes from more arid regions tending to have smaller but denser stomata. GWAS identified multiple loci associated with trait variation, including a major-effect region on chromosome 10 linked to stomatal size and abaxial contact angle. This locus overlapped with a tandem array of 3-ketoacyl-CoA synthase (KCS) genes and showed strong allele–climate and gene expression associations. Our findings reveal genetic and phenotypic variation consistent with local adaptation and suggest that future climates may favor alleles associated with smaller stomata, particularly under increasing aridity. This work provides insights into climate adaptation and breeding strategies for resilience in perennial crops.

Populus trichocarpa↗
DOE OSTI2026

Constitutive and inducible oleoresin defenses share genetic architectures and mechanisms in Pinus taeda

The oleoresin defense system of loblolly pine (Pinus taeda) protects trees from insects and pathogens and is an important source of renewable biofuels and chemicals, but the genetic basis of oleoresin production is poorly understood. We characterized the genetic architecture of oleoresin flow, resin canal number, stem wood terpene content, and monoterpene composition in two clonal populations of P. taeda. We used quantitative genetic analyses, genome-wide association studies (GWASs), multiplex network learning, and gene expression profiling to elucidate shared gene networks underlying defense traits and to identify high-quality candidates for breeding and engineering loblolly pine. Genetic analyses revealed polygenic inheritance and trait-to-trait correlations provide strong evidence for shared genes regulating constitutive and induced oleoresin flow. We identified 236 single nucleotide polymorphisms associated with oleoresin flow, resin canal number, and terpene composition and highlight candidate genes likely involved in terpene biosynthesis, cambial meristem reprogramming, and pathogen perception and immune signaling. Fourteen GWAS candidates were methyl jasmonate-responsive in tissues where resin canals initiate and terpene production occurs. Integrating quantitative genetics, GWAS, gene expression, and multiplex network analyses enabled the prioritization of high-quality candidate genes. This work advances the development of more resilient loblolly pine optimized for ecological performance, renewable chemical, and biofuel production.

genome-wide association study↗
DOE OSTI2026

Data for A Hybrid Biophysical-Machine Learning Framework for Diurnal Surface Energy Flux Estimation Using Proximal Sensing

Thermal infrared-based remote sensing of surface energy fluxes has traditionally relied on high spatial resolution satellite data with revisit frequencies on the order of weeks. In this study, we evaluate a biophysics-based analytical surface energy balance model for predicting latent energy (LE) and sensible heat (H) fluxes using proximal sensing observations. The Surface Temperature Initiated Closure (STIC1.2) model has been extensively validated across a wide range of spatial and temporal scales using various satellite-derived thermal infrared data sets. Here we extend this validation by applying STIC at sub-hourly temporal resolution over multiple growing seasons for four distinct agricultural systems. We further develop and evaluate novel STIC variants that incorporate machine learning (ML) techniques to eliminate the need for surface energy balance observations, specifically net radiation and soil heat flux, thereby enhancing model applicability in data-sparse settings. The integration of a ML component to estimate surface available energy is shown to have strong predictive performance for both LE (R2 = 0.81–0.94) and H (R2 = 0.46–0.72) across all agricultural systems examined here, demonstrating the potential of hybrid biophysical-machine learning approaches for surface energy balance modeling with minimal data requirements. This study concludes with a novel application of explainable machine learning (exML) to diagnose sources of model error. This exML framework attributes residual prediction errors to both model input variables and environmental drivers not explicitly included in the simulation experiments. This approach provides a new pathway for improving model design and integrating previously overlooked yet influential variables into future model iterations.

AI/ML↗
DOE OSTI2026

Data for: A hybrid biophysical-machine learning framework for diurnal surface energy flux estimation using proximal sensing

Thermal-based remote sensing of surface energy fluxes has traditionally relied on high spatial resolution satellite data with revisit frequencies on the order of weeks. In this study, we evaluate a biophysics-based analytical surface energy balance model for predicting latent energy (LE) and sensible heat (H) fluxes using proximal sensing observations. The Surface Temperature Initiated Closure (STIC1.2) model has been extensively validated across a wide range of spatial and temporal scales using various satellite-derived thermal datasets. Here we extend this validation by applying STIC at sub-hourly temporal resolution over multiple growing seasons for four distinct agricultural systems. We further develop and evaluate novel STIC variants that incorporate machine learning (ML) techniques to eliminate the need for specific surface energy balance observations, specifically net radiation and soil heat flux, thereby enhancing model applicability in data-sparse settings. The integration of an ML component to estimate surface available energy is shown to have strong predictive performance for both LE (R2 = 0.81-0.94) and H (R2 = 0.46-0.72) across all agricultural systems examined here, demonstrating the potential of hybrid biophysical – machine learning approaches for surface energy balance modeling with minimal data requirements. This study concludes with a novel application of explainable machine learning (exML) to diagnose sources of model error. This exML framework attributes residual prediction errors to both model input variables and environmental drivers not explicitly included in the simulation experiments. This approach provides a new pathway for improving model design and integrating previously overlooked yet influential variables into future model iterations.

Agricultural Sciences↗
DOE OSTI2025

Conductive Liquid Metal Vitrimer Composites for Reconfigurable and Recyclable Flexible Electronics

Liquid metal (LM) elastomer composites exhibit excellent functionality for stretchable electronics and wearables, but limited recycling and reuse pathways constrain their sustainable use. Here, to address these challenges amid growing concerns over electronic waste, a conductive LM–vitrimer composite is presented that enables recyclable and reconfigurable electronics. This soft and stretchable composite features uniformly distributed LM inclusions that enhance thermal conductivity by 6.53× and enable the formation of conductive traces with electrical self-healing, while the vitrimer provides structural restoration. The dynamic covalent bonds of the vitrimer matrix are leveraged for both reprocessing the composite and chemically recovering 94% of the LM. This liquid-state filler slightly reduces the vitrimer's stiffness to 2.63 MPa (≈20% lower), while maintaining its high stretchability (>135% strain) and thermal stability. It is further examined how ultrasonicated LM inclusions interact with the vitrimer matrix and demonstrate the composite's self-healing and recyclability through two distinct approaches: 1) thermomechanical reprocessing, which restores fragmented composites under heat and compression for circuit reconfiguration; and 2) chemical recycling, which recovers the embedded LM for reuse in fabricating new composites and redesigned circuitry. With the integration of recyclability and diverse functional capabilities, LM–vitrimer composites emerge as a promising material platform for sustainable, flexible electronics.

Han, Youngshang [Univ. of Washington, Seattle, WA ↗