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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 991 records · Page 55
All sourcesNASA NTRSDOE OSTI
DOE OSTI2022

Scaling of the strange-metal scattering in unconventional superconductors

Marked evolution of properties with minute changes in the doping level is a hallmark of the complex chemistry that governs copper oxide superconductivity as manifested in the celebrated superconducting domes and quantum criticality taking place at precise compositions. The strange-metal state, in which the resistivity varies linearly with temperature, has emerged as a central feature in the normal state of copper oxide superconductors. The ubiquity of this behaviour signals an intimate link between the scattering mechanism and superconductivity. However, a clear quantitative picture of the correlation has been lacking. In this study we report the observation of precise quantitative scaling laws among the superconducting transition temperature (T c ), the linear-in-T scattering coefficient (A 1 ) and the doping level (x) in electron-doped copper oxide La 2-x Ce x CuO 4 (LCCO). High-resolution characterization of epitaxial composition-spread films, which encompass the entire overdoped range of LCCO, has enabled us to systematically map its structural and transport properties with unprecedented accuracy and with increments of Δx = 0.0015. Additionally, we have uncovered the relations T c ~ (x c - x) 0.5 ~ (A 1 $\square$ ) 0.5 , where x c is the critical doping in which superconductivity disappears and A 1 $\square$ is the coefficient of the linear resistivity per CuO 2 plane. The striking similarity of the T c versus A 1 $\square$ relation among copper oxides, iron-based and organic superconductors may be an indication of a common mechanism of the strange-metal behaviour and unconventional superconductivity in these systems.

75 CONDENSED MATTER PHYSICS, SUPERCONDUCTIVITY AND↗
DOE OSTI2023

Intricate structure of the plasma Rayleigh–Taylor instability in shock tubes

Spikes and bubbles grow on unstable interfaces that are accelerated in high-energy-density conditions. If a shock propagates ahead of the interface, the plasma can be heated to extreme conditions where conduction and radiation fluxes influence the hydrodynamics. For example, a National Ignition Facility experiment found reduced single-mode nonlinear mixed-width growth in conditions scaled from a supernova explosion. We present high-resolution two-dimensional radiation hydrodynamic simulations with the Flash code that quantitatively reproduce the experiment. Radiative fluxes are primarily responsible for ablating the spike and removing the mushroom caps. The ablated plasma increases the mixed mass and forms a low-density halo with spikes forming in both directions. This is considerably more complex than classical instability. The halo is sensitive to ablative physics, so radiographing it may aid in the verification of energy transport modeling. Although ablation changes the spike shape, it has little effect on the overall mixed width for these parameters. This is because ablation enhances the bubble velocity but it has the opposite effect on the spike. The radiation transport instead suppresses the growth via increasing the shocked foam density, thus decreasing the Atwood number. Furthermore, a terminal velocity model including the rarefaction expansion agrees with the experimental mixed-width growth.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗
DOE OSTI2021

COVID-19 Biomarkers and Advanced Sensing Technologies for Point-of-Care (POC) Diagnosis

COVID-19, also known as SARS-CoV-2 is a novel, respiratory virus currently plaguing humanity. Genetically, at its core, it is a single-strand positive-sense RNA virus. It is a beta-type Coronavirus and is distinct in its structure and binding mechanism compared to other types of coronaviruses. Testing for the virus remains a challenge due to the small market available for at-home detection. Currently, there are three main types of tests for biomarker detection: viral, antigen and antibody. Reverse Transcription-Polymerase Chain Reaction (RT-PCR) remains the gold standard for viral testing. However, the lack of quantitative detection and turnaround time for results are drawbacks. This manuscript focuses on recent advances in COVID-19 detection that have lower limits of detection and faster response times than RT-PCR testing. The advancements in sensing platforms have amplified the detection levels and provided real-time results for SARS-CoV-2 spike protein detection with limits as low as 1 fg/mL in the Graphene Field Effect Transistor (FET) sensor. Additionally, using multiple biomarkers, detection levels can achieve a specificity and sensitivity level comparable to that of PCR testing. Proper biomarker selection coupled with nano sensing detection platforms are key in the widespread use of Point of Care (POC) diagnosis in COVID-19 detection.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2024

Defect Equilibria from First Principles: From Widegap Oxides to Topological Semimetals

Materials functionality and performance is rarely determined by the ideal crystal alone but is usually affected by formation of imperfections and the solution of impurities. In some applications, such as solar thermochemical hydrogen generation, defect formation is the fundamentally enabling mechanism of the desired functionality. In other cases, such as Cd3As2 topological semimetals, unintentional self-doping presents an obstacle to the access to the unique electronic properties. In either case, a quantitative understanding of the relevant defect mechanism is essential for developing design strategies. This presentation will touch upon numerous aspects in the computational simulation of defect equilibria, including non-equilibrium design strategies, the coupling of solid state and gas-phase reactions, dopant-defect and defect-defect interactions, both attractive and repulsive, the accuracy of total energy functionals and electronic structure methods, and the role of the shape of the density of states for the charge balance condition and Fermi level position, as well as machine-learning prediction of defect energies (1). Specific materials systems include Ga2O3 (2), Cd3As2 (3), and (Sr,Ce)MnO3 (4). (1) M.D. Witman, A. Goyal, T. Ogitsu, A.H. McDaniel, S. Lany, Nat. Comput. Sci. 3, 675 (2023). (2) A. Goyal, A. Zakutayev, V. Stevanovic, S. Lany, J. Appl. Phys. 129, 245704 (2021). (3) C. Brooks, M. van Schilfgaarde, D. Pashov, J.N. Nelson, K. Alberi, D.S. Dessau, S. Lany, Phys. Rev. B 107, 224110 (2023). (4) A. Goyal, M.D. Sanders, R.P. O'Hayre, S. Lany, PRX Energy 3, 013008 (2024).

CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSICS,M↗
DOE OSTI2025

Unusually Strong Near‐Infrared Photoluminescence of Highly Transparent Bulk InSe Flakes

Abstract Bulk γ‐InSe has a direct bandgap of 1.24 eV, which corresponds to near infrared wavelengths ( λ = 1.0 µm) useful in optoelectronic applications from biometric detectors to silicon photonics. However, its potential for optoelectronic applications is largely untapped due in part to the lack of quantitative studies of its optical properties. Here, the unusually low absorptance and high photoluminescence quantum efficiency of single‐crystalline InSe flakes with thickness in the hundreds of nanometers are studied. InSe emits brightly at room temperature from its direct bandgap with a peak photoluminescence quantum yield (PLQY) of 20%, despite displaying indirect bandgap like low absorption coefficient due to the symmetry of its crystal structure. By performing pump‐dependent PLQY measurements, the radiative and nonradiative recombination coefficients are extracted, including the Shockley‐Read‐Hall and Auger coefficients. Finally, a proof‐of‐concept alternating current electroluminescent device at low temperature is demonstrated to show the promise of InSe in optoelectronic technology such as highly transparent, bright NIR light sources.

Geng, Jamie↗
DOE OSTI2020

Experimental Modal Analysis Using Phase Quantities from Phase-Based Motion Processing and Motion Magnification

Phase-based motion processing and the associated Motion Magnification that it enables has become popular not only for the striking videos that it can produce of traditionally stiff structures visualized with very large deflections, but also for its ability to pull information out of the noise floor of images so that they can be processed with more traditional optical techniques such as digital image correlation or feature tracking. While the majority of papers in the literature have utilized the Phase-based Image Processing approach as a pre-processor for more quantitative analyses, the technique itself can be used directly to extract modal parameters from an image, noting that the extracted phases are proportional to displacements in the image. Therefore, once phases are extracted, they can be fit using traditional experimental modal analysis techniques. This produces a mode “shape” where the degrees of freedom are phases instead of physical motions. These phases can be scaled to produce on-image visualizations of the mode shapes, rather than operational shapes produced by bandpass filtering. Modal filtering techniques can also be used to visualize motions from an environment on an image using the modal phases as a basis for the expansion.

47 OTHER INSTRUMENTATION↗
DOE OSTI2021

Twinning-mediated anomalous alignment of rutile films revealed by synchrotron X-ray nanodiffraction

Nanotwin structures in materials engender fascinating exotic properties. However, twinning usually alter the crystal orientation, resulting in random orientation and limited performances. Here, we report a well-aligned rutile TiO 2 nanotwin film with superior preferential orientation than its isostructural substrate. By means of the synchrotron X-ray Laue nanodiffraction technique, the crystal orientation, twin boundaries, and deviatoric stresses of the film were quantitatively imaged at unprecedented spatial resolution to unravel the underlying mechanism of this anomalous alignment. Massive {101}-type rutile nanotwins were observed, and a crystallographic relationship of the heteroepitaxy was proposed. The rapid twinning and twin-controlled heteroepitaxy are responsible for the texture improvement. This work would open up opportunities for rational design of better twin-based functional materials, and implies the powerful capabilities of X-ray nanodiffraction technique for multidisciplinary applications.

36 MATERIALS SCIENCE↗
DOE OSTI2026

QCD Theory and Transverse Momentum Dependent Physics

A major challenge for QCD theorists is to understand how to different categories of QCD physics contribute to a single observable. This is the goal of QCD factorization theory. Today, QCD physicists are tasked with describing increasingly detailed properties of transverse momentum dependent particle distributions, including their shapes and normalizations, their variation with energy and other kinematical variables, the identity of particles involved, correlations with spin, and the relationship between transverse momentum distributions in different processes. Embedded within that broad effort are specific fundamental questions at the heart of QCD theory. Namely, how does one give precise, quantitative meaning to quark and gluon concepts in hard scattering events, and what are the limits of those concepts? In the past two decades, there has been a renaissance in TMD QCD, with a broad spectrum of motivations driving the study of TMD observables. TMD QCD studies are in a transformative stage; subfields that traditionally progressed nearly independently of one other, such as hadronic structure studies and the study of perturbative QCD gluon radiation in high-energy collisions, are becoming intertwined. It is thus becoming increasingly clear that unified approaches -- those that incorporate all the relevant phenomena into a single formalism rooted in QCD fundamentals -- will be critical to the mission to expand the current understanding of QCD nuclear/hadronic dynamics. TMD QCD factorization theorems and their generalizations will be central to these efforts. The factorization theorems, in addition, provide the practical prescription for applying specific calculations to phenomenology. The goal of this project was to unify and push the boundaries of traditional TMD research and prepare it for use in future phenomenological applications.

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗
DOE OSTI2023

LDRD 22A1059-068FP Tailoring the Properties of Multi-Phase Materials Through the Use of Correlative Microscopy and Machine Learning - Poster

High strength alloys with good ductility, hardness, and toughness are needed to meet stringent design requirements for extreme environments. One complication in this pursuit is the evidence that metals rarely exhibit both high strength and good fracture toughness as the underlying mechanisms work in opposition. An exception to this behavior is found in multiphase alloys that form complex microstructures of mixed phases with variable grain sizes and shapes that provide increased fracture toughness by the arrangement of their constituent elements. We propose to explore this phenomenon using state-of-the-art machine learning (ML) techniques in a new and novel manner to identify and correlate the critical microstructural features in a Titanium-10Vanadium-2Iron-3Aluminum (Ti-10V-2Fe-3Al) alloy that is reported to exhibit high strength and fracture toughness. Additionally, we will employ multiple, complementary characterization techniques such as optical microscopy, electron backscatter diffraction (EBSD), energy dispersive spectroscopy (EDS) and scanning electron microscopy to provide multi-layer, quantitative ground truth measures of the microstructures. This data will be used to train a Convolutional Neural Network (CNN) in a semi-supervised environment to identify key microstructural features such as ? platelet dimensions and locations and ?/? phase boundaries and correlate those features with the strength and toughness. Here the ? and ? nomenclature refers to hexagonal close pack (hcp) and body center cubic (bcc) crystal structures, respectively. Previous work has focused on popular alloys and typically used one characterization technique. This research is focused on a promising titanium alloy, uses multiple complimentary characterization tools to provide precise microstructural information and correlates to improved fracture toughness. The resulting ML tool can be trained for additional microstructural features, different alloy(s), and or target mechanical properties.

36 MATERIALS SCIENCE↗
DOE OSTI2023

Computational Screening of Cationic Zeolites for n -Butane/Methane Separations Using Quantitatively Accurate First-Principles-Derived Force Fields

We developed force fields for linear alkanes in Na and Ca-exchanged zeolites based on periodic first-principles calculations with coupled cluster corrections. These force fields were validated by comparing the simulated adsorption properties with extensive experimental data. As an example of using these force fields, we screened silica and aluminosilicate zeolites for separation of n-butane/methane mixtures in several different separation processes. A key step in this screening was the use of an interpolation method to create accurate zeolite structures that take account of the lattice constant changes for different Si/Al ratios for a given zeolite topology. We focused on zeolite structures that have known synthesis routes, so the materials selected from our calculations can be tested experimentally. Several promising materials that show good separation performance were selected for more detailed simulations.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2024

Large exchange-driven intrinsic circular dichroism of a chiral 2D hybrid perovskite

Abstract In two-dimensional chiral metal-halide perovskites, chiral organic spacers endow structural and optical chirality to the metal-halide sublattice, enabling exquisite control of light, charge, and electron spin. The chiroptical properties of metal-halide perovskites have been measured by transmissive circular dichroism spectroscopy, which necessitates thin-film samples. Here, by developing a reflection-based approach, we characterize the intrinsic, circular polarization-dependent complex refractive index for a prototypical two-dimensional chiral lead-bromide perovskite and report large circular dichroism for single crystals. Comparison with ab initio theory reveals the large circular dichroism arises from the inorganic sublattice rather than the chiral ligand and is an excitonic phenomenon driven by electron-hole exchange interactions, which breaks the degeneracy of transitions between Rashba-Dresselhaus-split bands, resulting in a Cotton effect. Our study suggests that previous data for spin-coated films largely underestimate the optical chirality and provides quantitative insights into the intrinsic optical properties of chiral perovskites for chiroptical and spintronic applications.

25 ENERGY STORAGE↗
DOE OSTI2023

NeRVI: Compressive neural representation of visualization images for communicating volume visualization results

We present NeRVI, a new deep-learning approach that compresses a large collection of visualization images generated from time-varying data for communicating volume visualization results. Based on an image-based implicit neural representation, our approach represents tens of thousands of high-resolution rendering images parametrized by different parameters via a hybrid model of multilayer perceptrons and convolutional neural networks. Here, our model predicts images and corresponding masks, and the masks are utilized for loss computation and network training to capture fine structural details and small components. In conjunction with model quantization and weight encoding, NeRVI yields highly compact compressive neural representations while preserving the image fidelity well. We demonstrate the effectiveness of NeRVI with isosurface rendering and direct volume rendering images generated from multiple data sets and compare NeRVI with other state-of-the-art deep learning-based (InSituNet, SIREN, NeRF, and NeRV) methods. Quantitative and qualitative results show that NeRVI provides an alternative solution that augments domain scientists' ability to manage, represent, and communicate scientific visualization output.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2026

Molecular concentration field design using closed-form steady-state solutions

Control over spatial concentration fields represents a fundamental challenge in designing synthetic biological systems and programmable soft materials. While nature creates morphogen gradients that orchestrate complex developmental processes, synthetic approaches have largely relied on empirical optimization and computationally intensive simulations. Here, we present an analytical framework for steady-state concentration fields generated by finite-sized localized sources in diffusion–degradation systems and derive closed-form solutions for one-, two-, and three-dimensional geometries. By expressing these solutions in dimensionless form, we show that gradient steepness and spatial structure are organized by the Thiele modulus, which captures the competition between diffusion and degradation length scales. The analysis reveals distinct design regimes: in degradation-dominated systems, gradient shape is governed by exponential decay and becomes dimension-independent, whereas in diffusion-dominated systems, gradient magnitude and extent follow dimension-dependent power-law scaling. Building on these results, we introduce a quantitative design strategy that uses threshold-based criteria to program concentration ranges by tuning physically accessible parameters, most directly the production rate, while holding transport and degradation properties fixed. Comparisons with numerical solutions and reported experimental systems demonstrate consistency with the predicted scaling behavior. Together, this work provides a generalizable and physically transparent framework for designing steady-state concentration fields in synthetic biological and soft matter systems, enabling predictive control of gradient-mediated organization without reliance on extensive numerical optimization.

Kim, Dong Woo [Johns Hopkins University, Baltimore↗
DOE OSTI2020

Direct imaging of plasma waves using ultrafast electron microscopy

A femtosecond plasma imaging modality based on a new development of ultrafast electron microscope is introduced. We investigated the laser-induced formation of high-temperature electron microplasmas and their subsequent non-equilibrium evolution. Based on a straightforward field imaging principle, we directly retrieve detailed information about the plasma dynamics, including plasma wave structures, particle densities, and temperatures. We discover that directly subjected to a strong magnetic field, the photo-generated microplasmas manifest in novel transient cyclotron echoes and form new wave states across a broad range of field strengths and different laser fluences. Intriguingly, the transient cyclotron waves morph into a higher frequency upper-hybrid wave mode with the dephasing of local cyclotron dynamics. The quantitative real-space characterizations of the non-equilibrium plasma systems demonstrate the feasibilities of a new microscope system in studying the plasma dynamics or transient electric fields with high spatiotemporal resolutions.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗
DOE OSTI2024

Enhancing Power Grid Resilience with Causal Loops Diagram and Bayesian Networks

Enhancing power grid resilience through improved analysis and planning of Distributed Energy Resources is a key for power system planner. This paper explores the integration of Causal Loop Diagrams (CLDs) and Bayesian Networks (BNs) for enhancing resilience in power systems, focusing on Distributed Energy Resources (DER) planning. By automating CLD analysis in Python's matplotlib, we present a tool for rapid model validation and structural accuracy, crucial for power system planners. This hybrid approach utilizes BNs for inferential depth and CLDs for dynamic system modeling, offering a comprehensive framework for policy formulation and collaborative strategy development against disruptions. Here, we highlight the tool's capability to identify and analyze interconnected feedback loops, facilitating a deeper understanding of DER integration's impact on network resilience. This work aims to bridge quantitative analysis and qualitative insights, addressing the limitations of each method while providing a robust model for power system resilience assessment.

14 SOLAR ENERGY↗
DOE OSTI2022

Building surrogate models of nuclear density functional theory with Gaussian processes and autoencoders

From the lightest Hydrogen isotopes up to the recently synthesized Oganesson (Z = 118), it is estimated that as many as about 8,000 atomic nuclei could exist in nature. Most of these nuclei are too short-lived to be occurring on Earth, but they play an essential role in astrophysical events such as supernova explosions or neutron star mergers that are presumed to be at the origin of most heavy elements in the Universe. Understanding the structure, reactions, and decays of nuclei across the entire chart of nuclides is an enormous challenge because of the experimental difficulties in measuring properties of interest in such fleeting objects and the theoretical and computational issues of simulating strongly-interacting quantum many-body systems. Nuclear density functional theory (DFT) is a fully microscopic theoretical framework which has the potential of providing such a quantitatively accurate description of nuclear properties for every nucleus in the chart of nuclides. Thanks to high-performance computing facilities, it has already been successfully applied to predict nuclear masses, global patterns of radioactive decay like β or γ decay, and several aspects of the nuclear fission process such as, e.g., spontaneous fission half-lives. Yet, predictive simulations of nuclear spectroscopy—the low-lying excited states and transitions between them—or of nuclear fission, or the quantification of theoretical uncertainties and their propagation to basic or applied nuclear science applications, would require several orders of magnitude more calculations than currently possible. However, most of this computational effort would be spent into generating a suitable basis of DFT wavefunctions. Such a task could potentially be considerably accelerated by borrowing tools from the field of machine learning and artificial intelligence. In this paper, we review different approaches to applying supervised and unsupervised learning techniques to nuclear DFT.

73 NUCLEAR PHYSICS AND RADIATION PHYSICS↗
DOE OSTI2021

Galaxy assembly bias and large-scale distribution: a comparison between IllustrisTNG and a semi-analytic model

In this work, we compare large scale structure observables for stellar mass selected samples at z = 0, as predicted by two galaxy models, the hydrodynamical simulation IllustrisTNG and the Santa-Cruz semi-analytic model (SC-SAM). Although both models have been independently calibrated to match observations, rather than each other, we find good agreement between the two models for two-point clustering and galaxy assembly bias signatures. The models also show a qualitatively similar response of occupancy and clustering to secondary halo parameters other than mass, such as formation history and concentration, although with some quantitative differences. Thus, our results demonstrate that the galaxy–halo relationships in SC-SAM and TNG are quite similar to first order. However, we also find areas in which the models differ. For example, we note a strong correlation between halo gas content and environment in TNG, which is lacking in the SC-SAM, as well as differences in the occupancy predictions for low-mass haloes. Moreover, we show that higher order statistics, such as cumulants of the density field, help us to accurately describe the galaxy distribution and discriminate between models that show degenerate behaviour for two-point statistics. Our results suggest that SAMs are a promising cost-effective and intuitive method for generating mock catalogues for next generation cosmological surveys.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2026

Supramolecular Assembly of Multielement Ribbon-like Structures Derived from Halide Perovskites

Halide perovskites are crucial materials with broad applications owing to their exceptional optoelectronic properties. Vacancy-ordered double perovskites, featuring highly tunable transition metal sites, enable controllable optoelectronic properties through multielement compositional design. Here, in this study, we introduced 18-crown-6 into the vacancy-ordered double perovskites system and developed two-dimensional ribbon-like single crystals (18C6@K) 2 {PtSnTeIrRe} 1 Cl 6 via an antisolvent supramolecular assembly method, demonstrating morphology modulation through multielement composition design. The crystals crystallize in the centrosymmetric space group $P\bar{1}$. The dumbbell-shaped structural units (crown ether@A)2MX6 pack along a and b axes to form a 2-dimensional (2D) monolayer, and these monolayers further stack along the c axis to generate the ribbon-like single crystals. Energy-dispersive X-ray spectroscopy (EDX) qualitatively confirmed the uniform distribution of the five transition metals throughout the crystal, while inductively coupled plasma atomic emission spectroscopy (ICP–AES) quantitatively verified their atomic ratios. We further investigated the origin of the morphology, distinct from the previously reported cube-like single crystals with the $R\bar{3}$ space group. When acetonitrile was used as the solvent, three-dimensional crystals with $R\bar{3}$ symmetry were obtained, whereas dimethylformamide (DMF) was essential for forming two-dimensional ribbon-like single crystals. The essential role of DMF could be ascribed to its capability to maintain a higher concentration of the building blocks. Moreover, Ir 4+ and Pt 4+ cations also played critical roles in inducing the two-dimensional ribbon-like morphology. The three-element (18C6@K) 2 {PtSnTe}1Cl 6 single crystals exhibited bright yellow emission under 375 nm laser excitation, demonstrating the tunability of optoelectronic properties of this class of material.

Zhu, Heqing [University of California, Berkeley, C↗