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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 361 records · Page 20
All sourcesNASA NTRSDOE OSTI
DOE OSTI2025

Subsurface Interface Structure Controlling Local Electronic Properties of Epitaxial Graphene on SiC(0001)

Recently realized high-mobility semiconducting epitaxial graphene on silicon carbide (SiC) [Zhao, J. Nature 2024, 625 (7993), 60−65, 10.1038/s41586-023-06811-0] provided an important step toward integration of the graphene-based system into active components in postsilicon micro- and nanoelectronics. However, the exact atomic-scale structure and complex bonding configurations of the first epitaxial graphene carbon layer (C buffer ) remain an open problem. Our recent report [Kolmer, M. Communications Physics 2024, 7 (1), 16, 10.1038/s42005-023-01515-3] has shed new light on understanding this interface, where the external transverse electric field-dependent dynamic switching behavior of the C buffer –SiC bonds was observed. Here, using scanning tunneling microscopy and spectroscopy (STM and STS), we present direct evidence of silicon (Si) vacancies at the interface and provide their distribution at the topmost reconstructed SiC(0001) layer. Bias voltage and epitaxial graphene thickness-dependent characterization of the collective C buffer –SiC interface showed that “Si” vacancy sites beneath C buffer are stable under STM electric fields. Moreover, the vacancies introduce localized electronic states below the Fermi level, thereby enhancing the charge-transfer phenomenon across the interface.

Thupakula, Umamahesh [Ames Laboratory (AMES), Ames↗
DOE OSTI2025

Directed Self-Assembly of the Organic Semiconductor C8-BTBT-C8 in Anodic Aluminum Oxide Nanopores

Controlling the self-assembly of organic semiconductors at the nanoscale is critical for advancing high-performance electronic and photonic devices, yet it remains challenging due to their intrinsic anisotropic crystallization and sensitivity to processing conditions. Here, in this study, we demonstrate that cylindrical nanoconfinement within anodic aluminum oxide membranes provides a versatile platform to precisely tune the molecular orientation and phase behavior of the prototypical organic semiconductor 2,7-dioctyl[1]benzothieno[3,2-b][1]benzothiophene (C8-BTBT-C8). Combining temperature-dependent high-resolution synchrotron X-ray scattering with optical birefringence measurements, we uncover that confinement geometries (pore diameters 25–180 nm) and surface chemistry govern the emergence of distinct smectic A textures, featuring molecular layers either parallel or perpendicular to the pore axis. The competition between axial and radial smectic layering is modulated by pore size, surface hydrophilicity, and thermal history, enabling reversible control over domain orientations and transitions between liquid crystalline and crystalline states. Notably, nanoconfinement stabilizes the smectic phase over an expanded temperature range compared to bulk, while inducing complex multidomain configurations owing to geometric constraints and anchoring conditions. Our results elucidate fundamental mechanisms by which anisotropic nanoscale confinement directs the self-organization of highly conjugated organic molecules, with implications for optimizing directional charge transport and anisotropic optical responses in organic–inorganic hybrid nanoarchitectures. This study establishes nanoconfinement as a powerful strategy to engineer morphology and functional properties in organic semiconducting materials with nanoscale precision.

36 MATERIALS SCIENCE↗
DOE OSTI2020

Fast predictions of liquid-phase acid-catalyzed reaction rates using molecular dynamics simulations and convolutional neural networks

The rates of liquid-phase, acid-catalyzed reactions relevant to the upgrading of biomass into high-value chemicals are highly sensitive to solvent composition and identifying suitable solvent mixtures is theoretically and experimentally challenging. We show that the complex atomistic configurations of reactant–solvent environments generated by classical molecular dynamics simulations can be exploited by 3D convolutional neural networks to enable accurate predictions of Brønsted acid-catalyzed reaction rates for model biomass compounds. We develop a 3D convolutional neural network, which we call SolventNet, and train it to predict acid-catalyzed reaction rates using experimental reaction data and corresponding molecular dynamics simulation data for seven biomass-derived oxygenates in water–cosolvent mixtures. We show that SolventNet can predict reaction rates for additional reactants and solvent systems an order of magnitude faster than prior simulation methods. This combination of machine learning with molecular dynamics enables the rapid, high-throughput screening of solvent systems and identification of improved biomass conversion conditions.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2022

Passenger Boarding Station and Curbfront Configuration Concepts for On-Demand Services with Small Automated Vehicles

This paper explores the various configurations of off-line stations and vehicle berthing that have been found to be fundamentally important parameters in the analysis of system performance, operations, and capacity. The proposed automated transit network system concept, which provides a direct ride between a passenger's origin and destination station, is applicable to both fixed guideway automated transit and to systems with self-driving vehicles. Addressed at a conceptual level is the vehicle/station interface in terms of Americans with Disability Act compliance and station capacity concerns. The concept of open-edge platforms monitored by advanced sensing technology combined with artificial intelligence-powered perception for the purpose of protecting passengers from entering the active vehicle lanes is also discussed. Finally, the paper draws initial conclusions on how these station configurations, operational complexities, and associated costs can be better understood through appropriate research and development.

ADVANCED PROPULSION SYSTEMS↗
DOE OSTI2026

An analytic approximation to the covariance between pre- and post-reconstruction galaxy two-point statistics

We present a simple analytic approximation for the covariance between pre-reconstruction galaxy power spectrum measurements and post-reconstruction two-point correlation functions. This cross-covariance is essential for joint analyses that combine full-shape clustering information with baryon acoustic oscillation (BAO) measurements, as commonly performed in modern spectroscopic surveys. Our model builds on the disconnected contribution to the covariance and accounts for the damping of correlations due to the BAO reconstruction process. We validate our analytic prescription against numerical simulations from the Dark Energy Spectroscopic Instrument (DESI), testing both idealized cubic geometries and realistic survey configurations including complex footprints and fiber assignment effects. Despite neglecting survey window functions in the analytic calculation, we find excellent agreement with simulation-based covariances and demonstrate that cosmological parameter constraints are virtually unchanged when using our approximation. Our results show that the pre-post cross-covariance is sufficiently small that even approximate treatments are adequate for cosmological inference, opening a pathway toward fully analytic covariance matrices for next-generation galaxy surveys.

baryon acoustic oscillations↗
DOE OSTI2021

Direct observation of ion micromotion in a linear Paul trap

In this paper, the first direct observation of micromotion for multiple ions in a laser-cooled trapped ion crystal is discussed along with a novel measurement technique for micromotion amplitude. Micromotion is directly observed using a time-resolving, single-photon sensitive camera that provides both fluorescence and position data for each ion on the nanosecond time scale. Micromotion amplitude and phase for each ion in the crystal is measured which allows this method to be sensitive to tilts and shifts of the ion chain from the null of the radiofrequency quadrupole potential in the linear trap. Spatial resolution makes this micromotion detection technique suitable for complex ion configurations, including two-dimensional geometries. It does not require any additional equipment or laser beams, and the modulation of the cooling lasers or trap voltages is not necessary for detection, as it is in other methods.

74 ATOMIC AND MOLECULAR PHYSICS↗
DOE OSTI2022

Dissipative Reactions with Intermediate-Energy Beams: A Novel Approach to Populate Complex-Structure States in Rare Isotopes

A novel pathway for the formation of multiparticle-multihole excited states in rare isotopes is reported from highly energy- and momentum-dissipative inelastic-scattering events measured in reactions of an intermediate-energy beam of 38 Ca on a Be target. The negative-parity, complex-structure final states in 38 Ca are observed following the in-beam γ-ray spectroscopy of events in the 9 Be( 38 Ca, 38 Ca + γ)X reaction in which the scattered projectile loses longitudinal momentum of order Δp || = 700 MeV/c. Here, the characteristics of the observed final states are discussed and found to be consistent with the formation of excited states involving the rearrangement of multiple nucleons in a single, highly energetic projectile-target collision. Unlike the far-less-dissipative, surface-grazing reactions usually exploited for the in-beam γ-ray spectroscopy of rare isotopes, these more energetic collisions appear to offer a practical pathway to nuclear-structure studies of more complex multiparticle configurations in rare isotopes—final states conventionally thought to be out of reach with high-luminosity fast-beam-induced reactions.

73 NUCLEAR PHYSICS AND RADIATION PHYSICS↗
DOE OSTI2024

Linear-frequency conversion with time-varying metasurfaces

Frequency conversion is a hallmark of nonlinearity. The spectral manifestations, emergent within a system, can typically be attributed to a marked nonlinearity within the material properties, complex geometric configurations, and/or the unique functional form of interactions taking place in the constitutive subsystems. These phenomena, irrespective of their origins, have been harnessed and exploited in applications ranging from the generation of entangled photons, a cornerstone in quantum technologies, to nanomechanical frequency mixing, advancing subsurface scanning probe microscopy. Here, we propose a frequency conversion mechanism based on time-varying metasurfaces, an emerging frontier in metamaterial research. We show how temporal properties of metasurfaces can effectively emulate a nonlinear medium, thereby facilitating frequency conversion. The proposed material configuration has the potential not only to advance integrated photonics and quantum optics, but also to create opportunities in quantum sensing, quantum materials, and crucially quantum communications. Published by the American Physical Society 2024

75 CONDENSED MATTER PHYSICS, SUPERCONDUCTIVITY AND↗
DOE OSTI2024

Learning Molecular Mixture Property Using Chemistry-Aware Graph Neural Network

Recent advances in machine learning (ML) are expediting materials discovery and design. One significant challenge facing ML for materials is the expansive combinatorial space of potential materials formed by diverse constituents and their flexible configurations. This complexity is particularly evident in molecular mixtures, a frequently explored space for materials, such as battery electrolytes. Owing to the complex structures of molecules and the sequence-independent nature of mixtures, conventional ML methods have difficulties in modeling such systems. Here, we present MolSets, a specialized ML model for molecular mixtures, to overcome the difficulties. Representing individual molecules as graphs and their mixture as a set, MolSets leverages a graph neural network and the deep sets architecture to extract information at the molecular level and aggregate it at the mixture level, thus addressing local complexity while retaining global flexibility. We demonstrate the efficacy of MolSets in predicting the conductivity of lithium battery electrolytes and highlight its benefits in the virtual screening of the combinatorial chemical space. Published by the American Physical Society 2024

Zhang, Hengrui (ORCID:0000000231831654)↗
DOE OSTI2025

Using 3D Measurements from MRI to Improve VVUQ

Magnetic resonance velocimetry (MRV) is a diagnostic that can measure 3D, three-component turbulent velocity fields in arbitrarily complex flow configurations. The approach uses magnetic resonance imaging (MRI), which is a system commonly available in radiology departments or medical research centers. MRV acquires signal from hydrogen protons in water channel flows. Despite the obvious utility of the measurements for investigating a variety of flows, comparisons with computational fluid dynamic (CFD) simulations largely retain traditional metrics, comparing velocity profiles and planes at discrete regions within the flow rather than using the 3D nature of the measured field. In this effort, MRV was conducted in a simple water channel with six spanwise-centered periodic obstacles roughly shaped as cubes. At a fully turbulent Reynolds number of 15,000, the channel includes two flows: a main streamwise flow and, in the wake of the second obstacle, an injected flow oriented perpendicular to the streamwise flow. The flow geometry includes partial obstacles on the side walls and is inherently 3D because of the interaction of the two streams and the wake features, making the flow challenging to completely measure with traditional optical techniques. Aside from line profiles and planar comparisons, two 3D metrics are used to compare the data with a steady Reynolds-averaged Navier–Stokes (RANS) simulation result. A brief discussion about the comparison is provided, including comments about uncertainty.

Benson, Mike [ORNL] (ORCID:000000023210116X)↗
DOE OSTI2021

Muon reconstruction and identification efficiency in ATLAS using the full Run 2 $pp$ collision data set at $\sqrt{s}=13$ TeV

This article documents the muon reconstruction and identification efficiency obtained by the ATLAS experiment for 139 fb$^{-1}$ of $pp$ collision data at $\sqrt{s}=13$ TeV collected between 2015 and 2018 during Run 2 of the LHC. The increased instantaneous luminosity delivered by the LHC over this period required a reoptimisation of the criteria for the identification of prompt muons. Improved and newly developed algorithms were deployed to preserve high muon identification efficiency with a low misidentification rate and good momentum resolution. The availability of large samples of $Z\to\mu\mu$ and $J/\psi\to\mu\mu$ decays, and the minimisation of systematic uncertainties, allows the efficiencies of criteria for muon identification, primary vertex association, and isolation to be measured with an accuracy at the per-mille level in the bulk of the phase space, and up to the percent level in complex kinematic configurations. Excellent performance is achieved over a range of transverse momenta from 3 GeV to several hundred GeV, and across the full muon detector acceptance of $|\eta|<2.7$.

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗
DOE OSTI2022

Techno-economic assessment for the production of algal fuels and value-added products: opportunities for high-protein microalgae conversion

Abstract Background Microalgae possess numerous advantages for use as a feedstock in producing renewable fuels and products, with techno-economic analysis (TEA) frequently used to highlight the economic potential and technical challenges of utilizing this biomass in a biorefinery context. However, many historical TEA studies have focused on the conversion of biomass with elevated levels of carbohydrates and lipids and lower levels of protein, incurring substantial burdens on the ability to achieve high cultivation productivity rates relative to nutrient-replete, high-protein biomass. Given a strong dependence of algal biomass production costs on cultivation productivity, further TEA assessment is needed to understand the economic potential for utilizing potentially lower-cost but lower-quality, high-protein microalgae for biorefinery conversion. Results In this work, we conduct rigorous TEA modeling to assess the economic viability of two conceptual technology pathways for processing proteinaceous algae into a suite of fuels and products. One approach, termed mild oxidative treatment and upgrading (MOTU), makes use of a series of thermo-catalytic operations to upgrade solubilized proteins and carbohydrates to hydrocarbon fuels, while another alternative focuses on the biological conversion of those substrates to oxygenated fuels in the form of mixed alcohols (MA). Both pathways rely on the production of polyurethanes from unsaturated fatty acids and valorization of unconverted solids for use as a material for synthesizing bioplastics. The assessment found similar, albeit slightly higher fuel yields and lower costs for the MA pathway, translating to a residual solids selling price of $899/ton for MA versus $1033/ton for MOTU as would be required to support a $2.50/gallon gasoline equivalent (GGE) fuel selling price. A variation of the MA pathway including subsequent upgrading of the mixed alcohols to hydrocarbon fuels (MAU) reflected a required solids selling price of $975/ton. Conclusion The slight advantages observed for the MA pathway are partially attributed to a boundary that stops at oxygenated fuels versus fungible drop-in hydrocarbon fuels through a more complex MOTU configuration, with more comparable results obtained for the MAU scenario. In either case, it was shown that an integrated algal biorefinery can be economical through optimal strategies to utilize and valorize all fractions of the biomass.

09 BIOMASS FUELS↗
DOE OSTI2023

Tomographic optical emission spectroscopy of atmospheric pressure plasma interacting with complex surfaces

Plasma distribution in 3D space is heavily influenced by complex surfaces and the coupling interactions between plasma properties and interfacing material properties. For example, guided streamers that transition to surface ionization waves (SIWs) and propagate over structured dielectrics experience field enhancements that can lead to localized increases in ionization rates and complex 3D configurations that are difficult to analyze. Investigating these configurations requires techniques than can provide a more complete 3D picture. To help address this capability gap, a tomographic optical emission spectroscopy (tomo-OES) diagnostic system has been developed at Sandia National Laboratories that can resolve SIWs. The system includes four intensified cameras that measure the angular projections of the plasma light emission through bandpass filters. A dot calibration target co-registers each angular projection to the same voxel grid and an algebraic reconstruction technique (ART) recovers the light intensity at each voxel. An atmospheric pressure plasma jet (APPJ), provided by Peter Bruggeman, has been investigated and representative results are shown in Figure 1. Here, a bandpass filter was used to isolate emission from the N 2 second positive system (SPS) at 337.1 nm to capture the transition of the streamer to SIW on a planar dielectric surface (relative permittivity 3.3) located 3 mm below the APPJ [3]. The surface wave velocity was 3.5x10 4 (m/s), consistent with measurements made by Steven Shannon. Characterization of this APPJ will support the group effort of standing up a reproducible APPJ across institutions for applications such as liquid treatment, catalysis, and plasma aided combustion. Future work will investigate non-planar surfaces and eventually develop tomographic laser-induced fluorescence (tomo-LIF) approaches.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗
DOE OSTI2025

Unified Universal Control and Coordination of Inverter-Based Resources, and Validation for a PV + Battery Hybrid Plant

As renewable energy deployment grows, hybrid power plants (HPPs) combining photovoltaic (PV) and battery systems must evolve to offer both energy and grid stability services. These systems typically include a mix of grid-following (GFL) and grid-forming (GFM) inverters, presenting unique coordination and control challenges. This Department of Energy–funded project developed and validated a Unified Universal Control and Coordination (UUCC) framework for such PV + battery hybrid plants, enabling seamless and stable operation, including ultrafast black start, autonomous synchronization, and robust frequency and voltage regulation, under different grid conditions. The project significantly advanced the understanding of inverter-based resource (IBR) control by developing and validating three complementary system-level approaches for hybrid GFL/GFM operation: 1. A combined Virtual Resistance (VR)-based GFL and Virtual Oscillator Control (VOC)-based GFM method, where each inverter type is governed by a specialized control strategy. Together, these achieve stable, fast-response coordination, eliminating inrush current and enabling smooth black start and grid synchronization across a wide range of grid strengths. 2. A Deadbeat-based UUCC strategy, which uses discrete-time, switching-cycle-level control for both GFL and GFM inverters. This approach replaces traditional PI/PLL control with a control parameter-free, high-bandwidth framework that supports stable LVRT and instantaneous synchronization under all conditions. 3. A benchmark comparison with Siemens’ commercial GFM microgrid controller, which provided a fast baseline platform. The commercial approach decoupled v & f control was implemented on a commercial microgrid controller.The baseline commercial benchmark helped highlight superior transient response and black start performance offered by the deadbeat and VOC approaches. These technical contributions offer substantial improvements over conventional inverter control schemes, which often rely on slow phase-locked loop (PLL)-based synchronization, require careful control parameters tuning, and prone to unstable in weak grids with GFL inverters and in stiff grid with GFM inverters therefore challenging for hybrid GFL+GFM under all grid conditions. The deadbeat-based UUCC framework enables simpler, faster, and more robust operation of hybrid IBR systems using wide-bandgap (WBG) devices such as SiC power semiconductors. The rapid expansion of hybrid distributed energy resources (DERs), including residential and commercial PV-BESS installations such as Tesla Powerwall, PV with vehicle-to-grid (V2G) capability, and other integrated configurations, presents complex operational challenges for medium-voltage radial distribution feeders. These networks are subject to frequent disturbances such as faults, switching operations, rapid reclosing sequences, and feeder reconfigurations, all of which introduce dynamic stress on IBRs. In addition, planned feeder segmentation and deliberate islanding for resilience will require DERs that can autonomously perform blackstart, establish voltage and frequency references, and resynchronize with the main grid. The advanced deadbeat-based UUCC control and blackstart functionalities developed in this project directly address these requirements, enabling decentralized and autonomous operation of inverter-dominated DERs in distribution systems under a wide range of fault and reconfiguration scenarios. From a public benefit perspective, these innovations enable more reliable and cost-effective integration of renewable energy into distribution networks. The ability to autonomously black start and stabilize grids under varying grid conditions support accelerates recovery from outages and support decentralized resilient energy systems. By reducing system complexity and improving performance, this project lays critical groundwork for future inverter-dominated power grids that are clean, reliable, and accessible to all.

14 SOLAR ENERGY↗
DOE OSTI2025

Data for: Subsurface Interface Structure Controlling Local Electronic Properties of Epitaxial Graphene on SiC(0001)

Recently realized high-mobility semiconducting epitaxial graphene on silicon carbide, provided an important step towards integration of the graphene-based system into active components in post-silicon micro- and nano-electronics. However, the exact atomic-scale structure and the complex bonding configurations of the first epitaxial graphene carbon layer remain an open problem. Our recent report has shed new light on understanding this interface, where the external transverse electric field-dependent dynamic switching behavior of the Cbuffer-SiC bonds was observed. Here, using scanning tunneling microscopy and spectroscopy (STM and STS), we present the direct evidence of silicon (Si) vacancies at the interface and provide their distribution at the topmost reconstructed SiC(0001) layer. Experimental STM and density functional theory modeling data were used in the preparation of figures in a published article in the Journal of Physical Chemistry Letters. Files related to the figures and supplementary materials in the article are present in this dataset in .txt format.

Condensed matter imaging↗
DOE OSTI2025

Data for: Subsurface Interface Structure Controlling Local Electronic Properties of Epitaxial Graphene on SiC(0001)

Recently realized high-mobility semiconducting epitaxial graphene on silicon carbide, provided an important step towards integration of the graphene-based system into active components in post-silicon micro- and nano-electronics. However, the exact atomic-scale structure and the complex bonding configurations of the first epitaxial graphene carbon layer remain an open problem. Our recent report has shed new light on understanding this interface, where the external transverse electric field-dependent dynamic switching behavior of the Cbuffer-SiC bonds was observed. Here, using scanning tunneling microscopy and spectroscopy (STM and STS), we present the direct evidence of silicon (Si) vacancies at the interface and provide their distribution at the topmost reconstructed SiC(0001) layer. Experimental STM and density functional theory modeling data were used in the preparation of figures in a published article in the Journal of Physical Chemistry Letters. Files related to the figures and supplementary materials in the article are present in this dataset in .txt format.

Condensed matter imaging↗
DOE OSTI2024

On the Feasibility of Deep Geothermal Wells Using Numerical Reservoir Simulation

This study examines the geothermal energy extraction potential from the basement rock within the Denver–Julesburg Basin, focusing on the flow performance and heat extraction efficiency of different geothermal well configurations. It specifically compares U-shaped, V-shaped, inclined V-shaped, and pipe-in-pipe configurations against enhanced geothermal system setups. Through numerical modeling, we evaluated the thermal behavior of these systems under various operational scenarios and fracture conditions. The results suggest that while closed-loop systems offer moderate temperature increases, Enhanced geothermal system configurations show substantial potential for high-temperature extraction. This underscores the importance of evaluating well configurations in complex geological settings. The insights from this study aid in strategic geothermal energy planning and development, marking significant advancements in geothermal technology and setting a foundation for future explorations and optimizations.

15 GEOTHERMAL ENERGY↗