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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 613 records · Page 34
All sourcesNASA NTRSDOE OSTI
NASA NTRS2005

Hybrid Verification of an Air Traffic Operational Concept

A concept of operations for air traffic management consists of a set of flight rules and procedures aimed to keep aircraft safely separated. This paper reports on the formal verification of separation properties of the NASA's Small Aircraft Transportation System, Higher Volume Operations (SATS HVO) concept for non-towered, non-radar airports. Based on a geometric description of the SATS HVO air space, we derive analytical formulas to compute spacing requirements on nominal approaches. Then, we model the operational concept by a hybrid non-deterministic asynchronous state transition system. Using an explicit state exploration technique, we show that the spacing requirements are always satisfied on nominal approaches. All the mathematical development presented in this paper has been formally verified in the Prototype Verification System (PVS). Keywords. Formal verification, hybrid systems, air traffic management, theorem proving

Munoz, Cesar A.↗
NASA NTRS2016

Multi-Step Deep Reactive Ion Etching Fabrication Process for Silicon-Based Terahertz Components

A multi-step silicon etching process has been developed to fabricate silicon-based terahertz (THz) waveguide components. This technique provides precise dimensional control across multiple etch depths with batch processing capabilities. Nonlinear and passive components such as mixers and multipliers waveguides, hybrids, OMTs and twists have been fabricated and integrated into a small silicon package. This fabrication technique enables a wafer-stacking architecture to provide ultra-compact multi-pixel receiver front-ends in the THz range.

Jung-Kubiak, Cecile↗
DOE OSTI2020

Hybrid Nanostructured Ni(OH) 2 /NiO for High Capacity Lithium–ion Battery Anodes

A straightforward hydrothermal process followed by a controlled calcination technique is introduced for the synthesis of a Ni(OH) 2 modified NiO nanohybrid structure. Conversion materials as Li-ion battery anodes, NiO in this case, suffer from capacity fade and structural/morphological instability during lithiation and delithiation. The novelty of this work is in utilizing this hybrid configuration to increase the specific capacity and enable reversible electrochemistry. Here, we study the lithiation/delithiation process of NiO using a suite of spectroscopy and microscopy techniques from the atomic to electrode scale. We propose a mechanism for a reversible redox couple behavior of the NiO electrode by means of a hybrid Ni(OH)2/NiO structure. The ultimate objective of this work is to guide the development of anode with rationally-designed heterogeneity to create high capacity Li-ion batteries with excellent cycling and rate performance.

25 ENERGY STORAGE↗
DOE OSTI2021

Solvent Impregnated Polymers Loaded with Liquid-Like Nanoparticle Organic Hybrid Materials for Enhanced Kinetics of Direct Air Capture and Point Source CO 2 Capture

Hybrid CO 2 capture materials, solvent impregnated polymers (SIPs), are developed based on a simple and scalable encapsulation technique to enhance CO 2 capture kinetics of water-lean solvents with high viscosity. Liquid-like nanoparticle organic hybrid materials functionalized with polyethylenimine (NOHM-I-PEI) are incorporated into a shell material and UV-cured to produce gas-permeable solid sorbents with uniform NOHMs loading (NPEI-SIPs). The CO 2 capture kinetics of NPEI-SIPs show a remarkable 50-fold increase compared to that of neat NOHM-I-PEI due to a large increase in the NOHMs-CO 2 interfacial surface area provided by the SIP design. The optimum NOHM-I-PEI loading and sorption temperature are found to be ≈49 wt% and 50 °C, respectively, and NPEI-SIPs exhibit great thermal stability over 20 CO 2 capture/sorbent regeneration temperature swing cycles. The pseudoequilibrium CO 2 loadings of NPEI-SIPs under humid conditions are as high as 3.1 mmol CO 2 g -1 NPEI - SIPs for 15 vol% CO 2 (postcombustion capture) and 1.7 mmol CO 2 g -1 NPEI - SIPs for 400 ppm (direct air capture). Overall, these findings suggest that NPEI-SIPs can effectively capture CO 2 from a wide range of CO 2 concentrations including direct air capture while allowing the flexible design of CO 2 capture reactors by combining the benefits of liquid solvents and solid sorbents.

36 MATERIALS SCIENCE↗
DOE OSTI2022

High-Cooperativity Coupling of Rare-Earth Spins to a Planar Superconducting Resonator

The interfacing of a superconducting microwave resonator with rare-earth-doped crystals presents a promising hybrid quantum system for applications including spin-assisted transducers and memories. The coupling strength between the spins of the rare-earth ions and the microwave photons is characterized by the cooperativity. Here, we report an ultrahigh cooperativity of C~650 between rare-earth spins and a planar superconducting microwave resonator that features a highly uniform magnetic field for harnessing the strong anisotropic coupling strength of erbium-doped yttrium orthosilicate. This cooperativity rivals that from a bulk dielectric resonator and may pave a path for efficient coupling of spins with microwave photons on an integrated platform.

75 CONDENSED MATTER PHYSICS, SUPERCONDUCTIVITY AND↗
DOE OSTI2025

Hybrid data-driven cement-stabilized soil design: An integration of machine learning, multi-objective optimization, and life cycle assessment

Soil stabilization is crucial in geotechnical engineering, yet conventional methods are often time-consuming, resource-intensive, and environmentally unsustainable. Despite growing interest in Machine Learning (ML) and optimization tools for mix design, few studies integrate these methods with decision-making techniques and environmental assessment to support practical implementation. This study proposes a hybrid data-driven framework for predicting strength, optimizing mix compositions, and evaluating environmental impacts via life cycle assessment of cement-stabilized soft soils. Six ML models were evaluated, and the top-performing eXtreme Gradient Boosting (XGB) model was further improved using the Grey Wolf Optimizer (GWO). The optimized XGB-GWO model, integrated with a polynomial cost function, served as the objective function in a multi-objective optimization problem solved via the Non-Dominated Sorting Genetic Algorithm II (NSGA-II), with final mix selection guided by the entropy-weighted TOPSIS method. Validation through a case study produced mix designs offering superior strength-cost trade-offs, with the optimal mix achieving 2243.2 kPa unconfined compressive strength and a 16.07 % reduction in carbon emissions compared to the highest-cost design. In conclusion, this study offers a sustainable, scalable approach to soil stabilization and supports informed decision-making in construction.

Life cycle assessment↗
DOE OSTI2021

Next-generation yeast-two-hybrid analysis with Y2H-SCORES identifies novel interactors of the MLA immune receptor

Protein-protein interaction networks are one of the most effective representations of cellular behavior. In order to build these models, high-throughput techniques are required. Next-generation interaction screening (NGIS) protocols that combine yeast two-hybrid (Y2H) with deep sequencing are promising approaches to generate interactome networks in any organism. However, challenges remain to mining reliable information from these screens and thus, limit its broader implementation. Here, we present a computational framework, designated Y2H-SCORES, for analyzing high-throughput Y2H screens. Y2H-SCORES considers key aspects of NGIS experimental design and important characteristics of the resulting data that distinguish it from RNA-seq expression datasets. Three quantitative ranking scores were implemented to identify interacting partners, comprising: 1) significant enrichment under selection for positive interactions, 2) degree of interaction specificity among multi-bait comparisons, and 3) selection of in-frame interactors. Using simulation and an empirical dataset, we provide a quantitative assessment to predict interacting partners under a wide range of experimental scenarios, facilitating independent confirmation by one-to-one bait-prey tests. Simulation of Y2H-NGIS enabled us to identify conditions that maximize detection of true interactors, which can be achieved with protocols such as prey library normalization, maintenance of larger culture volumes and replication of experimental treatments. Y2H-SCORES can be implemented in different yeast-based interaction screenings, with an equivalent or superior performance than existing methods. Proof-of-concept was demonstrated by discovery and validation of novel interactions between the barley nucleotide-binding leucine-rich repeat (NLR) immune receptor MLA6, and fourteen proteins, including those that function in signaling, transcriptional regulation, and intracellular trafficking.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2025

Online thermal profile prediction for large format additive manufacturing: A hybrid CNN-LSTM based approach

Large format additive manufacturing (LFAM) is an advanced 3D printing technique that efficiently fabricates large-scale components through a layer-by-layer extrusion and deposition process. Accurate surface layer temperature monitoring is essential to prevent manufacturing failures and ensure final product quality. Traditional physics-based offline approaches for simulating thermal behavior are often inefficient and complex, posing challenges on real-time, in-situ monitoring. Here, to address this, we propose a data-driven hybrid CNN-LSTM model to predict sequential thermal images of arbitrary length using real-time infrared thermal imaging. In this approach, a Convolutional Neural Networks (CNN) is trained offline to capture spatial features, reduce dimensional complexity, and enhance time efficiency, while a stacked Long Short-Term Memory (LSTM) is applied online to capture temporal information for improved prediction of future thermal behavior in subsequent printing layers. Model performance is evaluated using MSE, SSIM, and PSNR metrics and is benchmarked against stacked LSTM and convolutional LSTM models, demonstrating superior accuracy and applicability. Additionally, to mitigate noise from moving extruders and gantry backgrounds in thermal images, a fine-tuned semantic segmentation model is implemented offline to extract printing geometry, enabling precise temperature tracking along the tool path for further thermal analysis. The frameworks developed in this study significantly advance temperature monitoring, thermal analysis, and in-situ manufacturing control for LFAM, bridging the gap between theoretical modeling and practical application.

Geometry extraction↗
DOE OSTI2025

Inspection of next-generation EUV resists with nano-projectile secondary ion mass spectrometry

There is a rapidly growing need for new materials for extreme ultraviolet (EUV) lithography, which incorporate high EUV absorbing elements. Hybrid resists, which are a combination of inorganic and organic moieties, are promising as they offer both high EUV sensitivity and high etch resistance. However, there is a glaring lack of methods to examine the uniformity of these important materials at the nanoscale. We examine the capabilities of nano-projectile secondary ion mass spectrometry (NP-SIMS) to investigate hybrid resists. NP-SIMS is a mass spectrometry-based technique with high lateral resolution. Using NP-SIMS, a surface is probed stochastically with a suite of individual projectiles, 10 6 to 10 7 in total, separated in time and space. Examining these individual mass spectra allows for nanoscale investigation of the uniformity of the surface. We evaluated the performance of NP-SIMS using samples of poly(methyl methacrylate) (PMMA) infiltrated with varying amounts of InO x via vapor-phase infiltration (VPI), an organic–inorganic hybridization method derived from atomic layer deposition. Here, we found that NP-SIMS measurements contained abundant characteristic ions related to both the PMMA and infiltrated In. The intensity of In atomic ions increased linearly with the number of infiltration cycles; however, the uniformity of In and PMMA varied with the number of infiltration cycles. After one cycle, we found that both the PMMA and In were relatively inhomogeneous. The homogeneity improved with subsequent infiltration cycles. In addition, NP-SIMS measurements contained characteristic ions related to the infiltration reaction and provided insights into the VPI mechanism. Overall, the results show that NP-SIMS is capable of examining both the inorganic and organic moieties in a hybrid resist and will be an important method for understanding the performance of these materials for use in EUV lithography.

36 MATERIALS SCIENCE↗
NASA NTRS2009

Parameter Estimation for a Hybrid Adaptive Flight Controller

This paper expands on the hybrid control architecture developed at the NASA Ames Research Center by addressing issues related to indirect adaptation using the recursive least squares (RLS) algorithm. Specifically, the hybrid control architecture is an adaptive flight controller that features both direct and indirect adaptation techniques. This paper will focus almost exclusively on the modifications necessary to achieve quality indirect adaptive control. Additionally this paper will present results that, using a full non -linear aircraft model, demonstrate the effectiveness of the hybrid control architecture given drastic changes in an aircraft s dynamics. Throughout the development of this topic, a thorough discussion of the RLS algorithm as a system identification technique will be provided along with results from seven well-known modifications to the popular RLS algorithm.

Campbell, Stefan F.↗
DOE OSTI2021

Hybridizing Machine Learning and Physically-based Earth System Models to Improve Prediction of Multivariate Extreme Events (AI Exploration of Wildland Fire Prediction)

Focal Areas: This project responds to two focal areas identified in the DOE Call for AI4ESP White Papers: 1) Predictive modeling through the use of artificial intelligence (AI) techniques, and 2) insights gleaned from complex data using explainable AI and big data analytics. Science Challenge: Large wildland fires (hereafter wildfires) appearing as high-impact compound climate extreme events are closely related to hydroclimate and water cycle extremes that modulate surface fuel supply and combustibility. These compound events have multivariate climatic features (e.g., temperature, precipitation, relative humidity, wind, lightning) and societal drivers (e.g., forest management, land use change, human caused ignitions). Meanwhile, they induce strong feedbacks to the coupled atmosphere, biosphere, and hydrosphere by perturbing regional and global radiation budget as well as ecological, biogeochemical, and water cycles across multiple spatiotemporal scales. The nonlinear interactions between these natural and anthropogenic components of the Earth system are too complex to be completely and adequately represented in today’s Earth system models (ESMs). The inherent stochastic nature of fire activity at all scales further increases the difficulty of its prediction using ESMs that are usually developed from deterministic equations and parameterizations. Besides, concurrence of long-term (decadal to interdecadal) global climate change and fire regime shifts overlapping with short-term (intraseasonal to interannual) variations of regional fire weather and burning activity confound predictability of these compound extreme events. We propose to address the above scientific challenges by using machine learning (ML)-based data-driven modeling techniques to integrate observations and physically-based ESMs’ simulations in a computationally efficient hybrid prediction system. This prediction system is supposed to characterize the wildfire’s sensitivity to climate and exogenous drivers at high resolution (~ 0.25°) on subseasonal to seasonal (S2S) timescales providing improved predictability and explainability. We will use the system to help identify: (1) What are the computational elements of a hybrid system needed to predict compound climate extreme events such as global wildfires? (2) What are the key drivers (either natural or anthropogenic) that modulate short-term variations of multivariate fire weather and burning activity over different regions? How can one take advantage of those driver-response relationships to improve the predictability of large wildfires on S2S time scales? (3) What are the underlying physical mechanisms and sources of improved predictability? Which ML techniques are optimal in revealing and adapting these mechanisms?

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2023

A Hybrid Optimization and Deep Learning Algorithm for Cyber-Resilient DER Control

With the proliferation of distributed energy resources (DERs) in the distribution grid, it is a challenge to effectively control a large number of DERs resilient to the communication and security disruptions, as well as to provide the online grid services, such as voltage regulation and virtual power plant (VPP) dispatch. To this end, a hybrid feedback-based optimization algorithm along with deep learning forecasting technique is proposed to specifically address the cyber-related issues. The online decentralized feedback-based DER optimization control requires timely, accurate voltage measurement from the grid. However, in practice such information may not be received by the control center or even be corrupted. Therefore, the long short-term memory (LSTM) deep learning algorithm is employed to forecast delayed/missed/attacked messages with high accuracy. The IEEE 37-node feeder with high penetration of PV systems is used to validate the efficiency of the proposed hybrid algorithm. The results show that 1) the LSTM-forecasted lost voltage can effectively improve the performance of the DER control algorithm in the practical cyber-physical architecture; and 2) the LSTM forecasting strategy outperforms other strategies of using previous message and skipping dual parameter update.

cyber-resilient algorithm↗
NASA NTRS1995

A Numerical Analysis of Electromagnetic Scattering from Two-Dimensional Edge Terminations

Several techniques that influence the low frequency scattering from penetrable edge terminations are evaluated using a hybrid finite element and boundary element method code. The edge terminations consist of a dielectric skin forming an exterior shape with an internal conducting bulkhead. Some of the techniques considered are bulkhead shaping, internal material loading, placement of resistive cards, and the placement of lossy dielectric material rods. The intent of the various treatments is to find a combination or combinations that influence(s) the backscattered field to acceptable levels over a range of frequencies for both transverse magnetic and transverse electric polarizations.

Gray, B. E.↗
NASA NTRS1980

Cascade Solar Cell Workshop report

Issues related to the feasibility, research and development, and demonstration of a 30% AMO cascade solar cell discussed include the material selection, growth and fabrication techniques, and device development strategy for a monolithic (two terminal) cascade cell, a hybrid (four terminal) cascade cell, and a spectral splitting device (three cells). Workshop recommendations include: (1) initiate a long range research program to develop a three junction, monolithic, cascade cell using either AlGaAsSb-GaAsSb or AlGaInAs-GaInAs material system; (2) emphasize OM-CVD epitaxial growth technique, perhaps combined with other technologies in the near term to obtain tunnel junctions; (3) develop a two junction device first; (4) initiate a cascade solar cell modeling program to study and compare performance of two and four terminal cascade devices exposed to electron and proton irradiation; and (5) encourage and be open to new ideas for developing four terminal, hybrid, cascade cells exploiting novel component cell interconnect technologies.

Hutchby, J. A.↗
DOE OSTI2021

Coherent Gate Operations in Hybrid Magnonics

Electromagnonics-the hybridization of spin excitations and electromagnetic waves-has been recognized as a promising candidate for coherent information processing in recent years. Among its various implementations, the lack of available approaches for real-time manipulation on the system dynamics has become a common and urgent limitation. In this work, by introducing a fast and uniform modulation technique, we successfully demonstrate a series of benchmark coherent gate operations in hybrid magnonics, including semiclassical analogies of Landau-Zener transitions, Rabi oscillations, Ramsey interference, and controlled mode swap operations. Finally, our approach lays the groundwork for dynamical manipulation of coherent signals in hybrid magnonics and can be generalized to a broad range of applications.

75 CONDENSED MATTER PHYSICS, SUPERCONDUCTIVITY AND↗
DOE OSTI2025

Ferroelectric Switching in Hybrid Molecular-Beam-Epitaxy-Grown BaTiO 3 Films

Molecular beam epitaxy (MBE) is a promising synthesis technique for both heterostructure growth and epitaxial integration of ferroelectric BaTiO 3 . However, direct measurement of the remnant polarization (P r ) has not been previously reported in MBE-grown BaTiO 3 films. We report the in situ growth of an all-epitaxial SrRuO 3 /BaTiO 3 /SrRuO 3 heterostructure on Nb-doped SrTiO 3 (001) substrates by hybrid MBE using metal–organic precursors. This capacitor structure consisting of 16 nm SrRuO 3 /40 nm BaTiO 3 /16 nm SrRuO 3 shows hysteretic polarization–electric field (P–E) curves with P r ∼ 15 μC cm –2 at frequencies ranging from 500 Hz to 20 kHz, after isolating the intrinsic ferroelectric response from non-ferroelectric contributions using the Positive-Up-Negative-Down (PUND) method. We hypothesize that the asymmetry in switching behavior and current leakage has origins in structural defects. Furthermore, this work opens the door to defect-engineered ferroelectric BaTiO 3 -based heterostructures grown by hybrid MBE for future electronic, photonic and spintronic applications.

SrRuO3 electrode↗
DOE OSTI2023

Vehicle Powertrain Simulation Accuracy for Various Drive Cycle Frequencies and Upsampling Techniques

As connected and automated vehicle technologies emerge and proliferate, lower frequency vehicle trajectory data is becoming more widely available. In some cases, entire fleets are streaming position, speed, and telemetry at sample rates of less than 10 seconds. This presents opportunities to apply powertrain simulators such as the National Renewable Energy Laboratory's Future Automotive Systems Technology Simulator to model how advanced powertrain technologies would perform in the real world. However, connected vehicle data tends to be available at lower temporal frequencies than the 1-10 Hz trajectories that have typically been used for powertrain simulation. Higher frequency data, typically used for simulation, is costly to collect and store and therefore is often limited in density and geography. This paper explores the suitability of lower frequency, high availability, connected vehicle data for detailed powertrain simulation. A large data set of 1 Hz trajectories is used to quantify the accuracy loss when simulating energy consumption for conventional, hybrid, and battery electric powertrains using less than 1 Hz data. Techniques to upsample lower frequency drive cycle data in order to increase accuracy are also explored. Median energy consumption errors when simulating energy consumption for a 1/10 Hz trajectory are found to be 3-6% when compared to 1 Hz trajectories. Applying upsampling and interpolation techniques are shown to reduce the simulation errors by roughly 50%. The findings in this work can guide connected vehicle data collection specifications and processing techniques applied when using collected data for powertrain simulation.

ADVANCED PROPULSION SYSTEMS↗
DOE OSTI2026

Plasmon-Induced Resonance Energy Transfer in Hybrid Nanomaterials

Plasmon-induced resonance energy transfer (PIRET) has emerged as a powerful mechanism for harnessing and redirecting plasmon energy before it dissipates into hot carriers or heat. By matching plasmon resonance frequencies with acceptor absorption bands, PIRET extends plasmon-driven processes beyond the charge transfer pathway, enabling selective energy flow into excitonic transitions. Here, this review highlights important progress in elucidating the fundamental plasmon decay processes and transitioning them into hybrid nanomaterials under PIRET. Employing specialized single-particle spectroscopic techniques based on scattering, extinction, and emission enables demonstration of PIRET in the face of competing mechanisms, such as interfacial charge transfer and thermalization. Finally, we complete this review by addressing strategies for active modulation of PIRET and present applications, ranging from plasmon photocatalysis to intracellular biochemical sensing.

Energy transfer↗