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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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All sourcesNASA NTRSDOE OSTI
DOE OSTI2025

High-Speed and Hysteresis-Free Near-Infrared Optical Hydrogen Sensor Based on Ti/Pd Bilayer Thin Films

Palladium (Pd) and titanium (Ti) exhibit opposite dielectric responses upon hydrogenation, with stronger effects observed in the near-infrared (NIR) region. Leveraging this contrast, we investigated Ti/Pd bilayer thin films as a platform for NIR hydrogen sensing—particularly at telecommunication-relevant wavelengths, where such devices have remained largely unexplored. Ti/Pd bilayers coated with Teflon AF (TAF) and fabricated via sequential electron-beam and thermal evaporation were characterized using optical transmission measurements under repeated hydrogenation cycles. The Ti (5 nm)/Pd (x = 2.5 nm)/TAF (30 nm) architecture showed a 2.7-fold enhancement in the hydrogen-induced optical contrast at 1550 nm compared to Pd/TAF reference films, attributed to the hydrogen ion exchange between the Ti and Pd layers. The optimized structure, with a Pd thickness of x = 1.9 nm, exhibited hysteresis-free sensing behavior, a rapid response time (t90 < 0.35 s at 4% H2), and a detection limit below 10 ppm. It also demonstrated excellent selectivity with negligible cross-sensitivity to CO2, CH4, and CO, as well as high durability, showing less than 6% signal degradation over 135 hydrogenation cycles. These findings establish a scalable, room-temperature NIR hydrogen sensing platform with strong potential for deployment in automotive, environmental, and industrial applications.

Chemistry↗
DOE OSTI2018

Sensitive and Selective NH3 Monitoring at Room Temperature Using ZnO Ceramic Nanofibers Decorated with Poly(styrene sulfonate)

Ammonia (NH3) gas is a prominent air pollutant that is frequently found in industrial and livestock production environments. Due to the importance in controlling pollution and protecting public health, the development of new platforms for sensing NH3 at room temperature has attracted great attention. In this study, a sensitive NH3 gas device with enhanced selectivity is developed based on zinc oxide nanofibers (ZnO NFs) decorated with poly(styrene sulfonate) (PSS) and operated at room temperature. ZnO NFs were prepared by electrospinning followed by calcination at 500 °C for 3 h. The electrospun ZnO NFs are characterized to evaluate the properties of the as-prepared sensing materials. The loading of PSS to prepare ZnO NFs/PSS composite is also optimized based on the best sensing performance. Under the optimal composition, ZnO NFs/PSS displays rapid, reversible, and sensitive response upon NH3 exposure at room temperature. The device shows a dynamic linear range up to 100 ppm and a limit of detection of 3.22 ppm and enhanced selectivity toward NH3 in synthetic air, against NO2 and CO, compared to pure ZnO NFs. Additionally, a sensing mechanism is proposed to illustrate the sensing performance using ZnO NFs/PSS composite. Therefore, this study provides a simple methodology to design a sensitive platform for NH3 monitoring at room temperature.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2026

BSMV-mediated genome editing exhibits host-specific heritability: germline transmission in barley and somatic edits in Nicotiana benthamiana

Plant RNA virus–mediated guide RNA (gRNA) delivery represents a transformative advance in genome editing technologies. Unlike conventional transformation methods that rely on labor-intensive tissue culture and regeneration for each individual gRNA delivery, viral vectors can rapidly and systemically transmit gRNAs into pre-established Cas-expressing plants, providing an accelerated route for functional genomics and trait discovery directly in planta . However, key design parameters, including subgenomic promoter choice, transcript architecture, and their effects on viral fitness and editing outcomes, remain to be elucidated for most viral platforms. We developed five Barley stripe mosaic virus (BSMV) vectors, each with distinct subgenomic promoter elements to drive single gRNA expression. These were initially evaluated in Cas9-expressing transgenic Nicotiana benthamiana plants targeting the Phytoene desaturase ( PDS ) gene to compare their editing efficiencies. Single gRNAs expressed under the duplicated γb subgenomic promoter or when fused directly to the γb genome achieved the highest mutation frequencies (up to 90% at 60 days post-inoculation), whereas β1- and β2-driven sgRNAs produced delayed and reduced editing. Thus, promoter selection critically determines gRNA accumulation and the efficacy of BSMV-mediated genome editing. The top-performing design was then applied to Cas9-expressing barley ( Hordeum vulgare ) targeting HvCMF7 (conferring green-white variegation) and HvGW2.1 (impacts grain width and weight). BSMV spread systemically throughout barley, inducing somatic and heritable mutations at frequencies up to 100%, with virus-free edited progeny. In contrast, despite robust somatic editing in N. benthamiana, no heritable mutations were detected indicating species-dependent limitations in germline transmission. Our systematic comparison of subgenomic promoter architectures establishes clear design principles for optimizing viral vector–mediated delivery. Promoter choice and transcript structure critically shape editing efficiency and viral stability. The host-specific boundary for germline editing, defined by efficient heritable editing in barley but not N. benthamiana , highlights where BSMV offers advantages and where alternative vectors or hybrid strategies are required, guiding rational platform selection for diverse crop species and applications. Collectively, these findings establish BSMV as a promising next-generation vector for rapid, tissue culture–free, and transformation-independent genome editing in cereals and other recalcitrant monocots.

barley↗
DOE OSTI2022

An OpenMP GPU-offload implementation of a non-equilibrium solidification cellular automata model for additive manufacturing

Here, in this paper, performance strategies on GPU-based HPC platforms of a cellular automata (CA) simulation code for non-equilibrium solidification, including nucleation, grain growth, solute partitioning and transport for the metal additive manufacturing (AM) process are investigated using OpenMP 4.5. To accurately report the speed-up for multicore CPUs and GPUs, a rigorous performance analysis employed optimizations appropriate for both CPU-only code (baseline) and GPU offload codes for an isothermal test problem. The performance results on Summit at the Oak Ridge Leadership Computing Facility indicate that using a precomputed list of interface cells significantly decreased the wall-clock time on GPUs. The speedup due to GPU acceleration was evaluated for a full Summit node and measured to be 1.8X when comparing a 6 MPI tasks run with 6 GPUs versus 36 MPI tasks on the CPU only. That speed-up was found to be 7.9X when comparing 6 MPI tasks with 6 GPUs versus the 6 MPI tasks running on the CPU only. Performance measurements showed that system total time is almost constant for runs with more than 96 MPI tasks (or GPUs), indicating that the GPU-accelerated code showed an excellent weak scaling performance. Finally, a rapid directional solidification problem was considered to demonstrate the CA code capability on Summit. It was found that a mesh size of at least 0.05 μm is recommended for the AM-like simulations in order to obtain accurate elongated grain microstructure and elongated subgrain features, which are in qualitative good agreement with experimental data. The results presented in this study indicate that the performance strategies on GPU-based HPC platforms for the CA code are appropriate for novel HPC exascale platforms.

36 MATERIALS SCIENCE↗
DOE OSTI2024

Conceptual Design of a Tension Leg Platform With 22.3 MW Vertical Axis Turbine

Here, this paper presents the conceptual design of a tension leg platform (TLP) for the ARCUS “towerless” vertical-axis wind turbine (VAWT). VAWTs are ideal for floating offshore sites and have several advantages over horizontal-axis wind turbines (HAWT) including reduced top mass, lower center of gravity, increased energy capture, and in turn lower cost. The towerless ARCUS VAWT drives these advantages further through increased structural efficiency and by enabling more optimized TLP designs with simplified installation procedures. For hull sizing, we have studied three turbine sizes with corresponding power ratings of 5.1 MW, 10.4 MW and 22.3 MW. The largest turbine was identified as having the greatest potential to reduce the levelized cost of energy (LCOE) and is the reference size used for the further detailed design process. The conceptual design of the VAWT TLP has been awarded with an ABS Approval in Principle Certificate. This paper contains brief analysis results and design findings for a TLP designed to house a VAWT, including the following topics: • Applicable Design Codes • Metocean Conditions • ARCUS Turbine Loads • Design Load Cases and Requirements - Pre-service TLP Stability - In-place TLP Global Performance • Platform Configurations, Hull Structure Scantling Design, Weight and CG Estimation, and General Arrangement Drawings • Hull Ballast Plan for both Pre-service and In-place Conditions • Pre-service Quayside Integration, Transportation and Wet Tow Stability Analysis • Global Performance Analysis for Motions and Tendon tensions • Summary of cost components and system levelized cost of energy

17 WIND ENERGY↗
DOE OSTI2026

Data for High Yield Production of 3-Hydroxypropionic Acid Using Issatchenkia orientalis

Biomanufacturing provides a more sustainable alternative to fossil-based chemical manufacturing. 3-Hydroxypropionic acid (3HP) is a top Department of Energy value-added chemical and precursor to bioplastics, yet cost-effective microbial production remains elusive. Here, we establish the acid-tolerant yeast Issatchenkia orientalis as a robust host for low-pH 3HP biosynthesis. Genome-scale modeling identifies the β-alanine pathway as optimal, offering the highest theoretical yield and lowest oxygen requirement. Thermodynamic analysis confirms its favorability under acidic conditions. Using sequence similarity network analysis, we discover highly active aspartate 1-decarboxylase (PAND), β-alanine-pyruvate aminotransferase (BAPAT), and 3HP dehydrogenase (YDFG), which significantly improve the pathway efficiency. Next, to further elevate the production, pathway optimization through multi-copy PAND integration, byproduct elimination (knockouts of pyruvate decarboxylase and glycerol-3-phosphate dehydrogenase), and reinforcement of aspartate flux by overexpression of pyruvate carboxylase and aspartate amino transferase improves the titer to 29 g/L in shake flasks. Fed-batch fermentation at pH 4 with low-cost corn steep liquor medium further increases the production to 92 g/L with 0.7 g/g yield and 0.55 g/L/h productivity. Techno-economic analysis indicates that such performance could potentially enable a financially viable process for sustainable acrylic acid production. This work establishes I. orientalis as a next-generation platform for cost-effective 3HP production and paves the way toward industrial commercialization.

Bioproducts↗
DOE OSTI2022

Cloud-Control of Legacy Building Automation System: A case study

As Internet of Things devices and cloud-based platforms become more mature, Energy Management and Information Systems (EMIS) are increasingly gaining momentum in the building industry. In large commercial buildings, Fault-Detection and Diagnostic (FDD) and energy information systems (EIS) are now established technologies with tens of providers and thousands of deployment sites across North America. The new frontier for the EMIS technology is now represented by control systems that use advanced system optimization (ASO) methods to improve the operations of the HVAC system. Given the complexity of the integration of such systems with the existing building automation systems (BAS) and the higher risk involved with direct control of the HVAC, these systems are still emerging in the market. This paper presents the results of a project in which a start-up company partnered with a research institution to develop a cloud-based software EMIS solution and deployed it in a university campus in California. The software system included advanced sensing, data acquisition, storage and advanced control and analytics applications developed on top of the native BAS. The new platform controls ten buildings on the campus and the FDD and the ASO applications deployed on this platform were able to generate energy savings of up to 35% and 25% in certain buildings for each functionality respectively. Where the platform did not save energy, it improved building service (air quality). Lessons learned include the importance of collaborating with and training the building operators and evaluating whether the legacy system can work reliably with the new technology.

Prakash, Anand Krishnan↗
DOE OSTI2024

Untargeted Spatial Metabolomics and Spatial Proteomics on the Same Tissue Section

An increasing number of spatial multiomic workflows have been recently developed. Some of these approaches have leveraged initial mass spectrometry imaging (MSI)-based spatial metabolomics to inform region of interest (ROI) selection for downstream spatial proteomics. However, these workflows have been limited by varied substrate requirements between modalities or have required analyzing serial sections (i.e., one section per modality). To mitigate these issues, we present a novel multiomic workflow that uses desorption electrospray ionization (DESI)-MSI to identify representative spatial metabolite patterns on-tissue prior to spatial proteomic analyses on the same tissue section. Further, this workflow is demonstrated here with a model mammalian tissue (coronal rat brain section) mounted on a polyethylene naphthalate-membrane slide. Initial DESI-MSI resulted in 160 annotations (SwissLipids) within to the METASPACE platform (≤20% false discovery rate). A segmentation map from the annotated ion images informed downstream ROI selection for spatial proteomics characterization from the same sample. The unspecific substrate requirements and minimal sample disruption inherent to DESI-MSI allowed for an optimized, downstream spatial proteomics assay, resulting in 3888 ± 240 to 4717 ± 48 proteins being confidently directed per ROI (200 µm x 200 µm). Finally, we demonstrate the integration of multiomic information, where we found ceramide localization to be correlated with SMPD3 abundance (ceramide synthesis protein), and we also utilized protein abundance to resolve metabolite isomeric ambiguity. Overall, the integration of DESI-MSI into the multiomic workflow allows for complementary spatial and molecular-level information to be achieved from optimized implementations of each MS assay inherent to the workflow itself.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2019

Ultralow-threshold, continuous-wave upconverting lasing from subwavelength plasmons

Miniaturized lasers are an emerging platform for generating coherent light for quantum photonics, in vivo cellular imaging, solid-state lighting and fast three-dimensional sensing in smartphones. Continuous-wave lasing at room temperature is critical for integration with opto-electronic devices and optimal modulation of optical interactions. Plasmonic nanocavities integrated with gain can generate coherent light at subwavelength scales, beyond the diffraction limit that constrains mode volumes in dielectric cavities such as semiconducting nanowires. However, insufficient gain with respect to losses and thermal instabilities in nanocavities has limited all nanoscale lasers to pulsed pump sources and/or low-temperature operation. Here, we show continuous-wave upconverting lasing at room temperature with record-low thresholds and high photostability from subwavelength plasmons. We achieve selective, single-mode lasing from Yb 3+ /Er 3+ -co-doped upconverting nanoparticles conformally coated on Ag nanopillar arrays that support a single, sharp lattice plasmon cavity mode and greater than wavelength λ/20 field confinement in the vertical dimension. The intense electromagnetic near-fields localized in the vicinity of the nanopillars result in a threshold of 70 W cm -2 , orders of magnitude lower than other small lasers. Our plasmon-nanoarray upconverting lasers provide directional, ultra-stable output at visible frequencies under near-infrared pumping, even after six hours of constant operation, which offers prospects in previously unrealizable applications of coherent nanoscale light.

36 MATERIALS SCIENCE↗
DOE OSTI2023

Benchmarking Quantum Simulators Using Ergodic Quantum Dynamics

Here we propose and analyze a sample-efficient protocol to estimate the fidelity between an experimentally prepared state and an ideal target state, applicable to a wide class of analog quantum simulators without advanced spatiotemporal control. Our protocol relies on universal fluctuations emerging from generic Hamiltonian dynamics, which we discover in the present work. It does not require fine-tuned control over state preparation, quantum evolution, or readout capability, while achieving near optimal sample complexity: a percent-level precision is obtained with ~103 measurements, independent of system size. Furthermore, the accuracy of our fidelity estimation improves exponentially with increasing system size. We numerically demonstrate our protocol in a variety of quantum simulator platforms, including quantum gas microscopes, trapped ions, and Rydberg atom arrays. We discuss applications of our method for tasks such as multiparameter estimation of quantum states and processes.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗
DOE OSTI2021

Big Data Analysis of Massive PMU Datasets: A Data Platform Perspective

The discovery of `event signatures' and useful insights from very large historical Phasor Measurement Unit (PMU) datasets is predicated on offline Big Data analysis approaches that rely on the generation of predictive features on a massive scale. This paper presents lessons learned from a data platform perspective towards reducing barriers to adoption of Big Data analytics against a real dataset of almost half a trillion data points drawn from over 400 PMUs distributed across the North American power grid. We demonstrate software abstractions and targeted performance optimizations that can lead to significant productivity gains for power systems researchers seeking to perform offline exploratory temporal analysis and modeling tasks, with a focus on feature generation. We describe how our optimized approach goes beyond a naive application of mainstream Big Data technologies, enabling feature generation tasks, that previously took days or even weeks, to now be completed in just a few hours.

Kumar, Vijay S.↗
DOE OSTI2020

RODeO (Revenue Operation and Device Optimization Model) [SWR 20-67]

The Revenue, Operation, and Device Optimization (RODeO) model explores optimal system design and operation considering different levels of grid integration, equipment cost, operating limitations, financing, and credits and incentives. RODeO is a price-taker model formulated as a mixed-integer linear programming (MILP) model in the GAMS modeling platform. The objective is to maximizes the net revenue for a collection of equipment at a given site. The equipment includes generators (e.g., gas turbine, steam turbine, solar, wind, hydro, fuel cells, etc.), storage systems (batteries, pumped hydro, gas-fired compressed air energy storage, long-duration systems, hydrogen), and flexible loads (e.g., electric vehicles, electrolyzers, flexible building loads). The input data required by RODeO can be classified into three bins: 1) utility service data, which refers to retail utility rate information (meter cost, energy and demand charges), 2) electricity market data, which include energy and reserve prices, 3) other inputs, which refer to additional electrical demand, product output demand, technological assumptions, financial properties, and operational parameters.

Guerra Fernandez, Omar Jose↗
DOE OSTI2016

Novus: An Unmanned Aerial System and Virtual Reality Flight Deck for Data Collection and Visualization

Many emerging technologies offer tremendous new capabilities that will, without question, redefine how we interact with, utilize, understand, and optimize the energy systems around us. Drones offer a new ability to remotely capture data in ways that have previously been impractical. However, their flight control systems remain rudimentary and limit the ability of researchers to fully take advantage of these platforms as remote sensing devices. In this project, we constructed a hexarotor drone and outfitted it with advanced (yet inexpensive) sensors as well as a telemetry data management and control package. We then created a virtual reality flight deck that allows a drone operator to define waypoints "on-the-fly" or take manual control over flight operations. The flight deck provides a live on-board video stream display, a 3-dimensional topographic map with the drone’s position in space overlaid onto it, as well as a data visualization framework that keeps critical information immediately available to the operator. These advances provide a new platform for scientific data collection such as thermography of building envelopes or solar arrays. As the drone is in flight and data are being collected, these data are relayed to the virtual reality environment where they can be inspected for integrity and the flight plan altered if necessary to collect higher quality data. Future work will focus on implementing 3-dimensional mapping using both visible and thermal imagery collected from the drone, and streaming those into the virtual reality flight deck in real time.

Source record↗
NASA NTRS1987

The dynamics and control of the Space Station polar platform

The Space Station polar platform will carry a variety of earth observation instruments for NASA and the National Oceanic and Atmospheric Administration. In this paper, the asymmetrical platform is modeled as three connected rigid bodies. A generalized angular momentum equation is employed to derive the rotational equations of motion. These equations are linearized and used for preliminary sizing of control devices using a classical control approach. Two control systems are considered to stabilize the platform and satisfy the pointing requirements. The first system is composed of a single variable-speed, double-gimbaled momentum wheel and the second consists of three-reaction wheels. The performance of each system is assessed using a linear optimal control approach.

Wahbah, M. M.↗
NASA NTRS2023

NASA's Collaborative Digital Departure Rerouting (CDDR) Technology Reduces Flight Delays and Emissions

NASA’s Digital Information Platform is creating a digital information ecosystem to exchange services and provide access to airspace information to enable fuel efficient operations. This system allows providers to make their services more accessible and consumers to access information and services they need to optimize their operations. In this video we will focus on the Collaborative Digital Departure Rerouting service, which provides airlines with routing options similar to how drivers navigate using cellphone apps.

Keenan Roach↗
DOE OSTI2022

NREL's Advanced Distribution Management System (ADMS) Test Bed

Transform utility electric distribution management systems to enable the integration and management of all assets and functions across the utility enterprise regardless of vendor or technology. Four program areas: Platform: Develop an open-source platform; evaluate advanced applications. Test bed: Build a vendor-neutral test bed to evaluate existing and future advanced distribution management system (ADMS) functionalities in a realistic setting. Applications: Develop an initial suite of ADMS applications. Advanced control: Develop new integrated optimization and control solutions.

ADMS test bed↗
DOE OSTI2020

Accelerating Parallel Applications in Cloud Platforms via Adaptive Time-Slice Control

Cloud platforms can provide flexible and cost-effective environments for parallel applications. However, the resource over-commitment issues, i.e., cloud providers often provide much more executable virtual CPUs than available physical CPUs, still impede the synchronization operations of parallel applications, causing severe performance degradation. Existing methods optimize parallel applications by promoting the priorities of involved VMs. They cannot fully explore the performance of parallel applications, because they ignore the time-slice requirements of different phases of parallel applications. Furthermore, non-parallel applications experience unsatisfied performance because of low scheduling priorities. Given empirical analysis on time-slices of virtual machines (VMs), we find that shortening time-slices can mitigate synchronization overhead which incurs during communication phases, while over-short time-slices cause frequent cache misses in computation phases. Accordingly, we propose an Adaptive Time-slice Control (ATC) mechanism. ATC first detects the phases of parallel applications based on lock latency or cache misses. Then, ATC shortens time-slices during communication phases and prolongs time-slices during computation phases for parallel applications, and sets a uniform time-slice for non-parallel applications. Finally, we evaluate ATC using seven well-known benchmarks with 25+ applications. Experiments show that ATC obtains 1.5-75x performance gain for running parallel applications than state-of-the-art solutions, with nearly unaffected impact on non-parallel applications.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2017

Evolutionary Optimization of a Charge Transfer Ionic Potential Model for Ta/Ta-Oxide Heterointerfaces

Heterostructures of tantalum and its oxide are of tremendous technological interest for a myriad of technological applications, including electronics, thermal management, catalysis and biochemistry. In particular, local oxygen stoichiometry variation in TaO x memristors comprising of thermodynamically stable metallic (Ta) and insulating oxide (Ta 2 O 5 ) have been shown to result in fast switching on the subnanosecond timescale over a billion cycles. This rapid switching opens up the potential for advanced functional platforms such as stateful logic operations and neuromorphic computation. Despite its broad importance, an atomistic scale understanding of oxygen stoichiometry variation across Ta/TaO x heterointerfaces, such as during early stages of oxidation and oxide growth, is not well understood. This is mainly due to the lack of a unified interatomic potential model for tantalum oxides that can accurately describe metallic (Ta), ionic (TaO x ) as well as mixed (Ta/TaO x interfaces) bonding environments simultaneously. To address this challenge, we introduce a Charge Transfer Ionic Potential (CTIP) model for Ta/Ta-oxide system by training against lattice parameters, cohesive energies, equations of state (EOS), elastic properties, and surface energies of the various experimentally observed Ta 2 O 5 polymorphs (hexagonal, orthorhombic and monoclinic) obtained from density functional theory (DFT) calculations. The best CTIP parameters are determined by employing a global optimization scheme driven by genetic algorithms followed by local Simplex optimization. Our newly developed CTIP potential accurately predicts structure, thermodynamics, energetic ordering of polymorphs, as well as elastic and surface properties of both Ta and Ta 2 O 5 , in excellent agreement with DFT calculations and experiments. We employ our newly parameterized CTIP potential to investigate the early stages of oxidation and atomic scale mechanisms associated with oxide growth on Ta surface at various temperatures. Furthermore, the CTIP potential developed in this work is an invaluable tool to investigate atomic-scale mechanisms and transport phenomena underlying the response of Ta/TaO x interfaces to external stimuli (e.g, temperature, pressure, strain, electric field etc.), as well as other interesting dynamical phenomena including the physics of switching dynamics in TaO x based memristors and neuromorphic devices.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗