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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 577 records · Page 32
All sourcesNASA NTRSDOE OSTI
DOE OSTI2021

Monitoring changes in human activity during the COVID-19 shutdown in Las Vegas using infrasound microbarometers

While studies of urban acoustics are typically restricted to the audio range, anthropogenic activity also generates infrasound (<20 Hz, roughly at the lower end of the range of human hearing). Shutdowns related to the COVID-19 pandemic unintentionally created ideal conditions for the study of urban infrasound and low frequency audio (20–500 Hz), as closures reduced human-generated ambient noise, while natural signals remained relatively unaffected. An array of infrasound sensors deployed in Las Vegas, NV, provides data for a case study in monitoring human activity during the pandemic through urban acoustics. The array records a sharp decline in acoustic power following the temporary shutdown of businesses deemed nonessential by the state of Nevada. This decline varies spatially across the array, with stations close to McCarran International Airport generally recording the greatest declines in acoustic power. Further, declines in acoustic power fluctuate with the time of day. As only signals associated with anthropogenic activity are expected to decline, this gives a rough indication of periodicities in urban acoustics throughout Las Vegas. This study's results reflect the city's response to the pandemic and suggest spatiotemporal trends in acoustics outside of shutdowns.

58 GEOSCIENCES↗
DOE OSTI2021

DETECTING FIRE WITH MACHINE LEARNING-ENABLED VISUAL MONITORING FOR NUCLEAR POWER PLANT ENVIRONMENTS

Nuclear power plants are experiencing significant cost challenges to remain competitive with other energy-generation utilities. Unlike other industries, the cost of operation and maintenance activities is mostly attributed to workforce costs. To mitigate this, nuclear power plant stakeholders are increasingly interested in the development and deployment of machine learning methods to potentially automate or augment manually intensive tasks to reduce costs, especially for monitoring activities. One monitoring function that is visually demanding and that can occur frequently to meet the requirements of a fire protection program is visually monitoring an area for fire occurrence. Currently, fire watch activities consist of a worker physically stationed at a given location with the sole responsibility of observing a given area to ensure a fire is detected and mitigated promptly. This effort focused on the development and evaluation of a suitable deep convolutional neural network to classify individual video frames at a sub-second frequency for the occurrence of “fire” and “no fire” in varying industrial environments similar to nuclear power plants. It is believed that a trained neural network model could be integrated with existing facility video surveillance camera feeds to generate alerts when fire inferences occur in individual frames captured at sub-second temporal resolutions. Extensive effort was dedicated to identifying and curating suitable imagery training data representing varying environments and scene settings with and without flame features to maximize generalization in nuclear power plant environments. The data collection effort resulted in the aggregation of a large, labeled image library exceeding 12,000 images to support model training for diverse industrial environments. A deep neural network model incorporating parallel multi-scale capabilities was developed and trained to support accurate image-based detection of flame incidents of varying sizes and spectral feature properties within heterogeneous scenes. Analysis results show that the trained model can achieve high inference accuracy despite heterogeneous scene environments and components. Testing accuracy exceeded 95.0 percent with very low false positive and false negative inferences.

46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND ↗
DOE OSTI2023

Radiative Reconnection-powered TeV Flares from the Black Hole Magnetosphere in M87

Abstract Active galactic nuclei in general, and the supermassive black hole in M87 in particular, show bright and rapid gamma-ray flares up to energies of 100 GeV and above. For M87, the flares show multiwavelength components, and the variability timescale is comparable to the dynamical time of the event horizon, suggesting that the emission may come from a compact region near the nucleus. However, the emission mechanism for these flares is not well understood. Recent high-resolution general-relativistic magnetohydrodynamic simulations show the occurrence of episodic magnetic reconnection events that can power flares near the black hole event horizon. In this work, we analyze the radiative properties of the reconnecting current layer under the extreme plasma conditions applicable to the black hole in M87 from first principles. We show that abundant pair production is expected in the vicinity of the reconnection layer, to the extent that the produced secondary pair plasma dominates the reconnection dynamics. Using analytic estimates backed by two-dimensional particle-in-cell simulations, we demonstrate that in the presence of strong synchrotron cooling, reconnection can produce a hard power-law distribution of pair plasma imprinted in the outgoing synchrotron (up to a few tens of MeV) and the inverse-Compton signal (up to TeV). We produce synthetic radiation spectra from our simulations, which can be directly compared with the results of future multiwavelength observations of M87* flares.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2024

A Semi‐Automated, High‐Throughput Approach for the Synthesis and Identification of Highly Photo‐Cytotoxic Iridium Complexes

Abstract The discovery of new compounds with pharmacological properties is usually a lengthy, laborious and expensive process. Thus, there is increasing interest in developing workflows that allow for the rapid synthesis and evaluation of libraries of compounds with the aim of identifying leads for further drug development. Herein, we apply combinatorial synthesis to build a library of 90 iridium(III) complexes (81 of which are new) over two synthesise‐and‐test cycles, with the aim of identifying potential agents for photodynamic therapy. We demonstrate the power of this approach by identifying highly active complexes that are well‐tolerated in the dark but display very low nM phototoxicity against cancer cells. To build a detailed structure–activity relationship for this class of compounds we have used density functional theory (DFT) calculations to determine some key electronic parameters and study correlations with the experimental data. Finally, we present an optimised semi‐automated synthesise‐and‐test protocol to obtain multiplex data within 72 hours.

Kench, Timothy↗
DOE OSTI2024

A Semi‐Automated, High‐Throughput Approach for the Synthesis and Identification of Highly Photo‐Cytotoxic Iridium Complexes

Abstract The discovery of new compounds with pharmacological properties is usually a lengthy, laborious and expensive process. Thus, there is increasing interest in developing workflows that allow for the rapid synthesis and evaluation of libraries of compounds with the aim of identifying leads for further drug development. Herein, we apply combinatorial synthesis to build a library of 90 iridium(III) complexes (81 of which are new) over two synthesise‐and‐test cycles, with the aim of identifying potential agents for photodynamic therapy. We demonstrate the power of this approach by identifying highly active complexes that are well‐tolerated in the dark but display very low nM phototoxicity against cancer cells. To build a detailed structure–activity relationship for this class of compounds we have used density functional theory (DFT) calculations to determine some key electronic parameters and study correlations with the experimental data. Finally, we present an optimised semi‐automated synthesise‐and‐test protocol to obtain multiplex data within 72 hours.

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

Ultra-sensitive radon assay using an electrostatic chamber in a recirculating system

Rare event searches such as neutrinoless double beta decay and Weakly Interacting Massive Particle detection require ultra-low background detectors. Radon contamination is a significant challenge for these experiments, which employ highly sensitive radon assay techniques to identify and select low-emission materials. This work presents the development of ultra-sensitive electrostatic chamber (ESC) instruments designed to measure radon emanation in a recirculating gas loop, for future lower background experiments. Unlike traditional methods that separate emanation and detection steps, this system allows continuous radon transport and detection. This is made possible with a custom-built recirculation pump. A Python-based analysis framework, PyDAn, was developed to process and fit time-dependent radon decay data. Radon emanation rates are given for various materials measured with this instrument. A radon source of known activity provides an absolute calibration, enabling statistically-limited minimal detectable activities of 20 µBq. These devices are powerful tools for screening materials in the development of low-background particle physics experiments.

47 OTHER INSTRUMENTATION↗
DOE OSTI2025

Defect Diffusion Graph Neural Networks for Materials Discovery in High-Temperature Energy Applications

Here, the migration of crystallographic defects dictates material properties and performance for a plethora of technological applications. Density functional theory (DFT)-based nudged elastic band (NEB) calculations are a powerful computational technique for predicting defect migration activation energy barriers, yet they become prohibitively expensive for high-throughput screening of defect diffusivities. Without introducing hand-crafted (i.e., chemistry- or structure-specific) descriptors, we propose a generalized deep learning approach to train surrogate models for NEB energies of vacancy migration by hybridizing graph neural networks with transformer encoders and simply using pristine host structures as input. With sufficient training data, computationally efficient and simultaneous inference of vacancy defect thermodynamics and migration activation energies can be obtained to compute temperature-dependent vacancy diffusivities and to down-select candidates for more thorough DFT analysis or experiments. Thus, as we specifically demonstrate for potential water-splitting materials, candidates with desired defect thermodynamics, kinetics, and host stability properties can be more rapidly targeted from open-source databases of experimentally validated or hypothetical materials.

14 SOLAR ENERGY↗
DOE OSTI2023

Leaving in the lead: Priorities for perovskite photovoltaics

The need for moving electricity generation to a sustainable model requires the development of low cost ubiquitous photovoltaics (PVs) to harvest the planet’s primary energy source, the Sun. Building upon the successes of Si-based and CdTe-based PV technologies, PVs with lower-embodied energy and requiring lower carbon dioxide equivalent to produce will be required to meet long-term sustainability goals. In particular, thin-film technologies, such as high-efficiency metal halide perovskite (MHP) PV modules, provide avenues to reduced embodied energy, lower energy payback times, and enabling energy-dense tandems [H. M. Wikoff et al., Joule 6(7), 1710–1725 (2022) and V. Fthenakis, Renewable Sustainable Energy Rev. 13(9), 2746–2750 (2009)]. The ability to improve efficiency and lower energy payback time of next generation thin-film PV modules is a critical foundation for green H2 and electrification more broadly. In this regard, Pb-based MHP-PVs have separated themselves as a result of the high-efficiencies that can be realized across a range of electronic gaps. Questions regarding Pb-based MHP-PVs that are often asked, as the challenges of efficiency and reliability are met, revolve around the “problem” of the Pb content. Specifically, “does Pb toxicity preclude MHP-PV modules from being deployed at the TW scale?” To provide this sense of scale, in 2021, the United States burned 10.5 quads of coal, with 90% of that used for electricity generation. Given the energy content of coal of 29 MJ/kg and a residual lead content in that coal of 30 mg/kg, electricity generation from coal resulted in more lead emitted into the atmosphere than what would be required to produce over 2 TW of MHP-PV name plate capacity (assuming a 20% module efficiency and an ∼700 nm active layer). This amounts to more PV power than has been deployed across all PV technologies and geographies to date. This only includes US coal consumption; the rest of the world would be much larger. This example illustrates the scale of the material usage relative to the energy production. Imagine a power-generation technology that offsets these Pb emissions from coal and essentially sequesters this Pb content between two sheets of impermeable glass. Why should we let Pb’s history of misuse prevent it from being included in next generation PV modules that can enable a sustainable energy future?

14 SOLAR ENERGY↗
DOE OSTI2023

Quasi-deterministic localization of Er emitters in thin film TiO 2 through submicron-scale crystalline phase control

With their shielded 4f orbitals, rare-earth ions (REIs) offer optical and electron spin transitions with good coherence properties even when embedded in a host crystal matrix, highlighting their utility as promising quantum emitters and memories for quantum information processing. Among REIs, trivalent erbium (Er 3+ ) uniquely has an optical transition in the telecom C-band, ideal for transmission over optical fibers, making it well suited for applications in quantum communication. The deployment of Er 3+ emitters into a thin film TiO 2 platform has been a promising step toward scalable integration; however, like many solid-state systems, the deterministic spatial placement of quantum emitters remains an open challenge. Here, we investigate laser annealing as a means to locally tune the optical resonance of Er 3+ emitters in TiO 2 thin films on Si. Using both nanoscale x-ray diffraction measurements and cryogenic photoluminescence spectroscopy, we show that tightly focused below-gap laser annealing can induce anatase to rutile phase transitions in a nearly diffraction-limited area of the films and improve local crystallinity through grain growth. As a percentage of Er:TiO 2 is converted to rutile, the Er 3+ optical transition blueshifts by 13 nm. We explore the effects of changing laser annealing time and show that the amount of optically active Er:rutile increases linearly with laser power. We additionally demonstrate local phase conversion on microfabricated Si structures, which holds significance for quantum photonics.

36 MATERIALS SCIENCE↗
DOE OSTI2024

Studying fast-ion populations using oscillations in solid-state neutral-particle analyzer signal and neutral-beam injection power

A method for determining the fast-ion population density in magnetically confined plasmas as a function of pitch-radius, (λ, R), using a solid-state neutral-particle analyzer (ssNPA) signal and neutral-beam injection (NBI) power-output data has been developed. Oscillations in the NBI power output are replicated only in the active part of the ssNPA signal, allowing this to be separated from the passive and background signals, which usually complicate data from this diagnostic. Results obtained using this method are compared with those from standard techniques using data from the Mega-Amp Spherical Tokamak Upgrade spherical tokamak.

Instruments & Instrumentation↗
DOE OSTI2022

U.S. advanced and novel accelerator beam test facilities

Demonstrating the viability of Advanced Accelerator Concepts (AAC) relies on experimental validation. Over the last three decades, the U.S. has maintained a portfolio of advanced and novel accelerator test facilities to support research critical to AAC. The facilities have enabled pioneering developments in a wide variety of beam and accelerator physics, including plasma-wakefield and structure-wakefield acceleration. This work provides an overview of the current portfolio of U.S. facilities possessing charged particle drive beams with high energies, on the order of tens of joules per pulse, or drive lasers with high peak powers, on the order of a petawatt, and are actively conducting AAC research.

43 PARTICLE ACCELERATORS↗
DOE OSTI2020

Optimizing Piezoelectric Material Location and Size for Multiple-Mode Vibration Reduction of Turbomachinery Blades

Modern turbomachinery blades have extremely low inherent damping, which can lead to high transient vibrations and failure through high-cycle fatigue. Smart materials enable vibration reduction while meeting strict blade requirements such as weight and aerodynamic efficiency. In particular, piezoelectric-based vibration reduction offers the potential to reduce vibration semi-actively while simultaneously harvesting sufficient energy to power the implementation. The placement and the size of the piezoelectric material is critical to the vibration reduction capabilities of the system. Furthermore, the implementation should target multiple vibration modes. In this study, we develop a procedure to optimize electromechanical coupling across multiple vibration modes for a representative turbomachinery blade with surface-mounted piezoelectric patches. Experimental validation demonstrates good coupling across three targeted modes with a single piezoelectric patch. Placing the piezoelectric material in regions of high signed strain energy for all targeted modes enables vibration reduction across all of the targeted modes.

36 MATERIALS SCIENCE↗
DOE OSTI2026

ObstacleSense: Low-Power Neuromorphic Vision for Corridor Obstacle Awareness in Low-Level ADAS

The automotive industry’s pursuit of Level 5 autonomy is constrained by substantial perception-compute power requirements, often reaching 1, 000 + watts in full autonomy stacks. Reducing this energy burden requires rethinking perception not only at the high-end autonomy level, but also at the foundational Advanced Driver Assistance Systems (ADAS) level where low-power, safety-critical sensing can have broad impact. Neuromorphic vision provides a promising starting point: HD Dynamic Vision Sensors (DVS) can operate below 100 mW at the sensor level by reporting only asynchronous brightness changes. However, low-power sensing alone is insufficient if downstream perception reintroduces dense, energy-intensive computation. In particular, many event-driven object-detection pipelines still rely on CNN backbones, while purely spiking alternatives often trade away accuracy or ignore deployment constraints. We introduce ObstacleSense, a highly compact, CNN-free hybrid ANN–SNN framework for Level 0–1 forward-corridor obstacle awareness. Instead of performing full-scene object detection with a convolutional feature backbone, ObstacleSense targets the safety-critical question of whether the ego corridor is occupied and how far the nearest obstacle is. The architecture combines polarity-conditioned event encoding, lightweight temporal spiking dynamics, axial spatial mixing, and coarse-to-fine range estimation within a regular fixed-grid compute pattern. This design avoids the dense CNN backbone commonly used in event-based detection while maintaining a small state footprint suitable for eventual small-FPGA deployment. Before hardware mapping, we evaluate the software implementation using a model-side power proxy derived from MACs, weight and activation traffic, and spiking state updates under shared FP16 assumptions. On simulated CARLA event corpora, the deployment-oriented model achieves 0.9464 objectness F1, 0.9978 grid-level mAP, and 0.8987 m distance Mean Absolute Error at an estimated 1.92 mW proxy cost, while maintaining performance on unseen generalization test sequences.

Johnson-Scott, Zac [ORNL]↗
DOE OSTI2025

Measurement techniques using the electron beam profile scanner at the Fermilab Main Injector

This work presents techniques for non-invasive transverse profile measurements of high-intensity proton beams using an Electron Beam Profile Scanner (EBPS). The EBPS utilizes low-energy electrons as a probe to analyze the transverse size of proton beams, allowing for potential analysis on a single-bunch basis. Recent upgrades to the Fermilab Main Injector have enhanced beam power on target to 1 MW, with future developments targeting 2 MW. The higher beam power has increased the demand for non-invasive diagnostics, as invasive methods can disrupt operations.The techniques presented include 1) the slow scan technique, which serves as a proof of concept for the probe beam, 2) the one-shot scan technique for measuring horizontal beam profiles, and 3) the raster scan technique for analyzing horizontal beam profiles as a function of the longitudinal distribution of the beam. The profiles obtained will be crucial for studying and understanding instabilities in high-power, high-intensity proton beams. This will contribute to optimizing the operation of high-power proton accelerators by minimizing beam loss, activation, and damage to both the diagnostics and the accelerator components.

Mwaniki, Matilda [IIT, Chicago]↗
DOE OSTI2020

Electron-Transfer Activation of Thiophene

The research supported by contract FG02-90ER14146 from the U.S. Department of Energy to Thomas B. Rauchfuss is summarized. Seven research themes are described. The work on thiophenes identified pathways for desulfurization that are initiated by electron transfer or hydrolysis. Using similar half-sandwich platforms, a large family of molecular cyanometallate cages were prepared. These cages exhibit host-guest behavior, with a notable affinity for cesium ions. A series of investigations made pioneering contributions to the understanding of the catalytic properties of the hydrogenase enzymes. Work on molecular metal sulfides focused on elucidating hydrogen activation pathways, relevant to the corresponding reactions that are invoked for metal sulfide heterogeneous catalysts. The theme of proton-activated catalysis was continued with the elucidation of the effects of Bronsted acids and oxidation on inducing H2 activation by metal-amides. The proton-activation theme was shown to be a powerful vehicle for upgrading bio-derives substrates including sugars. The project concluded with emphasis on developing ligand platforms that facilitate the use of earth-abundant metals in homogeneous catalysis.

09 BIOMASS FUELS↗
DOE OSTI2020

Fission Gas Measurements in Spent Fuel: Literature Review

The fission-derived noble gases, xenon(Xe) and krypton(Kr), have long been proposed as a tool for supporting nuclear safeguards and declarations verification. A vast body of literature outlines how these non-reactive fission gases released during reactor operation and shutdowns correlate to burnup level, power history, other reactor specifications and reprocessing activities. However, irradiation conditions can vary appreciably both radially and axially within a fuel pin.

07 ISOTOPE AND RADIATION SOURCES↗
DOE OSTI2022

Bounding Radionuclide Release Estimates for a Hypothetical Power Reactor Accident

The object of this study is to provide an estimate of bounding radionuclide releases from a nuclear power plant accident. The time frame of interest is the release phase from the initiating event through 30 days. The maximum credible initiating event includes an initial failure of the containment function with a primary system leak. All estimates include a complete loss-of-onsite power and no successful mitigative actions. The active safety injection systems are also assumed failed. The review considers the following commonly deployed reactor designs in the following order of interest: RBMK 1000, VVER-440, VVER-1000, 1000 MWe PWR, 1000 MWe BWR, BN-800, and the 600 MWe CANDU/PHWR. The review also considers spent fuel pool accident scenarios.

21 SPECIFIC NUCLEAR REACTORS AND ASSOCIATED PLANTS↗