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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 235 records · Page 13
All sourcesNASA NTRSDOE OSTI
DOE OSTI2026

Impact of Selenium and Vitamin E Deficiency on Zika Virus Pathogenesis and Immune Response in Mice

Micronutrient status is recognized to influence host susceptibility to viral infections, yet its impact on Zika virus (ZIKV) pathogenesis remains incompletely understood. We investigated the effects of dietary selenium and combined selenium plus vitamin E deficiency on ZIKV infection outcomes in a type I interferon α/β receptor knockout (Ifnar1 −/− ) murine model. Mice maintained on deficient diets exhibited significantly lower neutralizing antibody titers and reduced levels of key antiviral cytokines (IFN-γ, TNF-α, IFN-α, IFN-β, IL-12p70, CCL5) compared to controls. Correspondingly, higher viral RNA loads were detected in the brains of double-deficient mice, which also experienced greater weight loss and increased mortality. Deep sequencing revealed no major differences in overall viral genome diversity across diet groups; however, specific mutations, including V330L and D67E in the E gene, and V360I in the NS3 gene, were enriched or detected in nutritionally deficient animals. These findings suggest that antioxidant micronutrient deficiency impairs both humoral and cellular immune responses to ZIKV, potentially facilitating enhanced neuroinvasion. While the functional consequences of the identified mutations warrant further investigation, our results underscore the importance of adequate micronutrient intake for optimal antiviral defense. Further studies are needed to clarify the epidemiological significance of these observations.

Biological and medical sciences↗
DOE OSTI2025

Low-cost, portable optical sensors for critical mineral prospecting and process monitoring

A combination of projected demand increases for economically critical minerals and a monopolistic global marketplace for these minerals has caused concern over supply chain vulnerabilities, which would impact energy, national defense, and advanced technology production. Domestic production of critical minerals is hindered by expensive and non-portable methods for analyzing metal concentration, which is vital both for resource prospecting and process monitoring. Here, a suite of luminescent materials along with portable instrumentation is presented for fluorescence-based detection of trace concentrations of critical minerals (cobalt, rare earth elements, and aluminum) as well as common gangue metals that are removed during processing (iron and aluminum). A metal-organic framework comprised of zinc, adenine, and trimesic acid immobilized on a fiber optic tip is capable of sensing part-per-billion concentrations of terbium, europium, dysprosium, and samarium through a sensitization approach, in which individual elements produce characteristic emission bands enabling multiple rare earths to be simultaneously detected and distinguished. A deep-UV light emitting diode is used as the excitation source, while a portable spectrometer is used to detect the emitted light. A similar optical set-up is coupled with two different carbon dot materials, which can detect low part-per-million concentrations of cobalt and iron in acidic environments. Metal-organic framework thin films comprised of copper and 2-aminoterephthalic acid are used in tandem with a portable optical set-up to further detect aluminum, with successful deployment in real fly ash leachates. Taken together, these technologies provide an inexpensive and sensitive path towards characterizing economically critical metals.

characterization and extraction of rare earth elem↗
NASA NTRS2015

BILLIARDS: A Demonstration Mission for Hundred-Meter Class Near-Earth Asteroid Disruption

Collisions from near-Earth asteroids (NEAs) have the potential to cause widespread harm to life on Earth. The hypervelocity nature of these collisions means that a relatively small asteroid (about a quartermile in diameter) could cause a global disaster. Proposed strategies for deflecting or disrupting such a threatening asteroid include detonation of a nuclear explosive device (NED) in close proximity to the asteroid, as well as intercepting the asteroid with a hypervelocity kinetic impactor. NEDs allow for the delivery of large amounts of energy to a NEA for a given mass launched from the Earth, but have not yet been developed or tested for use in deep space. They also present safety and political complications, and therefore may only be used when absolutely necessary. Kinetic impactors require a relatively simple spacecraft compared to NEDs, but also deliver a much lower energy for a given launch mass. To date, no demonstration mission has been conducted for either case, and such a demonstration mission must be conducted prior to the need to utilize them during an actual scenario to ensure that an established, proven system is available for planetary defense when the need arises. One method that has been proposed to deliver a kinetic impactor with impact energy approaching that of an NED is the "billiard-ball" approach. This approach would involve capturing an asteroid approximately ten meters in diameter with a relatively small spacecraft (compared to the launch mass of an equivalent direct kinetic impactor), and redirecting it into the path of an Earth-threatening asteroid. This would cause an impact which would disrupt the Earth-threatening asteroid or deflect it from its Earth-crossing trajectory. The BILLIARDS Project seeks to perform a demonstration of this mission concept in order to establish a protocol that can be used in the event of an impending Earth/asteroid collision. In order to accomplish this objective, the mission must (1) rendezvous with a small (less than 10m), NEA (hereinafter "Alpha"), (2) maneuver Alpha to a collision with a approx. 100 m NEA (hereinafter "Beta"), and (3) produce a detectable deflection or disruption of Beta. In addition to these primary objectives, the BILLIARDS project will contribute to the scientific understanding of the physical properties and collision dynamics of asteroids, and provide opportunities for international collaboration.

Asteroid↗
NASA NTRS2024

JWST Sighting of Decameter Main-Belt Asteroids and View on Meteorite Sources

Asteroid discoveries are essential for planetary-defense efforts aiming to prevent impacts with Earth, including the more frequent megaton explosions from decameter impactors. While large asteroids (≥100 km) have remained in the main belt since their formation, small asteroids are commonly transported to the near-Earth object (NEO) population. However, due to the lack of direct observational constraints, their size-frequency distribution — which informs our understanding of the NEOs and the delivery of meteorite samples to Earth —varies significantly among models. Here, we report 138 detections of the smallest asteroids (⪆10 m) ever observed in the main belt, which were enabled by JWST’s infrared capabilities covering the asteroids’ emission peaks and synthetic tracking techniques. Despite small orbital arcs, we constrain the objects’ distances and phase angles using known asteroids as proxies, allowing us to derive sizes via radiometric techniques. Their size-frequency distribution exhibits a break at∼100 m (debiased cumulative slopes of q=−2.66±0.60 and−0.97±0.14 for diameters smaller and larger than∼100 m, respectively), suggestive of a population driven by collisional cascade. These asteroids were sampled from multiple asteroid families —most likely Nysa, Polana and Massalia — according to the geometry of pointings considered here. Through additional long-stare infrared observations, JWST is poised to serendipitously detect thousands of decameter-scale asteroids across the sky, probing individual asteroid families and the source regions of meteorites “in-situ”.

asteroid families↗
NASA NTRS1990

Codeless GPS Applications to Multi-Path: CGAMP

Cordless Global Positioning System (GPS) Applications to Multi-Path (CGAMP) is meeting the challenge of exploiting the L-band signals from the Global Positioning System (GPS) satellites for the measurement of the impulse response of radio transmission channels over space-Earth paths. This approach was originally suggested by E. K. Smith and has been pursued by J. Lemmon, without an affordable implementation being identifiable. In addition to the high cost of a suitable P code correlating GPS receiver, there is also the major impediment of the often announced Department of Defense policy of selective availability/anti-spoof (SA/AS) that clouds reliable access to the wideband (20 MHz) P channel of the GPS signals without cryptographic access. A technique proposed by MacDoran utilizes codeless methods for exploiting the P channel signals implemented by the use of a pair of antennas and cross correlation signal detection.

Macdoran, P. F.↗
NASA NTRS2003

A Study of Planetary System Formation and Evolution Using a Dual Channel Adaptive Optics Imaging Polarimeter

The award I received from the Graduate Student Research Program in 2001, and the renewal award in 2002, supported the gathering and processing of my thesis data, and the presentation of my scientific results at astronomy conferences. Ultimately, this led to a successful defense of my PhD thesis. In the thesis work, I built, calibrated, and observed with the first dual imaging polarimeter ever used in series with a high order adaptive optics system and on an 8 meter class telescope. The instrument was arguably the most sensitive instrument for the high-contrast detection of the scattered light from circumstellar disks. The success of the instrument has provided the impetus for the construction of several similar simultaneous adaptive optics imaging polarimeters. In the course of the observations, a number of circumstellar disks were resolved for the first time in their scattered light, such as the disk around the classical T-tauri stars, LkCa 15 and LkHa 262. A sample of 24 young, nearby, solar-analog stars were observed with the instrument in search of the scattered light signature from debris disks around these stars. Although none of the stars revealed any obvious scattered light signatures, a meaningful limit was placed on the dust population around these stars. Also, a brown dwarf binary system was found to be a companion to one of the solar-analog stars observed, HD130948. A careful monitoring of the orbit of the binary brown dwarf will result in a dynamical mass determination of the objects, providing a valuable calibration of the physical models explaining brown dwarf evolution.

Potter, Daniel E.↗
DOE OSTI2022

Networked Microgrid Cybersecurity Architecture Design Guide: A New Jersey TRANSITGRID Use Case

Microgrids require reliable communication systems for equipment control, power delivery optimization, and operational visibility. To maintain secure communications, Microgrid Operational Technology (OT) networks must be defensible and cyber-resilient. The communication network must be carefully architected with appropriate cyber-hardening technologies to provide security defenders the data, analytics, and response capabilities to quickly mitigate malicious and accidental cyberattacks. In this work, we outline several best practices and technologies that can support microgrid operations (e.g., intrusion detection and monitoring systems, response tools, etc.). Then we apply these recommendations to the New Jersey TRANSITGRID use case to demonstrate how they would be deployed in practice.

24 POWER TRANSMISSION AND DISTRIBUTION↗
DOE OSTI2020

Systematic Evaluation of Backdoor Data Poisoning Attacks on Image Classifiers

Backdoor data poisoning attacks have recently been demonstrated in computer vision research as a potential safety risk for machine learning (ML) systems. Traditional data poisoning attacks manipulate training data to induce unreliability of an ML model, whereas backdoor data poisoning attacks maintain system performance unless the MLmodel is presented with an input containing an embedded“trigger” that provides a predetermined response advantageous to the adversary. Our work builds upon prior back-door data-poisoning research for ML image classifiers and systematically assesses different experimental conditions including types of trigger patterns, persistence of trigger patterns during retraining, poisoning strategies, architectures (ResNet-50, NasNet, NasNet-Mobile), datasets (Flowers, CIFAR-10), and potential defensive regularization techniques (Contrastive Loss, Logit Squeezing, Manifold Mixup,Soft-Nearest-Neighbors Loss). Experiments yield four key findings. First, the success rate of backdoor poisoning at-tacks varies widely, depending on several factors, including model architecture, trigger pattern and regularization technique. Second, we find that poisoned models are hard to detect through performance inspection alone. Third, regularization typically reduces backdoor success rate, although it can have no effect or even slightly increase it, depending on the form of regularization. Finally, backdoors inserted through data poisoning can be rendered ineffective after just a few epochs of additional training on a small set of clean data without affecting the model’s performance.

Truong, Loc T.↗
DOE OSTI2021

Evaluating Probability of Containment Effectiveness at a GCS Sites using integrated assessment modeling approach with Bayesian decision Networks

Improved scientific and engineering understanding of the behavior of geologic CO2 storage together with established regulatory framework and incentive structures raise the prospects for accelerated, large-scale deployment of this greenhouse gas emissions reduction approach. Incentive structures call for the establishment of appropriate verification and accounting approaches to support claims of the integrity of a geologic storage complex and to justify taking credit for long-term storage. In this study, we present a framework for assessing the probability of containment effectiveness over the lifetime of a geologic carbon storage site (e.g., after 70 years of injection and post-injection site performance) using forward stochastic model realizations based on site characterization data and using a monitoring-informed Bayesian network based on hypothetical detectability from surface seismic surveys over the site injection and post-injection phases. The National Risk Assessment Partnership’s open-source Integrated Assessment Model (NRAP-Open-IAM) was utilized to develop an ensemble of 10,000 a priori stochastic forecasts of CO2 containment. Those simulations were used to train the Bayesian network model to estimate the prior probabilities of the CO2 leakage mass into overlying, monitorable aquifers considering the uncertainties in the reservoir properties, permeability of potentially leaky wells and the overlying aquifers. The conditional probabilities in the Bayesian network were either learned from the NRAP-Open-IAM simulations or derived from the predefined detection thresholds for the monitoring method. Observations obtained from monitoring, over time during the site operation phases were then used to generate updated posterior probabilities of containment (and any loss from containment) in the Bayesian network by propagating the prior probabilities through the conditional probabilities. We demonstrate how to construct and use the Bayesian network for verifying the long-term storage complex effectiveness informed by monitoring based on the NRAP-Open-IAM simulations previously developed for the FutureGen 2.0 site. This approach may have relevance for stake holders to demonstrate secure geologic storage, provide a defensible, probabilistic approach to claim credit for geologic storage, and to estimate the likelihood that any fraction of the claimed credit may need to be refunded to the creditor based on available monitoring information.

Bayesian network, Risk assessment, Monitoring, car↗
DOE OSTI2022

Detecting Large Explosions With Machine Learning Models Trained on Synthetic Infrasound Data

Explosions produce low-frequency acoustic (infrasound) waves capable of propagating globally, but the spatio-temporal variability of the atmosphere makes detecting events difficult. Machine learning (ML) is well-suited to identify the subtle and nonlinear patterns in explosion infrasound signals, but a previous lack of ground-truth data inhibited training of generalized models. We introduce a physics-based method that propagates infrasound sources through realistic atmospheres to create 28,000 synthetic events, which are used to train ML classifiers. A simple artificial neural network and modern temporal convolutional network discriminate synthetic events from background noise with >90% accuracy and, more importantly, successfully identify the majority of real-world explosion signals recorded during the Humming Road Runner experiment. ML models trained entirely on physics-based synthetics advance explosion detection capabilities and make ML more viable to related fields lacking training data.

45 MILITARY TECHNOLOGY, WEAPONRY, AND NATIONAL DEF↗
NASA NTRS1996

Spectral imaging applications: Remote sensing, environmental monitoring, medicine, military operations, factory automation and manufacturing

This paper reviews the activities at OKSI related to imaging spectroscopy presenting current and future applications of the technology. The authors discuss the development of several systems including hardware, signal processing, data classification algorithms and benchmarking techniques to determine algorithm performance. Signal processing for each application is tailored by incorporating the phenomenology appropriate to the process, into the algorithms. Pixel signatures are classified using techniques such as principal component analyses, generalized eigenvalue analysis and novel very fast neural network methods. The major hyperspectral imaging systems developed at OKSI include the Intelligent Missile Seeker (IMS) demonstration project for real-time target/decoy discrimination, and the Thermal InfraRed Imaging Spectrometer (TIRIS) for detection and tracking of toxic plumes and gases. In addition, systems for applications in medical photodiagnosis, manufacturing technology, and for crop monitoring are also under development.

National Defense↗
DOE OSTI2025

Harnessing Machine Learning for Agnostic Biodetection

The United States’ current list-based approach to biodefense is limited because it considers only known biological agents. Alternatively, developing and adopting a system based on agent-agnostic signatures would enable detection and characterization of both known and novel agents, thereby engendering greater adaptability in the face of an evolving threat landscape. Machine learning (ML) could aid in such a transition, as it can recognize and encode highly complex patterns from multiple input data modalities and has already demonstrated success in many healthcare and defense applications. Functionalizing ML for environmental biodetection requires understanding current technical capabilities. In this article, we provide a systematic review of existing ML platforms and discuss anticipated development efforts needed to achieve effective ML-enabled, agnostic biodetection.

60 APPLIED LIFE SCIENCES↗
NASA NTRSDate not supplied

Yeast strain development to test in-space bioproduction in the Lunar Explorer Instrument for space biology Applications (LEIA) mission

The Lunar Explorer Instrument for space biology Applications (LEIA) is investigating the effects of lunar radiation and gravity on yeast viability, growth, and metabolism. LEIA is part of the CP-22 Commercial Lunar Payload Services (CLPS) surface mission to the south polar region of the Moon. The biological payload will test genetic factors that are likely to influence the tolerance of yeast for deep space and lunar surface radiation. LEIA is also investigating in-space production of β-carotene in bioengineered yeast. This carotenoid is both an antioxidant and pro-vitamin A- an essential human micronutrient. We report progress on engineering carotenoid-expressing strains to test the impacts of strain background and specific genetic variants on growth and production of β-carotene. To test for enhanced sensitivity to the LEIA mission environment, we generated gene knockouts for the RAD51 DNA damage repair locus as well as the SOD1, SOD2, and TSA1 reactive oxygen species (ROS) defense enzymes. We are also generating strains expected to increase tolerance to abiotic stressors and ROS. To be included in the biology payload, each strain needs to satisfy a series of requirements to be compatible with the mission concept of operations. The LEIA mission will conduct experiments using an autonomous light emitting diode optical detection system and microfluidics incubator to quantify growth, metabolism, and carotenoid production. Strains must produce sufficient carotenoids for bioproduction to be detectable with this optical system. Cells will be loaded into microfluidics cards, desiccated, and stored for 8-12 months prior to the initiation of lunar surface operations. The CLPS lander will operate for one lunar day, and strains will need to grow to stationary phase within 96 hours to ensure that telemetry of LEIA data to Earth can be completed. Genetic variants also need to display expected phenotypes within these optical detection, storage, and growth cycle constraints.

Yeast Engineering↗
DOE OSTI2024

Live-Cell Quantum Dot-Based Tracking of Plant & Microbial Extracellular Vesicles

All cellular organisms examined to date release various types of small vesicles called extracellular vesicles (EVs). They were originally thought to be unimportant, but research into EVs as carriers of key messages for communication within large, multicellular organisms or between interacting organisms has gained interest in many different scientific areas of study. Recently, it was discovered that both plants and their pathogenic fungi generate EVs and they are important vehicles for sending plant signals to thwart invasion by fungal pathogens, and in response, fungi use EVs to help evade plant defenses. This project aimed to develop new ways to visualize EVs and their potential nucleic acid cargo isolated from the crop plant, sorghum. We adapted a click chemistry-based method for making quantum dot-based RNA sensors, which will be a useful tool for others studying small RNAs. These sensors were not used detect RNA in sorghum EVs because unlike EVs from Arabidopsis, sorghum EVs were not enriched for specific small RNAs. This interesting difference shifted the project in an exciting direction, which focused on comparing EV proteomes. Our study discovered that greater than 60% of Arabidopsis EV proteins were also found in the sorghum EV proteome, revealing a partial conservation between a eudicot and monocot plant. These studies have raised new, fundamental questions on the conserved function of EVs in plants.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2020

Perturbation-Based Diagnosis of False Data Injection Attack Using Distributed Energy Resources

Modern smart grid relies on various sensor measurements for its operational control. In a successful false data injection attack, the attacker manipulates the measurements from the grid sensors such that undetected errors are introduced into the estimates of the system parameters leading to catastrophic situations. This paper proposes a novel perturbation based false data injection attack detection mechanism that utilizes inverter based distributed energy resources (DERs) to create low magnitude perturbation signal in the distribution system voltage that is inconsequential to the normal grid operation. Two voltage sensitivity analysis based algorithms are designed to identify the optimal set of DERs that can create the voltage perturbation signal of desired magnitude. An analytical method of voltage sensitivity analysis is used to compute the magnitude of voltage perturbation signal at each node in a computationally efficient manner. Then, a detection mechanism is developed that checks for the presence of the perturbation sequence in each sensor measurement. A sensor measurement is deemed authentic if the voltage perturbation signal is present in the data. In case of sensor malfunction or cyber-attack, the perturbation signal will not be present in the measurement data. Performance of the proposed attack detection mechanism is validated via simulation of the IEEE 69 bus test system.

24 POWER TRANSMISSION AND DISTRIBUTION↗
DOE OSTI2020

Applying Waveform Correlation to Mining Blasts Using a Global Sparse Network

Agencies that monitor for underground nuclear tests are interested in techniques that automatically characterize mining blasts to reduce the human analyst effort required to produce high-quality event bulletins. Waveform correlation is effective in finding similar waveforms from repeating seismic events, including mining blasts. We report the results of an experiment that uses waveform templates recorded by multiple International Monitoring System stations of the Comprehensive Nuclear-Test-Ban Treaty for up to 10 years prior to detect and identify mining blasts that occur during single weeks of study. We discuss approaches for template selection, threshold setting, and event detection that are specialized for mining blasts and a sparse, global network. We apply the approaches to two different weeks of study for each of two geographic regions, Wyoming and Scandinavia, to evaluate the potential for establishing a set of standards for waveform correlation processing of mining blasts that can be effective for operational monitoring systems with a sparse network. We compare candidate events detected with our processing methods to the Reviewed Event Bulletin of the International Data Centre to develop an intuition about potential reduction in analyst workload.

45 MILITARY TECHNOLOGY, WEAPONRY, AND NATIONAL DEF↗
DOE OSTI2020

CENC PSN VOID FY20 Report

Maritime trade accounts for approximately 80 percent of international commerce. The high volume of vessels traversing domestic and international ports makes port areas prime targets for terrorism as well as illegal trafficking of drugs and arms (conventional or nuclear). Port security is therefore a worldwide concern affecting global economies, freedom of movement, and national security. However, extensive port monitoring is inherently complex and time consuming — making it truly viable only via an automated framework that can detect potential illicit activity and alert authorities in a timely manner. The development of image processing algorithms for this purpose requires access to large, labeled datasets that cover the breadth of targets of interest as well as the environments that they are observed within. Curated and labeled datasets of this nature are of enormous value to Sandia's Defense Nuclear Nonproliferation and National Security Program portfolios, as well as to Sandia's machine learning/automatic target recognition (ML/ATR) algorithm development and R&D communities. The goal of this project is to create a commercial satellite imagery dataset of labeled maritime vessels in port areas to support the development of ML/ATR algorithms for port security nonproliferation purposes. This dataset — Port Security Nonproliferation Vessel Overhead Imagery Dataset (PSN VOID) — has the potential to support a variety of other ancillary missions, such as maritime domain awareness, domestic and international security, drug interdiction, and weapons trafficking.

98 NUCLEAR DISARMAMENT, SAFEGUARDS, AND PHYSICAL P↗
DOE OSTI2023

Development of quantum dot materials for infrared cameras (Final CRADA Report)

The aim of this project was to develop scalable methods to produce infrared (IR) mercury telluride (HgTe) colloidal quantum dot (CQD) thin films and demonstrate their utility in a proof-of-concept monolithic SWIR focal plane array (FPA). These objectives were accomplished by scaling up the HgTe CQD synthesis, characterizing physical and electrical properties of HgTe CQDs, evaluating solution-processed coating methods for quality and efficiency, and developing a process flow to integrate HgTe CQDs with commercial-off-the-shelf silicon CMOS readout circuits by solution-processed coating to produce monolithic FPAs. The FPA is the image sensor in an infrared imaging system responsible for detecting and processing reflected or emitted light into an infrared image of the scene under observation. The quality of the image is determined by the sensitivity and resolution of the image sensor in the system. Higher resolution IR FPAs enable higher throughput in manufacturing quality assurance, wider field of view for autonomous navigation, and longer range surveillance for defense.

77 NANOSCIENCE AND NANOTECHNOLOGY↗