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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 55 records · Page 3
All sourcesNASA NTRSDOE OSTI
DOE OSTI2023

AI-accelerated protein-ligand docking for SARS-CoV-2 is 100-fold faster with no significant change in detection

Protein-ligand docking is a computational method for identifying drug leads. The method is capable of narrowing a vast library of compounds down to a tractable size for downstream simulation or experimental testing and is widely used in drug discovery. While there has been progress in accelerating scoring of compounds with artificial intelligence, few works have bridged these successes back to the virtual screening community in terms of utility and forward-looking development. We demonstrate the power of high-speed ML models by scoring 1 billion molecules in under a day (50 k predictions per GPU seconds). We showcase a workflow for docking utilizing surrogate AI-based models as a pre-filter to a standard docking workflow. Our workflow is ten times faster at screening a library of compounds than the standard technique, with an error rate less than 0.01% of detecting the underlying best scoring 0.1% of compounds. Our analysis of the speedup explains that another order of magnitude speedup must come from model accuracy rather than computing speed. In order to drive another order of magnitude of acceleration, we share a benchmark dataset consisting of 200 million 3D complex structures and 2D structure scores across a consistent set of 13 million “in-stock” molecules over 15 receptors, or binding sites, across the SARS-CoV-2 proteome. We believe this is strong evidence for the community to begin focusing on improving the accuracy of surrogate models to improve the ability to screen massive compound libraries 100 × or even 1000 × faster than current techniques and reduce missing top hits. The technique outlined aims to be a fast drop-in replacement for docking for screening billion-scale molecular libraries.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2024

Graph-Based Modeling for the Detection and Tracking of Sarin-Surrogate-Induced Neurotoxicity Using a Human-Relevant, In-Vitro Brain Model

Organophosphorus (OP) nerve agents are a chemical threat to the United States, to the civilian population (e.g., pesticides) and historically weaponized (e.g., sarin) as chemical warfare agents. The unprecedented, accelerated process from “bench-to-bedside” during the SARSCov2 pandemic has made it clear that technology and tools need to be readily available for immediate response. Advances in human organ tissue mimetic systems are a promising technology to evaluate the human-relevant response in vitro for basic and applied research and drug screening. In particular, current brain microphysiological systems (MPS) have the capability to monitor and detect changes in engineered human neural circuit activity. However, current data analytics approaches for these systems lack the granularity to functionally detect and distinguish the different mechanisms that occur in the brain following neurotoxicity, injury, and disease. The goal of this project was to advance the computational analytical capabilities of the brain MPS to detect functional changes in neural circuit structure at different stages of Sarin surrogate-induced neurotoxicity. We developed graph-based models to (1) identify the composition of the neural circuit structure; (2) detect and monitor how this structure changes following sarin-induced neurotoxicity; and (3) evaluate the analytical pipeline using known/promising oxime reactivators. Through experiments on the bMPS where in vitro neuronal cultures were exposed to a sarin surrogate, we demonstrated the capabilities of our computational pipeline to identify different responses in the functional networks of brain cells exposed to low and high concentrations of the nerve agent. We identified a biphasic response of human neural network activity following exposure to a sarin-surrogate that had not been reported in the literature before. The graph-based models and software developed in this project can be used for future studies that leverage the brain MPS technology, such as treatment efficacy assessment.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2021

Hybrid Analytics Solution to Improve Coal Power Plant Operations

This project focused on developing advanced methods for thermal performance monitoring of a coal-fueled power plant. The specific goal was to develop and demonstrate a new thermal performance monitoring approach using a hybrid model that integrates a physics-based heat balance model with a machine learning-based pattern recognition model. The hybrid model enables increased accuracy and scope of the thermal analysis and an improved ability to monitor and detect changes in plant operation. This new approach takes full advantage of the individual model capabilities and creates an important new set of capabilities not previously possible using the two types of models separately. Using the heat balance model, a rich set of derived parameters (virtual sensors) are calculated from the measured plant operating data at each time point. The combined measured and derived data values are used by machine learning algorithms to create pattern recognition models over the range of normal unit operation. To create the monitoring models, historical data from normal operation of the plant is first processed by the heat balance model to compute the derived parameter data. The result is a greatly expanded set of normal operating data that can be used as input to create the pattern recognition model. Once the models are calibrated for normal operation, the hybrid model is suitable for use in continuous online monitoring. During online monitoring, new plant operating data is processed first by the heat balance model and then by the pattern recognition model. Results from the pattern recognition model quantify the deviation of each measured or derived parameter from its expected value in normal operation. The hybrid models can detect abnormal changes in plant operating data with very high accuracy and sensitivity. When abnormal behavior is detected, alerts are generated automatically for evaluation by the plant monitoring staff. The new hybrid solution product was developed and verified in the performance of the project. The hybrid solution was tested first in a simulation environment that mimicked the plant data systems and infrastructure used by U.S. power generating plants and utilities. The hybrid solution was then deployed for real-time, online monitoring of an operating coal-fueled power plant at a field test site. Field testing demonstrated that all hybrid solution development objectives were accomplished. The project work was based on combining the capabilities of two existing software products to create the new hybrid solution product. One of these was the existing MapEx® heat balance product and the other was the existing SureSense® advanced pattern recognition product. Each of these separate products was assessed to be at a Technology Readiness Level (TRL) of 9 at the start of the effort. The hybrid solution product was assessed to be at a TRL of 2 at the start of the project based on early feasibility work by the project team. At completion of the field testing performed in the project, the hybrid solution product was assessed to be at a TRL of 7. The project team expects that the hybrid solution product will be deployed commercially and will achieve a TRL of 9 within one year after completion of the project.

01 COAL, LIGNITE, AND PEAT↗
DOE OSTI2021

Comparison of Surface Phenomena Created by Underground Chemical Explosions in Dry Alluvium and Granite Geology From Fully Polarimetric VideoSAR Data

Phase I of the Source Physics Experiment (SPE) series involved six underground chemical explosions, all of which were conducted at the same experimental pad. Research from the sixth explosion of the series (SPE-6) demonstrated that polarimetric synthetic aperture radar (PolSAR) is a viable technology for monitoring an underground chemical explosion when the geologic structure is Cretaceous granitic intrusive. It was shown that a durable signal is measurable by the H/A/α polarimetric decomposition parameters. After the SPE-6 experiment, the SPE program moved to the Phase II location, which is composed of dry alluvium geology (DAG). The loss of wavefront energy is greater through dry alluvium than through granite. In this article, we compare the SPE-6 analysis to the second DAG (DAG-2) experiment. We hypothesize that despite the geology at the DAG site being more challenging than at the Phase I location, combined with the DAG-2 experiment having a 3.37 times deeper scaled depth of burial than the SPE-6, a durable nonprompt signal is still measurable by a PolSAR sensor. We compare the PolSAR time-series measures from videoSAR frames, from the SPE-6 and DAG-2 experiments, with accelerometer data. We show which PolSAR measures are invariant to the two types of geology and which are geology dependent. We compare a coherent change detection (CCD) map from the DAG-2 experiment with the data from a fiber-optic distributed acoustic sensor to show the connection between the spatial extent of coherence loss in CCD maps and spallation caused by the explosion. Finally, we also analyze the spatial extent of the PolSAR measures from both explosions.

42 ENGINEERING↗
DOE OSTI2026

Probing Cellular Activity Via Charge‐Sensitive Quantum Nanoprobes

Nitrogen‐vacancy (NV) based quantum sensors hold great potential for real‐time single‐cell sensing with far‐reaching applications in fundamental biology and medical diagnostics. Although highly sensitive, the mapping of quantum measurements onto cellular physiological states has remained an exceptional challenge. Here, we introduce a novel quantum sensing modality capable of detecting changes in cellular activity. Our approach is based on the detection of environment‐induced charge depletion within an individual particle that, owing to a previously unaccounted transverse dipole term, induces systematic shifts in the zero‐field splitting (ZFS). Importantly, these charge‐induced shifts serve as a reliable indicator for lipopolysaccharide (LPS)‐mediated inflammatory response in macrophages. Furthermore, we demonstrate that surface modification of our diamond nanoprobes effectively suppresses these environment‐induced ZFS shifts, providing an important tool for differentiating electrostatic shifts caused by the environment from other unrelated effects, such as temperature variations. Notably, this surface modification also leads to significant reductions in particle‐induced toxicity and inflammation. Our findings shed light on systematic drifts and sensitivity limits of NV spectroscopy in a biological environment with ramifications for the critical discussion surrounding single‐cell thermogenesis. Notably, this work establishes the foundation for a novel sensing modality capable of probing complex cellular processes through straightforward physical measurements.

band bending↗
DOE OSTI2020

A Rapid Population Assessment Method for Wild Pigs Using Baited Cameras at 3 Study Sites

ABSTRACT Reliable and efficient population estimates are a critical need for effective management of invasive wild pigs ( Sus scrofa ). We evaluated the use of 10‐day camera grids for rapid population assessment (RPA) of wild pigs at 3 study sites that varied in vegetation communities and wild pig densities. Study areas included Buck Island Ranch, Florida; Tejon Ranch, California; and the Savannah River Site, South Carolina, USA, during 2016–2018. Rapid population assessments grids were composed of baited camera traps spaced approximately 500 or 750 m apart. Two RPA grids were deployed per study site and each grid was deployed twice (4–6 months apart) to assess changes in response to season or population control efforts. We assessed the ability of RPA grids to track population trends, how camera number influenced estimate precision, and how relative abundance indices related to density estimates. We detected changes in occupancy probability, detection probability, and N‐mixture estimates following removal operations and between seasons, but the ability of RPA grids to track population trends was dependent on the statistical method used and number of cameras traps. Increasing the number of cameras traps used in RPA grids increased precision, and these results can be used in determining survey design and estimate choice. We found that estimates of occupancy probability, detection probability, and N‐mixture estimates were positively correlated with spatially explicit capture–recapture density estimates. Thus, these less labor‐intensive estimates from RPA grids showed potential to index the relative abundance of wild pigs in some systems. Our evaluation of RPAs indicates that using study‐specific combinations of statistical method and number of cameras can provide a useful tool for monitoring wild pig presence, tracking population trends, and evaluating the effectiveness of management actions. © 2020 The Wildlife Society.

Schlichting, Peter E.↗
DOE OSTI2024

Optical sensing of magnetic fields using magnetically sensitive materials

The invention provides a method for measuring the magnetic field of an electromagnetic component having the steps of: instrumenting one or more portions of an electromagnetic component by placing an optical fiber in electromagnetic communication with the one or more portions of said electromagnetic component; energizing the electromagnetic component; interrogating the optical fiber using light and an optical detector; and determining changes in the magnetic field incident on the optical fiber based on the detected changes in the light received by the optical detector.

Ohodnicki, Paul↗
DOE OSTI2022

The Influence of Cr-Additives on the Polarization Resistance of Praseodymium-Doped Ceria Cathodes for Solid Oxide Fuel Cells

While Cr poisoning of the oxygen reduction reaction (ORR) at SOFC cathodes is widely agreed to involve deactivation of oxygen exchange sites, the degradation mechanism remains ambiguous. Here, we selected an alternative cathode material Pr 0.1 Ce 0.9 O 2− δ , free of Sr segregation, to systematically investigate the effect of Cr-induced degradation in ORR. We expand on our previous studies in which the acidity/basicity of binary additives was found to be a strong indicator of the rate of oxygen surface exchange, by electrochemically investigating the ORR activity of the PCO cathode by impedance spectroscopy. Serial infiltration with acidic Cr-based oxides was found to degrade ORR activity as reflected in a 20-fold increase in area specific resistance (ASR) without corresponding changes in activation energy, with the opposite trend obtained with Ca-based oxides. Detected changes in total capacitance, attributed to changes in surface capacitance, also suggest depressed/enhanced PCO surface redox behavior with Cr/Ca-based infiltrants. Taken together, these results point to a more universal source of ORR poisoning/activation, based on acidity/basicity, rather than physical blocking of active sites. With this improved understanding, one can expect progress to be made in the coming years in optimizing means for protecting catalytic surfaces from degradation and/or improving their performance.

25 ENERGY STORAGE↗
DOE OSTI2026

Host-Directed, Bioelectronic Immunomodulation for Protection Against Emerging Pathogens

Acute care of patients with severe infections often relies on systemic administration of pharmaceuticals and monitoring of complex physiological symptoms to identify immune system dysfunction, which can lead to increased mortality. Furthermore, determining disease-specific treatment plans often leads to a delay in patient care. To address this, we proposed an immune modulation system that electrically detects and responds to a patient’s immune system status, creating an agnostic means of treating illness and infection. Two pieces of hardware were developed for this task: a minimally-invasive sensor and a vagus nerve stimulator. Stimulation of the vagus nerve is known to modulate the immune system. The sensor is a microfabricated, silicon-based microneedle array capable of interfacing with interstitial fluid to detect small molecules such as inflammatory proteins (cytokines) and pharmaceuticals (vancomycin). Process optimization to manufacture the needles refined the silicon etch process, creating needle patches long enough to penetrate skin and reach interstitial fluid. The needles were tested for mechanical strength and stability, and did not shatter when inserted into skin models. The needles are coated with a thin film metal, turning them into electrodes for electrochemical sensing of our target molecules. We hybridized aptamers to the surface of the electrode to act as the sensing layer and were able to detect changes in the conformation of the aptamer electrochemically in the presence of the target molecule. The stimulator was a cuff electrode that encircled the vagus nerve. Rodent studies were conducted in which rodents were exposed to an inflammatory event and vagus nerve stimulation (VNS) was applied. It was demonstrated that optimized electrical stimulation of the vagus nerve created measurably different levels of cytokines in blood samples, and certain cytokines released during the inflammatory event were either upregulated or downregulated. In sum, this project successfully developed new platforms and technologies that can, with further development, enable better temporal insight into biomarker changes in the body, letting healthcare providers know of possible immune system dysfunction before they are detected physiologically. We also demonstrated the value of VNS and its possible use in treating immune system response to inflammation and illness.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2022

Detecting impacts of surface development near weather stations since 1895 in the San Joaquin Valley of California

Abstract Temperature readings observed at surface weather stations have been used for detecting changes in climate due to their long period of observations. The most common temperature metrics recorded are the daily maximum (TMax) and minimum (TMin) extremes. Unfortunately, influences besides background climate variations impact these measurements such as changes in (1) instruments, (2) location, (3) time of observation, and (4) the surrounding artifacts of human civilization (buildings, farms, streets, etc.) Quantifying (4) is difficult because the surrounding infrastructure, unique to each site, often changes slowly and variably and is thus resistant to general algorithms for adjustment. We explore a direct method of detecting this impact by comparing a single station that experienced significant development from 1895 to 2019, and especially since 1970, relative to several other stations with lesser degrees of such development (after adjustments for the (1) to (3) are applied). The target station is Fresno, California (metro population ~ 15,000 in 1900 and ~ 1 million in 2019) situated on the eastern side of the broad, flat San Joaquin Valley in which several other stations reside. A unique component of this study is the use of pentad (5-day averages) as the test metric. Results indicate that Fresno experienced + 0.4 °C decade −1 more nighttime warming (TMin) since 1970 than its neighbors—a time when population grew almost 300%. There was little difference seen in TMax trends between Fresno and non-Fresno stations since 1895 with TMax trends being near zero. A case is made for the use of TMax as the preferred climate metric relative to TMin for a variety of physical reasons. Additionally, temperatures measured at systematic times of the day (i.e., hourly) show promise as climate indicators as compared with TMax and especially TMin (and thus TAvg) due to several complicating factors involved with daily high and low measurements.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2023

Quantification of SARS-CoV-2 spike protein expression from mRNA vaccines using isotope dilution mass spectrometry

The advent of mRNA vaccine technology has been vital in rapidly creating and manufacturing COVID-19 vaccines at an industrial scale. To continue to accelerate this leading vaccine technology, an accurate method is needed to quantify antigens produced by the transfection of cells with a mRNA vaccine product. This will allow monitoring of protein expression during mRNA vaccine development and provide information on how changes to vaccine components affects the expression of the desired antigen. Developing novel approaches that allow for high-throughput screening of vaccines to detect changes in antigen production in cell culture prior to in vivo studies could aid vaccine development. Here, we have developed and optimized an isotope dilution mass spectrometry method to detect and quantify the spike protein expressed after transfection of baby hamster kidney cells with expired COVID-19 mRNA vaccines. Five peptides of the spike protein are simultaneously quantified and provide assurance that protein digestion in the region of the target peptides is complete since results between the five peptides had a relative standard deviation of less than 15 %. In addition, two housekeeping proteins, actin and GAPDH, are quantified in the same analytical run to account for any variation in cell growth within the experiment. IDMS allows a precise and accurate means to quantify protein expression by mammalian cells transfected with an mRNA vaccine.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2023

Imaging the Future Threats of the Sand Dunes Along the Northwestern Coast of Nile Delta Using SAR

Although the Sand dunes screen the coastal cities from the sand dunes hazards and their infrastructures from rising sea levels and storms, they might threaten and hide the surrounding developmental projects. Thus, imaging these coastal dunes' internal anatomy and dynamics are crucial to protect the environment. In this study, the Optical and Synthetic Aperture Radar (SAR) images were processed and integrated with field observations and surveys to extract information about the past, current, and future behavior of coastal dunes in the Nile Delta-Northwest sector. A conventional change detection method using the supervised Landsat-8 images for the years 2015 and 2017 were generated to show a rapid change in the sand dune cover, where the high-resolution images of Google Earth were used to digitize the dune crests and measure its encroachment rate, which has been reached about 4 m/year with the NW–SE direction. The full-polarimetric ALOS/PALSAR-2 images from the years 2015 and 2017 show very low coherence for the sand dunes, which means these dunes are dynamic, where the changes in both power and phase imbalances of different dune fields during the investigated years (2015 and 2017) show high differences in values, which reached 40 dB and 0.06°, respectively. Finally, the extracted radar facies from the field survey show two successive groups of coastal sand dunes show a decline in the heavy minerals content to a level that might threaten the properties at the shoreline due to sea level rise and wave storms if mitigation actions are not taken place soon.

Ramadan, Rabab↗
DOE OSTI2022

Global 10 m Land Use Land Cover Datasets: A Comparison of Dynamic World, World Cover and Esri Land Cover

The European Space Agency’s Sentinel satellites have laid the foundation for global land use land cover (LULC) mapping with unprecedented detail at 10 m resolution. We present a cross-comparison and accuracy assessment of Google’s Dynamic World (DW), ESA’s World Cover (WC) and Esri’s Land Cover (Esri) products for the first time in order to inform the adoption and application of these maps going forward. For the year 2020, the three global LULC maps show strong spatial correspondence (i.e., near-equal area estimates) for water, built area, trees and crop LULC classes. However, relative to one another, WC is biased towards over-estimating grass cover, Esri towards shrub and scrub cover and DW towards snow and ice. Using global ground truth data with a minimum mapping unit of 250 m 2 , we found that Esri had the highest overall accuracy (75%) compared to DW (72%) and WC (65%). Across all global maps, water was the most accurately mapped class (92%), followed by built area (83%), tree cover (81%) and crops (78%), particularly in biomes characterized by temperate and boreal forests. The classes with the lowest accuracies, particularly in the tundra biome, included shrub and scrub (47%), grass (34%), bare ground (57%) and flooded vegetation (53%). When using European ground truth data from LUCAS (Land Use/Cover Area Frame Survey) with a minimum mapping unit of <100 m 2 , we found that WC had the highest accuracy (71%) compared to DW (66%) and Esri (63%), highlighting the ability of WC to resolve landscape elements with more detail compared to DW and Esri. Although not analyzed in our study, we discuss the relative advantages of DW due to its frequent and near real-time data delivery of both categorical predictions and class probability scores. We recommend that the use of global LULC products should involve critical evaluation of their suitability with respect to the application purpose, such as aggregate changes in ecosystem accounting versus site-specific change detection in monitoring, considering trade-offs between thematic resolution, global versus. local accuracy, class-specific biases and whether change analysis is necessary. We also emphasize the importance of not estimating areas from pixel-counting alone but adopting best practices in design-based inference and area estimation that quantify uncertainty for a given study area.

54 ENVIRONMENTAL SCIENCES↗