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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 469 records · Page 26
All sourcesNASA NTRSDOE OSTI
DOE OSTI2025

Amidinate- and Dithiolene-Based Silicon Complexes

Reactions of the amidinato-silylene chloride PhC( t BuN) 2 SiCl (1) with imidazole-based dithione dimer 2, lithium dithiolene radical 3, and dithiolate dimer 4 result in the synthesis of a series of silicon complexes 5-7, respectively, containing both amidinato and dithiolene ligands. 7 is the first structurally characterized silicon(II) dithiolene complex. The structural and bonding characteristics of 5-7 have been probed by both experimental and theoretical methods.

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

Machine Learning Prediction of Tritium‐Helium Groundwater Ages in the Central Valley, California, USA

Abstract Groundwater ages provides insight into recharge rates, flow velocities, and vulnerability to contaminants. The ability to predict groundwater ages based on more accessible parameters via Machine Learning (ML) would advance our ability to guide sustainable management of groundwater resources. In this study, ML models were trained and tested on a large data set of tritium concentrations and tritium‐helium groundwater ages from the California Central Valley, a large groundwater basin with complex land use, irrigation, and water management practices. The ML models were trained on 63 features, including location, well construction information, landscape characteristics, and climate variables, water chemistry, and stable isotopes. The Bagging regressor method can accurately classify (F1‐score = 0.91) groundwater samples as either modern or pre‐modern whereas the accuracy of the ML prediction of continuous tritium‐helium groundwater ages is limited and explains only of the variability in this data set. In general, ML groundwater age prediction relies mostly on features related to (a) the source of groundwater recharge, (b) contaminant history, (c) aquifer materials, (d) well construction, and (e) geochemical reactions along flow paths.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2025

Supercritical preparation of doped (111) facetted nickel oxide for the oxygen evolution reaction

Green hydrogen is of great interest as a replacement for traditional fossil fuels in a variety of energy applications. However, due to the poor kinetics present in the oxygen evolution reaction (OER) half-reaction, nanostructured catalysts are needed to reduce the reaction overpotential. Nickel oxide has previously been shown to be a promising alternative to expensive Pt-group based catalysts for the OER in alkaline media. Herein, facetted NiO nanosheets have been doped with Fe, Mn, or Co to reduce its catalytic overpotential for the OER. A supercritical synthesis process was used to promote the mass transport of the reactants while preserving catalytic surface area. Microscopy, diffraction, spectroscopy, and adsorption techniques were used to understand the morphological changes resulting from the inclusion of each dopant, as well as characterize the surface chemistry presented by the doped (111) facet. The pH was found to affect the properties of mixing due to difference in hydrolysis rates and catalysis of the hydrolysis/condensation. The dopants exhibited distinct effects on OER activity: Mn increased the overpotential to 742 mV vs. RHE, while Co and Fe reduced it to 502 mV and 457 mV, respectively. In summary, a straightforward and novel synthesis method is presented to prepare doped NiO(111) nanosheets, and their surface characteristics are explored to understand their varied electrochemical performances.

08 HYDROGEN↗
DOE OSTI2023

A formally certified end-to-end implementation of Shor’s factorization algorithm

Quantum computing technology may soon deliver revolutionary improvements in algorithmic performance, but it is useful only if computed answers are correct. While hardware-level decoherence errors have garnered significant attention, a less recognized obstacle to correctness is that of human programming errors—“bugs.” Techniques familiar to most programmers from the classical domain for avoiding, discovering, and diagnosing bugs do not easily transfer, at scale, to the quantum domain because of its unique characteristics. To address this problem, we have been working to adapt formal methods to quantum programming. With such methods, a programmer writes a mathematical specification alongside the program and semiautomatically proves the program correct with respect to it. The proof’s validity is automatically confirmed—certified—by a “proof assistant.” Formal methods have successfully yielded high-assurance classical software artifacts, and the underlying technology has produced certified proofs of major mathematical theorems. As a demonstration of the feasibility of applying formal methods to quantum programming, we present a formally certified end-to-end implementation of Shor’s prime factorization algorithm, developed as part of a framework for applying the certified approach to general applications. By leveraging our framework, one can significantly reduce the effects of human errors and obtain a high-assurance implementation of large-scale quantum applications in a principled way.

Science & Technology - Other Topics↗
DOE OSTI2020

12 C(p,p') 12 C Reaction ( E p =19.5–30 MeV) for Active Interrogation of Special Nuclear Material

Passive detection of special nuclear material (SNM) is challenging due to its inherently low rate of spontaneous emission of penetrating radiation, the relative ease of shielding, and the fluctuating and frequently overwhelming background. Active interrogation, the use of external radiation to increase the emission rate of characteristic radiation from SNM, has long been considered to be a promising method to overcome those challenges. Current active-interrogation systems that incorporate radiography tend to use bremsstrahlung beams, which can deliver high radiation doses. Low-energy ion-driven nuclear reactions that produce multiple monoenergetic photons may be used as an alternative. The 12 C ( p , p ' ) 12 C reaction is one such reaction that could produce large yields of highly penetrating 4.4- and 15.1-MeV gamma rays. This reaction does not directly produce neutrons below the approximately 19.7 MeV threshold, and the 15.1-MeV gamma-ray line is well matched to the photofission cross section of 235 U and 238 U . In this article, we report the measurements of thick-target gamma-ray yields at 4.4 and 15.1 MeV from the 12 C ( p , p ' ) 12 C reaction at proton energies of 19.5, 25, and 30 MeV. Measurements are made with two 3 -in. EJ-309 cylindrical liquid scintillation detectors and thermoluminescent dosimeters placed at 0 ° and 90 ° , with an additional 1.5 -in. Na I ( Tl ) cylindrical scintillation detector at 0 ° . We estimate the highest yields of the 4.4- and 15.1-MeV gamma rays of 1.65 × 10 10 and 4.47 × 10 8 sr - 1 μ C - 1 at a proton energy of 30 MeV, respectively. The yields in all experimental configurations are greater than in a comparable deuteron-driven reaction that produces the same gamma-ray energies— 11 B ( d , n γ ) 12 C . However, a significant increase of the neutron radiation dose accompanies the proton energy increase from 19.5 to 30 MeV.

38 RADIATION CHEMISTRY, RADIOCHEMISTRY, AND NUCLEA↗
DOE OSTI2022

Finite-Difference Simulation for Infrasound Generated by Finite-Extent Ground Motions

Underground explosions can produce infrasound in the atmosphere, and the wavefield characteristics are often governed by the ground surface motions. Finite-difference methods are popular for infrasound simulation as their generality and robustness allow for complex atmospheric structures and surface topography. A simple point-source approximation is often used because infrasound wavelengths tend to be large relative to the source dimensions. However, this assumption may not be able to capture the complexity of explosion-induced ground motions if the surface area is not compact, and appropriate source models must be incorporated into the finite-difference simulations for accurate infrasound prediction. In this study, we develop a point source representation of the complex ground motions for infrasound sources. Instead of a single point source, we use a series of point sources distributed over the source area. These distributed point sources can be equivalent to air volume changes produced by the ground motions in the atmosphere. Further, we apply the distributed point-source method to a series of buried chemical explosions conducted during the Source Physics Experiment Phase I. Epicentral ground-motion measurements during the experiments provide a way to calculate accurate distributed point sources. We validate and evaluate the accuracy of distributed point source approach for infrasound simulations by direct comparison with acoustic observations in the field experiment.

58 GEOSCIENCES↗
DOE OSTI2023

The Integration of Wi-Fi Location-Based Services to Optimize Energy Efficient Commercial Building Operations

This project investigated and demonstrated the use of Wi-Fi Location-Based Services (LBS) to perform occupancy sensing in commercial buildings. Wi-Fi LBS can be used to detect the presence of Wi-Fi enabled mobile devices and laptops that accompany occupants as they move through the building. These signals can be used to determine occupant presence, head count, and location. When integrated with the building automation system, this emerging technology approach can be used to manage other connected systems such as lighting and HVAC to reduce energy usage in the building and improve occupant comfort. An open source location detection algorithm was developed, which uses data collected from three or more Wi-Fi access points to determine the presence and estimate the location of mobile devices and laptops. Access points can detect Wi-Fi enabled devices even if they are not connected to the existing Wi-Fi network. Building occupancy is determined based on the presence, location, and movement of these devices through the space. From lab and small-scale in-situ testing, the Location Detection Algorithm (LDA) was found to be accurate to within 10 feet and could be further refined by tuning the algorithm for the specific space characteristics such as layout and obstructions (walls, furniture, etc.). An open source method to integrate the occupancy data with existing building automations systems was investigated. The Wi-Fi occupancy sensing approach was then demonstrated and validated at commercial buildings located in Saint Paul, MN; Madison, WI; New York City; and Fort Worth, TX.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗
DOE OSTI2024

Plant Disease Detection Technology Assessment

Visual inspections by US Customs and Border Protection agriculture specialists have identified approximately 20,000 regulated, quarantined pests each year in agricultural products entering the United States. Most of these pests identified in the Agriculture Quarantine and Inspection (AQI) program are insects. Many pathogens are difficult to detect in agricultural products, particularly in early stages of infection. New technologies can help to detect plant pathogens and the diseases that they cause. This technology assessment was performed for the US Department of Homeland Security Science and Technology Directorate (DHS S&T) through the Food, Agriculture, and Veterinary Defense (FAV-D) program to identify emerging technologies that could address this hard problem. These emerging technologies differ in their diagnostic sensitivities and specificities, as well as in their measurement time and training requirements. New instruments that detect volatile organic compounds characteristic of plant disease or pathogens could provide a less invasive inspection method. Dogs, which can successfully detect many concealed agricultural products, have also been trained to detect some plant pests and pathogens. Simple immunological tests offer sensitive and specific detection of many pathogens at the point of use. Advanced imaging methods that use AI to sort fruits and vegetables and recognize anomalies at high speeds could be used in cooperation with exporters to improve food quality and reduce pests. Advances in nucleic acid–based detection methods that have become gold standards for confirmatory diagnostics are now making those methods available for faster, point-of-use detection. These new methods should be developed in the context of AQI operational requirements, which apply risk-based sampling protocols to protect agriculture and facilitate commerce and passenger transit.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2024

Preparation and Characterization of Materials for Low- to Intermediate-Temperature CO2 Adsorption

Global carbon dioxide emissions are rising and the use of fossil fuels in several sectors are the leading causes. As global population and economies continue to grow significantly, the most practical method of lowering such emissions is to capture CO2. Although other technologies are more developed, adsorption is very promising and has attracted much attention. To ensure this technology’s success, it is essential to have suitable CO2 adsorbent materials. In this work, several new hydrotalcites (HTs) with different initial concentrations of ion precursors were prepared for the first time by the co-precipitation method—it was possible to verify that the ion concentrations influence the characteristics of the materials. The prepared HTs were characterized by thermogravimetric analysis (TG), X-Ray diffraction (XRD), surface area measurements and temperature-programmed desorption of CO2 (TPD-CO2) to relate their CO2 capture capacity to their physicochemical properties; the CO2 adsorption equilibrium isotherms were determined at 35 and 300 °C for the prepared samples, as well as for some commercial materials: magnesium oxide, calcium oxide, aluminium oxide and Zeolite 13X. After determining which materials present the best CO2 adsorption capacity, these were submitted to adsorption-desorption cycles to study their stability. The main objective of the work was to prepare and study different CO2 adsorbents for processes that are carried out at low and intermediate temperatures. From the experimental results, it was possible to conclude that the Zeolite 13X showed the best capacity at 35 °C, 3.38 mmol·g−1 (@ pCO2 = 1 bar), and a prepared calcined HT (c-HT2) was the best at 300 °C, 0.97 mmol·g−1 (@ pCO2 = 1 bar). Moreover, it seems there is an optimum initial concentration of the ions’ solutions for the tested HTs, which depends on the final application—c-HT1 showed a better capacity at 35 °C and c-HT2 at 300 °C. From the adsorption-desorption cycles—performed at 35 and 300 °C with the best materials using a magnetic suspension microbalance at 1 bar of CO2 partial pressure —, a working cyclic capacity of 2.69 mmol∙g−1 was achieved by the Zeolite at 35 °C; in turn, c-HT2 showed a working cyclic capacity of 0.79 mmol∙g−1 at 300 °C.

Figueiredo, Anabela↗
DOE OSTI2024

Wireless Passive Ceramic Sensor for Far-Field Temperature Measurement at High Temperatures

A passive wireless high-temperature sensor for far-field applications was developed for stable temperature sensing up to 1000 °C. The goal is to leverage the properties of electroceramic materials, including adequate electrical conductivity, high-temperature resilience, and chemical stability in harsh environments. Initial sensors were fabricated using Ag for operation to 600 °C to achieve a baseline understanding of temperature sensing principles using patch antenna designs. Fabrication then followed with higher temperature sensors made from (In, Sn) O2 (ITO) for evaluation up to 1000 °C. A patch antenna was modeled in ANSYS HFSS to operate in a high-frequency region (2.5–3.5 GHz) within a 50 × 50 mm2 confined geometric area using characteristic material properties. The sensor was fabricated on Al2O3 using screen printing methods and then sintered at 700 °C for Ag and 1200 °C for ITO in an ambient atmosphere. Sensors were evaluated at 600 °C for Ag and 1000 °C for ITO and analyzed at set interrogating distances up to 0.75 m using ultra-wideband slot antennas to collect scattering parameters. The sensitivity (average change in resonant frequency with respect to temperature) from 50 to 1000 °C was between 22 and 62 kHz/°C which decreased as interrogating distances reached 0.75 m.

47 OTHER INSTRUMENTATION↗
DOE OSTI2023

Development of a Supercharged Octane Number and a Supercharged Octane Index

Gasoline knock resistance is characterized by the Research and Motor Octane Number (RON and MON), which are rated on the CFR octane rating engine at naturally aspirated conditions. However, modern automotive downsized boosted spark ignition (SI) engines generally operate at higher cylinder pressures and lower temperatures relative to the RON and MON tests. Using the naturally aspirated RON and MON ratings, the octane index (OI) characterizes the knock resistance of gasolines under boosted operation by linearly extrapolating into boosted “beyond RON” conditions via RON, MON, and a linear regression K factor. Using OI solely based on naturally aspirated RON and MON tests to extrapolate into boosted conditions can lead to significant errors in predicting boosted knock resistance between gasolines due to non-linear changes in autoignition and knocking characteristics with increasing pressure conditions. Here, a new “Supercharged Octane Number” (SON) method was developed on the CFR engine at increased intake pressures, which improved the correlation to boosted knock-limited automotive SI engine data over RON for several surrogate fuels and gasolines, including five “Co-Optima” RON 98 fuels and an E10 regular grade gasoline. Furthermore, the conventional OI was extended to a newly introduced Supercharged Octane Index (OI S ) based on SON and RON, which significantly improved the correlation to fuel knock resistance measurements from modern boosted SI engine knock-limited spark advance tests. This demonstrated the first proof of concept of a SON and OI S to better characterize a fuel’s knock resistance in modern boosted SI engines.

42 ENGINEERING↗
DOE OSTI2022

Using Wi-Fi Location-Based Services (LBS) for Commercial Building Occupancy Sensing

From May 2019 through October 2022, this DOE-funded project investigated and demonstrated the use of Wi-Fi Location-Based Services (LBS) to perform occupancy sensing in commercial buildings. Wi-Fi LBS can be used to detect the presence of Wi-Fi enabled mobile devices and laptops that accompany occupants as they move through the building. These signals can be used to determine occupant presence, head count, and location. When integrated with the building automation system, this emerging technology approach can be used to manage other connected systems such as lighting and HVAC to reduce energy usage in the building and improve occupant comfort. An open source location detection algorithm was developed, which uses data collected from three or more Wi-Fi access points to determine the presence and estimate the location of mobile devices and laptops. Access points can detect Wi-Fi enabled devices even if they are not connected to the existing Wi-Fi network. Building occupancy is determined based on the presence, location, and movement of these devices through the space. From lab and small-scale in-situ testing, the Location Detection Algorithm (LDA) was found to be accurate to within 10 feet and could be further refined by tuning the algorithm for the specific space characteristics such as layout and obstructions (walls, furniture, etc.). An open source method to integrate the occupancy data with existing building automations systems was investigated. The Wi-Fi occupancy sensing approach was then demonstrated and validated at two commercial buildings located in Minnesota and Wisconsin.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗
DOE OSTI2022

Generative adversarial networks for ensemble projections of future urban morphology

As city planners design and adapt cities for future resilience and intelligence, interactions among neighborhood morphological development with respect to changes in population and resultant built infrastructure's impact on the natural environment must be considered. For deep understanding of these interactions, explicit representation of future neighborhoods is necessary for future city modeling. Generative Adversarial Networks (GANs) have been shown to produce spatially accurate urban forms at scales representing entire cities to those at neighborhood and single building scale. Here we demonstrate a GAN method for generating an ensemble of possible new neighborhoods given land use characteristics and designated neighborhood type.

Dumas, Melissa↗
DOE OSTI2024

Heat Transfer Experiments of Ribbed, Serpentine Cooling Passages with Supercritical CO2

The results of heat transfer experiments with supercritical CO2 (sCO2) to capture the performance of ribbed, serpentine cooling passages for Reynolds numbers in the range of 100,000 - 400,000 are presented. The oxy-combustion turbine operating in an Allam-Fetvedt cycle has a turbine inlet temperature exceeding 1100°C, necessitating internally cooled turbine blades due to material creep strength limits. Reynolds numbers for sCO2, with its combination of high fluid density and low viscosity, can be up to an order of magnitude higher than comparable internal cooling conditions for an air-breathing engine where the majority of experimental data exists. To improve the prediction of cooling performance through turbulated passages, a test rig was designed and constructed with interchangeable insert capability for operation in sCO2 with heat transfer from a heater outlet flow stream to a lower temperature recuperator outlet flow stream. The inserts included serpentine passage geometry with five passes, one of the inserts including chevron ribs and the other a plain wall. The multi-pass serpentine geometry allowed for the evaluation of the effects 180 deg. tip turns have on the return passage flow characteristics, as they can depart significantly from fully-developed flow characteristics. The test procedure included the use of a modified Wilson plot method, with hot flow stream conditions kept effectively constant so that monitored changes in the overall thermal resistance could be attributed to manipulations in cooling flow stream conditions. Conditions during testing consisted of test section pressures of approximately 180 bar, cooling flow stream temperature of 175°C and hot flow stream temperature of 410°C. Test points at discrete cool flow Reynolds numbers up to 400,000 demonstrated an expected trend of decreased fluid thermal resistance with increasing Reynolds number, while at comparable Reynolds number there was an exhibited lower thermal resistance and higher passage pressure drop for the ribbed insert compared to the plain-walled insert. Processing of test data included establishment of Nusselt number enhancement ratios, so that for different internal cooling passage dimensions in the actual turbine blade at comparable Reynolds number the ratio could be applied to predict cooling performance.

Marshall, Michael↗
DOE OSTI2022

The Geothermal Artificial Intelligence for geothermal exploration

Exploration of geothermal resources involves analysis and management of a large number of uncertainties, which makes investment and operations decisions challenging. Remote Sensing (RS), Machine Learning (ML) and Artificial Intelligence (AI) have potential in managing the challenges of geothermal exploration. In this paper, we present a methodology that integrates RS, ML and AI to create an initial assessment of geothermal potential, by resorting to known indicators of geothermal areas namely mineral markers, surface temperature, faults and deformation. We demonstrated the implementation of the method in two sites (Brady and Desert Peak geothermal sites) that are close to each other but have different characteristics (Brady having clear surface manifestations and Desert Peak being a blind site). Here, we processed various satellite images and geospatial data for mineral markers, temperature, faults and deformation and then implemented ML methods to obtain pattern of surface manifestation of geothermal sites. We developed an AI that uses patterns from surface manifestations to predict geothermal potential of each pixel. We tested the Geothermal AI using independent data sets obtaining accuracy of 92-95%; also tested the Geothermal AI trained on one site by executing it for the other site to predict the geothermal / non-geothermal delineation, the Geothermal AI performed quite well in prediction with 72-76% accuracy.

15 GEOTHERMAL ENERGY↗
DOE OSTI2022

Comparing the shock sensitivity of insensitive energetic materials

We present a tabletop method to study the shock sensitivity of plastic-bonded explosives that are considered shock insensitive using high dynamic range optical emission spectroscopy with laser-launched km/s flyer plate impacts (2, 3, and 4 km/s), which measures the spectral radiance (the emission spectrum vs a calibrated intensity standard) every 0.8 ns in the nanosecond and microsecond regimes. The explosives were TATB (1,3,5 trinitro, 2,4,6 triamino benzene), FOX-7 (1,1-diamino-2,2-dinitroethylene), LLM-105 (2,6-diamino-3,5-dinitropyrazine-1-oxide), and NTO (nitrotriazolone), all with 20% Sylgard 182 binder. Time resolving emission from shocked explosives produces a unique fingerprint at each shock pressure, and the emission temporal profile can be used to understand each material's shock response and the underlying mechanisms within the framework of the well-known shock ignition and growth models. Ignition was characterized by the emission intensity during nanosecond hot spot ignition. The growth process, which occurs after the shock has unloaded, was characterized by the time derivative of microsecond emission intensity. Singular-value decomposition was used to determine a characteristic spectral radiance over the entire time range, and this radiance was fit to a graybody model to determine a characteristic temperature Tc. Even though these insensitive explosives have similar shock sensitivities, our method can clearly distinguish their shock response. FOX-7 and NTO were the most sensitive, with FOX-7 having the greatest shock ignition sensitivity, growth rate, and Tc. LLM-105 was much less sensitive than FOX-7 and NTO, and TATB consistently showed the lowest sensitivity by any measure.

Akhtar, Meysam (ORCID:0000000243897837)↗
DOE OSTI2020

High-Fidelity Large-Signal Order Reduction Approach for Composite Load Model

With the increasing penetration of electronic loads and distributed energy resources, conventional load models cannot capture their dynamics. Therefore, a new comprehensive composite load model is developed by Western Electricity Coordinating Council (WECC). However, this model is a complex high-order non-linear system with multi-time-scale property, which poses challenges on power system studies with heavy computational burden. In order to reduce the model complexity, the authors firstly develop a large-signal order reduction (LSOR) method using singular perturbation theory. In this method, the fast dynamics are integrated into the slow ones to preserve transient characteristics of the former. Then, accuracy assessment conditions are proposed and embedded into the LSOR to improve and guarantee the accuracy of reduced-order model. Finally, the reduced-order WECC composite load model is derived by using the proposed algorithm. Overall, simulation results show that the reduced-order large-signal model significantly alleviates the computational burden while maintaining similar dynamic responses as the original composite load model.

24 POWER TRANSMISSION AND DISTRIBUTION↗
DOE OSTI2022

Neptunium mononitride as a target material for Pu-238 production

Deep space exploration requires specialized sources for both thermal and power applications. Radioactive decay heat of plutonium-238 (238Pu) provides these sources in the form of radioisotope thermoelectric generators (RTGs). The 238 Pu is produced via neutron capture reaction involving neptunium-237 ( 237 Np) target material. Continual optimization of 237 Np target materials and evaluation of potential alternative targets for production of 238 Pu RTGs are advantageous for meeting ongoing space power system resource requirements. Current production of 238 Pu for RTGs for the United States space program utilizes neptunium dioxide ( 237 NpO 2 ) targets; however, the use of neptunium mononitride ( 237 NpN) presents an opportunity to increase the mass of 237 Np per target compared to the dioxide form, as well as increase the thermal conductivity of the target. To assess the viability of a 237 NpN target material, the material chemistry must be thoroughly evaluated, including synthesis methods and dissolution and reprocessing schemes. This review presents a summary of synthesis pathways for 237 NpN based on published literature on actinide mononitrides. Specific literature on 237 NpN is limited, necessitating evaluation of other actinide systems to gather parallels. This suggests a need for additional experimental studies on 237 NpN. A particular limitation in the existing literature is a lack of information on the differences in material characteristics, such as morphology, particle size, and trace chemical impurities, as a function of synthesis method. These parameters may affect subsequent reactor performance or dissolution of irradiated targets. The evaluation of existing literature is presented with a focus on the efficacy of 237 NpN targets for 238 Pu production.

38 RADIATION CHEMISTRY, RADIOCHEMISTRY, AND NUCLEA↗