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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 217 records · Page 12
All sourcesNASA NTRSDOE OSTI
DOE OSTI2020

Frequency and Time Domain Nuclear-Electronic Orbital Equation-of-Motion Coupled Cluster Methods: Combination Bands and Electronic-Protonic Double Excitations

The accurate description of excited vibronic states is important for modeling a wide range of photoinduced processes. The nuclear-electronic orbital (NEO) approach, which treats specified protons on the same level as the electrons, can describe excited electronic-protonic states. Herein the multicomponent equation-of-motion coupled cluster with singles and doubles (NEO-EOM-CCSD) method and its time-domain counterpart, TD-NEO-EOM-CCSD, are developed and implemented. The application of these methods to the HCN molecule highlights their capabilities. These methods predict qualitatively reasonable energies and intensities for a combination band corresponding to simultaneous excitation of two vibrational modes, as well as an overtone. These methods also describe states with double excitation character, such as excited electronic-protonic states corresponding to the simultaneous excitation of an electron and a proton. The ability of the NEO-EOM-CCSD method and its time-dependent counterpart to describe combination bands, overtones, and double excitations will enable a wide range of photochemical applications.

Pavosevic, Fabijan↗
DOE OSTI2023

Combined Stress Testing of Perovskite Solar Cells for Stable Operation in Space

Perovskite solar cells (PSCs) could provide new low-cost opportunities for powering spacecraft. Here we are designing a PSC resilient to lunar surface conditions, focusing on contact/barrier layers combinations that maximize stability. Thermal vacuum stress shows cells with gold back-contacts and SiOx protection retain >90% of initial efficiency and indium tin oxide (ITO)-contacted cells retain ~100% of initial efficiency after five months of stress. SiO x mitigates mechanical aspects of contact stress, retaining 99% of initial efficiency, whereas cells with silicone/cover glass decreased 27%. We further exposed cells to combined illumination, 75 °C heat, and 10 -6 Torr vacuum for 24 h and found that ITO-contacted cells outperformed Au-contacted cells.

14 SOLAR ENERGY↗
DOE OSTI2020

Mesoporous Silica Formation Mechanisms Probed Using Combined Spin–Echo Modulated Small-Angle Neutron Scattering (SEMSANS) and Small-Angle Neutron Scattering (SANS)

In this work, the initial formation stages of surfactant-templated silica thin films which grow at the air–water interface were studied using combined spin–echo modulated small-angle neutron scattering (SEMSANS) and small-angle neutron scattering (SANS). The films are formed from either a cationic surfactant or nonionic surfactant (C 16 EO 8 ) in a dilute acidic solution by the addition of tetramethoxysilane. Previous work has suggested a two stage formation mechanism with mesostructured particle formation in the bulk solution driving film formation at the solution surface. From the SEMSANS data, it is possible to pinpoint accurately the time associated with the formation of large particles in solution that go on to form the film and to show their emergence is concomitant with the appearance of Bragg peaks in the SANS pattern, associated with the two-dimensional hexagonal order. The combination of SANS and SEMSANS allows a complete depiction of the steps of the synthesis that occur in the subphase.

36 MATERIALS SCIENCE↗
DOE OSTI2021

Mechanistic Understanding of Methane Combustion over Ni/CeO 2 : A Combined Experimental and Theoretical Approach

Catalytic oxidation of methane (CH 4 ) over nonprecious Ni/CeO 2 catalysts has received a lot of attention due to the large natural gas reserves found in North America and the prohibitive cost of palladium-based catalysts, commonly used for CH 4 oxidation. However, the catalytic mechanism of CH 4 oxidation over Ni/CeO 2 still remains unclear. Moreover, the parameters affecting the reaction rates, the interaction between nickel and CeO 2 , and the reaction intermediates are still not well understood. In this study, kinetic model fitting, CH 4 temperature-programmed reduction-mass spectroscopy (CH 4 TPR-MS), in situ diffuse reflectance infrared Fourier transform spectroscopy (DRIFTS), and density functional theory (DFT) calculations were combined to elucidate the mechanism of complete oxidation of CH 4 over Ni/CeO 2 . CH 4 TPR-MS showed that the complete oxidation of CH 4 over Ni/CeO 2 requires 55–120 °C lower compared to bare CeO 2 or Ni/quartz sand; complete oxidation of CH 4 took place when the surface oxygen species were abundant, while partial oxidation products (CO, H 2 ) were formed when the oxygen species were depleted. In situ DRIFTS showed that CH 3 , CH 2 , CO, and CO 2 were formed after CH 4 activation over Ni/CeO 2 , while CH 3 O species were not observed. Combining those findings with kinetic model fitting, a redox Mars–van Krevelen (MvK) mechanism showed the best description of the experimental observations. The MvK mechanism involves the reaction of dissociated oxygen species with gas-phase CH 4 while water inhibits the reaction rate by adsorbing on the oxidized sites. Moreover, CH 4 activation leads to the reduction of the active sites and oxygen vacancy formation followed by reoxidation of the active sites by gas-phase O 2 . A CH 4 oxidation reaction pathway over Ni/CeO 2 is proposed by DFT calculations. In summary, the findings shown here suggest that CH 4 oxidation over Ni/CeO 2 follows a redox MvK mechanism and provides guidance for the rational design of non-precious-metal catalysts for CH 4 oxidation reactions.

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

Ripening of Rh Nanoparticle Catalysts in Reverse Water–Gas Shift via a Data-Driven Model Combining Physics, Theory, and Experiment

Degradation via sintering is an ongoing challenge that impedes the broad commercial success of supported metallic nanoparticle catalysts. To mitigate degradation via informed catalyst design and process operations, here we aim to disambiguate the underlying mechanisms of sintering by combining theory and experiment in a quantitative framework. While mechanistic sintering models exist, they only model a single sintering pathway, even though multiple sintering mechanisms can occur simultaneously or dominate at different stages of the process. Data-driven machine learning models have emerged as a means to represent complex processes through data regression. However, machine learning models have very large data needs and lack mechanistic insights due to their black-box encoding. To develop an interpretive model of catalyst degradation via sintering, we constructed a hybrid model combining mechanistic “physics-based” models and data-driven methods to obtain both reliable predictions and mechanistic insights regarding experimentally observed sintering phenomena. Focusing on nanoparticle sintering in the Rh–TiO 2 catalyst for the reverse water–gas shift (RWGS) reaction, the hybrid model couples a mechanistic term for Ostwald ripening with energy values calculated via density functional theory (DFT) with a parametric, data-driven discrepancy function term for unmodeled mechanisms. The hybrid model is trained using Bayesian inference with data collected from small-angle X-ray scattering (SAXS) in situ experiments wherein average nanoparticle diameter versus time was measured at three relevant operating temperatures. The calibrated hybrid model results show that an Ostwald ripening-only model parameterized with fixed DFT energies does not fully capture the time and temperature dependence of the SAXS-observed sintering kinetics, and that an additional functional contribution, or DFT energy calibration, is required to reconcile simulation and experiment. Analysis of the hybrid-model error confirms that the hybrid model outperforms both the purely mechanistic and purely data-driven alternatives in terms of expected predictive accuracy for time-evolving average particle sizes. Furthermore, the results support the hypothesis that the Ostwald ripening mechanism is less important for explaining the sintering phenomena as operating temperature increases under an assumed fixed DFT parameterization. This could be explained in one of two ways: either latent, unmodeled sintering mechanisms dominate at higher temperatures, or the DFT uncertainty increases with temperature. The proposed modeling approach directly links theory to experiments and simulations via a statistical hybrid modeling framework and can be extended to other catalytic systems to improve predictive models and mechanistic understanding.

Bayesian hybrid modeling↗
DOE OSTI2020

Combined Spatially Resolved Characterization of Antireflection and Antisoiling Coatings for PV Module Glass

Characterization of photovoltaic (PV) module materials throughout different stages of service life is crucial to understanding and improving the durability of these materials. Currently the large-scale of PV modules (>1 m 2 ) is imbalanced with the small-scale of most materials characterization tools (= 1 cm 2 ). Furthermore, understanding degradation mechanisms often requires a combination of multiple characterization techniques. Here, we present adaptations of three standard materials characterization techniques to enable mapping characterization over moderate sample areas (=25 cm 2 ). Contact angle, ellipsometry, and UV-Vis spectroscopy are each adapted and demonstrated on two representative samples: a commercial multi-functional coating for PV glass and an oxide combinatorial sample library. Best practices are discussed for adapting characterization techniques for large area mapping and combining mapping information from multiple techniques.

14 SOLAR ENERGY↗
DOE OSTI2024

Unraveling the Combined Photothermal Stability of Common Perovskite Solar Cell Compositions

In halide perovskite solar cells, certain compositions, especially those with a high mixture of anions, degrade rapidly. Here, in this study, a degradation study compares the photo (exposure to light), thermal (exposure to elevated temperatures), and photo-plus-thermal (combination) stability of three representative perovskite compositions chosen for their relatively high performance and to independently test anion versus cation effects. Based upon experience and reports, the compositions studied are triple cation with a high Br ratio (Cs 0.05 (FA 0.98 MA 0.02 ) 0.95 Pb(I 0.5 Br 0.5 ) 3 ), all iodide with a moderately high Cs ratio (FA 0.8 Cs 0.2 PbI 3 ), and FA-dominated triple cation (Cs 0.05 (FA 0.98 MA 0.02 ) 0.95 Pb(I 0.98 Br 0.02 ) 3 ). Cs 0.05 (FA 0.98 MA 0.02 ) 0.95 Pb(I 0.98 Br 0.02 ) 3 displayed the best combined photo-plus-thermal stability. Degradation mechanisms were investigated by comparing the morphology, surface composition, and bulk and interface carrier dynamics. Under the harshest aging conditions (photo-plus-thermal), phase segregation and vaporization of organic cations occurred, along with the appearance of high-energy states indicating the interaction with C 60 . The findings were verified on perovskite films blade-coated in air used in devices (FTO/NiOx/SAM/Perovskite/C 60 /BCP/Cu), achieving an efficiency of 21.9% alongside correlated stability.

14 SOLAR ENERGY↗
DOE OSTI2025

Combining Organic Cations of Different Sizes Grants Improved Control over Perovskitoid Dimensionality and Bandgap

Because mixed-halide wide-bandgap (1.6-2.0 eV) perovskite solar cells suffer from operating instability related to light-induced halide segregation, it is of interest to study alternative means of bandgap widening. Perovskitoids combine wide bandgaps and structural stability resulting from face- or edge-sharing octahedral connections in their crystal structures. Unfortunately, there existed no prior reports of three-dimensional (3D) perovskitoids having direct bandgaps with optical absorption edges less than 2.2 eV. As the most significant predictor of perovskitoid bandgaps is the fraction of corner-sharing in their crystal structures, we hypothesized that increasing the amount of corner-sharing would access lower bandgaps than previously reported. Here, we accomplished this by mixing a spacer cation within the size range for 3D perovskitoid formation with a smaller perovskite-forming cation. We explored three spacer cations of different sizes: ethylammonium (EA), cyclopropylammonium (c-C3A), and cyclobutylammonium (c-C4A), combining these with methylammonium (MA), and found that the middle cation, c-C3A, pairs with MA to form a 3D perovskitoid with the formula (c-C3A) 3 (MA) 3 Pb 5 I 16 and a direct bandgap with an optical absorption edge at 2.0 eV. Solution-processed films of this perovskitoid showed improved light stability over mixed-halide perovskites, and solar cells based on these films exhibit increased maximum power point operating stability compared to reference mixed-halide devices.

Gilley, Isaiah W. [Northwestern University, Evanst↗
DOE OSTI2021

Enhance Low Level Temperature and Moisture Profiles Through Combining NUCAPS, ABI Observations, and RTMA Analysis

Thermodynamic information from low levels in the atmosphere is crucial for operational weather forecasts and meteorological researchers. The NOAA Unique Combined Atmospheric Processing System (NUCAPS) sounding products have been proven beneficial to fill the data gap between synoptic radiosonde observations (RAOBs). However, compared with the upper troposphere, the accuracy of NUCAPS soundings in the low levels still needs improvement. In this study, a deep neural network (DNN) is applied to fuse multiple data sources to enhance the NUCAPS temperature and moisture profiles in the lower atmosphere. The network is developed by combining satellite observations, including NUCAPS sounding retrievals and high resolution geostationary satellite observations from the Advanced Baseline Imager, and surface analysis from the Real-Time Mesoscale Analysis (RTMA) as inputs, while collocated soundings from ECMWF re-analysis version 5 are used as the benchmark for the training. The performance of the model is evaluated by using the independent testing data set, data from a different year, as well as collocated RAOBs, showing improvement to the temperature and moisture profiles by reducing the root-mean-squared-error (RMSE) by more than 30% in the lower atmosphere (from 700 hPa to surface) in both clear sky and partially cloudy conditions. A convective event from June 18, 2017 is presented to illustrate the application of the enhanced low level soundings on high impact weather events. The enhanced soundings from fused data capture the large surface-based convective available potential energy structures in the preconvection environment, which is very useful for severe storm nowcasting and forecasting applications.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2021

RBSP-ECT Combined Pitch Angle Resolved Electron Flux Data Product

We describe a new data product combining pitch angle resolved electron flux measurements from the Radiation Belt Storm Probes (RBSP) Energetic Particle Composition and Thermal Plasma (ECT) suite on the National Aeronautics and Space Administration’s Van Allen Probes. Here, we describe the methodology used to combine each of the data sets and produce a consistent set of pitch-angle-resolved spectra for the entire Van Allen Probes mission. Three-minute-averaged flux spectra are provided spanning energies from 15 eV up to 20 MeV. This new data product offers researchers a consistent cross calibrated data set to explore the particle dynamics of the inner magnetosphere across a wide range of energies.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2021

Unraveling the Combined Effects of Ice and Permafrost on Arctic Delta Morphodynamics

Arctic delta channel dynamics and coastal deposition patterns are uniquely affected by ice and permafrost. Building on previous experiments that documented the individual effects of ice and permafrost, we conducted new numerical modeling experiments to examine the combined influences of ice thickness and permafrost erodibility. We quantified the effects of ice, permafrost, and their combination on channel migration, avulsion, and persistence; onshore and nearshore deposition; and large-scale delta morphology. Both ice and permafrost limited channel mobility, enhanced overbank deposition, and roughened shorelines. Thick ice increased offshore channel incision and sediment delivery. Permafrost stabilized channels and focused deposition that led to enhanced channel levee development. The increased levee deposition limited widespread channel infilling leading to the preservation of abandoned channels. Levee growth was enhanced by overbank flooding due to ice-driven changes in water surface elevations. Furthermore, this flooding led to more extensive levees on deltas with both ice and permafrost compared to deltas with only permafrost. Ice also preserved small channels, leading to size-dependent channel persistence: the presence of ice in the channels reduced the fraction of time small channels remained active relative to larger channels. Resistant permafrost damped this effect, as smaller and more distal channels were active earlier in the flood on deltas with resistant permafrost likely due to the smaller number of channels on permafrost-laden deltas. Finally, our results suggest that the loss of both ice and permafrost will increase channel mobility, decrease overbank flooding, increase shoreline progradation rates, and decrease offshore transport of riverine fluxes.

58 GEOSCIENCES↗
DOE OSTI2022

Detection of Forced Change Within Combined Climate Fields Using Explainable Neural Networks

Abstract Assessing forced climate change requires the extraction of the forced signal from the background of climate noise. Traditionally, tools for extracting forced climate change signals have focused on one atmospheric variable at a time, however, using multiple variables can reduce noise and allow for easier detection of the forced response. Following previous work, we train artificial neural networks to predict the year of single‐ and multi‐variable maps from forced climate model simulations. To perform this task, the neural networks learn patterns that allow them to discriminate between maps from different years—that is, the neural networks learn the patterns of the forced signal amidst the shroud of internal variability and climate model disagreement. When presented with combined input fields (multiple seasons, variables, or both), the neural networks are able to detect the signal of forced change earlier than when given single fields alone by utilizing complex, nonlinear relationships between multiple variables and seasons. We use layer‐wise relevance propagation, a neural network explainability tool, to identify the multivariate patterns learned by the neural networks that serve as reliable indicators of the forced response. These “indicator patterns” vary in time and between climate models, providing a template for investigating inter‐model differences in the time evolution of the forced response. This work demonstrates how neural networks and their explainability tools can be harnessed to identify patterns of the forced signal within combined fields.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2022

Combined Effects of Stream Hydrology and Land Use on Basin‐Scale Hyporheic Zone Denitrification in the Columbia River Basin

Abstract Denitrification in the hyporheic zone (HZ) of river corridors is crucial to removing excess nitrogen in rivers from anthropogenic activities. However, previous modeling studies of the effectiveness of river corridors in removing excess nitrogen via denitrification were often limited to the reach‐scale and low‐order stream watersheds. We developed a basin‐scale river corridor model for the Columbia River Basin with random forest models to identify the dominant factors associated with the spatial variation of HZ denitrification. Our modeling results suggest that the combined effects of hydrologic variability in reaches and substrate availability influenced by land use are associated with the spatial variability of modeled HZ denitrification at the basin scale. Hyporheic exchange flux can explain most of spatial variation of denitrification amounts in reaches of different sizes, while among the reaches affected by different land uses, the combination of hyporheic exchange flux and stream dissolved organic carbon (DOC) concentration can explain the denitrification differences. Also, we can generalize that the most influential watershed and channel variables controlling denitrification variation are channel morphology parameters (median grain size (D50), stream slope), climate (annual precipitation and evapotranspiration), and stream DOC‐related parameters (percent of shrub area). The modeling framework in our study can serve as a valuable tool to identify the limiting factors in removing excess nitrogen pollution in large river basins where direct measurement is often infeasible.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2022

Combining Deep Learning With Physics Based Features in Explosion-Earthquake Discrimination

This paper combines the power of deep-learning with the generalizability of physics-based features, to present an advanced method for seismic discrimination between earthquakes and explosions. The proposed method contains two branches: a deep learning branch operating directly on seismic waveforms or spectrograms, and a second branch operating on physics-based parametric features. These features are high-frequency P/S amplitude ratios and the difference between local magnitude (ML) and coda duration magnitude (MC). The combination achieves better generalization performance when applied to new regions than models that are developed solely with deep learning. Further, we also examined which parts of the waveform data dominate deep learning decisions (i.e., via Grad-CAM). Such visualization provides a window into the black-box nature of the machine-learning models and offers new insight into how the deep learning derived models use data to make decisions.

58 GEOSCIENCES↗
DOE OSTI2023

Closing in on Hydrologic Predictive Accuracy: Combining the Strengths of High-Fidelity and Physics-Agnostic Models

Applications of process-based models (PBM) for predictions are confounded by multiple uncertainties and computational burdens, resulting in appreciable errors. A novel modeling framework combining a high-fidelity PBM with surrogate and machine learning (ML) models is developed to tackle these challenges and applied for streamflow prediction. A surrogate model permits high computational efficiency of a PBM solution at a minimum loss of its accuracy. A novel probabilistic ML model partitions the PBM-surrogate prediction errors into reducible and irreducible types, quantifying their distributions that arise due to both explicitly perceived uncertainties (such as parametric) or those that are entirely hidden to the modeler (not included or unexpected). Using this approach, we demonstrate a substantial improvement of streamflow predictive accuracy for a case study urbanized watershed. Such a framework provides an efficient solution combining the strengths of high-fidelity and physics-agnostic models for a wide range of prediction problems in geosciences.

58 GEOSCIENCES↗
DOE OSTI2020

Combining mechanistic and machine learning models for predictive engineering and optimization of tryptophan metabolism

Abstract Through advanced mechanistic modeling and the generation of large high-quality datasets, machine learning is becoming an integral part of understanding and engineering living systems. Here we show that mechanistic and machine learning models can be combined to enable accurate genotype-to-phenotype predictions. We use a genome-scale model to pinpoint engineering targets, efficient library construction of metabolic pathway designs, and high-throughput biosensor-enabled screening for training diverse machine learning algorithms. From a single data-generation cycle, this enables successful forward engineering of complex aromatic amino acid metabolism in yeast, with the best machine learning-guided design recommendations improving tryptophan titer and productivity by up to 74 and 43%, respectively, compared to the best designs used for algorithm training. Thus, this study highlights the power of combining mechanistic and machine learning models to effectively direct metabolic engineering efforts.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2024

Crimean Congo hemorrhagic fever virus nucleoprotein and GP38 subunit vaccine combination prevents morbidity in mice

Immunizing mice with Crimean-Congo hemorrhagic fever virus (CCHFV) nucleoprotein (NP), glycoprotein precursor (GPC), or with the GP38 domain of GPC, can be protective when the proteins are delivered with viral vectors or as a DNA or RNA vaccine. Subunit vaccines are a safe and cost-effective alternative to some vaccine platforms, but Gc and Gn glycoprotein subunit vaccines for CCHFV fail to protect despite eliciting high levels of neutralizing antibodies. Here, we investigated humoral and cellular immune responses and the protective efficacy of recombinant NP, GP38, and GP38 forms (GP85 and GP160) associated with the highly glycosylated mucin-like (MLD) domain, as well as the NP + GP38 combination. Vaccination with GP160, GP85, or GP38 did not confer protection, and vaccination with the MLD-associated GP38 forms blunted the humoral immune responses to GP38, worsened clinical chemistry, and increased viral RNA in the blood compared to the GP38 vaccination. In contrast, NP vaccination conferred 100% protection from lethal outcome and was associated with mild clinical disease, while the NP + GP38 combination conferred even more robust protection by reducing morbidity compared to mice receiving NP alone. Thus, recombinant CCHFV NP alone is a promising vaccine candidate conferring 100% survival against heterologous challenge. Moreover, incorporation of GP38 should be considered as it further enhances subunit vaccine efficacy by reducing morbidity in surviving animals.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2022

Sensory stimulation for upper limb amputations modulates adaptability of cortical large-scale systems and combination of somatosensory and visual inputs

Abstract Touch-like phantom limb sensations can be elicited through targeted transcutaneous electrical nerve stimulation (tTENS) in individuals with upper limb amputation. The corresponding impact of sensory stimulation on cortical activity remains an open question. Brain network research shows that sensorimotor cortical activity is supported by dynamic changes in functional connections between relevant brain regions. These groups of interconnected regions are functional modules whose architecture enables specialized function and related neural processing supporting individual task needs. Using electroencephalographic (EEG) signals to analyze modular functional connectivity, we investigated changes in the modular architecture of cortical large-scale systems when participants with upper limb amputations performed phantom hand movements before, during, and after they received tTENS. We discovered that tTENS substantially decreased the flexibility of the default mode network (DMN). Furthermore, we found increased interconnectivity (measured by a graph theoretic integration metric) between the DMN, the somatomotor network (SMN) and the visual network (VN) in the individual with extensive tTENS experience. While for individuals with less tTENS experience, we found increased integration between DMN and the attention network. Our results provide insights into how sensory stimulation promotes cortical processing of combined somatosensory and visual inputs and help develop future tools to evaluate sensory combination for individuals with amputations.

60 APPLIED LIFE SCIENCES↗