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NASA NTRS1993

Broad-band linear polarization in cool stars. II - Amplitude and wavelength dependence for magnetic and scattering regions

We have developed a model to estimate the amplitude and wavelength dependence of broad-band linear polarization (BLP) from magnetic regions on cool stars. The model includes corrections both for line blends and for the partial cancellation of polarization in the vector sum over the stellar disk. Our results agree with recent calculations of BLP in the red, but show larger amplitudes and a different wavelength dependence in the blue. We find that the detailed wavelength dependence of the polarization is complex and varies with effective temperature and gravity due to changes in line blanketing. The BLP amplitudes depend strongly on field strength, blanketing, and magnetic region filling factor and geometry. We make rough estimates of the maximum BLP for stars of various spectral types and demonstrate a method for deriving a lower limit to the filling factor from the maximum observed BLP. This lower limit is related to the fractional area covered by the spatially asymmetric component of magnetic regions.

Saar, Steven H.↗

DOE OSTI2025

The microbiologist's guide to metaproteomics

Metaproteomics is an emerging approach for studying microbiomes, offering the ability to characterize proteins that underpin microbial functionality within diverse ecosystems. As the primary catalytic and structural components of microbiomes, proteins provide unique insights into the active processes and ecological roles of microbial communities. By integrating metaproteomics with other omics disciplines, researchers can gain a comprehensive understanding of microbial ecology, interactions, and functional dynamics. This review, developed by the Metaproteomics Initiative (www.metaproteomics.org), serves as a practical guide for both microbiome and proteomics researchers, presenting key principles, state-of-the-art methodologies, and analytical workflows essential to metaproteomics. Topics covered include experimental design, sample preparation, mass spectrometry techniques, data analysis strategies, and statistical approaches.

bioinformatics↗

NASA NTRS1990

High resolution derivative spectra in remote sensing

The use of derivative spectra is an established technique in analytical chemistry for the elimination of background signals and for resolving overlapping spectral features. Application of this technique for tackling analogous problems such as interference from soil background reflectance in the remote sensing of vegetation or for resolving complex spectra of several target species within individual pixels in remote sensing is proposed. Methods for generating derivatives of high spectral resolution data are reviewed. Results of experiments to test the use of derivatives for monitoring chlorosis in vegetation show that derivative spectral indices are superior to conventional broad-band spectral indices such as the near-infrared/red reflectance ratio. Conventional broad-band indices are sensitive to both leaf cover as well as leaf color. New derivative spectral indices which were able to monitor chlorosis unambiguously were identified. Potential areas for the application of this technique in remote sensing are considered.

Demetriades-Shah, Tanvir H.↗

NASA NTRS2001

Assimilation of GOES Land Surface Data into a Mesoscale Models

A technique has been developed for assimilating Geostationary Operational Environmental Satellite (GOES)-derived skin temperature tendencies and insolation into the surface energy budget equation of a mesoscale model so that the simulated rate of temperature change closely agrees with the satellite observations. A critical assumption of the technique is that the availability of moisture (either from the soil or vegetation) is the least known term in the model's surface energy budget. Therefore, the simulated latent heat flux, which is a function of surface moisture availability, is adjusted based upon differences between the modeled and satellite-observed skin temperature tendencies. An advantage of this technique is that satellite temperature tendencies are assimilated in an energetically consistent manner that avoids energy imbalances and surface stability problems that arise from direct assimilation of surface shelter temperatures. The fact that the rate of change of the satellite skin temperature is used rather than the absolute temperature means that sensor calibration is not as critical. The assimilation technique has been applied to the Oklahoma-Kansas region during the spring-summer 2000 time period when dynamic changes in vegetation cover occur. In April, central Oklahoma is characterized by large NDVI associated with winter wheat while surrounding areas are primarily rangeland with lower NDVI. In July the vegetation pattern reverses as the central wheat area changes to low NDVI due to harvesting and the surrounding rangeland is greener than it was in April. The goal of this study is to determine if assimilating satellite land surface data can improve simulation of the complex spatial distribution of surface energy and water fluxes across this region. The PSU/NCAR NM5 V3 system is used in this study. The grid configuration consists of a 36-km CONUS domain and a 12-km nest over the area of interest. Bulk verification statistics (BIAS and RMSE) of surface air temperature and dewpoint indicates that assimilation of the satellite data results reduces both the bias and RMSE for both state variables. In addition, comparison of model data with ARM/CART EBBR flux observations reveals that the assimilation technique adjusts the bowen ratio in a realistic fashion.

Lapenta, William M.↗

NASA NTRS2022

Machine-Learning Reveals Climate Forcing From Aerosols is Dominated by Increased Cloud Cover

Aerosol-cloud interactions have a potentially large impact on climate, but are poorly quantified and thus contribute a significant and long-standing uncertainty in climate projections. The impacts derived from climate models are poorly constrained by observations, because retrieving robust large-scale signals of aerosol-cloud interactions are frequently hampered by the considerable noise associated with meteorological co-variability. The Iceland-Holuhraun effusive eruption in 2014 resulted in a massive aerosol plume in an otherwise near-pristine environment and thus provided an ideal natural experiment to quantify cloud responses to aerosol perturbations. Here we disentangle significant signals from the noise of meteorological co-variability using a satellite-based machine-learning approach. Our analysis shows that aerosols from the eruption increased cloud cover by approximately 10%, and this appears to be the leading cause of climate forcing, rather than cloud brightening as previously thought. We find that volcanic aerosols do brighten clouds by reducing droplet size, but this has a significantly smaller radiative impact than changes in cloud fraction. These results add substantial observational constraints on the cooling impact of aerosols. Such constraints are critical for improving climate models, which still inadequately represent the complex macro-physical and micro-physical impacts of aerosol-cloud interactions.

Aerosols↗

NASA NTRS2016

Pluto is the new Mars!

Data from NASA's New Horizons encounter with Pluto in July 2015 revealed an astoundingly complex world. The surface seen on the encounter hemisphere ranged in age from ancient to recent. A vast craterless plain of slowly convecting solid nitrogen resides in a deep primordial impact basin, reminiscent of young enigmatic deposits in Mars' Hellas basin. Like Mars, regions of Pluto are dominated by valleys, though the Pluto valleys are thought to be carved by nitrogen glaciers. Pluto has fretted terrain and halo craters. Pluto is cut by tectonics of several different ages. Like Mars, vast tracts on Pluto are mantled by dust and volatiles. Just as on Mars, Pluto has landscapes that systematically vary with latitude due to past and present seasonal (and mega-seasonal) effects on two major volatiles. On Mars, those volatiles are H2O and CO2; on Pluto they are CH4 and N2. Like Mars, some landscapes on Pluto defy easy explanation. In the Plutonian arctic there is a region of large (approx. 40 km across) deep (approx. 3-4 km) pits that probably could not be formed by sublimation, or any other single process, alone. Equally bizarre is the Bladed terrain, which is composed of fields of often roughly aligned blade-like ridges covering the flanks and crests of broad regional swells. Topping the unexpected are two large mounds approximately150 km across, approx. 5-6 km high, with great central depressions at their summits. The central depressions are almost as deep as the mounds are tall. These mounds have many of the characteristics of volcanic mountains seen on Mars and elsewhere in the inner solar system. Hypotheses for the formation of these Plutonian mounds so far all have challenges, principally revolving around the need for H2O ice to support their relief and the difficulty imagining mechanisms that would mobilize H2O. From the perspective of one year after the encounter, our appreciation of the extent of Pluto's diversity and complexity is quite reminiscent of the perspective the science community had of Mars, with similar quality data sets, soon after the early reconnaissance of that planet in the late 1960s and early 70s. So certainly in this sense, Pluto is the new Mars.

Pluto↗

DOE OSTI2024

The first ensemble of kilometer-scale simulations of a hydrological year over the third pole

An accurate understanding of the current and future water cycle over the Third Pole is of great societal importance, given the role this region plays as a water tower for densely populated areas downstream. An emerging and promising approach for skillful climate assessments over regions of complex terrain is kilometer-scale climate modeling. As a foundational step towards such simulations over the Third Pole, we present a multi-model and multi-physics ensemble of kilometer-scale regional simulations for the hydrological year of October 2019 to September 2020. The ensemble consists of 13 simulations performed by an international consortium of 10 research groups, configured with a horizontal grid spacing ranging from 2.2 to 4 km covering all of the Third Pole region. These simulations are driven by ERA5 and are part of a Coordinated Regional Climate Downscaling EXperiment Flagship Pilot Study on Convection-Permitting Third Pole. The simulations are compared against available gridded and in-situ observations and remote-sensing data, to assess the performance and spread of the model ensemble compared to the driving reanalysis during the cold and warm seasons. Although ensemble evaluation is hindered by large differences between the gridded precipitation datasets used as a reference over this region, we show that the ensemble improves on many warm-season precipitation metrics compared with ERA5, including most wet-day and hour statistics, and also adds value in the representation of wet spells in both seasons. As such, the ensemble will provide an invaluable resource for future improvements in the process understanding of the hydroclimate of this remote but important region.

54 ENVIRONMENTAL SCIENCES↗

DOE OSTI2020

Tutorial review on structure – dendrite growth relations in metal battery anode supports

This tutorial review explains the emerging understanding of the surface and bulk chemistry – electrochemical performance relations in anode supports (aka secondary current collectors, substrates, templates, hosts) for lithium, sodium and potassium metal batteries (LMBs, SMBs or NMBs, and KMBs or PMBs). In relation to each section, the possible future research directions that may yield both new insight and improved cycling behavior are explored. Representative case studies from Li, Na and K metal anode literature are discussed. The tutorial starts with an overview of the solid electrolyte interphase (SEI), covering both the “classic” understanding of the SEI structure and the “modern” insights obtained by site-specific cryogenic stage TEM analysis. Next, the multiple roles of supports in promoting cycling stability are detailed. Without an optimized support architecture, the metal–electrolyte interface becomes geometrically unstable at a lower current density and cycle number. Taking into consideration the available literature on LMBs, SMBs and KMBs, it is concluded that effective architectures are geometrically complex and electrochemically lithiophilic, sodiophilic or potassiophilic, so as to promote conformal electrochemical wetting of the metal during plating/stripping. One way that philicity is achieved is through support oxygen surface chemistry, which yields a reversibly reactive metal–support interface. Examples of this include the well-known oxygen–carbon moieties in reduced graphene oxide (rGO), as well as classic ion battery reversible conversion reaction oxides such as SnO 2 . Unreactive surfaces lead to dewetted island growth of the metal, which is a precursor to dendrites, and possibly to non-uniform dissolution. Surveying the literature on various Li, Na and K metal supports, it is concluded that the key bulk thermodynamic property that will predict electrochemical wetting behavior is the enthalpy of infinite solution (Δ sol H ∞ ) of the metal (solute) into the support (solvent). Large and negative Δ sol H ∞ promotes uniform metal wetting on the support surface, corresponding to relatively low plating overpotential. Positive Δ sol H ∞ promotes dewetted islands and a relatively high overpotential. This simple rule explains a broad range of studies on Li, Na and K metal – support interactions, including the previously reported correlation between mutual solubility and wetting.

25 ENERGY STORAGE↗

DOE OSTI2023

Tree-Based Ensemble Learning Models for Wall Temperature Predictions in Post-Critical Heat Flux Flow Regimes at Subcooled and Low-Quality Conditions

Accurately predicting post-critical heat flux (CHF) heat transfer is an important but challenging task in water-cooled reactor design and safety analysis. Although numerous heat transfer correlations have been developed to predict post-CHF heat transfer, these correlations are only applicable to relatively narrow ranges of flow conditions due to the complex physical nature of the post-CHF heat transfer regimes. In this paper, a large quantity of experimental data is collected and summarized from the literature for steady-state subcooled and low-quality film boiling regimes with water as the working fluid in vertical tubular test sections. In addition, a low-quality water film boiling (LWFB) database is consolidated with a total of 22,813 experimental data points, which cover a wide flow range of the system pressure from 0.1 to 9.0 MPa, mass flux from 25 to 2750 kg/m 2 s, and inlet subcooling from 1 to 70 °C. Two machine learning (ML) models, based on random forest (RF) and gradient boosted decision tree (GBDT), are trained and validated to predict wall temperatures in post-CHF flow regimes. The trained ML models demonstrate significantly improved accuracies compared to conventional empirical correlations. To further evaluate the performance of these two ML models from a statistical perspective, three criteria are investigated and three metrics are calculated to quantitatively assess the accuracy of these two ML models. For the full LWFB database, the root-mean-square errors between the measured and predicted wall temperatures by the GBDT and RF models are 5.7% and 6.2%, respectively, confirming the accuracy of the two ML models.

22 GENERAL STUDIES OF NUCLEAR REACTORS↗

NASA NTRS1972

Automatic identification of land uses from ERTS-1 data obtained over Milwaukee, Wisconsin

The author has identified the following significant results. Spectrally, thirteen classes of ground cover were identified within Milwaukee County: five classes of water, grassy open areas, beach, two classes of road, woods, suburban, inner city, and industry. A distinct concentric pattern of land use was identified in the county radiating outward from the central business district. The first ring has a principal feature, termed the inner city, which is indicative of the older part of the county. In the second ring, the land use becomes more complex, consisting of suburban areas, parks, and varied institutional features. The third general ring consists primarily of open, grassy land, with scattered residential subdivisions, wood lots, and small water bodies. The five classes of water identified suggest differences in depth, turbidity, and/or color. A number of major roads were identified. Other spectrally identifiable features included the larger county parks and larger cemeteries.

Baumgardner, M. F.↗

NASA NTRS1983

Directivity of planar array feeds for satellite reflector applications

Array-fed reflector antennas are used extensively in today's contour and multiple beam satellite antennas. To determine the directivity of these antennas theoretically, the total radiated power of the array feed must be accurately computed. In this paper, a closed-form expression for the radiated power is obtained for arrays with the well-known (cos theta q-type element patterns. The formulation is general, and takes into account polarization, nonsymmetric E- and H-plane element patterns, nonuniform element spacings and arbitrary complex excitation coefficients. Selective numerical data are presented to demonstrate the usefulness of this result. Comparisons are made with the available results obtained using direct numerical integration techniques, and with other available data based on less general formulations. Excellent agreement is observed for all cases. In particular, data are presented on the directivity of seven-element cluster feeds used in multiple beam designs, and an array feed for producing a contour beam covering the United States Eastern Time Zone (ETZ).

Rahmat-Samii, Y.↗

NASA NTRS1992

Uncertainty management by relaxation of conflicting constraints in production process scheduling

Mathematical-analytical methods as used in Operations Research approaches are often insufficient for scheduling problems. This is due to three reasons: the combinatorial complexity of the search space, conflicting objectives for production optimization, and the uncertainty in the production process. Knowledge-based techniques, especially approximate reasoning and constraint relaxation, are promising ways to overcome these problems. A case study from an industrial CIM environment, namely high-grade steel production, is presented to demonstrate how knowledge-based scheduling with the desired capabilities could work. By using fuzzy set theory, the applied knowledge representation technique covers the uncertainty inherent in the problem domain. Based on this knowledge representation, a classification of jobs according to their importance is defined which is then used for the straightforward generation of a schedule. A control strategy which comprises organizational, spatial, temporal, and chemical constraints is introduced. The strategy supports the dynamic relaxation of conflicting constraints in order to improve tentative schedules.

Dorn, Juergen↗

DOE OSTI2020

Probe-Enabled Approaches for Function-Dependent Cell Sorting and Characterization of Microbiome Subpopulations

Understanding the roles that individual species or communities play within a microbiome is a significant challenge. The complexity and heterogeneity of microbiomes presents a challenge to researchers looking to unravel the function that microbiomes serve within larger environments. While identification of the species and proteins present in a microbiome can be accomplished through genomics approaches, strategies that report on enzyme activity are limited. In this chapter, we describe the application of small molecule chemical probes in the isolation and subsequent characterization of microbiome subpopulations based on enzymatic function. We will cover protocols for labeling microbes with appropriate probes, microbiome sample preparation, and using fluorescence-activated cell sorting to isolate subpopulations based on function. We hope that the strategies outlined here will serve as a resource for researchers studying the functional role that microbiomes play in the gut and soil.

Steiger, Andrea K.↗

DOE OSTI2022

An experimental and modeling study on auto-ignition kinetics of ammonia/methanol mixtures at intermediate temperature and high pressure

A rapid compression machine (RCM) has been applied to measure the ignition delay times of NH 3 /CH 3 OH mixtures covering pressures of 20 and 40 bar, equivalence ratios of 0.5, 1.0 and 2.0, and temperatures between 845 and 1100 K. Here the measurements show that the NH 3 /CH 3 OH mixtures become more reactive with increasing methanol addition. Addition of merely 1% (molar basis) of CH 3 OH to NH 3 lowers the ignition temperature around 100 K at 40 bar in comparison to pure NH 3 . The ignition delay is a complex function of fuel mixture and stoichiometry. For the 1% CH 3 OH mixture, the leaner mixtures are more reactive, while the reverse trend is found for mixtures with 5%, 20% and pure CH 3 OH. Analysis of the pressure profiles shows three distinct ignition modes for NH 3/ CH 3 OH mixtures, facilitated by the pre-ignition heat release from NH 3 consumption. A detailed mechanism for ignition of NH 3 /CH 3 OH fuel blends has been developed, capable of reproducing the ignition behavior of mixtures with reasonable accuracy. A subset for amine / methanol interactions was established, with rate constants for the key reaction between NH 2 and CH 3 OH calculated from ab initio theory. A sensitivity analysis indicates that the critical reactions during the auto-ignition process vary with the CH 3 OH mole fraction in the fuel. The ammonia chemistry, namely NH 2 + NO, NH2 + NO 2 and NH 2 + HO 2 , is dominant for the mixture with 1% CH 3 OH, while the reactions related to CH 3 OH and H 2 O 2 are more important for the 20% CH 3 OH mixture. The interaction between ammonia and methanol shows a more prominent effect on auto-ignition for mixtures with 5% CH 3 OH in fuel as compared to those with 1% and 20% CH 3 OH. According to the modeling results, methanol addition is found to enrich the O/H radical pool, consuming ammonia and promoting auto-ignition through different reaction pathways.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗

NASA NTRS1996

4482 Element Multispectral Hybrid PV/PC HgCdTe IRFPA for High Resolution Coverage of 3.7 - 15.4 Micrometers for the AIRS Instrument

The Atmospheric Infrared Sounder (AIRS) is a key facility instrument in the NASA Earth Observing System (EOS) program, being implemented to obtain comprehensive long-term measurements of earth processes affecting global change. The instrument performs passive IR remote sensing using a high resolution grating spectrometer with a wide spectral coverage (3.7 - 15.4 m) directing radiation onto a hybrid HgCdTe IRFPA operating at 58K in a vacuum package cooled to 155K. The hybrid HgCdTe FPA consists of twelve modules, 10 with multiplexed photovoltaic detectors and two with individually leaded out photoconductive detectors. The complex FPA has a large optical footprint, 53 mm x 66 mm, and receives energy dispersed from the grating through a precision filter assembly containing 17 narrow band filters. The backside illuminated PV detector arrays are fabricated from P-on-n double layer LPE grown heterojunction detectors in a bilinear format of 50 m x 100 m detectors, with from 232 to 420 detectors per module. For the MWIR bands four PV modules cover the 3.7 m to 8.22 m region. Low detector capacitance and low noise preamplifiers in the ROIC are key to achieving high sensitivities in these bands. Uniform quantum efficiencies and detectivities exceeding 3E13 cm-rtHz/W have been achieved. The LWIR region is covered by six PV modules spanning 8.8 m to 13.75 m. High detector resistance and very low ROIC preamplifier input noise are key to achieving high sensitivity. A detectivity exceeding 2E11 cm-rtHz/W has been achieved at the longest wavelength. Two additional PC modules cover the longest spectral bands out to 15.4 m. This high performance multispectral focal plane has been built and integrated with the dewar assembly, and is currently being integrated with the complete AIRS sensor.

Rutter, James↗

NASA NTRS2005

Effective Team Support: From Task and Cognitive Modeling to Software Agents for Time-Critical Complex Work Environments

The purpose of this research contract was to perform multidisciplinary research between CMU psychologists, computer scientists and NASA researchers to design a next generation collaborative system to support a team of human experts and intelligent agents. To achieve robust performance enhancement of such a system, we had proposed to perform task and cognitive modeling to thoroughly understand the impact technology makes on the organization and on key individual personnel. Guided by cognitively-inspired requirements, we would then develop software agents that support the human team in decision making, information filtering, information distribution and integration to enhance team situational awareness. During the period covered by this final report, we made substantial progress in completing a system for empirical data collection, cognitive modeling, and the building of software agents to support a team's tasks, and in running experiments for the collection of baseline data.

Remington, Roger W.↗

DOE OSTI2021

Improved accuracy and transferability of molecular-orbital-based machine learning: Organics, transition-metal complexes, non-covalent interactions, and transition states

We report molecular-orbital-based machine learning (MOB-ML) provides a general framework for the prediction of accurate correlation energies at the cost of obtaining molecular orbitals. The application of Nesbet’s theorem makes it possible to recast a typical extrapolation task, training on correlation energies for small molecules and predicting correlation energies for large molecules, into an interpolation task based on the properties of orbital pairs. We demonstrate the importance of preserving physical constraints, including invariance conditions and size consistency, when generating the input for the machine learning model. Numerical improvements are demonstrated for different datasets covering total and relative energies for thermally accessible organic and transition-metal containing molecules, non-covalent interactions, and transition-state energies. MOB-ML requires training data from only 1% of the QM7b-T dataset (i.e., only 70 organic molecules with seven and fewer heavy atoms) to predict the total energy of the remaining 99% of this dataset with sub-kcal/mol accuracy. This MOB-ML model is significantly more accurate than other methods when transferred to a dataset comprising of 13 heavy atom molecules, exhibiting no loss of accuracy on a size intensive (i.e., per-electron) basis. It is shown that MOB-ML also works well for extrapolating to transition-state structures, predicting the barrier region for malonaldehyde intramolecular proton-transfer to within 0.35 kcal/mol when only trained on reactant/product-like structures. Finally, the use of the Gaussian process variance enables an active learning strategy for extending the MOB-ML model to new regions of chemical space with minimal effort. We demonstrate this active learning strategy by extending a QM7b-T model to describe non-covalent interactions in the protein backbone–backbone interaction dataset to an accuracy of 0.28 kcal/mol.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗

DOE OSTI2020

3D Hybrid Plasmonic Framework with Au Nanopillars Embedded in Nitride Multilayers Integrated on Si

Integration of nanoscale photonic and plasmonic components on Si substrates is a critical step toward Si-based integrated nanophotonic devices. Herein, a set of unique complex 3D metamaterials with intercalated nanolayered and nanopillar structures with tunable plasmonic and optical properties on Si substrates is designed. More specifically, the 3D metamaterials combine metal (Au) nanopillars and alternating metal-nitride (Au-TiN and Au-TaN) nanolayers, epitaxially grown on Si substrates. The ultrafine Au nanopillars ( d ≈ 3 nm) continuously grow throughout all the nanolayers with high epitaxial quality. Novel optical properties, such as highly anisotropic optical property, high absorbance covering the entire visible spectrum regime, and hyperbolic property in the visible regime, are demonstrated. Furthermore, a waveguide based on a silicon nitride (Si 3 N 4 ) ridge with a multilayer structure is successfully fabricated. The demonstration of 3D nanoscale metamaterial design integrated on Si opens up a new route toward tunable metamaterials nanostructure designs with versatile material selection for various optical components in Si integrated photonics.

36 MATERIALS SCIENCE↗