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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 631 records · Page 35
All sourcesNASA NTRSDOE OSTI
NASA NTRS1978

A simple method for obtaining a three-dimensional proton distribution function from Voyager plasma data

The main sensor of the Vogager plasma experiment consists of a cluster of three, modulated-grid Faraday cups whose normals are arranged symmetrically about the symmetry axis of the cluster at an angle of 20 degrees to that axis. In interplanetary space, each cup explores the positive ion distribution by accepting particles from contiguous slices in velocity space. The slices are narrow in the direction of the normal to the modulating grid but are broad in planes parallel to that grid. The resulting three sets of measurements can be combined to yield the three-dimensional distribution function in the following way: the distribution function is assumed to be gyrotropic. For each value of speed in a frame of reference moving with the bulk velocity of the solar wind, the variation of the distribution function with angle from the field direction is represented by a series of Legendre polynomials. Effects such as double-streaming and heat flow can be well represented by using only the first three terms of the series which are fully specified by the measurements. Examples of the use of this method in the analysis of Voyager data are shown.

Olbert, S.↗
DOE OSTI2020

ATLAS: a Snakemake workflow for assembly, annotation, and genomic binning of metagenome sequence data

Background: Metagenomics and metatranscriptomics studies provide valuable insight into the composition and function of microbial populations from diverse environments, however the data processing pipelines that rely on mapping reads to gene catalogs or genome databases for cultured strains yield results that underrepresent the genes and functional potential of uncultured microbes. Recent improvements in sequence assembly methods have eased the reliance on genome databases, thereby allowing the recovery of genomes from uncultured microbes. However, configuring these tools, linking them with advanced binning and annotation tools, and maintaining provenance of the processing continues to be challenging for researchers. Results: Here we present ATLAS, a software package for customizable data processing from raw sequence reads to functional and taxonomic annotations using state-of-the-art tools to assemble, annotate, quantify, and bin metagenome and metatranscriptome data. Genome-centric resolution and abundance estimates are provided for each sample in a dataset. ATLAS is written in Python and the workflow implemented in Snakemake; it operates in a Linux environment, and is compatible with Python 3.5+ and Anaconda 3+ versions. The source code for ATLAS is freely available, distributed under a BSD-3 license. Conclusions: ATLAS provides a user-friendly, modular and customizable Snakemake workflow for metagenome and metatranscriptome data processing; it is easily installable with conda and maintained as open-source on GitHub at https://github.com/metagenome-atlas/atlas.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2018

Identifying an efficient, thermally robust inorganic phosphor host via machine learning

Abstract Rare-earth substituted inorganic phosphors are critical for solid state lighting. New phosphors are traditionally identified through chemical intuition or trial and error synthesis, inhibiting the discovery of potential high-performance materials. Here, we merge a support vector machine regression model to predict a phosphor host crystal structure’s Debye temperature, which is a proxy for photoluminescent quantum yield, with high-throughput density functional theory calculations to evaluate the band gap. This platform allows the identification of phosphors that may have otherwise been overlooked. Among the compounds with the highest Debye temperature and largest band gap, NaBaB 9 O 15 shows outstanding potential. Following its synthesis and structural characterization, the structural rigidity is confirmed to stem from a unique corner sharing [B 3 O 7 ] 5– polyanionic backbone. Substituting this material with Eu 2+ yields UV excitation bands and a narrow violet emission at 416 nm with a full-width at half-maximum of 34.5 nm. More importantly, NaBaB 9 O 15 :Eu 2+ possesses a quantum yield of 95% and excellent thermal stability.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
NASA NTRS2000

Hydrogen Biogeochemistry in Anaerobic and Photosynthetic Ecosystems

The simple biochemistry of molecular hydrogen is central to a large number of microbial processes, affecting the interaction of organisms with each other and with the environment. In anoxic sediments, a great majority of microbial redox processes involve hydrogen as a reactant, product or potential by-product. Accordingly, the energetics (thermodynamics) of each of these processes is affected by variations in local H2 concentrations. It has long been established that this effect is important in governing microbe-microbe interactions and there are multiple demonstrations that "interspecies hydrogen transfer" can alter the products of, inhibit/stimulate, or even reverse microbial metabolic reactions. In anoxic sediments, H2 concentrations themselves are thought to be controlled by the thermodynamics of the predominant H2-consuming microbial process. In sediments from Cape Lookout Bight, this relationship quantitatively describes the co-variation of H2 concentrations with temperature (for methanogens and sulfate reducers) and with sulfate concentration (for sulfate reducers). The quantitative aspect is import= for two reasons: 1) it permits the modeling of H2-sensitive biogeochemistry, such as anaerobic methane oxidation or pathways of organic matter remineralization, as a function of environmental controls; 2) for such a relationship to be observed requires that intracellular biochemistry and bioenergetics are being directly expressed in a component of the extracellular medium. H2 could therefore be utilized a non-invasive probe of cellular energetic function in intact microbial ecosystems. Based on the latter principle we have measured down-core profiles of H2 and other relevant physico-chemical parameters in order to calculate the metabolic energy yields (DG) that support microbial metabolism in Cape Lookout Bight sediments. Methanogens in this system apparently function with energy yields significantly smaller than the minimum requirements suggested by pure culture studies. Our recent work has extended the study of hydrogen to cyanobacterial mat communities. The large amounts of reducing power generated during photosynthetic activity carry the potential to contribute a swamping term to the H2 economy of the anaerobic microbial populations within the mat - and thereby to alter the population structure and biogeochemical function of the mat as a whole. In hypersaline microbial mats, we observe a distinct diel cycle in H2 production and a substantial corresponding flux. On an early Earth dominated by microbial mats, this transmission of photosynthetic reducing power may have carried important implications for both biospheric and atmospheric evolution.

Hoehler, Tori M.↗
DOE OSTI2026

Mechanistic Insights into Dinitrogen Reduction to Ammonia in Light-Controlled Nanocrystal:Nitrogenase Complexes

Developing systems that can efficiently capture photon energy and convert this energy into fuels and chemicals requires understanding how to assemble molecular components with diverse functions into complete systems possessing selectivity and efficiency in directing charge carriers to catalytic reactions. There are many challenges to achieving this goal. One promising approach is the development of hybrid systems that combine semiconductor nanocrystals (NCs) for light capture and enzymes as efficient catalysts. Such biohybrid systems capitalize on the tunable electronic and optical properties of NCs while leveraging the unmatched specificity and efficiency of enzymes in catalyzing chemical reactions, thereby offering opportunities to surpass the limitations of each component alone. Here, we focus on recent progress in developing a biohybrid system that combines CdS NCs for photon capture with the enzyme nitrogenase to accomplish light-driven dinitrogen (N 2 ) reduction to ammonia (NH 3 ). Integrating light-harvesting materials with biological catalysts requires a deep understanding of NC properties, protein stability, and electron transfer (ET), making it an inherently multidisciplinary problem. The reduction of N 2 to NH 3 is a challenging reaction, with a high demand in both agriculture and industrial chemical production. This reaction is intrinsically energy intensive, due to the need to activate the N≡N triple bond. The current standard industrial approach to N 2 reduction, the Haber−Bosch reaction, obtains the necessary energy input from fossil fuels, whereas biological systems capable of N 2 reduction utilize the hydrolysis of ATP as their energy source. Replacing these costly, energy-intensive inputs with renewable light energy represents a critical step toward sustainable NH 3 production. Recent progress has demonstrated that semiconductor CdS NCs can be coupled to the catalytic component of nitrogenase, the MoFe protein, to form a biohybrid CdS NC:MoFe protein complex, enabling light-driven N 2 reduction rather than energy input from fossil fuels or ATP. This illustrates how inorganic NCs can functionally replace the natural Fe protein partner, yielding a biohybrid catalyst that enables controlled electron delivery and provides not only light-driven NH 3 production but also new approaches for probing enzyme catalytic function. The CdS NC:MoFe protein biohybrid system enables light-initiated electron delivery at ambient temperature, as well as temperatures below freezing, allowing for stabilization and spectroscopic characterization of key reaction intermediates. These findings highlight how photochemical biohybrids can serve as both functional catalysts and mechanistic probes. Beyond studies of the nitrogenase mechanism, studies of the CdS NC:MoFe system reveal how variables such as NC size, electrostatic binding interactions, and sacrificial electron donors (SEDs) govern complex stability, charge transfer efficiency, and catalytic performance. In addition, studies of nitrogenase and the high activation barrier for N 2 reduction are enabling investigations of new and interesting questions regarding the properties and limitations of NC biocatalysis. In this Account, we describe the key features of CdS NC:MoFe protein biohybrids and the parameters for optimal light-driven N 2 reduction, and how controlling ET with light illuminates the path to new insights into the nitrogenase mechanism.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2023

The Low-Yield Nuclear Monitoring (LYNM) Experimental Science Plan

The Low-Yield Nuclear Monitoring (LYNM) Program is a long-term NNSA research and development effort designed to improve the United States’ explosion monitoring capabilities, particularly with respect to low-yield and potentially evasive underground nuclear testing. The LYNM Program focuses on researching, discovering, and exploiting unique and useful signatures, from all available technologies and sensors (e.g., seismic, acoustic, electromagnetic, gases, and particulates (both stable and radioactive)). Four Department of Energy laboratories participate in LYNM and together are referred to as the ‘quad-lab’. The R&D program execution is performed under NNSA defined structures known as “ventures”. Four science ventures were established: 1) Explosion Source Functions; 2) Containment of Low-Yield Underground Tests; 3) Local Signatures; 4) Dynamic Monitoring Networks. To organize and execute the large field scale experiment a fifth venture was established: 5) Physics Experiment One (PE-1). As part of the LYNM Program, a series of experiments are planned at various scales and levels of venture involvement. These vary from those that involve a subset of labs and/or LYNM ventures (e.g., small experiments), to full quad-lab LYNM Program field-scale integrated experiments. Such experiments may involve chemical explosions with tracer materials or other means of simulating the expected signals from a nuclear explosion. The LYNM Program does not conduct actual nuclear explosions. Since 1992, the U.S. has observed a moratorium on underground nuclear explosions. This document is intended to provide the underlying scientific basis for the LYNM planned experimental work. Each specific LYNM experiment will develop a goals, objectives, and requirements (GOR) plan following the guidance in this document. The LYNM technical staff will define the numbers and types of experiments required over the course of the Program based on technical needs and within funding constraints. As with any scientific experiment series, the number and types of experiments may change based upon the experimental results obtained. An experiment that agrees with models/codes/software signature predictions may need fewer repetitions/variations, depending upon the level of statistical rigor desired, as compared to one in which the predictions and experimental data do not match. The large LYNM field-scale integrated experiments require the longest lead-time for planning, and these are discussed in more detail near the end of this document.

58 GEOSCIENCES↗
DOE OSTI2024

Biosensor and machine learning-aided engineering of an amaryllidaceae enzyme

A major challenge to achieving industry-scale biomanufacturing of therapeutic alkaloids is the slow process of biocatalyst engineering. Amaryllidaceae alkaloids, such as the Alzheimer’s medication galantamine, are complex plant secondary metabolites with recognized therapeutic value. Due to their difficult synthesis they are regularly sourced by extraction and purification from the low-yielding daffodil Narcissus pseudonarcissus. Here, we propose an efficient biosensor-machine learning technology stack for biocatalyst development, which we apply to engineer an Amaryllidaceae enzyme in Escherichia coli. Directed evolution is used to develop a highly sensitive (EC 50 = 20 μM) and specific biosensor for the key Amaryllidaceae alkaloid branchpoint 4’-O-methylnorbelladine. A structure-based residual neural network (MutComputeX) is subsequently developed and used to generate activity-enriched variants of a plant methyltransferase, which are rapidly screened with the biosensor. Functional enzyme variants are identified that yield a 60% improvement in product titer, 2-fold higher catalytic activity, and 3-fold lower off-product regioisomer formation. A solved crystal structure elucidates the mechanism behind key beneficial mutations.

60 APPLIED LIFE SCIENCES↗
NASA NTRS2001

Characteristics of human dendritic cells generated in a microgravity analog culture system

Generation of an effective immune response requires that antigens be processed and presented to T lymphocytes by antigen-presenting cells, the most efficient of which are dendritic cells (DC). Because of their influence on both the innate and the acquired arms of immunity, a defect in DC would be expected to result in a broad impairment of immune function, not unlike that observed in astronauts during or after space flight. In the study reported here, we investigated whether DC generation and function are altered in a culture environment that models microgravity, i.e., the rotary-cell culture system (RCCS). We observed that RCCS supported the generation of DC identified by morphology, phenotype (HLA-DR+ and lacking lineage-associated markers), and function (high allostimulatory activity). However, the yield of DC from RCCS was significantly lower than that from static cultures. RCCS-generated DC were less able to phagocytose Aspergillus fumigatus conidia and expressed a lower density of surface HLA-DR. The proportion of DC expressing CD80 was also significantly reduced in RCCS compared to static cultures. When exposed to fungal antigens, RCCS-generated DC produced lower levels of interleukin-12 and failed to upregulate some costimulatory/adhesion molecules involved in antigen presentation. These data suggest that DC generation, and some functions needed to mount an effective immune response to pathogens, may be disturbed in the microgravity environment of space.

Non-NASA Center↗
DOE OSTI2024

FLOWERS AEP: An Analytical Model for Wind Farm Layout Optimization

Annual energy production (AEP) is commonly used in objective functions for wind farm layout optimization. AEP is proportional to wind farm power production integrated over an annual distribution of free-stream wind conditions. Physics-based estimates of wind farm power production typically rely on low-fidelity engineering wake models that approximate the steady-state wind farm flow field. AEP estimates are then obtained by performing independent simulations for discrete wind conditions and using rectangular quadrature to account for each condition's expected frequency of occurrence. Depending on the number of simulated discrete wind conditions, this numerical integral could be hampered by poor accuracy or high computational costs. The FLOWERS AEP model instead poses an analytical integral of the engineering wake model over the variable wind conditions, yielding a closed-form, analytical function for wind farm AEP. This paper derives the analytical functions for FLOWERS AEP and its derivatives with respect to turbine position, which are useful for gradient-based wind farm layout optimization, in nondimensional form. We then analyze the benefits of the FLOWERS AEP model over conventional reference models, focusing on its low cost, adequate wake loss predictions, and smooth design space. Although the FLOWERS approach is found to predict the exact value of AEP with some error relative to the reference model (within 14% on average), it dramatically reduces computation time by an order of magnitude, produces a qualitatively similar design space at relatively low resolution, and yields comparable optimal layouts. This significant speed improvement is critical in layout optimization applications, where determining an optimal layout in an efficient manner is more important than precise AEP prediction.

17 WIND ENERGY↗
DOE OSTI2024

Smooth Dispersion Is Physically Appropriate: Assessing and Amending the D4 Dispersion Model

The addition of dispersion corrections to density functionals is essential for accurate energy and geometry predictions. Among them, the D4 scheme is popular due to its low computational cost and high accuracy. However, due to its design, the D4 correction can occasionally lead to anomalies, such as unphysical curvature and bumps in the potential energy surface. We find these anomalies are common in the D4 model, although observable consequences are rarer than in the D3 model for reasons we explain. Nevertheless, we uncover instances of unphysical local minima and stationary points with the D4 scheme and propose two solutions that yield smoother dispersion energy as a function of nuclear position. One is trivial to implement, based on a smoother reparametrization of Gaussian weighting (D4S) to find the effective coordination number. The other replaces Gaussian weighting with soft linear interpolation (D4SL). These new approaches usually remove artificial extremum points, while maintaining accuracy.

Tkachenko, Nikolay V↗
DOE OSTI2026

Functionalized benzylamines from commercial kraft lignin

Benzylamines are key intermediates in pharmaceuticals, agrochemicals, and polymers, but their conventional production relies on benzyl chloride - a petroleum-derived compound with high toxicity and energy demands. Lignin, accounting for up to 30% of plant biomass, is the largest renewable source of aromatic carbon on Earth. However, its highly complex and recalcitrant structure poses a major barrier to efficient conversion into high-value chemicals. Here, in this study, we developed a catalytic approach to convert commercial kraft lignin into phenolic benzylamines through selective depolymerization and subsequent functionalization. We systematically evaluated the effects of three alcohol solvents, formic acid (FA), and a ruthenium-on‑carbon (Ru/C) catalyst on monophenol yield and selectivity. Up to 6.5 wt% monophenol yield was achieved using methanol (MeOH), FA, and Ru/C at 300 °C for 2 h. Quantum thermodynamic simulations based on the COSMO-RS model confirmed the superior solvation and reactivity of the MeOH + FA system, rationalizing observed product yield. The purified monophenolic products, primarily guaiacol and alkyl guaiacols, were then converted into functionalized benzylamines with >90% yield via a multicomponent Mannich reaction under mild conditions. Techno economic analysis (TEA) and life cycle assessment (LCA) underscore the importance of improving lignin depolymerization yields and expanding biorefinery scale. Solvent-only configurations outperform other options in both cost and emissions, with the methanol-only case performing the best ($\$$105 /kg and 26 kg CO 2e /kg) at a large-scale facility. This study establishes a scalable, bio-based pathway for producing benzylamines from commercial kraft lignin, advancing lignin valorization and offering a sustainable alternative to produce petrochemical-based benzylamines.

Catalytic reduction↗
DOE OSTI2017

Unified halo-independent formalism from convex hulls for direct dark matter searches

Using the Fenchel-Eggleston theorem for convex hulls (an extension of the Caratheodory theorem), we prove that any likelihood can be maximized by either a dark matter 1- speed distribution F ( v ) in Earth's frame or 2- Galactic velocity distribution f gal ( ) , consisting of a sum of delta functions. The former case applies only to time-averaged rate measurements and the maximum number of delta functions is ( -1) , where is the total number of data entries. The second case applies to any harmonic expansion coefficient of the time-dependent rate and the maximum number of terms is . Using time-averaged rates, the aforementioned form of F ( v ) results in a piecewise constant unmodulated halo function 0 BF ( v min ) (which is an integral of the speed distribution) with at most ( -1) downward steps. The authors had previously proven this result for likelihoods comprised of at least one extended likelihood, and found the best-fit halo function to be unique. This uniqueness, yet, cannot be guaranteed in the more general analysis applied to arbitrary likelihoods. Thus we introduce a method for determining whether there exists a unique best-fit halo function, and provide a procedure for constructing either a pointwise confidence band, if the best-fit halo function is unique, or a degeneracy band, if it is not. Using measurements of modulation amplitudes, the aforementioned form of f gal ( ) , which is a sum of Galactic streams, yields a periodic time-dependent halo function BF ( v min , t ) which at any fixed time is a piecewise constant function of v min with at most downward steps. In this case, we explain how to construct pointwise confidence and degeneracy bands from the time-averaged halo function. Lastly, we show that requiring an isotropic Galactic velocity distribution leads to a Galactic speed distribution F ( u ) that is once again a sum of delta functions, and produces a time-dependent BF ( v min , t ) function (and a time-averaged 0 BF ( v min )) that is piecewise linear, differing significantly from best-fit halo functions obtained without the assumption of isotropy.

79 ASTRONOMY AND ASTROPHYSICS↗
NASA NTRS1976

Satellite observations of snow and ice with an imaging passive microwave spectrometer

The scanning microwave spectrometer (SCAMS) on the Nimbus-6 satellite continuously maps the terrestrial surface with a resolution of about 150 km at 22.235 and 31.400 GHz. SCAMS observes at six angles besides nadir, yielding brightness temperatures which are a function of the distribution and character of various types of snow and ice, including microstructure and subsurface profiles in refractive index, loss (moisture or salinity), and temperature. Spectral signatures exhibiting interesting topographical structure have been observed. To aid in the interpretation of these data, a model was developed to describe the propagation of microwave intensity in a scattering medium characterized by three-dimensional random fluctuations of refractive index in addition to nonrandom variations in permittivity, temperature, and loss. The model combines Maxwell's equations in the Born approximation with radiative-transfer theory; this approach yields the variation of intensity with polarization, direction, and position.

Fisher, A. D.↗
DOE OSTI2026

Many-Body Benchmark of Electronic Charge and Spin Densities for Li 1–x NiO 2

Accurate benchmarks are particularly important for highly correlated oxides as mean-field approximations often fail to describe the subtle balance of charge transfer and magnetism in these materials with an accuracy comparable to experimental needs. Here we present accurate diffusion Monte Carlo (DMC) results of the electronic charge and spin densities for the tunable highly correlated oxide Li 1–x NiO 2 for x = 0, 1/2, and 1. To enable quantitative comparisons, we introduce a robust density-partitioning scheme, extending Voronoi analysis to assign atomic charges from spatially noisy DMC densities. We then benchmark common approximations used in density functional theory (DFT). Comparison against DMC shows that r 2 SCAN delivers the most balanced performance across charge, spin, and radial density descriptors, nearly reproducing DMC results for LiNiO 2 and apical Ni sites in Li 0.5 NiO 2 . Hybrid functionals (PBE0, SCAN0) perform unexpectedly poorly, and PBE + U + V yields inconsistent trends between charge and spin densities. Therefore, the r 2 SCAN functional minimizes errors relative to DMC while capturing the variable valence of the Ni ion and also retaining the computational efficiency of DFT for large-scale simulations of the tunable structural and electronic phases of Li1−xNiO2. Our study highlights the importance of accurate benchmarking of the fundamental quantities involved in DFT to select appropriate DFT approximations in order to advance the predictive modeling of charge-transfer-driven phenomena in correlated electron systems.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
NASA NTRS1993

Classifying multispectral data by neural networks

Several energy functions for synthesizing neural networks are tested on 2-D synthetic data and on Landsat-4 Thematic Mapper data. These new energy functions, designed specifically for minimizing misclassification error, in some cases yield significant improvements in classification accuracy over the standard least mean squares energy function. In addition to operating on networks with one output unit per class, a new energy function is tested for binary encoded outputs, which result in smaller network sizes. The Thematic Mapper data (four bands were used) is classified on a single pixel basis, to provide a starting benchmark against which further improvements will be measured. Improvements are underway to make use of both subpixel and superpixel (i.e. contextual or neighborhood) information in tile processing. For single pixel classification, the best neural network result is 78.7 percent, compared with 71.7 percent for a classical nearest neighbor classifier. The 78.7 percent result also improves on several earlier neural network results on this data.

Telfer, Brian A.↗
DOE OSTI2017

Complete fold annotation of the human proteome using a novel structural feature space

Recognition of protein structural fold is the starting point for many structure prediction tools and protein function inference. Fold prediction is computationally demanding and recognizing novel folds is difficult such that the majority of proteins have not been annotated for fold classification. Here we describe a new machine learning approach using a novel feature space that can be used for accurate recognition of all 1,221 currently known folds and inference of unknown novel folds. We show that our method achieves better than 94% accuracy even when many folds have only one training example. We demonstrate the utility of this method by predicting the folds of 34,330 human protein domains and showing that these predictions can yield useful insights into potential biological function, such as prediction of RNA-binding ability. Finally, our method can be applied to de novo fold prediction of entire proteomes and identify candidate novel fold families.

59 BASIC BIOLOGICAL SCIENCES↗
NASA NTRS1990

An analysis of scatter decomposition

A formal analysis of a powerful mapping technique known as scatter decomposition is presented. Scatter decomposition divides an irregular computational domain into a large number of equal sized pieces, and distributes them modularly among processors. A probabilistic model of workload in one dimension is used to formally explain why, and when scatter decomposition works. The first result is that if correlation in workload is a convex function of distance, then scattering a more finely decomposed domain yields a lower average processor workload variance. The second result shows that if the workload process is stationary Gaussian and the correlation function decreases linearly in distance until becoming zero and then remains zero, scattering a more finely decomposed domain yields a lower expected maximum processor workload. Finally it is shown that if the correlation function decreases linearly across the entire domain, then among all mappings that assign an equal number of domain pieces to each processor, scatter decomposition minimizes the average processor workload variance. The dependence of these results on the assumption of decreasing correlation is illustrated with situations where a coarser granularity actually achieves better load balance.

Nicol, David M.↗