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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 433 records · Page 24
All sourcesNASA NTRSDOE OSTI
DOE OSTI2020

Relevance of the Unfolded Protein Response to Spaceflight-Induced Transcriptional Reprogramming in Arabidopsis

Plants are primary producers of food and oxygen on Earth and will likewise be indispensable to the establishment of large-scale sustainable ecosystems and human survival in space. To contribute to the understanding of how plants respond to spaceflight stress, we examined the significance of the unfolded protein response (UPR), a conserved signaling cascade that responds to a number of unfavorable environmental stresses, in the model plant Arabidopsis thaliana. To do so, we performed a large-scale comparative transcriptome profiling in wild type and various UPR-defective mutants during the SpaceX-CRS12 mission to the International Space Station. We established that orbital culture substantially alters the expression of hundreds of stress-related genes compared with ground control conditions. Although expression of those genes varied in the UPR mutants on the ground, it was largely similar across the genotypes in the spaceflight condition. Furthermore, our results have yielded new information on how plants respond to growth in orbit and support the hypothesis that spaceflight induces the activation of signaling pathways that compensate for the loss of UPR regulators in the control of downstream transcriptional regulatory networks.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2022

Implications of Microstructure in Helium-Implanted Nanocrystalline Metals

Helium bubbles are known to form in nuclear reactor structural components when displacement damage occurs in conjunction with helium exposure and/or transmutation. If left unchecked, bubble production can cause swelling, blistering, and embrittlement, all of which substantially degrade materials and—moreover—diminish mechanical properties. On the mission to produce more robust materials, nanocrystalline (NC) metals show great potential and are postulated to exhibit superior radiation resistance due to their high defect and particle sink densities; however, much is still unknown about the mechanisms of defect evolution in these systems under extreme conditions. Here, the performances of NC nickel (Ni) and iron (Fe) are investigated under helium bombardment via transmission electron microscopy (TEM). Bubble density statistics are measured as a function of grain size in specimens implanted under similar conditions. While the overall trends revealed an increase in bubble density up to saturation in both samples, bubble density in Fe was over 300% greater than in Ni. To interrogate the kinetics of helium diffusion and trapping, a rate theory model is developed that substantiates that helium is more readily captured within grains in helium-vacancy complexes in NC Fe, whereas helium is more prone to traversing the grain matrices and migrating to GBs in NC Ni. Our results suggest that (1) grain boundaries can affect bubble swelling in grain matrices significantly and can have a dominant effect over crystal structure, and (2) an NC-Ni-based material can yield superior resistance to irradiation-induced bubble growth compared to an NC-Fe-based material and exhibits high potential for use in extreme environments where swelling due to He bubble formation is of significant concern.

36 MATERIALS SCIENCE↗
DOE OSTI2020

An Accurate Vegetation and Non-Vegetation Differentiation Approach Based on Land Cover Classification

Accurate vegetation detection is important for many applications, such as crop yield estimation, land cover land use monitoring, urban growth monitoring, drought monitoring, etc. Popular conventional approaches to vegetation detection incorporate the normalized difference vegetation index (NDVI), which uses the red and near infrared (NIR) bands, and enhanced vegetation index (EVI), which uses red, NIR, and the blue bands. Although NDVI and EVI are efficient, their accuracies still have room for further improvement. In this paper, we propose a new approach to vegetation detection based on land cover classification. That is, we first perform an accurate classification of 15 or more land cover types. The land covers such as grass, shrub, and trees are then grouped into vegetation and other land cover types such as roads, buildings, etc. are grouped into non-vegetation. Similar to NDVI and EVI, only RGB and NIR bands are needed in our proposed approach. If Laser imaging, Detection, and Ranging (LiDAR) data are available, our approach can also incorporate LiDAR in the detection process. Results using a well-known dataset demonstrated that the proposed approach is feasible and achieves more accurate vegetation detection than both NDVI and EVI. In particular, a Support Vector Machine (SVM) approach performed 6% better than NDVI and 50% better than EVI in terms of overall accuracy (OA).

54 ENVIRONMENTAL SCIENCES↗
NASA NTRS1975

High dispersion observations of Venus during 1972 - The CO2 band at 7820 A

Results are reported for analysis of 47 photographic plates of Venus which show the spectrum of the CO2 band at 7820 A. Evidence is found for a semiregular four-day variation in the CO2 abundance over the disk of the planet as well as for temporal variations in the rotational temperature of the 7820-A band and temperature variations over the disk. No apparent relationship is found between the CO2 abundance and temperature, although an increase in temperature is usually accompanied by a decrease in the CO2 abundance. A curve-of-growth analysis assuming a constant CO2 line width yields an average rotational temperature of about 249 K, which is several degrees higher than previously obtained rotational temperatures for the same band. Temperature variations are found to indicate that the average temperature in the equatorial and polar regions is 2.3 K higher than that near the limb or terminator.

Young, L. D. G.↗
NASA NTRS1975

On fracture toughness in the nonlinear range

A general definition of fracture toughness is developed which is appropriate to situations of subcritical crack growth and/or large-scale crack border plastic yield. The theoretical basis as well as comparisons with other proposed measures of fracture toughness are discussed. A simple method is given for evaluating fracture toughness which is based on use of the load-displacement test record.

Eftis, J.↗
NASA NTRS1979

Proceedings of Technical Sessions, Volumes 1 and 2: the LACIE Symposium

The technical design of the Large Area Crop Inventory Experiment is examined and data acquired over 3 global crop years is analyzed with respect to (1) sampling and aggregation; (2) growth size estimation; (3) classification and mensuration; (4) yield estimation; and (5) accuracy assessment. Seventy-nine papers delivered at conference sessions cover system implementation and operation; data processing systems; experiment results and accuracy; supporting research and technology; and the USDA application test system.

Source record↗
NASA NTRS1985

Web team overview

Analytical and experimental work were combined to develop a stress model to determine the temperature profile which will yield a satisfactory web. A thermal model of the web will be used to determine the thermal configuration which will yield the desired temperature profile. Thermal analysis of growth interface and the susceptor melt will be developed to determine a thermal configuration which will improve stability of growth process. The thermal configurations will be designed, fabricated, and tested and the results fed back into the models.

Mcdonald, R. R.↗
NASA NTRS1988

Oscillator strengths for Si II

New accurate results are presented for absorption oscillator strengths of Si II resonance lines. In these ab initio calculations, an extensive basis set of 67 electronic configuration are employed, including fine-structure states and relativistic effects in the Breit-Pauli approximation. For the strong Si II lines, good agreement is found with previous LS-coupling f-values of Dufton et al. (1983). Oscillator strengths of selected weaker lines disagree in some cases with empirically determined f-values by Shull, Snow, and York (1981) and by Van Buren (1986). The theoretical results are combined with curve-of-growth analyses of Copernicus and IUE data to yield a list of recommended Si II oscillator strengths consistent with the errors in both theoretical and observational analyses. These f-values have been used (Van Steenberg and Shull) for a Galactic survey of interstellar silicon abundances with the International Ultraviolet Explorer satellite.

Luo, Ding↗
NASA NTRS1998

Heterogeneity Determination and Purification of Commercial Hen Egg-White Lysozyme

Hen egg-white lysozyme (HEWL) is widely used as a model protein, although its purity has not been adequately characterized by modern biochemical techniques. We have identified and quantified the protein heterogeneities in three commercial HEWL preparations by sodium dodecyl sulfate polyacrylamide gel electrophoresis with enhanced silver staining, reversed-phase fast protein liquid chromatography (FPLC) and immunoblotting with comparison to authentic protein standards. Depending on the source, the contaminating proteins totalled 1-6%(w/w) and consisted of ovotransferrin, ovalbumin, HEWL dimers, and polypeptides with approximate M(sub r) of 39 and 18 kDa. Furthermore, we have obtained gram quantities of electrophoretically homogeneous [> 99.9%(w/w)] HEWL by single-step semi-preparative scale cation-exchange FPLC with a yield of about 50%. Parallel studies of crystal growth kinetics, salt repartitioning and crystal perfection with this highly purified material showed fourfold increases in the growth-step velocities and significant enhancement in the structural homogeneity of HEWL crystals.

Thomas, B. R.↗
NASA NTRS1999

The Oxidation Rate of SiC in High Pressure Water Vapor Environments

CVD SiC and sintered alpha-SiC samples were exposed at 1316 C in a high pressure burner rig at total pressures of 5.7, 15, and 25 atm for times up to 100h. Variations in sample emittance for the first nine hours of exposure were used to determine the thickness of the silica scale as a function of time. After accounting for volatility of silica in water vapor, the parabolic rate constants for Sic in water vapor pressures of 0.7, 1.8 and 3.1 atm were determined. The dependence of the parabolic rate constant on the water vapor pressure yielded a power law exponent of one. Silica growth on Sic is therefore limited by transport of molecular water vapor through the silica scale.

Opila, Elizabeth J.↗
DOE OSTI2020

Extending juvenility in grasses

The present invention relates to compositions and methods for modulating the juvenile to adult developmental growth transition in plants, such as grasses (e.g. maize). In particular, the invention provides methods for enhancing agronomic properties in plants by modulating expression of GRMZM2G362718, GRMZM2G096016, or homologs thereof. Modulation of expression of one or more additional genes which affect juvenile to adult developmental growth transition such as Glossy15 or Cg1, in conjunction with such modulation of expression is also contemplated. Nucleic acid constructs for down-regulation of GRMZM2G362718 and/or GRMZM2G096016 are also contemplated, as are transgenic plants and products produced there from, that demonstrate altered, such as extended juvenile growth, and display associated phenotypes such as enhanced yield, improved digestibility, and increased disease resistance. Plants described herein may be used, for example, as improved forage or feed crops or in biofuel production.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2022

Radar Retrieval Evaluation and Investigation of Dendritic Growth Layer Polarimetric Signatures in a Winter Storm

Abstract This study evaluates ice particle size distribution and aspect ratio φ Multi-Radar Multi-Sensor (MRMS) dual-polarization radar retrievals through a direct comparison with two legs of observational aircraft data obtained during a winter storm case from the Investigation of Microphysics and Precipitation for Atlantic Coast-Threatening Snowstorms (IMPACTS) campaign. In situ cloud probes, satellite, and MRMS observations illustrate that the often-observed K dp and Z DR enhancement regions in the dendritic growth layer can either indicate a local number concentration increase of dry ice particles or the presence of ice particles mixed with a significant number of supercooled liquid droplets. Relative to in situ measurements, MRMS retrievals on average underestimated mean volume diameters by 50% and overestimated number concentrations by over 100%. IWC retrievals using Z DR and K dp within the dendritic growth layer were minimally biased relative to in situ calculations where retrievals yielded −2% median relative error for the entire aircraft leg. Incorporating φ retrievals decreased both the magnitude and spread of polarimetric retrievals below the dendritic growth layer. While φ radar retrievals suggest that observed dendritic growth layer particles were nonspherical (0.1 ≤ φ ≤ 0.2), in situ projected aspect ratios, idealized numerical simulations, and habit classifications from cloud probe images suggest that the population mean φ was generally much higher. Coordinated aircraft radar reflectivity with in situ observations suggests that the MRMS systematically underestimated reflectivity and could not resolve local peaks in mean volume diameter sizes. These results highlight the need to consider particle assumptions and radar limitations when performing retrievals. significance statement Developing snow is often detectable using weather radars. Meteorologists combine these radar measurements with mathematical equations to study how snow forms in order to determine how much snow will fall. This study evaluates current methods for estimating the total number and mass, sizes, and shapes of snowflakes from radar using images of individual snowflakes taken during two aircraft legs. Radar estimates of snowflake properties were most consistent with aircraft data inside regions with prominent radar signatures. However, radar estimates of snowflake shapes were not consistent with observed shapes estimated from the snowflake images. Although additional research is needed, these results bolster understanding of snow-growth physics and uncertainties between radar measurements and snow production that can improve future snowfall forecasting.

Meteorology & Atmospheric Sciences↗
NASA NTRS1996

Seed-to-seed growth of superdwarf wheat and arabidopsis using red light-emitting diodes (LED's): A report on baseline tests conducted for NASA's proposed Plant Research Unit (PRU)

To determine the influence of narrow-spectrum red light-emitting diodes (LED's) on plant growth and seed production, wheat (Triticum aestivum L.cv Superdwarf) and Arabidopsis (Arabidopsis thaliana (L.) Heynh, race Columbia) plants were grown under red LED's (peak emission 660 nm) and compared to plants grown under daylight fluorescent (white) light and red LED's supplemented with either 1 percent or 10 percent blue fluorescent (BF) light. Wheat growth under red LED's alone appeared normal, whereas Arabidopsis under red LED's alone developed curled leaf margins and a spiraling growth pattern. Both wheat and Arabidopsis under red LED's alone or red LED's + 1 percent BF light had significantly lower seed yield than plants grown under white light. However, the addition of 10 percent BF light to red LED's partially alleviated the adverse effect of red LED's on yield. Irrespective of the light treatment, viable seeds were produced by wheat(75-92 percent germination rate) and Arabidopsis (85-100 percent germination rate). These results indicate that wheat, and to a lesser extent Arabidopsis, can be successfully grown under red LED's alone, but supplemental blue light is required with red LED's to sufficiently match the growth characteristics and seed yield associated with plants grown under white light.

Goins, G. D.↗
NASA NTRSDate not supplied

Low-Temperature Formation of Carbonaceous Grains from PAHs

Astronomical observations have detected interstellar carbon in the form of polycyclic aromatic hydrocarbons(PAHs) as well as carbonaceous dust, confirming its presence in both gaseous and solid phases. The goal of this study is to experimentally investigate low-temperature chemical pathways between these two phases in order to better understand the evolution of cosmic carbon. Cosmic dust analogs are produced in the supersonic expansion of an argon jet seeded with aromatic molecules: benzene, naphthalene, anthracene, phenanthrene, and pyrene. These are prototype aromatic molecules of compact and non compact structure, and are used to evaluate the effect of precursor structure on the molecular complexity of the resulting grains. The seeded jet is exposed to an electrical discharge and the carbonaceous grains are collected and probed ex situ via laser desorption mass spectrometry. Mass spectra reveal a rich molecular diversity within the grains, including fragments of the parent molecule but also growth into larger complex organic molecules (COMs). In all experiments, the largest number of products is found in the m/z range 200-250, and C16H10 (attributed to pyrene and/or its isomers) is found to be a dominant product, pointing at the formation of this stable PAH as a preferential route in the growth to larger PAHs. Comparison to mass spectra from the Murchison meteorite reveals a similar dominance of compounds related toC16H10 at m/z = 202. Evidence of the methyl addition-cyclization mechanism in the anthracene experiment is reported. PAH structure is found to impact the dust production yield, as seen by the greater yield for the anthracene compared to the phenanthrene experiment. PAH growth at low temperatures via barrierless routes involving the addition of alkyl- and phenyl-type radicals is suggested as a viable pathway to COMs. These results suggest that PAH growth and dust formation from PAHs are feasible at low temperatures in photon-dominated regions and circumstellar envelopes.

L Gavilan↗
DOE OSTI2025

A WRKY transcription factor confers broad-spectrum resistance to biotic stresses and yield stability in rice

Plants are subject to attack by diverse pests and pathogens. Few genes conferring broad-spectrum resistance to both insects and pathogens have been identified. Because of the growth–defense tradeoff, it is often challenging to balance biotic stress resistance and yield for crops. Here, we report thatOsWRKY36suppresses the resistance to insects and pathogens via transcriptional repression ofPhenylalanine Ammonia Lyases(PALs), a key enzyme in phenylpropanoid pathway in rice. Knocking outOsWRKY36causes elevated lignin biosynthesis and increased sclerenchyma thickness of leaf sheath, leading to enhanced resistance to multiple pests and pathogens. Additionally, loss ofOsWRKY36also derepresses the transcription ofIdeal Plant Architecture 1(IPA1) andMONOCULM2(MOC2), resulting in increased spikelet number per panicle and tiller number. These findings provide mechanistic insights into biotic stress tolerance in rice and offer a promising strategy to breed rice cultivars with broad-spectrum resistance to insects and pathogens while maintaining stable yield.

Science & Technology - Other Topics↗
DOE OSTI2024

Integrating multi-modal remote sensing, deep learning, and attention mechanisms for yield prediction in plant breeding experiments

In both plant breeding and crop management, interpretability plays a crucial role in instilling trust in AI-driven approaches and enabling the provision of actionable insights. The primary objective of this research is to explore and evaluate the potential contributions of deep learning network architectures that employ stacked LSTM for end-of-season maize grain yield prediction. A secondary aim is to expand the capabilities of these networks by adapting them to better accommodate and leverage the multi-modality properties of remote sensing data. In this study, a multi-modal deep learning architecture that assimilates inputs from heterogeneous data streams, including high-resolution hyperspectral imagery, LiDAR point clouds, and environmental data, is proposed to forecast maize crop yields. The architecture includes attention mechanisms that assign varying levels of importance to different modalities and temporal features that, reflect the dynamics of plant growth and environmental interactions. The interpretability of the attention weights is investigated in multi-modal networks that seek to both improve predictions and attribute crop yield outcomes to genetic and environmental variables. This approach also contributes to increased interpretability of the model's predictions. The temporal attention weight distributions highlighted relevant factors and critical growth stages that contribute to the predictions. The results of this study affirm that the attention weights are consistent with recognized biological growth stages, thereby substantiating the network's capability to learn biologically interpretable features. Accuracies of the model's predictions of yield ranged from 0.82-0.93 R 2 ref in this genetics-focused study, further highlighting the potential of attention-based models. Further, this research facilitates understanding of how multi-modality remote sensing aligns with the physiological stages of maize. The proposed architecture shows promise in improving predictions and offering interpretable insights into the factors affecting maize crop yields, while demonstrating the impact of data collection by different modalities through the growing season. By identifying relevant factors and critical growth stages, the model's attention weights provide valuable information that can be used in both plant breeding and crop management. The consistency of attention weights with biological growth stages reinforces the potential of deep learning networks in agricultural applications, particularly in leveraging remote sensing data for yield prediction. To the best of our knowledge, this is the first study that investigates the use of hyperspectral and LiDAR UAV time series data for explaining/interpreting plant growth stages within deep learning networks and forecasting plot-level maize grain yield using late fusion modalities with attention mechanisms.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2024

Interplay between the non-resonant streaming instability and self-generated pressure anisotropies

The non-thermal particles escaping from collision-less shocks into the surrounding medium can trigger a non-resonant streaming instability that converts parts of their drift kinetic energy into large amplitude magnetic field perturbations, and promote the confinement and acceleration of high energy cosmic rays. We present simulations of the instability using an hybrid-Particle-in-Cell approach including Monte Carlo collisions, and demonstrate that the development of the non-resonant mode is associated with important ion pressure anisotropies in the background plasma. Depending on the initial conditions, the anisotropies may act on the instability by lowering its growth and trigger secondary micro-instabilities. Introducing collisions with neutrals yield a strong reduction of the magnetic field amplification as predicted by linear fluid theory. In contrast, Coulomb collisions in fully ionized plasmas are found to mitigate the self-generated pressure anisotropies and promote the growth of the magnetic field.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2025

Leveraging System Dynamics to Predict the Commercialization Success of Emerging Energy Technologies: Lessons from Wind Energy

The United States urgently needs to tackle the climate crisis while enhancing energy security and resiliency. The complexity of the U.S. energy system, with its interconnected elements, makes predicting future states challenging, especially with the introduction of novel energy systems like wind, solar, clean hydrogen, and advanced nuclear technologies. Modern systems engineering methods and tools can provide deeper insights into these dynamics and future behaviors. This research aims to develop a comprehensive model that captures the main elements and behaviors of new energy technologies within the existing energy system. We hypothesized that the market uptake of novel energy systems is influenced by multiple diverse factors, such as technological learning, availability of resources, and economic incentives; examined the history of electricity generation using land-based wind technologies; and developed a system dynamics model to investigate the relationships between capacity growth and influencing factors, both internal and external. The developed model yielded outcomes that confirmed the hypothesized dynamics of wind energy system diffusion through a quantitative comparison of installed capacity and highlighted the significant influence of resource availability, federal incentives (production tax credits), and technological learning on capacity growth and cost reduction. This research aims to support informed decision-making for investments in novel energy systems and aid in developing effective policies for technology deployment.

17 WIND ENERGY↗