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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 19 records
All sourcesNASA NTRSDOE OSTI
NASA NTRSDate not supplied

Comparison of Multispectral Imaging and Traditional Fundoscopy in the Detection of Terrestrial Retinal and Optic Nerve Pathologies like those Encountered During and/or Immediately Following Long-Duration Spaceflight

INTRODUCTION: The purpose of this investigation was to evaluate if MultiColor Imaging (MCI) can replace color fundus photography (CFP) as a diagnostic screening tool during spaceflight. MCI significantly reduces crew time (approx. 115 minutes/session, 36 hours/year) by eliminating nominal on-orbit fundoscopy sessions, while also providing the option to capture a larger field of view (55 vs. 35). METHODS: A comprehensive PubMed literature search was conducted using the following key words: multicolor, multispectral, imaging, retina, choroid, optic nerve, optic disc, and papilledema. Publications were filtered based on optic nerve and chorioretinal pathologies matching those seen during or immediately after spaceflight: optic disc edema (ODE), cotton wool spots (CWS), retinal hemorrhage, pigment epithelial detachment (PED), and serous chorioretinopathy (SCR). In a separate effort, 44 multicolor images (30 abnormal) of terrestrial patients were graded and compared to corresponding color fundus images acquired at the Doheny Eye Centers and UCLA. RESULTS: The search identified 340 articles; 9 describing MCI in relevant pathologies, 6 comparing MCI to CFP. MCI is superior in detecting CWS (1 paper), PED (2 papers), retinal hemorrhages (2 papers), and choroidal folds (1 paper), and can better delineate extent or boundaries of subretinal fluid and identify areas of RPE damage in SCR (2 papers). On MCI, ODE was described as a hyperreflective ring with a green shift and indistinct disc margins, with equaldetectability as using CFP (3 papers). Grading at Doheny Eye Institute confirmed these findings. DISCUSSION: MCI can effectively detect all retinal and optic nerve findings detectable by CFP during and immediately post-spaceflight and represents a suitable replacement as an on-orbit diagnostic screening tool. Additionally, by eliminating the nominal on-orbit fundoscopy sessions, dozens of crew hours are spared per year by utilizing MCI.

Jorge Nagel↗
DOE OSTI2023

Delayed leaf greening involves a major shift in the expression of cytosolic and mitochondrial ribosomes to plastid ribosomes in the highly phosphorus-use-efficient Hakea prostrata (Proteaceae)

Abstract Background and aims Hakea prostrata (Proteaceae) is a highly phosphorus-use-efficient plant native to southwest Australia. It maintains a high photosynthetic rate at low leaf phosphorus (P) and exhibits delayed leaf greening, a convergent adaptation that increases nutrient-use efficiency. This study aimed to provide broad physiological and gene expression profiles across leaf development, uncovering pathways leading from young leaves as nutrient sinks to mature leaves as low-nutrient, energy-transducing sources. Methods To explore gene expression underlying delayed greening, we analysed a de novo transcriptome for H. prostrata across five stages of leaf development. Photosynthesis and respiration rates, and foliar pigment, P and nitrogen (N) concentrations were determined, including the division of P into five biochemical fractions. Key results Transcripts encoding functions associated with leaf structure generally decreased in abundance across leaf development, concomitant with decreases in foliar concentrations of 85% for anthocyanins, 90% for P and 70% for N. The expression of genes associated with photosynthetic function increased during or after leaf expansion, in parallel with increases in photosynthetic pigments and activity, much later in leaf development than in species that do not have delayed greening. As leaves developed, transcript abundance for cytosolic and mitochondrial ribosomal proteins generally declined, whilst transcripts for chloroplast ribosomal proteins increased. Conclusions There was a much longer temporal separation of leaf cell growth from chloroplast development in H. prostrata than is found in species that lack delayed greening. Transcriptome-guided analysis of leaf development in H. prostrata provided insight into delayed greening as a nutrient-saving strategy in severely phosphorus-impoverished landscapes.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2025

Impact of salinity on morphology, growth, and pigment profiles of Scenedesmus obliquus HTB1 under ambient air and elevated CO 2 (10 %) conditions

Certain microalgal species, such as Scenedesmus obliquus strain HTB1, thrive under high CO 2 concentrations, making them promising for carbon sequestration to mitigate climate change. Isolated from the Baltimore Inner Harbor, HTB1 grows faster with 10 % CO 2 than with ambient air. To investigate its responses to salinity and elevated CO 2 , two experiments were conducted. In the first, HTB1 was cultured at seven different salinities (0, 17.5, 20, 22.5, 25, 27.5, and 30 ppt) (parts per thousand) under ambient air. Higher salinity caused cell shrinkage, color changes from green to pale white, reduced pigments like zeaxanthin, lutein, and chlorophyll b, but increased canthaxanthin. Growth declined significantly above 22.5 ppt. The second experiment compared HTB1's response to salinity (0, 10, 20 ppt) under air and 10 % CO 2 . Cultures under 10 % CO 2 showed minimal color changes, while those under air shifted from green to brown, with salinity having less inhibitory effects on growth under elevated CO 2 . Interestingly, lutein and canthaxanthin levels rose with salinity in 10 % CO 2 . These findings indicate that elevated CO 2 mitigates salt stress in HTB1, reducing its impact on growth and promoting adaptive pigment changes. This study sheds light on how salinity and CO 2 interact to influence HTB1's morphology, growth, and pigment composition, enhancing our understanding of its resilience and potential applications.

20 FOSSIL-FUELED POWER PLANTS↗
NASA NTRS2001

Optical metrology for testing an all-composite 2-meter diameter mirror

The Herschel Space Observatory (formerly known as FIRST) consists of a 3.5 m space telescope designed for use in the long IR and sub-milimeter wavebands. To demonstrate the viability of a carbon fiber composite telescope for this application, Composite Optics Incorporated (COI) manufactured a fast (f/1), large (2 m), lightweight (10.1 kg/m squared) demonstration mirror. A key challenge in demonstrating the performance of this novel mirror was to characterize the surface accuracy at cryogenic (70 K) temperatures. A wide variety of optical metrology techniques were investigated and a brief survey of empirical test results and limitations of the various techniques will be presented in this paper. Two complementary infrared (IR)techniques operating at a wavelength of 10.6 microns were chosen for further development: (1) IR Twyman-Green Phase Shifting Interferometry (IR PSI) and (2) IR Shack-Hartmann (IR SH) Wavefront Sensing. Innovative design modifications made to an existing IR PSI to achieve high-resolution, scannable, infrared measurements of the composite mirror are described. The modified interferometer was capable of measuring surface gradients larger than 350 microradians. The design and results of measurements made with a custom-built IR SH Wavefrong Sensor operating at 10.6 microns are also presented. A compact experimental setup permitting simultaneous operation of both the IR PSI and IR SH tools is shown. The advantages and the limitations of the two key IR metrology tools are discussed.

IR metrology phase shifting interferometry Shack-H↗
DOE OSTI2022

Shifting from Fossil Fuel Reliance to Green Energy Sovereignty: Ute Mountain Ute Tribe

Self-determination has been an on-going effort for Native American people and gained much traction with the passing of The Energy Policy Act of 2005, which included the Indian Tribal Energy Development and Self-Determination Act. Congress passed this act to assist Native American tribes and Alaska Native villages with planning, development, and assistance to achieve their energy goals. The Ute Mountain Ute Tribe (UMUT) has relied on oil and natural gas for economic support the last 70 years. Burning fossil fuels, along with oil and gas development, decreases the quality of air and leads to increased greenhouse gas emissions. Subsequently, the burning of fossil fuels to produce energy is now more costly than many renewable energy sources, including solar photovoltaic (PV) systems. Environmental stewardship, along with the need to maintain revenue generation, has led UMUT’s efforts to achieve energy self-determinism employing PV and exploring other technology. In the past, the tribe completed a 1 megawatt PV project near Towaoc, Colorado, which serves as a case study on the tribe’s energy goals: a future where renewables will dominate their energy landscape. This paper explores UMUT’s past and on-going efforts toward energy independence and how it relates to the broader landscape of Native American energy sovereignty.

29 ENERGY PLANNING, POLICY, AND ECONOMY↗
DOE OSTI2020

Giant defect emission enhancement from ZnO nanowires through desulfurization process

Zinc oxide (ZnO) is a stable, direct bandgap semiconductor emitting in the UV with a multitude of technical applications. It is well known that ZnO emission can be shifted into the green for visible light applications through the introduction of defects. However, generating consistent and efficient green emission through this process is challenging, particularly given that the chemical or atomic origin of the green emission in ZnO is still under debate. In this work we present a new method, for which we coin term desulfurization, for creating green emitting ZnO with significantly enhanced quantum efficiency. Solution grown ZnO nanowires are partially converted to ZnS, then desulfurized back to ZnO, resulting in a highly controlled concentration of oxygen defects as determined by X-ray photoelectron spectroscopy and electron paramagnetic resonance. Using this controlled placement of oxygen vacancies we observe a greater than 40-fold enhancement of integrated emission intensity and explore the nature of this enhancement through low temperature photoluminescence experiments.

36 MATERIALS SCIENCE↗
NASA NTRS2005

Satellite Studies of Storm-Time Thermospheric Winds

In this project we have studied the climatology and storm-time dependence of longitude-averaged mid- and low-latitude thermospheric neutral winds observed by the WINDII instrument on board the UARS satellite. This satellite is in a circular, 57 deg inclination orbit at a height of 585 km; the orbit precesses at a rate of 5 deg per day. WINDII is a Michelson interferometer that measures Doppler shifts of the green line (557.7 nm) and red line (630.0 nm) airglow emissions at the Earth's limb, covering latitudes up to 72 deg.

Fejer, Bela G.↗
DOE OSTI2025

Interplay of Topography, Fire History, and Climate on Interior Alaska Boreal Forest Vegetation Dynamics in the 21st Century: A Landsat Time-Series Analysis

This study investigates vegetation dynamics in boreal forests of Interior Alaska, focusing on topography, fire history, and climate influences. The study area includes Bonanza Creek Experimental Forest (BCEF) and surrounding region, categorized by topography (upland, floodplain, lowland) and fire history. Using Mann–Kendall trend and Theil–Sen slope analyses on Landsat-derived spectral metrics: Normalized Difference Vegetation Index (NDVI) and Normalized Burn Ratio (NBR), we observed a shift from browning to greening trends, particularly in historically burned areas. The photosynthetic activity in burned upland converged with unburned areas ~30 years post-fire, coincident with a shift towards deciduous dominance during post-fire succession. Normalized Difference Moisture Index (NDMI) trends revealed a significant increase in vegetation moisture content across all topographies. We introduce Effective Seasonal Precipitation Index (ESPI), which combines prior-year annual precipitation with current-year spring snow depth. Its positive correlation with NDMI highlights its potential for monitoring vegetation moisture dynamics at the landscape scale. Furthermore, by correlating dendrochronology-based climate indices, we found strong correlation between NDMI and normalized Supplemental Precipitation Index (nSPI), across topographies. Overall, this research provides critical insights into how climate and fire influence interior boreal vegetation, highlighting the effects of increased precipitation, and topography on shaping differential vegetation responses across the landscape.

Google Earth Engine↗
DOE OSTI2023

Chemically sensitive fluorescence imaging of colliding microdroplets

Here, we present a simple optical capability for generating spatially resolved chemical concentration maps of mixing fluids using a chemically sensitive dye, 1-hydroxy-3,6,8-pyrenetrisulfonic acid, detected by planar laser induced fluorescence. To demonstrate an application of this capability, we investigate the collision and mixing of a pair of microdroplets in air. The two microdroplets are composed of different fluids, methanol and water, with the dye initially in the methanol droplet. When the droplets collide and mixing process develops, the fluorescence of the dye shifts from blue to green as the solvent environment changes. A series of spectral-temporal images of the collision and subsequent mixing are recorded, from which we extract the distribution of the two intermixing droplet species reflected in the spatially resolved dye spectra. Images reveal material transfer between droplets in both coalescing and non-coalescing droplet collisions.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
NASA NTRS2015

Advances in Remote Sensing for Vegetation Dynamics and Agricultural Management

Spaceborne remote sensing has led to great advances in the global monitoring of vegetation. For example, the NASA Global Inventory Modeling and Mapping Studies (GIMMS) group has developed widely used datasets from the Advanced Very High Resolution Radiometer (AVHRR) sensors as well as the Moderate Resolution Imaging Spectroradiometer (MODIS) map imagery and normalized difference vegetation index datasets. These data are valuable for analyzing vegetation trends and variability at the regional and global levels. Numerous studies have investigated such trends and variability for both natural vegetation (e.g., re-greening of the Sahel, shifts in the Eurasian boreal forest, Amazonian drought sensitivity) and crops (e.g., impacts of extremes on agricultural production). Here, a critical overview is presented on recent developments and opportunities in the use of remote sensing for monitoring vegetation and crop dynamics.

variability↗
DOE OSTI2025

An integrated integral projection model ( IPM 2 ) to disentangle size‐structured harvest and natural mortality

Abstract Body size is one of the most important traits governing individual‐level demographic rates and modulating population‐level processes. Multiple size‐dependent demographic rates can simultaneously change population structure, so distinguishing their individual contributions to overall population dynamics remains a challenge. Disentangling size‐dependent harvest rates from other demographic rates is critical for assessing the impact of removal on populations of invasive species. Inference about invasive populations can be difficult, however, as observations are often collected opportunistically as part of removal programs, rather than experimentally designed. Yet accurate inference is essential for understanding the feasibility of population suppression and optimising management decisions. We develop an integrated integral projection model (IPM 2 ) that leverages the strengths of the integrated population model and integral projection model to enable inference about complex, size‐structured demographic rates from imperfect observations. We apply the IPM 2 in the context of invasive European green crab ( Carcinus maenas ), a species for which individual body size strongly regulates both the observation‐generating process and latent, population dynamics. The IPM 2 facilitates the distinct estimation of green crab size‐structured harvest and natural mortality rates, parameters for which no explicit data is collected and that are unidentifiable in component datasets of the integrated population model. The model represents how the green crab population changes over time, providing the first estimates of size‐structured abundance of this high‐priority species. By forecasting the stable size distribution and equilibrium population size under varying removal efforts, we demonstrate that extremely high levels of removal effort can reduce the equilibrium green crab population size. Yet these high mortality rates also shift the stable size distribution and increase the equilibrium abundance of smaller crabs, since size‐selective removal alters intraspecific interactions. The ecological outcome of this shift in size structure will be variable, as green crab size modulates only some of its interactions with other species. These results highlight the value of the IPM 2 framework for inferring complex population dynamics with information needs that outpace information in individual observational datasets, providing a path forward for accurate assessment of conservation programs.

Keller, Abigail G. [Department of Environment Scie↗
NASA NTRS1991

Wavelength shifts in fluorescence maxima of stressed and non-stressed Norway spruce needles over the growing season

Laboratory fluorescence measurements of first and third year metal stressed and non stressed Norway spruce needles collected in May, Jul. Sep. and Nov. display significant wavelength shifts in the intensity maxima in the blue, green, red, and near infrared spectral regions, with the largest shifts occurring in the blue spectral region for both first and third year needles from Nov. Smaller, but the otherwise significant shifts also take place in the blue spectral region for first year needles from Sep. in the red spectral region for third year neddles from May, Jul. and Sep. and in the near infrared spectral region for first and third year needles from Jul. and Sep. Wavelength shifts in needle fluorescence maxima over the growing season are greatest in the blue and to a lesser extent, greenspectral regions from Sep. to Nov. but are also significant in the red and near infrared spectral regions from Jul. to Sep. and Sep. to Nov., and in the near infrared spectral region also from May to Jul.

Banninger, Cliff↗
NASA NTRS2020

150 Shades of Green: Using the Full Spectrum of Remote Sensing Reflectance to Elucidate Color Shifts in the Ocean

This article proposes a simple and intuitive classification system by which to define full spectral remote sensing reflectance (Rrs(λ)) data with a quantitative output that enables a more manageable handling of spectral information for aquatic science applications. The weighted harmonic mean of the Rrs(λ) wavelengths outputs an Apparent Visible Wavelength (in units of nanometers), representing a one-dimensional geophysical metric of color that is inherently correlated to spectral shape. This dimensionality reduction of spectral information combined with the output along a continuum of wavelength values offers a robust and user-friendly means to describe and analyze spectral Rrs(λ) in terms of spatial and temporal trends and variability. The uncertainty in the algorithm's estimation of spectral shape is demonstrated on a global scale, in addition to the utility of the algorithm to discern spectral-spatial-temporal trends in the ocean, on a per-pixel basis for the entire 22 year continuous ocean color (SeaWiFS and MODIS-Aqua) time-series. This technique can be applied to datasets of varying multi- and hyper-spectral resolutions, providing continuity between heritage and future satellite sensors, and further enabling an effective means of elucidating similarities or differences in complex spectral signatures within the constraints of two dimensions. This straightforward means of conceptualizing multi-dimensional variability can help maximize the potential of the spectral information embedded in remote sensing data.

ocean color↗
DOE OSTI2023

XANES analysis of phosphate glasses melted with Tb 4 O 7 and SnO: evaluating the impact of valence states on structural, thermal, and luminescent properties

Barium phosphate glasses were prepared with 0.5 mol% Tb 4 O 7 added alongside SnO up to 5 mol% with the purpose of evaluating the resulting terbium and tin oxidation states and their impact on glass structural, thermal, and luminescent properties. In this work, following material synthesis by melt-quenching, the composition-structure–property investigation was pursued encompassing measurements by X-ray diffraction (XRD), X-ray absorption near-edge spectroscopy (XANES), Raman spectroscopy, differential scanning calorimetry (DSC), dilatometry, and photoluminescence (PL) spectroscopy. While XRD confirmed the amorphous nature of the glasses, results from XANES indicated that terbium occurs as terbium(III) with a predisposition for tin to exist as tin(IV) which decreased at high SnO content. The structural as well as the thermal properties appeared to be mostly impacted by the presence of tin(IV). Specifically, glass depolymerization was indicated to be induced by Sn 4+ ions, and their concentration was observed to correlate with glass transition and softening temperatures. On the other hand, the tin(II) remnants were observed to exert an impact on the luminescent properties shifting light emission from the green towards the blue-green (cyan). It is indicated that Tb 4 O 7 reacting to produce Tb 2 O 3 supports the oxidation of tin(II) to tin(IV) which in turn dominates the physical properties. However, this was somewhat circumvented at the highest SnO content wherein tin(IV) appeared to be lower.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2024

DeepPhenoMem V1.0: deep learning modelling of canopy greenness dynamics accounting for multi-variate meteorological memory effects on vegetation phenology

Abstract. Vegetation phenology plays a key role in controlling the seasonality of ecosystem processes that modulate carbon, water and energy fluxes between the biosphere and atmosphere. Accurate modelling of vegetation phenology in the interplay of Earth's surface and the atmosphere is thus crucial to understand how the coupled system will respond to and shape climatic changes. Phenology is controlled by meteorological conditions at different timescales: on the one hand, changes in key meteorological variables (temperature, water, radiation) can have immediate effects on the vegetation development; on the other hand, phenological changes can be driven by past environmental conditions, known as memory effects. However, the processes governing meteorological memory effects on phenology are not completely understood, resulting in their limited performance of vegetation phenology represented in land surface models. A deep learning model, specifically a long short-term memory network (LSTM), has the potential to capture and model the meteorological memory effects on vegetation phenology. Here, we apply the LSTM to model the vegetation phenology using meteorological drivers and high-temporal-resolution canopy greenness observations through digital repeat photography by the PhenoCam network. We compare a multiple linear regression model, a no-memory-effect LSTM model and a full-memory-effect LSTM model to predict the whole seasonal greenness trajectory and the corresponding phenological transition dates across 50 sites and 317 site years during 2009–2018, covering deciduous broadleaf forests, evergreen needleleaf forests and grasslands. Results show that the deep learning model outperforms the multiple linear regression model, and the full-memory-effect LSTM model performs better than the no-memory-effect model for all three plant function types (median R2 of 0.878, 0.957 and 0.955 for broadleaf forests, evergreen needleleaf forests and grasslands). We also find that the full-memory-effect LSTM model is capable of predicting the seasonal dynamic variations of canopy greenness and reproducing trends in shifting phenological transition dates. We also performed a sensitivity analysis of the full-memory-effect LSTM model to assess its plausibility, revealing its coherence with established knowledge of vegetation phenology sensitivity to meteorological conditions, particularly changes in temperature. Our study highlights that (1) multi-variate meteorological memory effects play a crucial role in vegetation phenology, and (2) deep learning opens up new avenues for improving the representation of vegetation phenological processes in land surface models via a hybrid modelling approach.

Geology↗