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All sourcesNASA NTRSDOE OSTI
DOE OSTI2024

Community Input on the Need, Scope, and Development Roadmap of a Proposed National Virtual Biosecurity for Bioenergy Crops Center (NVBBCC)

Brookhaven National Laboratory (BNL) was awarded a pilot project in FY22 under the U.S. Department of Energy (DOE) Office of Science Biopreparedness Research Virtual Environment (BRaVE) initiative, to define research priorities, needs, and requirements for a national virtual center devoted to the biosecurity of bioenergy crops. The mission of the proposed center, referred to as the National Virtual Biosecurity for Bioenergy Crop Center (NVBBCC), would be to provide the scientific basis and tools to detect, characterize, model, and mitigate biothreats to bioenergy crops. This function will be essential to ensure the projected increased US reliance over the next few decades on key plant-based energy products, such as biojet fuel. The NVBBCC is envisioned as a distributed, virtual center with multiple national laboratories at its core to maximize the use of existing unique facilities and expertise across the DOE complex. A major goal of the pilot project was to develop a roadmap for establishing NVBBCC through a series of meetings to gather community input. A total of about 150 individuals, drawn from DOE laboratories, the USDA, academia, NIH, DHS and the private sector participated in six planning meetings held in FY23. Four of the meetings were focused on specific research topics (disease detection, dispersion and disease propagation, biomolecular characterization of plant-pathogen interaction, and mitigation strategies). These four meetings were followed by a meeting that focused on computational needs to support collaborative, data-intensive research within a distributed center as well as workforce development. A final meeting focused on establishing and maintaining preparedness within NVBBCC to respond to an emerging disease within bioenergy crops and how it would collaborate and coordinate with USDA and DHS.

09 BIOMASS FUELS↗
DOE OSTI2023

Heterologous expression of formate dehydrogenase enables photoformatotrophy in the emerging model microalga, Picochlorum renovo

Rising global greenhouse gas emissions and the impacts of resultant climate change necessitate development and deployment of carbon capture and conversion technologies. Amongst the myriad of bio-based conversion approaches under evaluation, a formate bio-economy has recently been proposed, wherein CO 2 -derived formate serves as a substrate for concurrent carbon and energy delivery to microbial systems. To date, this approach has been explored in chemolithotrophic and heterotrophic organisms via native or engineered formatotrophy. However, utilization of this concept in phototrophic organisms has yet to be reported. Herein, we have taken the first steps to establish formate utilization in Picochlorum renovo, a recently characterized eukaryotic microalga with facile genetic tools and promising applied biotechnology traits. Plastidial heterologous expression of a formate dehydrogenase (FDH) enabled P. renovo growth on formate as a carbon and energy source. Further, FDH expression enhanced cultivation capacity on ambient CO 2 , underscoring the potential for bypass of conventional CO 2 capture and concentration limitations. This work establishes a photoformatotrophic cultivation regime that leverages light energy-driven formate utilization. The resultant photosynthetic formate platform has widespread implications for applied phototrophic cultivation systems and the bio-economy at large.

09 BIOMASS FUELS↗
NASA NTRS2005

Evaluation of Aerosol Properties over Ocean from Moderate Resolution Imaging Spectroradiometer (MODIS) during ACE-Asia

The Aerosol Characterization Experiment-Asia (ACE-Asia) was conducted in March-May 2001 in the western North Pacific in order to characterize the complex mix of dust, smoke, urban/industrial pollution, and background marine aerosol that is observed in that region in springtime. The Moderate Resolution Imaging Spectroradiometer (MODIS) provides a large-scale regional view of the aerosol during the ACE-Asia time period. Focusing only on aerosol retrievals over ocean, MODIS data show latitudinal and longitudinal variation in the aerosol characteristics. Typically, aerosol optical depth (tau(sub a)) values at 0.55 micrometers are highest in the 30 deg. - 50 deg. latitude band associated with dust outbreaks. Monthly mean tau(sub a) in this band ranges approx. 0.40-70, although large differences between monthly mean and median values indicate the periodic nature of these dust outbreaks. The size parameters, fine mode fraction (eta), and effective radius (r(sub eff)) vary between monthly mean values of eta = 0.47 and r(sub eff)= 0.75 micrometers in the cleanest regions far offshore to approximately eta = 0.85 and r(sub eff) =.30 micrometers in near-shore regions dominated by biomass burning smoke. The collocated MODIS retrievals with airborne, ship-based, and ground-based radiometers measurements suggest that MODIS retrievals of spectral optical depth fall well within expected error (DELTA tau(sub a) = plus or minus 0.03 plus or minus 0.05 tau(sub a)) except in situations dominated by dust, in which cases MODIS overestimate both the aerosol loading and the aerosol spectral dependence. Such behavior is consistent with issues related to particle nonsphericity. Comparisons of MODIS-derived r(sub eff) with AERONET retrievals at the few occurrences of collocations show MODIS systematically underestimates particle size by 0.2 micrometers. Multiple-year analysis of MODIS aerosol size parameters suggests systematic differences between the year 2001 and the years 2000 and 2002, which are traced to instrumental electronic cross talk. Sensitivity studies show that such calibration errors are negligible in tau(sub a) retrievals but are more pronounced in size parameter retrievals, especially for dust and sea salt.

Chu, D. A.↗
DOE OSTI2021

X-Ray Characterization of Real Fuel Sprays for Gasoline Direct Injection

Here, the effects of fuel blend properties on spray and injector performance has been investigated in a side-mount injector for gasoline direct injection (GDI) using two certification fuel blends: Euro 5 and Euro 6. Several X-ray diagnostic techniques were conducted to characterize the injector and spray morphology. Detailed internal geometry of the GDI injector was resolved to 1.8 μm, through the use of hard X-ray tomography. The geometry characterization of this six-hole GDI, side mount injector, quantifies relevant hole and counterbore dimensions and reveals the intricate details within the flow passages, including surface roughness and micron-sized features. Internal valve motion was measured with a temporal resolution of 20 μs and a spatial resolution of 2.0 μs, for three injection pressures and several injector energizing strategies. The needle motion for both fuels exhibits similar lift profiles for common energizing commands. A combination of X-ray radiography and ultra-small-angle X-ray scattering (USAXS) was used to characterize the fuel mass distribution and the droplet sizing, respectively. Tomographic spray radiography revealed the near-nozzle distribution of fuel mass for each of the fuels and the asymmetry produced by the angled nozzles. Under evaporative conditions, the two fuels show minor differences in peak fuel mass distribution during steady injection, though both exhibit fluctuations in injection during the early, transient phase. USAXS measurements of the path-specific surface area of the spray indicated lower peak values for the more evaporative conditions in the near nozzle region.

09 BIOMASS FUELS↗
DOE OSTI2022

Estimating biofuel contaminant concentration from 4D ERT with mixing models

Here, we present the results of a lab-scaled feasibility study to assess the performance of electrical resistivity tomography for detection, characterization, and monitoring of fuel grade ethanol releases to the subsurface. Further, we attempt to determine the concentration distribution of the ethanol from the electrical resistivity tomography data using mixing-models. Ethanol is a renewable fuel source as well as an oxygenate fuel additive currently used to replace the known carcinogen methyl tert-butyl ether; however, ethanol is preferentially biodegraded and a cosolvent. When introduced to areas previously impacted by nonethanol-based fuels, it will facilitate the persistence of carcinogenic fuel compounds like benzene and ethylbenzene, as well as remobilize them to the ground water. These compounds would otherwise be retained in the soil column undergoing active or passive remediation processes such as soil vapor extraction or natural attenuation. Here, we introduce ethanol to a saturated Ottawa sand in a tank instrumented for four-dimensional geoelectrical measurements. Forward model results suggest pure phase ethanol released into a water saturated silica sand should present a detectable target for electrical resistivity tomography relative to a saturated silica sand only. We observe the introduction of ethanol to the closed hydraulic system and subsequent migration over the duration of the experiment. One-dimensional and three–dimensional temporal data are assessed for the detection, characterization, and monitoring of the ethanol release. Results suggest one-dimensional geoelectrical measurements may be useful for monitoring a release, while three-dimensional geoelectrical field imaging would be useful to characterize, monitor, and design effective remediation approaches for an ethanol release, assuming field conditions do not preclude the application of geoelectrical methods. We then attempt to use predictive mixing models to calculate the distribution of ethanol concentration within the measurement domain. For this study we examine four different models: a nested parallel mixing model, a nested cubic mixing model, the complex refractive index model (CRIM), and the Lichtenecker-Rother (L-R) model. The L-R model, modified to include an electrical formation factor geometry term, provided the best agreement with expected EtOH concentrations.

09 BIOMASS FUELS↗
DOE OSTI2020

Pyrolysis Vapor and Bio-Oil Preconditioning via Ex Situ Hydrodeoxygenation and Alkylation Using a Heteropolyacid Catalyst

Ex situ catalytic preconditioning of biomass pyrolysis vapors (partial deoxygenation) has the potential for both reducing the oxygen content and beneficially influencing the oxygen speciation of condense bio-oils for enhancement of downstream condensed phase upgrading processes. Here, condensed phase upgrading processes may refer to both coprocessing and hydroprocessing with vacuum gas-oil (VGO). Reduction of oxygen content to produce bio-oils with greater hydrophobicity can enhance VGO-solubility while tailoring of oxygen speciation by removal of reactive carbonyls (e.g., acetic acid) can lessen catalytic deactivation via coking reactions. 1,2 Furthermore, enhanced VGO-solubility and reduced coking leads to greater biogenic carbon incorporation in end-products from condensed phase upgrading processes. By leveraging the hydrodeoxygenation and alkylation activity of a titania-supported molybdenum-heteropolyacid (Mo-HPA/TiO2) catalyst, pyrolysis vapors and resulting condensed bio-oils were effectively preconditioned for downstream upgrading. 3 Data regarding the use of Mo-HPA/TiO2 for pyrolysis vapor preconditioning will be presented on, including method of employment, catalyst characterization, and impacts on bio-oil in relation to downstream condensed phase upgrading processes. Our approach is to: Compare Mo-HPA/TiO2 product distribution to HZSM-5; Investigate Mo-HPA/TiO2 regeneration and characterize changes in acid site character; Assess product differences in terms of enhanced VGO-solubility.

29 ENERGY PLANNING, POLICY, AND ECONOMY↗
NASA NTRSDate not supplied

Springtime SEA Air Is Consistently Both Smoky and Humid, but This Relationship (and Its Radiative Effects) Varies Spatially and Seasonally

The atmosphere over the southeast Atlantic Ocean (SEA) sees a consistent springtime biomass burning (BB) smoke from widespread agricultural fires on the African continent. This smoke layer is initially lofted high in a continental mixed layer (~5-6km) and is then transported westward in the free troposphere, where it overlies and ultimately mixes into the SEA stratocumulus-topped oceanic boundary layer. Coincident with this smoke is an elevated humidity signal which is present from the time a given airmass is over the continental source region. ORACLES (ObseRvations of Aerosols above CLouds and their intEractionS) was a NASA Earth Venture Suborbital mission with the goal of measuring aerosol, cloud, and atmospheric properties over this region during three deployments in September 2016, August 2017, and October 2018. A previous study discussed the spatial characteristics of the water vapor-BB plume observations in the September 2016 flight data and their degree of agreement with several atmospheric reanalyses and models. In the present work, we first discuss atmospheric chemistry, structure, and aerosol results from all three ORACLES deployments to place these results in a broader seasonal context. Despite the differing locations and season of each deployment, we show that there continues to be good agreement between the airborne ORACLES dataset and large-scale reanalyses, specifically the ECMWF ERA5 and CAMS reanalyses. Other reanalyses (specifically NASA's MERRA-2) preserved the observed correlation between meteorology and biomass burning conditions, but, as was seen for 2016, was frequently displaced spatially relative to the observations. The CAMS reanalysis performs better with water vapor than with CO. Results comparing CAMS column AOD to the field measurements show slight overestimates, consistent with previous analysis, but the good relationship observed between AOD and inlet-based aerosol extinction demonstrates this metric's utility to broader studies. The good ERA5/CAMS/ORACLES agreement allows us to next examine the multi-year seasonal patterns and trends beyond the three ORACLES deployment periods. Looking at seven years of reanalysis data for the BB season, we find distinct variations between each month/deployment in terms of vertical smoke distribution and correlation to atmospheric specific humidity, due to changing conditions through the BB season. Using k-means clustering of these climatological reanalysis, we identify six canonical atmospheric profile types of varying total atmospheric humidity and vertical structure and describe their changing incidence spatially and throughout the season, and six analogous profile types for carbon monoxide, allowing us to characterize the atmospheric structure of both vapor and BB over time throughout the SEA region. This classification will allow for a more complete analysis of the broader radiative and dynamical effects of humid aerosols overlying stratocumulus clouds.

Kristina Marie Myers Pistone↗
DOE OSTI2024

Physics-informed machine learning to predict solvatochromic parameters of designer solvents with case studies in CO 2 and lignin dissolution

The polarity of solvents plays a critical role in various research applications, particularly in their solubilities. Polarity is conveniently characterized by the Kamlet-Taft parameters that is, the hydrogen bonding acidity (α), the basicity (β), and the polarizability (π*). Obtaining Kamlet-Taft parameters is very important for designer solvents, namely ionic liquids (ILs) and deep eutectic solvents (DESs). However, given the unlimited theoretical number of combinations of ionic pairs in ILs and hydrogen-bond donor/acceptor pairs in DESs, experimental determination of their Kamlet-Taft parameters is impractical. To address this, the present study developed two different machine learning (ML) algorithms to predict Kamlet-Taft parameters for designer solvents using quantum chemically derived input features. The ML models developed in the present study showed accurate predictions with high R 2 and low RMSE values. Further, in the context of present interest in the circular bioeconomy, the relationship between the basicities and acidities of designer solvents and their ability to dissolve lignin and carbon dioxide (CO 2 ) is discussed. Our method thus guides the design of effective solvents with optimal Kamlet-Taft parameter values dissolving and converting biomass and CO 2 into valuable chemicals.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2021

A comparative study of machine learning models for predicting the state of reactive mixing

Mixing phenomena are important mechanisms controlling flow, species transport, and reaction processes in fluids and porous media. Accurate predictions of reactive mixing are critical for many Earth and environmental science problems such as contaminant fate and remediation, macroalgae growth, and plankton biomass evolution. Here, to investigate the evolution of mixing dynamics under different scenarios (e.g., anisotropy, fluctuating velocity fields), a finite-element-based numerical model was built to solve the fast, irreversible bimolecular reaction-diffusion equations to simulate a range of reactive-mixing scenarios. A total of 2,315 simulations were performed using different sets of model input parameters comprising various spatial scales of vortex structures in the velocity field, time-scales associated with velocity oscillations, the perturbation parameter for the vortex-based velocity, anisotropic dispersion contrast (i.e., ratio of longitudinal-to-transverse dispersion), and molecular diffusion. The outputs comprised concentration profiles of reactants and products. The inputs to and outputs from these simulations were concatenated into feature and label matrices, respectively, to train 20 different machine learning (ML) models intended to emulate system behavior. These 20 ML emulators, based on linear methods, Bayesian methods, ensemble learning methods, and multilayer perceptrons (MLPs), were trained to classify the state of mixing and predict three quantities of interest (QoIs) characterizing species production, decay (i.e., average concentration, square of average concentration), and degree of mixing (i.e., variances of species concentration). Unsurprisingly, linear classifiers and regressors failed to reproduce the QoIs; however, ensemble methods (classifiers and regressors) and the MLP model accurately classified the state of reactive mixing and the QoIs. Among ensemble methods, random forest and decision-tree-based AdaBoost faithfully predicted the QoIs. At run time, trained ML emulators produced results times faster than the finite-element simulations. Due to their low computational expense and high accuracy, ensemble and MLP models are excellent emulators for these numerical simulations and great utilities in uncertainty quantification exercises, which can require 1,000s of forward model runs.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2023

Non-canonical d-xylose and l-arabinose metabolism via d-arabitol in the oleaginous yeast Rhodosporidium toruloides

Abstract R. toruloides is an oleaginous yeast, with diverse metabolic capacities and high tolerance for inhibitory compounds abundant in plant biomass hydrolysates. While R. toruloides grows on several pentose sugars and alcohols, further engineering of the native pathway is required for efficient conversion of biomass-derived sugars to higher value bioproducts. A previous high-throughput study inferred that R. toruloides possesses a non-canonical l -arabinose and d -xylose metabolism proceeding through d -arabitol and d -ribulose. In this study, we present a combination of genetic and metabolite data that refine and extend that model. Chiral separations definitively illustrate that d -arabitol is the enantiomer that accumulates under pentose metabolism. Deletion of putative d -arabitol-2-dehydrogenase (RTO4_9990) results in > 75% conversion of d -xylose to d -arabitol, and is growth-complemented on pentoses by heterologous xylulose kinase expression. Deletion of putative d -ribulose kinase (RTO4_14368) arrests all growth on any pentose tested. Analysis of several pentose dehydrogenase mutants elucidates a complex pathway with multiple enzymes mediating multiple different reactions in differing combinations, from which we also inferred a putative l -ribulose utilization pathway. Our results suggest that we have identified enzymes responsible for the majority of pathway flux, with additional unknown enzymes providing accessory activity at multiple steps. Further biochemical characterization of the enzymes described here will enable a more complete and quantitative understanding of R. toruloides pentose metabolism. These findings add to a growing understanding of the diversity and complexity of microbial pentose metabolism.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2023

Light Energy Transduction in Green Sulfur Bacteria

Green sulfur bacteria (GSB) are exquisitely adapted for growth at extraordinarily low light intensities. They are important primary producers of biomass in many anoxic environments, and they contribute significantly to the biogeochemical cycling of carbon, nitrogen, and sulfur on Earth. Green bacteria more generally share the property of using chlorosomes for light-harvesting, and these unusual organelles have many unique features. These include the presence of a monolayer lipid-protein envelope, self-assembling bacteriochlorophylls (BChl) in which pigment-pigment interactions predominate, and the presence of redox components ([2Fe-2S] ferredoxins and quinones) that play a role in regulating excitation energy transfer to the type-1 homodimeric reaction centers. The reaction centers of GSB are related to Photosystem I of cyanobacteria and higher plants but also exhibit several unique structural and functional features. The long-term objectives of this research program are to understand the structure, functions, and biogenesis of the chlorosomes, reaction centers, and electron transport chains that carry out the photochemical transduction of light energy into chemical energy in the model green sulfur bacterium, Chlorobaculum (formerly Chlorobium) tepidum. Over a period of 26 years, we characterized chlorosomes in detail. We identified the proteins in the chlorosome envelopes of diverse organisms, identified the nearest neighbors of those proteins, and characterized the chlorosomes of mutant strains lacking one to five of these proteins. After the genome sequence of Cba. tepidum became available, we identified all genes encoding enzymes for BChl a , BChl c/d/e/ƒ , and Chl a biosynthesis Cba. tepidum . We additionally identified all genes encoding enzymes for carotenoid biosynthesis. By constructing a bchQRU mutant strain that produces [Et, Me]-BChl d , together with collaborators who are specialists in solid-state NMR and cryo-electron microscopy, we solved the structure of the BChls in chlorosomes. Using similar methods and chlorosomes containing [Et, Me]-BChl c , we then showed that alternative structures were possible using the same syn-anti BChl dimer. The structures were refined by introducing spectroscopic data from single-chlorosome measurements. Over the course of this project, we sequenced the genomes of approximately twenty GSB strains, which provided important information for comparative analyses. Through analyses of metagenomic data from Mushroom and Octopus Springs in Yellowstone National Park, we identified a novel chlorophototroph belonging to the phylum Acidobacteriota. We successfully isolated an axenic culture of this organism. We characterized the photosynthetic apparatus of this bacterium, named Chloracidobacterium thermophilum, in significant detail, in particular its type-1, homodimeric reaction centers. Surprisingly, these reaction centers contain three types of Chls, BChl a , Chl a , and Zn-BChl a' . We showed that a dimer of Zn-BChl a' is the primary donor and Chl a the primary acceptor of electrons in this reaction center by using advanced spectroscopic techniques. We isolated eight additional strains of Chloracidobacterium spp. from Mushroom Spring and Rupite hot springs in Bulgaria. Comparative genomes showed that these represent three species, Cab. thermophilum , Cab. aggregatum , and Cab. validum . By applying comparative genomics, genetic, biochemical biophysical and physiological approaches to study green bacteria, we produced a wealth of new information about the remarkable light-harvesting and energy transduction capabilities of these poorly characterized microorganisms.

59 BASIC BIOLOGICAL SCIENCES↗
NASA NTRS2014

The Uncertainty of Biomass Estimates from LiDAR and SAR Across a Boreal Forest Structure Gradient

In this study, weexamined the uncertainty of aboveground live biomass (AGB) estimates based on light detection and ranging (LiDAR) and synthetic aperture radar (SAR) measurements distributed across a low-biomass vegetation structure gradient fromforest to non-forest in boreal-like ecosystems. The conifer-dominant structure gradient was compiled from ground data amassed from multiple field expeditions in central Maine (USA), Aurskog (Norway), and across central Siberia (Russia). Single variable empirical models were built tomodel AGB fromremote sensingmetrics. Using thesemodels, we calculated a rootmean square error (RMSE) and a 95% confidence interval (CI) of the RMSE fromthe difference between the remote sensing AGB predictions and the ground reference AGB estimates within AGB intervals across a 0-100 Mg ha(exp.−1) boreal forest structure gradient. The results show that the error in AGB predictions (RMSE) and the error uncertainty (the CI) from LiDAR and SAR change across a forest gradient. The errors of airborne LiDAR and SAR metrics and spaceborne LiDAR platforms show a general trend of reduced relative errors as AGBmagnitudes increase, particularly from0 to 60 Mg ha (exp.−1). Empirical models relating spaceborne metrics to AGB and estimates of spaceborne LiDAR error uncertainty demonstrate the difficulty of characterizing differences in AGB at the site-level with current spaceborne sensors, particularly below 80 Mg ha (exp.−1) with less than 50-100% error.

Taiga↗
DOE OSTI2023

AmeriFlux FLUXNET-1F US-Lin Lindcove Orange Orchard

This is the AmeriFlux Management Project (AMP) created FLUXNET-1F version of the carbon flux data for the site US-Lin Lindcove Orange Orchard. This is the FLUXNET version of the carbon flux data for the site US-Lin Lindcove Orange Orchard produced by applying the standard ONEFlux (1F) software. Site Description - The experimental site was a citrus orchard about three km west of the UC Lindcove Research and Experiment Station. The site is characterized by a Mediterranean climate typical of Central California, with warm dry summers and cool wet winters. The soil texture for the upper horizon (0-33 cm) was loam with a particle size distribution of 42% sand, 38% silt, and 20% clay (Kearney Ag Center). Soil pH was 7.4 (Valley Tech Soil Agricultural Laboratory Services). The orchard was drip-irrigated with water applied twice per week in the warm season to ensure water avail- ability close to field capacity. The site was kept clean from understory vegetation and mechanical pruning operations took place twice a year to limit the size ofthe orange trees for harvesting and site maintenance. The block of trees in which the tower and instruments were located was ‘Valencia’ orange on trifoliate rootstock, with a planting date in the 1960’s. The square 4 ha block had dimensions of 200 m NeS and EeW. At the end of the study, we harvested a ‘Valencia’ citrus tree from within the study block to measure citrus leaf area index (LAI) and biomass density. The tree height was 3.7 m as measured with a telescoping pole. We measured canopy radii in the four cardinal directions to calculate a planar area (as seen from above) of 12.2 m2. The mean specific leaf area (SLA) of citrus leaves was 85.4 cm2 g-1 obtained by measuring leaf area of five groups of leaf samples with a LiCor Leaf area meter (mod. LI-3100C) followed by drying and weighing. LAI for the orchard was 3.00, derived using the SLA value and the total dry mass of leaves from the harvested tree, multiplied by the plant population of 237 plants per ha as obtained from spacing measurements and Google Maps imagery.

Fares, Silvano↗
DOE OSTI2024

Metal Phosphide Nanoparticles Generated via a Molecular Precursor Route for Hydrotreatment of Methyl Laurate

Transition metal phosphide nanoparticles supported on silica were used as catalysts to investigate the hydrodeoxygenation of methyl laurate (used as a model compound for vegetable oils). Ni 2 P, Ni 1 Mo 1 P, and Ni 1.6 Mo 0.4 P were synthesized using a molecular precursor route. The nanoparticles were added to the silica support without any changes in their structure or particle size. As a reference and for comparison, MoP/SiO 2 was also prepared by the phosphite method. The prepared catalysts were characterized by X-ray diffraction, X-ray photoelectron spectroscopy, transmission electron microscopy, and chemical analysis. A higher conversion is reached with the MoP/SiO 2 monometallic catalyst, close to that with Ni 1 Mo 1 P/SiO 2 . However, this observation is misleading because the differences in dispersion obscure the result that the intrinsic activity (turnover frequency) of the bimetallic catalyst, prepared by a molecular precursor route, is significantly higher than that of the monometallic catalyst, prepared by the phosphite route, revealing a synergistic effect on the catalytic activity due to the formation of the bimetallic phosphide.

09 BIOMASS FUELS↗
DOE OSTI2024

Biofuel production from palm oil deoxygenation using nickel-molybdenum on zirconia catalyst using glycerol as a hydrogen donor

The growing demand for renewable energy has generated interest in biofuels as alternatives to fossil fuels. Second-generation biofuels, derived from deoxygenating fats and oils, have garnered a higher level of interest from industry and academia due to their potential for direct replacement of diesel and jet fuels. Palm oil, mostly cultivated in Thailand and composed of C16 and C18 fatty acids, is a primary feedstock sought for biofuel production. Palm oil deoxygenation contains several pathways that may or may not require hydrogen gas. This study aimed to produce biofuels in different fuel ranges, such as gasoline, jet fuel, and diesel, through palm oil deoxygenation using glycerol as a hydrogen source. Glycerol, a low-value byproduct, was used as a hydrogen donor, whereas nickel-molybdenum-supported catalysts were chosen for their high efficiency in deoxygenation and cost-effectiveness. The study investigated the impact of reaction time, temperature, and catalyst activation method on palm oil deoxygenation. Catalyst characterization methods, including XRD, SEM, TEM, XPS, FTIR, TGA, and nitrogen-sorption, were employed to understand the role of catalysts’ activity during palm oil upgrading. Findings indicated that alkane hydrocarbons are the major components in liquid products. The presence of excess hydrogen in post reaction gaseous phase proves the hydrogen donation capability of glycerol. Increased reaction time and temperature facilitated the removal of oxygen from palm oil. Nickel-molybdenum on zirconia activated by sulfidation demonstrated higher stability than by reduction activation.

09 BIOMASS FUELS↗
NASA NTRS2001

Observations of Ocean Primary Productivity Using MODIS

Measuring the magnitude and variability of oceanic net primary productivity (NPP) represents a key advancement toward our understanding of the dynamics of marine ecosystems and the role of the ocean in the global carbon cycle. MODIS observations make two new contributions in addition to continuing the bio-optical time series begun with Orbview-2's SeaWiFS sensor. First, MODIS provides weekly estimates of global ocean net primary productivity on weekly and annual time periods, and annual empirical estimates of carbon export production. Second, MODIS provides additional insight into the spatial and temporal variations in photosynthetic efficiency through the direct measurements of solar-stimulated chlorophyll fluorescence. The two different weekly productivity indexes (first developed by Behrenfeld & Falkowski and by Yoder, Ryan and Howard) are used to derive daily productivity as a function of chlorophyll biomass, incident daily surface irradiance, temperature, euphotic depth, and mixed layer depth. Comparisons between these two estimates using both SeaWiFS and MODIS data show significant model differences in spatial distribution after allowance for the different integration depths. Both estimates are strongly dependence on the accuracy of the chlorophyll determination. In addition, an empirical approach is taken on annual scales to estimate global NPP and export production. Estimates of solar stimulated fluorescence efficiency from chlorophyll have been shown to be inversely related to photosynthetic efficiency by Abbott and co-workers. MODIS provides the first global estimates of oceanic chlorophyll fluorescence, providing an important proof of concept. MODIS observations are revealing spatial patterns of fluorescence efficiency which show expected variations with phytoplankton photo-physiological parameters as measured during in-situ surveys. This has opened the way for research into utilizing this information to improve our understanding of oceanic NPP variability. Deriving the ocean bio-optical properties places severe demands on instrument performance (especially band to band precision) and atmospheric correction. Improvements in MODIS instrument characterization and calibration over the first 16 mission months have greatly improved the accuracy of the chlorophyll input fields and FLH, and therefore the estimates of NPP and fluorescence efficiency. Annual estimates now show the oceanic NPP accounts for 40-50% of the global total NPP, with significant interannual variations related to large scale ocean processes. Spatial variations in ocean NPP, and exported production, have significant effects on exchange of CO2 between the ocean and atmosphere. Further work is underway to improve both the primary productivity model functions, and to refine our understanding of the relationships between fluorescence efficiency and NPP estimates. We expect that the MODIS instruments will prove extremely useful in assessing the time dependencies of oceanic carbon uptake and effects of iron enrichment, within the global carbon cycle.

Esaias, Wayne E.↗
NASA NTRS2012

G-LiHT: Goddard's LiDAR, Hyperspectral and Thermal Airborne Imager

Scientists at NASA's Goddard Space Flight Center have developed an ultra-portable, low-cost, multi-sensor remote sensing system for studying the form and function of terrestrial ecosystems. G-LiHT integrates two LIDARs, a 905 nanometer single beam profiler and 1550 nm scanner, with a narrowband (1.5 nanometers) VNIR imaging spectrometer and a broadband (8-14 micrometers) thermal imager. The small footprint (approximately 12 centimeters) LIDAR data and approximately 1 meter ground resolution imagery are advantageous for high resolution applications such as the delineation of canopy crowns, characterization of canopy gaps, and the identification of sparse, low-stature vegetation, which is difficult to detect from space-based instruments and large-footprint LiDAR. The hyperspectral and thermal imagery can be used to characterize species composition, variations in biophysical variables (e.g., photosynthetic pigments), surface temperature, and responses to environmental stressors (e.g., heat, moisture loss). Additionally, the combination of LIDAR optical, and thermal data from G-LiHT is being used to assess forest health by sensing differences in foliage density, photosynthetic pigments, and transpiration. Low operating costs (approximately $1 ha) have allowed us to evaluate seasonal differences in LiDAR, passive optical and thermal data, which provides insight into year-round observations from space. Canopy characteristics and tree allometry (e.g., crown height:width, canopy:ground reflectance) derived from G-LiHT data are being used to generate realistic scenes for radiative transfer models, which in turn are being used to improve instrument design and ensure continuity between LiDAR instruments. G-LiHT has been installed and tested in aircraft with fuselage viewports and in a custom wing-mounted pod that allows G-LiHT to be flown on any Cessna 206, a common aircraft in use throughout the world. G-LiHT is currently being used for forest biomass and growth estimation in the CONUS and Mexico in support of NASA's Carbon Monitoring System (CMS) and AMIGA-Carb (AMerican Icesat Glas Assessment of Carbon). For NASA's CMS, wall-to-wall G-LiHT data have been acquired over intensive study sites with historic LiDAR datasets, dense inventory data, stem maps and flux tower observations. For AMIGA-Carb, G-LiHT transects have been acquired over ICESat tracks and USDA-FS inventory plots throughout the CONUS, and similar data will be acquired in Mexico during 2013. This talk will highlight recent science results from continental-scale transects landscape-scale deployments of G-LiHT, as well as seasonal forest dynamics from repeat pass G-LiHT acquisitions.

Cook, Bruce↗