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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 523 records · Page 29
All sourcesNASA NTRSDOE OSTI
NASA NTRS2014

Elastic Model Transitions: A Hybrid Approach Utilizing Quadratic Inequality Constrained Least Squares (LSQI) and Direct Shape Mapping (DSM)

A method for transitioning linear time invariant (LTI) models in time varying simulation is proposed that utilizes a hybrid approach for determining physical displacements by augmenting the original quadratically constrained least squares (LSQI) algorithm with Direct Shape Mapping (DSM) and modifying the energy constraints. The approach presented is applicable to simulation of the elastic behavior of launch vehicles and other structures that utilize discrete LTI finite element model (FEM) derived mode sets (eigenvalues and eigenvectors) that are propagated throughout time. The time invariant nature of the elastic data presents a problem of how to properly transition elastic states from the prior to the new model while preserving motion across the transition and ensuring there is no truncation or excitation of the system. A previous approach utilizes a LSQI algorithm with an energy constraint to effect smooth transitions between eigenvector sets with no requirement that the models be of similar dimension or have any correlation. This approach assumes energy is conserved across the transition, which results in significant non-physical transients due to changing quasi-steady state energy between mode sets, a phenomenon seen when utilizing a truncated mode set. The computational burden of simulating a full mode set is significant so a subset of modes is often selected to reduce run time. As a result of this truncation, energy between mode sets may not be constant and solutions across transitions could produce non-physical transients. In an effort to abate these transients an improved methodology was developed based on the aforementioned approach, but this new approach can handle significant changes in energy across mode set transitions. It is proposed that physical velocities due to elastic behavior be solved for using the LSQI algorithm, but solve for displacements using a two-step process that independently addresses the quasi-steady-state and non-steady-state contributions to the elastic displacement. For structures subject to large external forces, such as thrust or atmospheric drag, it is imperative to capture these forces when solving for elastic displacement. To simplify the mathematical formulation, assumptions are made regarding mass matrix normalization, constant external forcing, and constant viscous damping. These simplifications allow for direct solutions to the quasi-steady-state displacements through a process titled Direct Shape Mapping. DSM solves for the displacements using the eigenvalues of the elastic modes and the external forcing and returns a set of elastic displacements dictated by the eigenvectors of the post-transition mode set. For the non-steady-state contributions to displacement we formulate a LSQI problem that is constrained by energy of the non-steady state terms. The contributions from the quasi-steady-state and non-steady state solutions are then combined to obtain the physical displacements associated with the new set of eigenvectors. Results for the LSQI-DSM approach show significant reduction/complete removal of transients across mode set transitions while maintaining elastic motion from the prior state. For time propagation applications employing discrete elastic models that need to be transitioned in time and where running with full a full mode set is not feasible, the method developed offers a practical solution to simulating vehicle elasticity.

Hannan, Mike R.↗
NASA NTRS2017

Using Satellite Remote Sensing to Map Changes in Aquatic Invasive Plant Cover in the Sacramento-San Joaquin River Delta of California

Waterways of the Sacramento San Joaquin Delta have recently become infested with invasive aquatic weeds such as floating water hyacinth (Eichhoria crassipes) and water primrose (Ludwigia peploides). These invasive plants cause many negative impacts, including, but not limited to: the blocking of waterways for commercial shipping and boating; clogging of irrigation screens, pumps and canals; and degradation of biological habitat through shading. Zhang et al. (1997, Ecological Applications, 7(3), 1039-1053) used NASA Landsat satellite imagery together with field calibration measurements to map physical and biological processes within marshlands of the San Francisco Bay. Live green biomass (LGB) and related variables were correlated with a simple vegetation index ratio of red and near infra-red bands from Landsat images. More recently, the percent (water area) cover of water hyacinth plotted against estimated LGB of emergent aquatic vegetation in the Delta from September 2014 Landsat imagery showed a 80% overall accuracy. For the past two years, we have partnered with the U. S. Department of Agriculture (USDA) and the Department of Plant Sciences, University of California at Davis to conduct new validation surveys of water hyacinth and water primrose coverage and LGB in Delta waterways. A plan is underway to transfer decision support tools developed at NASA's Ames Research Center based on Landsat satellite images to improve Delta-wide integrated management of floating aquatic weeds, while reducing chemical control costs. The main end-user for this application project will be the Division of Boating and Waterways (DBW) of the California Department of Parks and Recreation, who has the responsibility for chemical control of water hyacinth in the Delta.

Map Changes↗
DOE OSTI2019

A new method of relative radiometric calibration for hyperspectral imaging based on skylight monitor

A new method of relative radiometric calibration based on the skylight monitor is presented for airborne hyperspectral remote sensing to eliminate the negative effect on data resulting from the time-varying skylight during a long flight. In terms of large area operation of mapping or spectral imaging, a static radiation model hardly reveals actual cases as external environment keeps changing. Compared with common considerable factors, fluctuation of skylight is the primary one that may observably affect spectral imaging continuously leading to radiation changes upon flight lines. To get rid of the influence of skylight on spectral imagery and monitoring the variation during the flight, we assemble a set of skylight monitor fixed to the aircraft cabin for the sake of recording the semi-spherical skylight keeping synchronization with hyperspectral imaging system and the position and orientation system. The skylight monitor consists of a cosine corrector on the top receiving the skylight, a stable optical fiber and a visible and near-infrared linear array modular spectrometer. The corresponding matched data of skylight with each frame of images is used to correct related digital number after stripe non-uniformity correction, achieving mean relative standard deviation (coefficient of variation or CV) of under 6% for all 256 bands. The final result shows a satisfactory calibrated image of an appropriate evaluation criteria with contrast of the original image.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗
DOE OSTI2020

High-resolution mapping of spatial heterogeneity in ice wedge polygon geomorphology near Prudhoe Bay, Alaska

It is well known that microtopography associated with ice wedge polygons drives pronounced, meter-scale spatial gradients in hydrologic and ecological processes on the tundra. However, highresolution maps of polygonal geomorphology are rarely available, due to the complexity and subtlety of ice wedge polygon relief at landscape scales. Here we present a sub-meter resolution map of >106 discrete ice wedge polygons across a ~1200 km2 landscape, delineated within a lidar-derived digital elevation model. The delineation procedure relies on a convolutional neural network paired with a set of common image processing operations and permits explicit measurement of relative elevation at the center of each ice wedge polygon. The resulting map visualizes meter- to kilometer-scale spatial gradients in polygonal geomorphology across an extensive landscape with unprecedented detail. This high-resolution inventory of polygonal geomorphology provides rich spatial context for extrapolating observations of environmental processes across the landscape. The map also represents an extensive baseline dataset for quantifying contemporary land surface deformation (i.e., thermokarst) at the survey area, through future topographic surveys.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2020

Mechanisms of Plutonium Redox Reactions in Nitric Acid Solutions

Plutonium (Pu) exhibits a complex redox behavior in acidic solutions due to its ability to adapt a wide range of oxidation states typically from +3 to +6 and the tendency for dynamic interconversion between the oxidation states that depends upon electrochemical potential, acid concentration and coordination environments. In particular, the accurate redox speciation of Pu in nitric acid solutions and mapping the interconversion between the various oxidation states mechanistically is challenging due to overlapping chemical and electrochemical reactions leading to significant experimental uncertainties. The last several decades have seen the extensive use of spectroscopic methods to identify and characterize Pu solution species in the various oxidation states. However, dynamic redox mobility of Pu has led to discrepancies among the relevant spectral databases for Pu in nitric acid solutions. This work utilizes a combination of voltammetry and controlled-potential in-situ vis–NIR spectroelectrochemistry to overcome these challenges, and map the interconversion between Pu species in +3, +4, and +6 oxidation states. This allowed elucidation of the mechanisms of the involved redox reactions and an in-depth understanding of the relative stability of the Pu oxidation states through mapping of the Pu redox processes as a function of redox potentials and nitric acid concentrations. The NIR-spectroelectrochemistry studies used to spectroscopically map the redox speciation of Pu as a function of oxidation potentials with varying HNO3 concentrations allows a new perspective into the plutonium redox transformations.

Chatterjee, Sayandev↗
DOE OSTI2020

Time-resolved site-selective imaging of predissociation and charge transfer dynamics: the CH 3 I B-band

The predissociation dynamics of the 6s (B 2 E) Rydberg state of gas-phase CH 3 I were investigated by time-resolved Coulomb-explosion imaging using extreme ultraviolet (XUV) free-electron laser pulses. Inner-shell ionization at the iodine 4d edge was utilized to provide a site-specific probe of the ensuing dynamics. The combination of a velocity-map imaging (VMI) spectrometer coupled with the pixel imaging mass spectrometry (PImMS) camera permitted three-dimensional ionic fragment momenta to be recorded simultaneously for a wide range of iodine charge states. In accord with previous studies, initial excitation at 201.2 nm results in internal conversion and subsequent dissociation on the lower-lying A-state surface on a picosecond time scale. Examination of the time-dependent yield of low kinetic energy iodine fragments yields mechanistic insights into the predissociation and subsequent charge transfer following multiple ionization of the iodine products. The effect of charge transfer was observed through differing delay-dependencies of the various iodine charge states, from which critical internuclear distances for charge transfer could be inferred and compared to a classical over-the-barrier model. Time-dependent photofragment angular anisotropy parameters were extracted from the central slice of the Newton sphere, without Abel inversion, and highlight the effect of rotation of the parent molecule before dissociation, as observed in previous works. Our results demonstrate the ability to perform three-dimensional ion imaging at high event rates and showcase the potential benefits of this approach, particularly in relation to further time-resolved studies at free-electron laser facilities.

74 ATOMIC AND MOLECULAR PHYSICS↗
NASA NTRS1993

Separation of topographic and intrinsic backscatter variations in biscopic radar images: A magic airbrush

Shaded-relief maps portraying landforms as they would appear in the absence of variations in the intrinsic brightness of the surface are a venerable and extremely useful tool in planetary geology. Such maps have traditionally been produced by a highly labor intensive manual process. Skilled cartographer-artists develop detailed mental images of landforms by meticulous scrutiny of all available data, and are able to use an airbrush and electric eraser to draw these images on a map. This process becomes increasingly time-consuming or even impossible if - as is true for radar data in general and Magellan data in particular - the effects on image brightness of varying scattering properties greatly outweigh those of slope variations. Because of the difficulty of interpreting relief in the Magellan images, the airbrush technique is being used only to remove obvious artifacts from low-resolution, shaded-relief images computed digitally from altimetric data. A surprisingly simple digital-processing technique that can be applied to pairs of radar images to produce shaded-relief-like results at the full image resolution is described. These shaded-relief images can be used not only as base maps, but to improve the accuracy of quantitative topographic mapping by radarclinometry and stereoanalysis.

Kirk, R. L.↗
NASA NTRS1984

A preliminary study of the feasibility of detecting the floating macrophytes by means of digital processing of MSS/LANDSAT data

The feasibility of mapping the floating macrophytes (mostly water hyacinth: (Eichornia crassipes) in the Salto Grande reservoir in Americana, SP, by means of digital processing of MSS/LANDSAT data is described. The area occupied by the macrophytes was extracted by exclusion after the merging of two thematic masks representing the area of vegetation free water surface. One of the masks was obtained from a date when the vegetal cover is insignificant, while the other represents a large infestation episode. The utilization of digital processing of MSS/LANDSAT data techniques for the documentation of macrophytes infestation is feasible only when the phenomenon occurs in large areas.

Valeriano, D. D.↗
NASA NTRS2005

Analysis of Multi-Scale Radiometric Data Collected during the Cold Land Processes Experiment-1 (CLPX-1)

Brightness temperature maps at 18.7 and 37 GHz collected at the Fraser and North Park Meso-Scale Areas during the Cold Land Processes Experiment by the NOAA Polarimetric Scanning Radiometer (PSWA) airborne sensor are analyzed. The Fraser site is mostly covered by forest with a typical snowpack depth of 1 m while North Park has no forest cover and is characterized by patches of shallow snow. We examine histograms of the brightness temperatures at 500 m resolution for both the Fraser and North Park areas. The histograms can be modelled by a log-normal distribution in the case of the Fraser MSA and by a bi-modal distribution in the case of the North Park MSA. The histograms of the brightness temperatures at coarser resolutions are also plotted to study the effects of sensor resolution on the shape of the distribution, on the values of the average brightness temperatures and standard deviations. Finally, the values of brightness temperatures obtained by re-sampling (aggregating) the data at 25 km resolution are compared with the values of the brightness temperatures collected by the Advanced Microwave Scanning Radiometer (AMSR-E) and Special Sensor Microwave/Imager (SSMII) satellite radiometers. The results show that in both areas for sensor footprint larger than 5000 m, the brightness temperatures show a flat distribution and the memory of the initial distribution is lost. The values of the brightness temperatures measured by the satellite radiometers are in good agreement with the values obtained averaging the airborne data, even if some discrepancies occur.

Tedesco, M.↗
DOE OSTI2017

Deterministic direct aperture optimization using multiphase piecewise constant segmentation

Purpose Direct aperture optimization (DAO) attempts to incorporate machine constraints in the inverse optimization to eliminate the post‐processing steps in fluence map optimization (FMO) that degrade plan quality. Current commercial DAO methods utilize a stochastic or greedy approach to search a small aperture solution space. In this study, we propose a novel deterministic direct aperture optimization that integrates the segmentation of fluence map in the optimization problem using the multiphase piecewise constant Mumford‐Shah formulation. Methods The Mumford‐Shah based direct aperture optimization problem was formulated to include an L2‐norm dose fidelity term to penalize differences between the projected dose and the prescribed dose, an anisotropic total variation term to promote piecewise continuity in the fluence maps, and the multiphase piecewise constant Mumford‐Shah function to partition the fluence into pairwise discrete segments. A proximal‐class, first‐order primal‐dual solver was implemented to solve the large scale optimization problem, and an alternating module strategy was implemented to update fluence and delivery segments. Three patients of varying complexity—one glioblastoma multiforme (GBM) patient, one lung (LNG) patient, and one bilateral head and neck (H&N) patient with 3 PTVs—were selected to test the new DAO method. For each patient, 20 non‐coplanar beams were first selected using column generation, followed by the Mumford‐Shah based DAO (DAO MS ). For comparison, a popular and successful approach to DAO known as simulated annealing—a stochastic approach—was replicated. The simulated annealing DAO (DAO SA ) plans were then created using the same beam angles and maximum number of segments per beam. PTV coverage, PTV homogeneity , and OAR sparing were assessed for each plan. In addition, high dose spillage, defined as the 50% isodose volume divided by the tumor volume, as well as conformity, defined as the van't Riet conformation number, were evaluated. Results DAO MS achieved essentially the same OAR doses compared with the DAO SA plans for the GBM case. The average difference of OAR D max and D mean between the two plans were within 0.05% of the plan prescription dose. The lung case showed slightly improved critical structure sparing using the DAO MS approach, where the average OAR D max and D mean were reduced by 3.67% and 1.08%, respectively, of the prescription dose. The DAO MS plan substantially improved OAR dose sparing for the H&N patient, where the average OAR D max and D mean were reduced by over 10% of the prescription dose. The DAO MS and DAO SA plans were comparable for the GBM and LNG PTV coverage, while the DAO MS plan substantially improved the H&N PTV coverage, increasing D99 by 6.98% of the prescription dose. For the GBM and LNG patients, the DAO MS and DAO SA plans had comparable high dose spillage but slightly worse conformity with the DAO MS approach. For the H&N plan, DAO MS was considerably superior in high dose spillage and conformity to the DAO SA . The deterministic approach is able to solve the DAO problem substantially faster than the simulated annealing approach, with a 9.5‐ to 40‐fold decrease in total solve time, depending on the patient case. Conclusions A novel deterministic direct aperture optimization formulation was developed and evaluated. It combines fluence map optimization and the multiphase piecewise constant Mumford‐Shah segmentation into a unified framework, and the resulting optimization problem can be solved efficiently. Compared to the widely and commercially used simulated annealing DAO approach, it showed comparable dosimetry behavior for simple plans, and substantially improved OAR sparing, PTV coverage, PTV homogeneity, high dose spillage, and conformity for the more complex head and neck plan.

Nguyen, Dan↗
DOE OSTI2022

Coarse-Grained Density Functional Theory Predictions via Deep Kernel Learning

Scalable electronic predictions are critical for soft materials design. Recently, the Electronic Coarse-Graining (ECG) method was introduced to renormalize all-atom quantum chemical (QC) predictions to coarse-grained (CG) resolutions using deep neural networks (DNNs). While DNNs can learn complex representations that prove challenging for kernel-based methods, they are susceptible to overfitting and the overconfidence of uncertainty estimations. Here, we develop ECG within a GPU-accelerated Deep Kernel Learning (DKL) framework to enable CG QC predictions using range-separated hybrid density functional theory (DFT), obtaining a 107 speedup relative to naive all-atom QC. By treating the predicted electronic properties as random Gaussian Processes, DKL incorporates CG mapping degeneracy by learning the distribution of electronic energies as a function of CG configuration. DKL-ECG accurately reproduces molecular orbital energies from range-separated DFT while facilitating efficient training via active learning using the uncertainties provided by DKL. Further, we show that while active learning algorithms enable efficient sampling of a more diverse configurational space relative to random sampling, all explored query methods exhibit comparable performance for the examined system. We attribute this result to the significant overlap of the feature space and output property distributions across multiple temperatures.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2023

Manifold projection image segmentation for nano-XANES imaging

As spectral imaging techniques are becoming more prominent in science, advanced image segmentation algorithms are required to identify appropriate domains in these images. We present a version of image segmentation called manifold projection image segmentation (MPIS) that is generally applicable to a broad range of systems without the need for training because MPIS uses unsupervised machine learning with a few physically motivated hyperparameters. We apply MPIS to nanoscale x-ray absorption near edge structure (XANES) imaging, where XANES spectra are collected with nanometer spatial resolution. We show the superiority of manifold projection over linear transformations, such as the commonly used principal component analysis (PCA). Moreover, MPIS maintains accuracy while reducing computation time and sensitivity to noise compared to the standard nano-XANES imaging analysis procedure. Finally, we demonstrate how multimodal information, such as x-ray fluorescence data and spatial location of pixels, can be incorporated into the MPIS framework. We propose that MPIS is adaptable for any spectral imaging technique, including scanning transmission x-ray microscopy, where the length scale of domains is larger than the resolution of the experiment.

36 MATERIALS SCIENCE↗
DOE OSTI2024

Development of an MKID frequency-to-pixel LED mapper for SPT-3G+

SPT-3G+ is the next-generation camera for the South Pole Telescope (SPT). SPT is designed to measure the cosmic microwave background (CMB) and the mm/sub-mm sky. The planned focal plane consists of 34,000 microwave kinetic inductance detectors (MKIDs), divided among three observing bands centered at 220, 285, and 345 GHz. Each readout line is designed to measure 800 MKIDs over a 500 MHz bandwidth, which places stringent constraints on the accuracy of the frequency placement required to limit resonator collisions that reduce the overall detector yield. To meet this constraint, we are developing a two-step process that first optically maps the resonance to a physical pixel location, and then next trims the interdigitated capacitor (IDC) to adjust the resonator frequency. We present a cryogenic LED apparatus operable at 300 mK for the optical illumination of SPT-3G+ detector arrays. We demonstrate integration of the LED controls with the GHz readout electronics (RF-ICE) to take data on an array of prototype SPT-3G+ detectors. We show that this technique is useful for characterizing defects in the resonator frequency across the detector array and will allow for improvements in the detector yield.

46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND ↗
DOE OSTI2020

Ultrafast structural response to charge redistribution within a photosinthetic reaction centre

This data set contains stream files, difference maps and SVD maps obtained from data processing of time-resolved serial femtosecond crystallography measurements performed at LCLS XFEL, used to construct the images and perform the analysis published in the paper "Ultrafast structural response to charge redistribution within a photosinthetic reaction centre". Two (2) time points plus a dark state from 2015 ('a' label) as well as five (5) time points plus a dark state from 2016 ('b' label) are provided.

CXI↗
DOE OSTI2024

EAGLE-I Power Outage Data 2023

The provided EAGLE-I historic dataset includes 1 year of power outage information at the county level for 2023 at 15-minute intervals collected by the EAGLE-I program at ORNL. The data has been collected from utility's public outage maps using an ETL process. The dataset details FIPS code, county name, state name, total number of customers without power, and a date/timestamp. Also included is the EAGLE-I coverage of each state for each year. For detailed metadata, refer to the metadata DOI.

24 POWER TRANSMISSION AND DISTRIBUTION↗
DOE OSTI2025

EAGLE-I Power Outage Data 2024

The provided EAGLE-I historic dataset includes power outage information at the county level for 2024 at 15-minute intervals collected by the EAGLE-I program at ORNL. The data has been collected from utility's public outage maps using an ETL process. The dataset details FIPS code, county name, state name, total number of customers without power, total customers per county, and a date/timestamp. For detailed metadata, refer to the linked metadata DOI.

24 POWER TRANSMISSION AND DISTRIBUTION↗
DOE OSTI2023

EAGLE-I Power Outage Data 2014 - 2022

The provided EAGLE-I historic dataset includes eight years of power outage information at the county level from 2014 to 2022 at 15-minute intervals collected by the EAGLE-I program at ORNL. The data has been collected from utility's public outage maps using an ETL process. The dataset details FIPS code, county name, state name, total number of customers without power, and a date/timestamp. Also included is the EAGLE-I coverage of each state for each year. For detailed metadata, refer to the metadata DOI.

24 POWER TRANSMISSION AND DISTRIBUTION↗
DOE OSTI2024

Climate adaptation and sustainability in switchgrass: exploring plant-microbe-soil interactions across continental scale environmental gradients

Less carbon-intensive energy sources are needed to reduce greenhouse gas emissions and their predicted role in climate change. There is growing interest in the potential of biofuels for meeting this need. A critical question is whether large-scale biofuel production can be sustainable over the time scales needed to mitigate our carbon debt from fossil fuel consumption. The carbon balance and ultimately the sustainability of biofuel feedstock production is the result of complex climate-coupled interactions between carbon fixation, sequestration, and release through combustion. Similarly, the long-term productivity of biofuels depends on the environmental factors limiting plant growth. These factors are often related to soil resources which involve complex interactions at the plant-microbe-soil interface impacting their availability and cycling. Our collaborative project addressed sustainable switchgrass (Panicum virgatum) production by exploring Plant Systems, Plant-Microbiome Interactions, and Ecosystem Processes through the integrating lens of Multi-Scale Modeling. Our research was based on detailed characterization of genetically diverse switchgrass genotypes planted in common gardens across a continental latitudinal gradient. The underlying theme of our Plant Systems research was the use of locally adapted plant material to explore plant function, to understand the mechanistic basis of environmental interactions, and to discover the plant genes important for adaptation and sustainability in the face of climate change. Our Plant-Microbiome Interaction project characterized the microbial communities associated with switchgrass using genomic tools. Our Ecosystem Processes research focused on carbon cycle responses at the ecosystem level using stand level plantings. Finally, our Multi-Scale Modeling helped to define conditions of a sustainable biofuel system and identify key tradeoffs between genetic diversity, productivity, and ecosystem services. Genome-wide association analyses were used to identify alleles that contribute to successful establishment and biomass production across North America. Together, our work provided a baseline analyses of the potential of switchgrass as a biofuel feedstock. Our project resulted in a number of successful outcomes. First, we were successful in collecting switchgrass germplasm across the species range, propagating the material, and establishing common garden experiments across the species range. In collaboration with DOE JGI, we successfully assembled the first tetraploid switchgrass genome and published this resource with an analyses of the genetic basis local adaptation from our gardens (Lowry et al. 2019, Lovell et al. 2021). The gardens were used to characterize the genetic architecture for a number of important plant phenotypes. Our project also conducted extensive sampling and sequencing to characterize the bacterial and fungal associates of switchgrass roots and leaves. We showed that host genotype, location, and harvesting practices can play a role in microbiome assembly (Singer et al. 2019 & 2022, Van Wallendael et al. 2020 & 2022, Edwards et al. 2023). Our ecosystem processes work created baseline dataset of carbon and nutrient cycling in realistic stand plantings of switchgrass. Data from this experiment provided new insight into the role of plant traits, phenology, and local environments in ecosystem processes like soil respiration, net-ecosystem exchange, and dynamics of soil and plant nutrients (Ricketts et al. 2023). Finally, our crop modelling experiments help to characterize the sensitivity of common modeling frameworks to parameters, identify key limiters of productivity across large geographic scales, and leverage patterns of local adaptation in prediction. Ultimately, these studies help to identify critical plant-microbe-soil traits that may be manipulated, through breeding or agronomic management, to improve the sustainability of biofuel feedstocks.

09 BIOMASS FUELS↗