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At least 667 records · Page 37

Small angle scattering methods to study porous materials under high uniaxial strain

We developed a high pressure cell for the in situ study of the porosity of solids under high uniaxial strain using neutron small angle scattering. The cell comprises a hydraulically actioned piston and a main body equipped with two single-crystal sapphire windows allowing for the neutron scattering of the sample. The sample cavity is designed to allow for a large volume variation as expected when compressing highly porous materials. We also implemented a loading protocol to adapt an existing diamond anvil cell for the study of porous materials by X-ray small angle scattering under high pressure. The two techniques are complementary as the radiation beam and the applied pressure are in one case perpendicular to each other (neutron cell) and in the other case parallel (X-ray cell). We will illustrate the use of these two techniques in the study of lamellar porous systems up to a maximum pressure of 0.1 GPa and 0.3 GPa for the neutron and X-ray cells, respectively. These devices allow obtaining information on the evolution of porosity with pressure in the pore dimension subdomain defined by the wave-numbers explored in the scattering process. The evolution with the applied load of such parameters as the fractal dimension of the pore-matrix interface or the apparent specific surface in expanded graphite and in expanded vermiculite is used to illustrate the use of the high pressure cells.

46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND ↗

Super-resolution for scanning light stimulation systems

Super-resolution (SR) is a technique used in digital image processing to overcome the resolution limitation of imaging systems. In this process, a single high resolution image is reconstructed from multiple low resolution images. SR is commonly used for CCD and CMOS (Complementary Metal-Oxide-Semiconductor) sensor images, as well as for medical applications, e.g., magnetic resonance imaging. Here, we demonstrate that super-resolution can be applied with scanning light stimulation (LS) systems, which are common to obtain space-resolved electro-optical parameters of a sample. For our purposes, the Projection Onto Convex Sets (POCS) was chosen and modified to suit the needs of LS systems. To demonstrate the SR adaption, an Optical Beam Induced Current (OBIC) LS system was used. The POCS algorithm was optimized by means of OBIC short circuit current measurements on a multicrystalline solar cell, resulting in a mean square error reduction of up to 61% and improved image quality.

46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND ↗

Amoeba species colonizing the gills of rainbow trout ( Oncorhynchus mykiss ) in Swiss aquaculture

Nodular gill disease (NGD) is an infectious condition characterized by proliferative gill lesions leading to respiratory problems, oxygen deficiency and mortality in fish. Globally, NGD primarily impacts freshwater salmonids in intensive aquaculture systems. In recent years, numerous outbreaks of severe gill disease have affected more than half of the larger rainbow trout ( Oncorhynchus mykiss ) farms in Switzerland, mainly during spring and early summer. Mortality has reached up to 50% in cases where no treatment was administered. Freshwater amoeba are the presumed aetiologic agent of NGD. The gross gill score (GS) categorising severity of gill pathology is a valuable first-line diagnostic tool aiding fish farmers in identifying and quantifying amoebic gill disease (AGD) in farmed marine salmonids. In this study, the GS was adapted to the NGD outbreak in farmed trout in Switzerland. In addition to scoring disease severity, gill swabs from NGD-affected rainbow trout were sampled and amoeba were cultured from these swabs. Morphologic and molecular methods identified six amoeba strains: Cochliopodium sp., Naegleria sp., Vannella sp., Ripella sp., Saccamoeba sp. and Mycamoeba sp. However, the importance of the different amoeba species for the onset and progression of NGD still has to be evaluated. This paper presents the first description of NGD with associated amoeba infection in farmed rainbow trout in Switzerland.

59 BASIC BIOLOGICAL SCIENCES↗

The Target-Matrix Optimization Paradigm for High-Order Meshes

Here, we describe a framework for controlling and improving the quality of high-order finite element meshes based on extensions of the Target-Matrix Optimization Paradigm (TMOP) of [P. Knupp, Eng. Comput., 28 (2012), pp. 419--429]. This approach allows high-order applications to have a very precise control over local mesh quality, while still improving the mesh globally. We address the adaption of various TMOP components to the settings of general isoparametric element mappings, including the mesh quality metric in 2D and 3D, the selection of sample points and the solution of the resulting mesh optimization problem. We also investigate additional practical concerns, such as tangential relaxation and restricting the deviation from the original mesh. The benefits of the new high-order TMOP algorithms are illustrated on a number of test problems and examples from a high-order arbitrary Lagrangian--Eulerian (ALE) application [BLAST: High-order curvilinear finite elements for shock hydrodynamics, http://www.llnl.gov/CASC/blast]. Our implementation is freely available in an open-source library form [MFEM: Modular parallel finite element methods library, http://mfem.org].

97 MATHEMATICS AND COMPUTING↗

Longitudinal Antigenic Sequences and Sites from Intra-Host Evolution (LASSIE) identifies immune-selected HIV variants

Within-host genetic sequencing from samples collected over time provides a dynamic view of how viruses evade host immunity. Immune-driven mutations might stimulate neutralization breadth by selecting antibodies adapted to cycles of immune escape that generate within-subject epitope diversity. Comprehensive identification of immune-escape mutations is experimentally and computationally challenging. With current technology, many more viral sequences can readily be obtained than can be tested for binding and neutralization, making down-selection necessary. Typically, this is done manually, by picking variants that represent different time-points and branches on a phylogenetic tree. Such strategies are likely to miss many relevant mutations and combinations of mutations, and to be redundant for other mutations. Longitudinal Antigenic Sequences and Sites from Intrahost Evolution (LASSIE) uses transmitted founder loss to identify virus “hot-spots” under putative immune selection and chooses sequences that represent recurrent mutations in selected sites. LASSIE favors earliest sequences in which mutations arise. Here, with well-characterized longitudinal Env sequences, we confirmed selected sites were concentrated in antibody contacts and selected sequences represented diverse antigenic phenotypes. Finally, practical applications include rapidly identifying immune targets under selective pressure within a subject, selecting minimal sets of reagents for immunological assays that characterize evolving antibody responses, and for immunogens in polyvalent “cocktail” vaccines.

59 BASIC BIOLOGICAL SCIENCES↗

Resolved Imaging of the AR Puppis Circumbinary Disk

Circumbinary disks are common around post-asymptotic giant branch (post-AGB) stars with a stellar companion on orbital timescales of a few 100 to few 1000 days. The presence of a disk is usually inferred from the system’s spectral energy distribution and confirmed, for a sub-sample, by interferometric observations. We used the Spectro-Polarimetric High-contrast Exoplanet REsearch (SPHERE) instrument on the Very Large Telescope to obtain extreme adaptive optics assisted scattered light images of the post-AGB binary system AR Puppis. Data have been obtained in the V, I, and H bands. Our observations have produced the first resolved images of AR Puppis’s circumbinary disk and confirm its edge-on orientation. In our high-angular-resolution and high-dynamic-range images we identify several structural components such as a dark mid-plane, the disk surface, and arc-like features. We discuss the nature of these components and use complementary photometric monitoring to relate them to the orbital phase of the binary system. Because the star is completely obscured by the disk at visible wavelengths, we conclude that the long-term photometric variability of the system must be caused by variable scattering, not extinction, of starlight by the disk over the binary orbit. Finally, we discuss how the short disk lifetimes and fast evolution of the host stars compared to the ages at which protoplanetary disks are typically observed make systems like AR Puppis valuable extreme laboratories to study circumstellar disk evolution and constrain the timescale of dust grain growth during the planet formation process.

79 ASTRONOMY AND ASTROPHYSICS↗

SPATIALLY RESOLVED IMAGING AND SPECTROSCOPY OF CANDIDATE DUAL ACTIVE GALACTIC NUCLEI

When galaxies merge, both central supermassive black holes are immersed in a dense and chaotic environment. If there is sufficient gas in the nuclear regions, one expects to see close pairs of active galactic nuclei (AGNs), or dual AGNs, in a fraction of galaxy mergers. However, finding them remains a challenge. The presence of double-peaked [O iii] emission lines has been proposed as a technique to select dual AGNs efficiently. We studied a sample of double-peaked narrow [O iii] emitting AGNs from Sloan Digital Sky Survey (SDSS) DR7. By obtaining new and archival high spatial resolution images taken with the Keck II Laser Guide Star Adaptive Optics system and the near-infrared camera NIRC2, we show that 30% of 140 double-peaked [O iii] emission line SDSS AGNs have two spatial components within a 3″ radius. However, spatially resolved spectroscopy or X-ray observations are needed to confirm these galaxy pairs as systems containing two AGNs. We followed up three spatially double candidate dual AGNs with integral field spectroscopy from Keck OSIRIS and 10 candidates with long-slit spectroscopy from the Shane Kast Double Spectrograph at Lick Observatory. We find that the double-peaked emission lines in our sample of 12 candidates are caused by: one dual AGN (SDSS J114642.47+511029.6), one confirmed outflow and four likely outflows, two pairs of star-forming galaxies, one candidate indeterminate due to sky line interference, and three AGNs with spatially coincident double [O iii] peaks, likely due to unresolved complex narrow line kinematics, outflows, binary AGN, or small-scale jets.

79 ASTRONOMY AND ASTROPHYSICS↗

Single-object Imaging and Spectroscopy to Enhance Dark Energy Science from LSST

Single-object imaging and spectroscopy on telescopes with apertures ranging from ~4 m to 40 m have the potential to greatly enhance the cosmological constraints that can be obtained from LSST. Two major cosmological probes will benefit greatly from LSST follow-up: accurate spectrophotometry for nearby and distant Type Ia supernovae will expand the cosmological distance lever arm by unlocking the constraining power of high-z supernovae; and cosmology with time delays of strongly-lensed supernovae and quasars will require additional high-cadence imaging to supplement LSST, adaptive optics imaging or spectroscopy for accurate lens and source positions, and IFU or slit spectroscopy to measure detailed properties of lens systems. We highlight the scientific impact of these two science drivers, and discuss how additional resources will benefit them. For both science cases, LSST will deliver a large sample of objects over both the wide and deep fields in the LSST survey, but additional data to characterize both individual systems and overall systematics will be key to ensuring robust cosmological inference to high redshifts. Community access to large amounts of natural-seeing imaging on ~2-4 m telescopes, adaptive optics imaging and spectroscopy on 8-40 m telescopes, and high-throughput single-target spectroscopy on 4-40 m telescopes will be necessary for LSST time domain cosmology to reach its full potential. In two companion white papers we present the additional gains for LSST cosmology that will come from deep and from wide-field multi-object spectroscopy.

79 ASTRONOMY AND ASTROPHYSICS↗

An algebraic convolution formulation for multiple-scattering correction in small-angle neutron scattering

Multiple scattering in small-angle neutron scattering (SANS) redistributes spectral weight and distorts structural interpretation, particularly for thick or strongly scattering samples. We develop a finite-dimensional spectral desmearing framework that corrects multiple scattering without resorting to integral transforms or model-dependent extrapolation. The primary intensity is expanded in an orthonormal basis adapted to the isotropic transverse-momentum measure, under which convolution reduces to a recursive tensor contraction, allowing the Poisson-weighted multiple-scattering series to be evaluated directly in a finite-dimensional basis representation. This formulation yields a stable forward–inverse mapping between apparent and primary spectra. Numerical tests demonstrate convergence under repeated convolution and accurate recovery of the single-scattering intensity. Application to SANS measurements collected at multiple neutron facilities, including the Spallation Neutron Source, the High Flux Isotope Reactor, and the Institut Laue-Langevin, shows the quantitative reconstruction of the underlying primary spectrum across a wide range of transmission conditions, including strongly attenuating samples. Here, the method provides a stable, model-agnostic framework for multiple-scattering correction in SANS and enables consistent structural interpretation across instruments and scattering regimes.

Tung, Chi-Huan [Oak Ridge National Laboratory (ORN↗

Observation of tantalum deposition and growth on TiB2 and ZrB2 from PISCES-RF deuterium and helium plasma exposures

Deuterium and helium plasma exposures on bulk TiB2 and ZrB2 samples were performed using the PISCES-RF linear plasma device. 40 and 90 eV deuterium ion plasma exposures were performed at 240 and 800 °C sample temperatures, and 80 eV helium ion plasma exposures were performed at 800 °C sample temperatures. Following plasma exposures, it was discovered that two plasma conditions (90 eV deuterium and 80 eV helium at 800 °C) resulted in thick (>200 nm) tantalum-rich (>10 at%) surface features on the targets, presumably from tantalum sourced from a tantalum adapter mask or cap used as part of the target holder. This work aims to characterize these tantalum-rich features and examine the mechanisms of impurity deposition.Plasma-induced surface morphology of the tantalum-rich surface layers depends on plasma properties and target temperature and chemistry. Greater titanium sputtering compared to zirconium resulted in more distinct surface features in the TiB2 samples compared to the ZrB2 samples via increased, prompt deposition onto tantalum surface impurities. There is still uncertainty as to why thick tantalum deposition only occurred under some plasma exposure conditions but not others; it is likely due to tantalum sputtering by a combination of boron molecules from the targets and carbon-impurities in the tantalum mask or targets. Impurity driven surface features are a well-documented phenomena in samples exposed to plasma from linear plasma device facilities—this work confirms the occurrence of this and emphasizes the need for chemistry characterization of isolated post-mortem surface features in plasma-exposed samples.

Nuckols, Lauren↗

Natural selection maintains species despite frequent hybridization in the desert shrub Encelia

Natural selection is an important driver of genetic and phenotypic differentiation between species. For species in which potential gene flow is high but realized gene flow is low, adaptation via natural selection may be a particularly important force maintaining species. For a recent radiation of New World desert shrubs ( Encelia : Asteraceae), we use fine-scale geographic sampling and population genomics to determine patterns of gene flow across two hybrid zones formed between two independent pairs of species with parapatric distributions. After finding evidence for extremely strong selection at both hybrid zones, we use a combination of field experiments, high-resolution imaging, and physiological measurements to determine the ecological basis for selection at one of the hybrid zones. Our results identify multiple ecological mechanisms of selection (drought, salinity, herbivory, and burial) that together are sufficient to maintain species boundaries despite high rates of hybridization. Given that multiple pairs of Encelia species hybridize at ecologically divergent parapatric boundaries, such mechanisms may maintain species boundaries throughout Encelia .

59 BASIC BIOLOGICAL SCIENCES↗

Site Characterization and Adaptation - Lessons Learned from the Port Granby LLRW Project in Canada - 19255

The Port Hope Area Initiative (PHAI) is a community-based solution for the long-term management of historic low level radioactive waste (LLRW) resulting from 60 years of uranium and radium processing operations in the Town of Port Hope, Ontario. The Eldorado refinery, on the shores of Lake Ontario, began refining radium-226 from pitchblende ore, later transitioning to the refining of uranium ore. Process residues were deposited at the Welcome Waste Management Facility in the Town of Port Hope until the mid-1950's switching to the Port Granby Waste Management Facility (PGWMF), which continued to accept waste until the late 1980's. The Port Granby Project is part of the broader PHAI being completed by Canadian Nuclear Laboratories (CNL). The scope of remediation activities at Port Granby includes: 1) constructing a new, secure long-term waste management facility (LTWMF) and supporting infrastructure, and 2) excavating and relocating LLRW and marginally contaminated soil from the Port Granby Waste Management Facility (PGWMF) to the newly constructed LTWMF. Remediation design required extensive site investigations of waste buried in unmarked and unlined trenches across the PGWMF area. Investigations included reviews of historical records, interviews with past staff of Eldorado and Atomic Energy Canada Limited (the entity managing the PHAI cleanup prior to CNL), radiological surveys, and the advancement of boreholes across the site to facilitate the physical sampling of soil and groundwater contaminants indicative of the presence of LLRW. This paper describes the challenges associated with characterizing and quantifying waste prior to remediation and lessons-learned to adjust procedural adaptations during the characterization phase of a project to better anticipate the unanticipated nature and growth of the contamination profile that often occurs during remediation. Despite substantial pre-remediation investigative efforts, current PGWMF remediation activities have resulted in growth of both the areal and vertical extent of contamination. The contamination profile was shown to be dramatically influenced by leaching of contaminants deeper into native soils. In addition, remedial excavation discovered specific waste types such as unidentified compressed gas cylinders outside of the location indicated by historical records, as well as a quantity of unknown liquids and unmarked drums. These discoveries have resulted in higher waste quantity estimates and have required a partial redesign of the LTWMF mound (a higher final elevation and steeper slopes) to accommodate the increased waste volume. In addition, the means and methods necessary to execute the remedial construction had to be modified to account for the new conceptual model developed from the new contamination data. Despite extensive historical review and site characterization to define the nature and extent of LLRW buried at the PGWMF, actual conditions encountered during remediation efforts necessitated taking an adaptive approach to both the design of the LTWMF and the means and methods used for excavation and transport of waste. (authors)

12 MANAGEMENT OF RADIOACTIVE AND NON-RADIOACTIVE W↗

Using scalable computer vision to automate high-throughput semiconductor characterization

Abstract High-throughput materials synthesis methods, crucial for discovering novel functional materials, face a bottleneck in property characterization. These high-throughput synthesis tools produce 10 4 samples per hour using ink-based deposition while most characterization methods are either slow (conventional rates of 10 1 samples per hour) or rigid (e.g., designed for standard thin films), resulting in a bottleneck. To address this, we propose automated characterization (autocharacterization) tools that leverage adaptive computer vision for an 85x faster throughput compared to non-automated workflows. Our tools include a generalizable composition mapping tool and two scalable autocharacterization algorithms that: (1) autonomously compute the band gaps of 200 compositions in 6 minutes, and (2) autonomously compute the environmental stability of 200 compositions in 20 minutes, achieving 98.5% and 96.9% accuracy, respectively, when benchmarked against domain expert manual evaluation. These tools, demonstrated on the formamidinium (FA) and methylammonium (MA) mixed-cation perovskite system FA 1−x MA x PbI 3 , 0 ≤ x ≤ 1, significantly accelerate the characterization process, synchronizing it closer to the rate of high-throughput synthesis.

Science & Technology - Other Topics↗

Pretraining Billion-Scale Geospatial Foundational Models on Frontier

As AI workloads increase in scope, generalization capability becomes challenging for small task-specific models and their demand for large amounts of labeled training samples increases. On the contrary, Foundation Models (FMs) are trained with internet-scale unlabeled data via self-supervised learning and have been shown to adapt to various tasks with minimal fine-tuning. Although large FMs have demonstrated significant impact in natural language processing and computer vision, efforts toward FMs for geospatial applications have been restricted to smaller size models, as pretraining larger models requires very large computing resources equipped with state-of-the-art hardware accelerators. Current satellite constellations collect 100+TBs of data a day, resulting in images that are billions of pixels and multimodal in nature. Such geospatial data poses unique challenges opening up new opportunities to develop FMs. We investigate billion scale FMs and HPC training profiles for geospatial applications by pretraining on publicly available data. We studied from end-to-end the performance and impact in the solution by scaling the model size. Our larger 3B parameter size model achieves up to 30% improvement in top1 scene classification accuracy when comparing a 100M parameter model. Moreover, we detail performance experiments on the Frontier supercomputer, America's first exascale system, where we study different model and data parallel approaches using PyTorch's Fully Sharded Data Parallel library. Specifically, we study variants of the Vision Transformer architecture (ViT), conducting performance analysis for ViT models with size up to 15B parameters. By discussing throughput and performance bottlenecks under different parallelism configurations, we offer insights on how to leverage such leadership-class HPC resources when developing large models for geospatial imagery applications.

Tsaris, Aristeidis (aris)↗

A Practical Framework for Sharing and Rendering Real-World Bidirectional Scattering Distribution Functions

The utilization of real-world materials has been hindered by a lack of standards for sharing and interpreting measured data. This paper presents an XML representation and an Open Source C library to support bidirectional scattering distribution functions (BSDFs) in data-driven lighting simulation and rendering applications.The library provides for the efficient representation, query, and Monte Carlo sampling of arbitrary BSDFs in amodel-free framework. Currently, we support two BSDF data representations: one using a fixed subdivision of thehemisphere, and one with adaptive density. The fixed type has advantages for certain matrix operations, while theadaptive type can more accurately represent highly peaked data. We discuss advanced methods for data-drivenBSDF rendering for both types, including the proxy of detailed geometry to enhance appearance and accuracy.We also present an advanced interpolation method to reduce measured data into these standard representations.We end with our plan for future extensions and sharing of BSDF data.

29 ENERGY PLANNING, POLICY, AND ECONOMY↗

The GGCMI Phase 2 emulators: global gridded crop model responses to changes in CO 2 , temperature, water, and nitrogen (version 1.0)

Statistical emulation allows combining advantageous features of statistical and process-based crop models for understanding the effects of future climate changes on crop yields. We describe here the development of emulators for nine process-based crop models and five crops using output from the Global Gridded Model Intercomparison Project (GGCMI) Phase 2. The GGCMI Phase 2 experiment is designed with the explicit goal of producing a structured training dataset for emulator development that samples across four dimensions relevant to crop yields: atmospheric carbon dioxide (CO2) concentrations, temperature, water supply, and nitrogen inputs (CTWN). Simulations are run under two different adaptation assumptions: that growing seasons shorten in warmer climates, and that cultivar choice allows growing seasons to remain fixed. The dataset allows emulating the climatological-mean yield response of all models with a simple polynomial in mean growing-season values. Climatological-mean yields are a central metric in climate change impact analysis; we show here that they can be captured without relying on interannual variations. In general, emulation errors are negligible relative to differences across crop models or even across climate model scenarios; errors become significant only in some marginal lands where crops are not currently grown. We demonstrate that the resulting GGCMI emulators can reproduce yields under realistic future climate simulations, even though the GGCMI Phase 2 dataset is constructed with uniform CTWN offsets, suggesting that the effects of changes in temperature and precipitation distributions are small relative to those of changing means. The resulting emulators therefore capture relevant crop model responses in a lightweight, computationally tractable form, providing a tool that can facilitate model comparison, diagnosis of interacting factors affecting yields, and integrated assessment of climate impacts.

54 ENVIRONMENTAL SCIENCES↗

Benchmarking image processing techniques for porosity measurement in polymer additive manufacturing: Review and experimental analysis

An image processing workflow is proposed for porosity measurement in polymer additive manufacturing. Various techniques, including global and local thresholding, region growing, and K-means clustering, were applied to microscopic images of carbon fiber reinforced acrylonitrile butadiene styrene (CF-ABS) and benchmarked for their ability to accurately measure porosity. Global methods included Otsu, minimum error, iterative, and entropy-based thresholding, while local methods included Niblack, Bernsen, Sauvola, and Bradley-Roth algorithms. Artificial uneven illumination was introduced to test local adaptive thresholds. Results showed significant differences in porosity values across methods. Otsu, region growing, and K-means clustering excelled under uniform illumination, while Sauvola and Bradley-Roth performed better with uneven illumination. Comparison with X-ray computed tomography (XCT) revealed slightly lower porosity values (2.55 %) than optimized methods (2.73–2.79 %) due to XCT's lower resolution excluding smaller pores. While XCT offers finer pore detection, it limits sample volume and underestimates porosity due to spatial variation. Validation using artificial grayscale images with 5 % porosity confirmed that Otsu, Bradley-Roth, region growing, and Sauvola algorithms produced accurate results. Although tested on a single material system, these methods can be adapted to others with optimization. In conclusion, given XCT's high computational and time costs, this study highlights suitable image processing techniques as cost-effective alternatives for porosity analysis in polymer composites.

Additive manufacturing↗

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↗