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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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All sourcesNASA NTRSDOE OSTI
DOE OSTI2024

Architecture and functional regulation of a plant PSII-LHCII megacomplex

Photosystem II (PSII) splits water in oxygenic photosynthesis on Earth. The structure and function of the C 4 S 4 M 2 -type PSII-LHCII (light-harvesting complex II) megacomplexes from the wild-type and PsbR-deletion mutant plants are studied through electron microscopy (EM), structural mass spectrometry, and ultrafast fluorescence spectroscopy [time-resolved fluorescence (TRF)]. The cryo-EM structure of a type I C 4 S 4 M 2 megacomplex demonstrates that the three domains of PsbR bind to the stromal side of D1, D2, and CP43; associate with the single transmembrane helix of the redox active Cyt b 559 ; and stabilize the luminal extrinsic PsbP, respectively. This megacomplex, with PsbR and PsbY centered around the narrow interface between two dimeric PSII cores, provides the supramolecular structural basis that regulates the plastoquinone occupancy in Q B site, excitation energy transfer, and oxygen evolution. PSII-LHCII megacomplexes (types I and II) and LHC aggregation levels in Arabidopsis psbR mutant were also interrogated and compared to wild-type plants through EM and picosecond TRF.

Science & Technology - Other Topics↗
DOE OSTI2020

Cellulose synthase interactive1- and microtubule-dependent cell wall architecture is required for acid growth in Arabidopsis hypocotyls

Auxin-induced cell elongation relies in part on the acidification of the cell wall, a process known as acid growth that presumably triggers expansin-mediated wall loosening via altered interactions between cellulose microfibrils. Cellulose microfibrils are a major determinant for anisotropic growth and they provide the scaffold for cell wall assembly. Little is known about how acid growth depends on cell wall architecture. To explore the relationship between acid growth-mediated cell elongation and plant cell wall architecture, two mutants (jia1-1 and csi1-3) that are defective in cellulose biosynthesis and cellulose microfibril organization were analyzed. The study revealed that cell elongation is dependent on CSI1-mediated cell wall architecture but not on the overall crystalline cellulose content. We observed a correlation between loss of crossed-polylamellate walls and loss of auxin- and fusicoccin-induced cell growth in csi1-3. Furthermore, induced loss of crossed-polylamellate walls via disruption of cortical microtubules mimics the effect of csi1 in acid growth. We hypothesize that CSI1- and microtubule-dependent crossed-polylamellate walls are required for acid growth in Arabidopsis hypocotyls.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2022

Carbohydrate-aromatic interface and molecular architecture of lignocellulose

Abstract Plant cell walls constitute the majority of lignocellulosic biomass and serve as a renewable resource of biomaterials and biofuel. Extensive interactions between polysaccharides and the aromatic polymer lignin make lignocellulose recalcitrant to enzymatic hydrolysis, but this polymer network remains poorly understood. Here we interrogate the nanoscale assembly of lignocellulosic components in plant stems using solid-state nuclear magnetic resonance and dynamic nuclear polarization approaches. We show that the extent of glycan-aromatic association increases sequentially across grasses, hardwoods, and softwoods. Lignin principally packs with the xylan in a non-flat conformation via non-covalent interactions and partially binds the junction of flat-ribbon xylan and cellulose surface as a secondary site. All molecules are homogeneously mixed in softwoods; this unique feature enables water retention even around the hydrophobic aromatics. These findings unveil the principles of polymer interactions underlying the heterogeneous architecture of lignocellulose, which may guide the rational design of more digestible plants and more efficient biomass-conversion pathways.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2022

PRMI: A Dataset of Minirhizotron Images for Diverse Plant Root Study

Understanding a plant's root system architecture (RSA) is crucial for a variety of plant science problem domains including sustainability and climate adaptation. Minirhizotron (MR) technology is a widely-used approach for phenotyping RSA non-destructively by capturing root imagery over time. Precisely segmenting roots from the soil in MR imagery is a critical step in studying RSA features. In this paper, we introduce a large-scale dataset of plant root images captured by MR technology. In total, there are over 72K RGB root images across six different species including cotton, papaya, peanut, sesame, sunflower, and switchgrass in the dataset. The images span a variety of conditions including varied root age, root structures, soil types, and depths under the soil surface. All of the images have been annotated with weak image-level labels indicating whether each image contains roots or not. The image-level labels can be used to support weakly supervised learning in plant root segmentation tasks. In addition, 63K images have been manually annotated to generate pixel-level binary masks indicating whether each pixel corresponds to root or not. These pixel-level binary masks can be used as ground truth for supervised learning in semantic segmentation tasks. By introducing this dataset, we aim to facilitate the automatic segmentation of roots and the research of RSA with deep learning and other image analysis algorithms.

Xu, Weihuang↗
DOE OSTI2020

Molecular Mechanism of Polysaccharide Acetylation by the Arabidopsis Xylan O-acetyltransferase XOAT1

Xylans are a major component of plant cell walls. O-Acetyl moieties are the dominant backbone substituents of glucuronoxylan in dicots and play a major role in the polymer-polymer interactions that are crucial for wall architecture and normal plant development. Here, we describe the biochemical, structural, and mechanistic characterization of Arabidopsis (Arabidopsis thaliana) xylan O-acetyltransferase 1 (XOAT1), a member of the plant-specific Trichome Birefringence Like (TBL) family. Detailed characterization of XOAT1-catalyzed reactions by real-time NMR confirms that it exclusively catalyzes the 2-O-acetylation of xylan, followed by nonenzymatic acetyl migration to the O-3 position, resulting in products that are monoacetylated at both O-2 and O-3 positions. In addition, we report the crystal structure of the catalytic domain of XOAT1, which adopts a unique conformation that bears some similarities to the a/ß/a topology of members of the GDSL-like lipase/acylhydrolase family. Finally, we use a combination of biochemical analyses, mutagenesis, and molecular simulations to show that XOAT1 catalyzes xylan acetylation through formation of an acyl-enzyme intermediate, Ac–Ser-216, by a double displacement bi-bi mechanism involving a Ser-His-Asp catalytic triad and unconventionally uses an Arg residue in the formation of an oxyanion hole.

acetylation↗
DOE OSTI2021

Spontaneous rearrangement of acetylated xylan on hydrophilic cellulose surfaces

The interaction of xylan, an abundant plant polysaccharide, with cellulose microfibrils is essential for secondary cell wall strength. A deeper understanding of these interactions is crucial both to improve our understanding of plant cell wall architecture and to design alternate strategies to overcome cellulose recalcitrance for the production of biofuels and sustainable biomaterials. Naturally occurring acetate or glucuronic acid substitutions on xylan have been shown to influence xylan-cellulose interactions. In this work, we use unrestrained molecular dynamics simulations to determine the interactions with the (110) hydrophilic face of cellulose fibers of four different xylans. In the absence of cellulose, all xylans, independent of the substitution pattern, adopt a highly flexible threefold helical screw conformation. However, when xylan is spatially close to a cellulose surface 1,2 linked acetyl xylans (2AcX) adopt rigid twofold helical screw conformations. The 2AcX conformations are primarily stabilized by interactions between the acetylated oxygen and the glycosidic linkage with C-O6 of cellulose. In contrast, the glycosidic oxygens and acetyl decorations for 1,3 linked acetyl groups (3AcX) are oriented away from the cellulose surface and the 3AcX xylans maintain threefold helical screw conformations on the cellulose surface. Our results show that evenly spaced chemical functionalization (with acetyl groups) and the position of substitution (1,2) on xylan backbone play key roles in tuning the xylan-cellulose interactions to stabilize the twofold helical screw conformations of xylan on the cellulose surface. A comparison with previous experimental findings further suggests that 1,2 substitutions induce twofold helical screw conformations of xylan on the cellulose surface irrespective of the chemical nature of the substituent, while 1,3 substitutions primarily bind lignin in threefold helical screw conformations rather than cellulose in plant cell walls.

1,2 Ac↗
DOE OSTI2020

Structural Insights into Low and High Recalcitrance Natural Poplar Variants Using Neutron and X-ray Scattering

Here, we used small-angle neutron scattering (SANS) and wide-angle X-ray scattering (WAXS) to study two Populus trichocarpa variants, GW-11012 and BESC-316, with lignin contents of 17.8 and 23.2%, respectively, to understand why the low lignin variant has increased sugar release during liquid hot water (LHW) pretreatment. SANS showed differences in cell wall polymer arrangement in the variants. In GW-11012, the cellulose microfibrils were similar in size to BESC-316 but were not equally spaced and lignin was aggregated rather than homogenously interspersed with other matrix co-polymers. However, after LHW pretreatments, the SANS profiles of the pretreated variants were almost indistinguishable, indicating that the nanoscale structural features could not adequately explain the increased sugar release observed in GW-11012. WAXS analysis revealed higher cellulose crystallinity and larger crystallite size in native and pretreated GW-11012. However, the most significant difference was that the crystallite orientation was approximately 2-fold higher in GW-11012. This suggests that higher crystallite orientation may be favorable for the action of processive cellulases leading to increased sugar release from GW-11012. Our study shows that there is a complex interplay between cell wall polymers during their deposition resulting in changes in lignin distribution and cellulose structure, highlighting the importance of investigating plant cell wall architecture across multiple length scales.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2022

Photovoltaic System Health-State Architecture for Data-Driven Failure Detection

The timely detection of photovoltaic (PV) system failures is important for maintaining optimal performance and lifetime reliability. A main challenge remains the lack of a unified health-state architecture for the uninterrupted monitoring and predictive performance of PV systems. To this end, existing failure detection models are strongly dependent on the availability and quality of site-specific historic data. The scope of this work is to address these fundamental challenges by presenting a health-state architecture for advanced PV system monitoring. The proposed architecture comprises of a machine learning model for PV performance modeling and accurate failure diagnosis. The predictive model is optimally trained on low amounts of on-site data using minimal features and coupled to functional routines for data quality verification, whereas the classifier is trained under an enhanced supervised learning regime. The results demonstrated high accuracies for the implemented predictive model, exhibiting normalized root mean square errors lower than 3.40% even when trained with low data shares. The classification results provided evidence that fault conditions can be detected with a sensitivity of 83.91% for synthetic power-loss events (power reduction of 5%) and of 97.99% for field-emulated failures in the test-bench PV system. Finally, this work provides insights on how to construct an accurate PV system with predictive and classification models for the timely detection of faults and uninterrupted monitoring of PV systems, regardless of historic data availability and quality. Such guidelines and insights on the development of accurate health-state architectures for PV plants can have positive implications in operation and maintenance and monitoring strategies, thus improving the system’s performance.

photovoltaics↗
DOE OSTI2021

Monitoring Based Commissioning: Step-by-Step Approach to Implementation in Chiller Plants

Chilled water systems consume a significant portion of the energy in manufacturing, and inefficient operation could significantly influence utility bills and the environmental footprint of a facility. One way to ensure long-term energy-efficient plant operation is to ensure that chilled water system equipment, such as chillers, cooling towers, and pumps, are running at their best. Periodic operational assessments help to meet efficiency objectives for cooling plants. However, occasional set point and operational changes resulting from short-term fluctuations cause the plant operation to deviate from optimal. These changes accumulate over time, and operational efficiency deteriorates. Significant energy efficiency improvement potential in chilled water systems can be achieved by operational changes alone. Recent developments in information technology and the industrial internet of things data processing ability have overcome traditional problems and given rise to a new paradigm: monitoring-based commissioning (MBCx). Commissioning platforms and fault detection and diagnostics capabilities have evolved over time and presented more sophisticated abilities to efficiently control process cooling plants. The typical architectures of the MBCx process and a detailed approach to implement the process for chilled water systems are presented. A case study highlighting the energy-and cost-savings potential of MBCx systems is also presented.

11 NUCLEAR FUEL CYCLE AND FUEL MATERIALS↗
DOE OSTI2022

WPGNN and PLayGen (Wind Plant Graph Neural Network and Plant Layout Generator) [SWR-21-90]

WPGNN is the graph neural network machine learning based surrogate model and software that provides a streamlined approximation of wind plant wake models. It can rapidly estimate the energy production of the plant and turbines for any arbitrarily sized wind plant and layout under any inflow condition. Associated tools include graphing and visualization capabilities as well as a wind plant generator capable of creating randomized realizations of canonical wind plant layouts. The WPGNN architecture and application is extendable to multiple wake models, turbine technologies and features, and supports downstream optimizations of wind plant layouts and control strategies. In addition to the WPGNN, we include the code for the plant layout generator (PLayGen) playgen.py. This generator can produce random realizations of realistic wind plant layouts from one of the four canonical styles: cluster, single string, multiple string, or parallel string. The PLayGen_demo.ipynb notebook provides a demonstration of how to use the generator tool.

Harrison-Atlas, Dylan↗
DOE OSTI2021

Machine learning enables national assessment of wind plant controls with implications for land use

Summary As deployment of wind energy continues to expand, computationally efficient tools for predicting wind plant performance over a wide range of layout designs, technology innovations, and spatial locations are increasingly important for policy and investment decisions. We demonstrate two approaches to training a surrogate model to predict annual energy production (AEP) of parameterized wind plant layouts: one using a Gaussian process (GP) and the other using a fully convolutional neural network (FCNN). We leverage the powerful FCNN architecture by encoding wind plant design parameters and output response surface as an image. The FCNN produces more accurate results than the GP with mean absolute errors equivalent to 1% and 1.9% of plant rated power, respectively, although the GP performs well under limited training data and provides useful uncertainty information. We also evaluate a surrogate model for wake steering, enabling a nationwide assessment of the impact of plant control strategies and plant layout decisions. Across two million locations, we find that wake steering strategies boost AEP with relative gains upwards of 3%. Gains are most pronounced at sites without a dominant wind direction and where layout optimization is less fruitful. Additionally, we perform a nationwide sensitivity analysis showing that wake steering can mitigate wake losses from higher density plant layouts. Our results suggest that regions which have not been previously viable for wind deployment due to moderate wind resources are especially well enhanced by wake steering strategies that could help overcome land constraints and inflexible layout options, potentially identifying new deployment opportunities.

17 WIND ENERGY↗
DOE OSTI2020

Super resolution for root imaging

Premise High‐resolution cameras are very helpful for plant phenotyping as their images enable tasks such as target vs. background discrimination and the measurement and analysis of fine above‐ground plant attributes. However, the acquisition of high‐resolution images of plant roots is more challenging than above‐ground data collection. An effective super‐resolution (SR) algorithm is therefore needed for overcoming the resolution limitations of sensors, reducing storage space requirements, and boosting the performance of subsequent analyses. Methods We propose an SR framework for enhancing images of plant roots using convolutional neural networks. We compare three alternatives for training the SR model: (i) training with non‐plant‐root images, (ii) training with plant‐root images, and (iii) pretraining the model with non‐plant‐root images and fine‐tuning with plant‐root images. The architectures of the SR models were based on two state‐of‐the‐art deep learning approaches: a fast SR convolutional neural network and an SR generative adversarial network. Results In our experiments, we observed that the SR models improved the quality of low‐resolution images of plant roots in an unseen data set in terms of the signal‐to‐noise ratio. We used a collection of publicly available data sets to demonstrate that the SR models outperform the basic bicubic interpolation, even when trained with non‐root data sets. Discussion The incorporation of a deep learning–based SR model in the imaging process enhances the quality of low‐resolution images of plant roots. We demonstrate that SR preprocessing boosts the performance of a machine learning system trained to separate plant roots from their background. Our segmentation experiments also show that high performance on this task can be achieved independently of the signal‐to‐noise ratio. We therefore conclude that the quality of the image enhancement depends on the desired application.

Ruiz‐Munoz, Jose F.↗
DOE OSTI2024

MultiGreen: A multiplexing architecture for GreenGate cloning

Genetic modification of plants fundamentally relies upon customized vector designs. The ever-increasing complexity of transgenic constructs has led to increased adoption of modular cloning systems for their ease of use, cost effectiveness, and rapid prototyping. GreenGate is a modular cloning system catered specifically to designing bespoke, single transcriptional unit vectors for plant transformation—which is also its greatest flaw. MultiGreen seeks to address GreenGate’s limitations while maintaining the syntax of the original GreenGate kit. The primary limitations MultiGreen addresses are 1) multiplexing in series, 2) multiplexing in parallel, and 3) repeated cycling of transcriptional unit assembly through binary intermediates. MultiGreen efficiently concatenates bespoke transcriptional units using an additional suite of level 1acceptor vectors which serve as an assembly point for individual transcriptional units prior to final, level 2, condensation of multiple transcriptional units. Assembly with MultiGreen level 1 vectors scales at a maximal rate of 2*⌈ log 6 n ⌉+3 days per assembly, where n represents the number of transcriptional units. Further, MultiGreen level 1 acceptor vectors are binary vectors and can be used directly for plant transformation to further maximize prototyping speed. MultiGreen is a 1:1 expansion of the original GreenGate architecture’s grammar and has been demonstrated to efficiently assemble plasmids with multiple transcriptional units. MultiGreen has been validated by using a truncated violacein operon from Chromobacterium violaceum in bacteria and by deconstructing the RUBY reporter for in planta functional validation. MultiGreen currently supports many of our in-house multi transcriptional unit assemblies and will be a valuable strategy for more complex cloning projects.

Science & Technology - Other Topics↗
DOE OSTI2022

Registration of the sorghum carbon–partitioning nested association mapping (CP–NAM) population

The sorghum [ Sorghum bicolor (L.) Moench] carbon-partitioning nested association mapping (CP_NAM) (Reg. no. MP-4, NSL 542189 MAP) population was developed at Clemson University, SC, using 11 diverse, male founder accessions, each crossed with a recurrent female parent ‘Grassl’. The male parents represent all five major botanical races and the four major agronomic types: cellulosic (5), sweet (3), grain (2) and forage (1). A set of 11 recombinant inbred line (RIL) families CP_NAM01 to CP_NAM011 were maintained, which consisted of 2,484 (F 6 ) individuals. Each RIL family contained a minimum of 193 individuals (CP_NAM01) and a maximum of 287 individuals (CP_NAM06). For the development of this population, the founder lines were judiciously selected from the sorghum Bioenergy Association Panel based on carbon-partitioning phenotypes that make this population an ideal genetic resource for dissecting a wide range of agronomic and compositional traits for basic and applied research. The founder accessions of the CP_NAM were phenotypically characterized for various traits, including agronomic, biomass and related components, and additional compositional components. Each of the 11 F 6 RIL families of the CP_NAM were genotyped using genotyping-by-sequencing analysis, and 144,087 single nucleotide polymorphisms were generated for each individual. Genotypic information along with phenotypic data were used for the characterization of this population and to explore the range of phenotypes that permits the understanding of carbon-partitioning dynamics. This population is a unique resource for researchers to study a wide range of contrasting carbon-partitioning characteristics in sorghum to understand the genetic architecture underlying whole-plant carbon partitioning and allocation.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2023

Multigenic resistance to Xylella fastidiosa in wild grapes ( Vitis sps.) and its implications within a changing climate

Xylella fastidiosa is a bacterium that infects crops like grapevines, coffee, almonds, citrus and olives. There is little understanding of the genes that contribute to plant resistance, the genomic architecture of resistance, and the potential role of climate in shaping resistance, in part because major crops like grapevines (Vitis vinifera) are not resistant to the bacterium. Here we study a wild grapevine species, V. arizonica, that segregates for resistance. Using genome-wide association, we identify candidate resistance genes. Resistance-associated kmers are shared with a sister species of V. arizonica but not with more distant species, suggesting that resistance evolved more than once. Finally, resistance is climate dependent, because individuals from low (< 10 °C) temperature locations in the wettest quarter were typically susceptible to infection, likely reflecting a lack of pathogen pressure in colder climates. In fact, climate is as effective a predictor of resistance phenotypes as some genetic markers. We extend our climate observations to additional crops, predicting that increased pathogen pressure is more likely for grapevines and almonds than some other susceptible crops.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2025

Structural features of xylan dictate reactivity and functionalization potential for bio-based materials

Plant-based materials have the potential to replace some petroleum-based products, offering compostability and biodegradability as critical advantages. Xylan-rich biomass sources are gaining recognition due to their abundance and underutilization in current industrial applications. Research of potential xylan applications has been complicated by the complex and heterogeneous structure that varies for different xylan feedstocks. Acylation is a broadly used reaction in functionalization of polysaccharides at an industrial scale. However, the efficiency of this reaction varies with the xylan source. To optimize xylan valorization, a systematic understanding of structure–reactivity relationships is essential. This study explores, characterizes, and compares various xylan feedstocks in the acylation process. Xylan feedstocks were analyzed for their chemical composition, degree of polymerization, branching, solubility, and presence of impurities. These features were correlated with xylan glycotypes’ reactivity toward functionalization with succinic anhydride in an optimized DMSO/KOH condition, achieving carboxyl contents of up to 1.46. We used principal component analysis and hierarchical clustering to identify key structural features of xylan that promote its reactivity. Our findings reveal that xylans with higher xylose content and lower degrees of branching exhibit enhanced reactivity, achieving higher carboxyl content and yields. Structural analyses confirmed successful modification, and light scattering analyses showed dramatic changes in the solution properties. Succinylation improves the solubility and film-forming properties of native xylans. This study shows key structure–reactivity relationships in xylan succinylation, establishing that low branching, high xylose content, and reduced lignin impurity enhance chemical functionalization. The results offer a framework for selecting optimal biomass feedstocks and support future efforts in genetic and synthetic biology to design plants with tunable xylan architectures. These findings advance the hemicellulose valorization for applications in coatings and packaging.

Acylation↗
DOE OSTI2025

The spherical tokamak advanced reactor (STAR) fusion power plant design

Scientific and technical advancements have been made that improve fusion’s prospects to provide a new energy source, showing enhanced plasma confinement conditions with plasma temperatures reaching or exceeding 100 million degrees. Overshadowing this progress is the challenge involved in developing an economically viable fusion power plant design. Many proposed next-step DEMO and pilot plant designs are extensions of existing physics-focused experimental devices defined to understand and control plasma operations to achieve and sustain a fusion reaction. Transitioning scientific and technical advancements into a functional power plant requires a dedicated focus on architectural designs that integrate diverse technologies, while optimizing physics conditions, with a focus on economic viability. This holistic approach is essential in turning the promise of fusion energy into a reality. The Spherical Tokamak Advanced Reactor (STAR) is a fusion power plant conceptual design with the architectural focus that strives to balance physics, engineering, and cost considerations. In conclusion, it has been set up to introduce relevant physics, engineering and concept features that an intermediate pilot plant might follow, with the goal of meeting system performances and economic requirements that lead to a commercially competitive fusion power plant.

Blanket segmentation↗