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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 73 records · Page 4
All sourcesNASA NTRSDOE OSTI
DOE OSTI2021

Boosting the signal-to-noise of low-field MRI with deep learning image reconstruction

Recent years have seen a resurgence of interest in inexpensive low magnetic field (< 0.3 T) MRI systems mainly due to advances in magnet, coil and gradient set designs. Most of these advances have focused on improving hardware and signal acquisition strategies, and far less on the use of advanced image reconstruction methods to improve attainable image quality at low field. We describe here the use of our end-to-end deep neural network approach (AUTOMAP) to improve the image quality of highly noise-corrupted low-field MRI data. We compare the performance of this approach to two additional state-of-the-art denoising pipelines. We find that AUTOMAP improves image reconstruction of data acquired on two very different low-field MRI systems: human brain data acquired at 6.5 mT, and plant root data acquired at 47 mT, demonstrating SNR gains above Fourier reconstruction by factors of 1.5- to 4.5-fold, and 3-fold, respectively. In these applications, AUTOMAP outperformed two different contemporary image-based denoising algorithms, and suppressed noise-like spike artifacts in the reconstructed images. The impact of domain-specific training corpora on the reconstruction performance is discussed. The AUTOMAP approach to image reconstruction will enable significant image quality improvements at low-field, especially in highly noise-corrupted environments.

42 ENGINEERING↗
NASA NTRS2008

NASA Tech Briefs, October 2008

Topics covered include: Control Architecture for Robotic Agent Command and Sensing; Algorithm for Wavefront Sensing Using an Extended Scene; CO2 Sensors Based on Nanocrystalline SnO2 Doped with CuO; Improved Airborne System for Sensing Wildfires; VHF Wide-Band, Dual-Polarization Microstrip-Patch Antenna; Onboard Data Processor for Change-Detection Radar Imaging; Using LDPC Code Constraints to Aid Recovery of Symbol Timing; System for Measuring Flexing of a Large Spaceborne Structure; Integrated Formation Optical Communication and Estimation System; Making Superconducting Welds between Superconducting Wires; Method for Thermal Spraying of Coatings Using Resonant-Pulsed Combustion; Coating Reduces Ice Adhesion; Hybrid Multifoil Aerogel Thermal Insulation; SHINE Virtual Machine Model for In-flight Updates of Critical Mission Software; Mars Image Collection Mosaic Builder; Providing Internet Access to High-Resolution Mars Images; Providing Internet Access to High-Resolution Lunar Images; Expressions Module for the Satellite Orbit Analysis Program Virtual Satellite; Small-Body Extensions for the Satellite Orbit Analysis Program (SOAP); Scripting Module for the Satellite Orbit Analysis Program (SOAP); XML-Based SHINE Knowledge Base Interchange Language; Core Technical Capability Laboratory Management System; MRO SOW Daily Script; Tool for Inspecting Alignment of Twinaxial Connectors; An ATP System for Deep-Space Optical Communication; Polar Traverse Rover Instrument; Expert System Control of Plant Growth in an Enclosed Space; Detecting Phycocyanin-Pigmented Microbes in Reflected Light; DMAC and NMP as Electrolyte Additives for Li-Ion Cells; Mass Spectrometer Containing Multiple Fixed Collectors; Waveguide Harmonic Generator for the SIM; Whispering Gallery Mode Resonator with Orthogonally Reconfigurable Filter Function; Stable Calibration of Raman Lidar Water-Vapor Measurements; Bimaterial Thermal Compensators for WGM Resonators; Root Source Analysis/ValuStream[Trade Mark] - A Methodology for Identifying and Managing Risks; Ensemble: an Architecture for Mission-Operations Software; Object Recognition Using Feature-and Color-Based Methods; On-Orbit Multi-Field Wavefront Control with a Kalman Filter; and The Interplanetary Overlay Networking Protocol Accelerator.

Source record↗
NASA NTRS1983

Spatial variation of corn canopy temperature as dependent upon soil texture and crop rooting characteristics

A soil plant atmosphere model for corn (Zea mays L.) together with the scaling theory for soil hydraulic heterogeneity are used to study the sensitivity of spatial variation of canopy temperature to field averaged soil texture and crop rooting characteristics. The soil plant atmosphere model explicitly solves a continuity equation for water flux resulting from root water uptake, changes in plant water storage and transpirational flux. Dynamical equations for root zone soil water potential and the plant water storage models the progressive drying of soil, and day time dehydration and night time hydration of the crop. The statistic of scaling parameter which describes the spatial variation of soil hydraulic conductivity and matric potential is assumed to be independent of soil texture class. The field averaged soil hydraulic characteristics are chosen to be representative of loamy sand and clay loam soils. Two rooting characteristics are chosen, one shallow and the other deep rooted. The simulation shows that the range of canopy temperatures in the clayey soil is less than 1K, but for the sandy soil the range is about 2.5 and 5.0 K, respectively, for the shallow and deep rooted crops.

Choudhury, B. J.↗
DOE OSTI2020

Carbon flow through energycane agroecosystems established post-intensive agriculture

As part of an integrated energy and climate system, biomass production for bioenergy based on the tropical perennial C4 grass energycane can both offset fossil fuels and store soil carbon (C). We measured energycane yields, root biomass, soil C pools, and soil C stocks in a 4 year field trial and modeled C flow from plants to soils in the surface layer of no-till energycane planted after more than a century of intensive sugarcane agriculture. Aboveground yields ranged from 16.7 to 19.0 Mg C/ha over the 4 year trial. Although total C stocks did not significantly differ in the surface layer (approx. 0–20 cm) during the study, C in free and occluded light fractions decreased, whereas C in the mineral-rich dense fraction increased over 4 years. Belowground system inputs, estimated from measurements and informed by convergence in the final soil fraction model, were set to 2.5 Mg C ha -1 year -1 . With this input value, we estimated that surface soils retained photosynthetically fixed C predominantly within the mineral-associated organic matter pool for a mean and median transit time of 177 and 110 years, respectively. Although we did not model C flow to deep soil layers (approx. 0–100 cm), observed C accumulation (11.4 Mg C ha -1 year -1 ) and root growth down to 120 cm suggest that soil processes and resulting C sequestration at the surface are likely to persist deeper into the soil profile. Energycane, as a strong candidate for climate change mitigation and land degradation remediation, showed high biomass yields and allocation of resources to roots, with sequestered soil C expected to persist for over a century.

09 BIOMASS FUELS↗
NASA NTRS2023

3D Printed Substrate

In preparation for long-term, manned, deep space missions, NASA requires a sustainable system for crop and food production. This system has a variety of benefits, including a fresh food supply, improvements in air quality, a lower need to resupply and psychological benefits for the gardeners. Specifically, NASA is attempting to improve and iterate their Vegetable Production System (VEGGIE), a plant growth unit currently aboard the International Space Station (ISS). The VEGGIE unit is highly dependent on a variety of factors, including passive water delivery, growth lights, rooting pillows and cabin conditions. While previous teams have improved and redesigned water reservoirs, the primary objective of the project was to create a new substrate unit to replace the current rooting pillow design. The current design consists of an electrostatic bag filled with arcillite and a slow-release fertilizer pellet. The prototype design retains the fertilizer pellet for nutrient consistency, however the outer bag and arcillite fillings have been replaced with a 3D printed lattice block. This lattice block has several key points that allow it to function in similar ways to the current design: 1. The lattice planes are porous, with each pore offset such that the overall porosity is the same as the arcillite filling. 2. The block demonstrates a wicking nature and is able to pull water upwards towards the roots. This minimizes time needed to integrate with the existing water reservoir. 3. The filament used is highly flexible and is able to pull apart to accommodate root growth. 4. The lattice planes are connected by microfibers left behind from the printing process. These fibers provide support for the growing roots and keep the lattice planes properly aligned. The substrate block design seeks to reduce the payload costs by being entirely 3D printed. While filament would still need to be provided to the ISS, it is far less expensive than shipping arcillite due to the significantly lower weight. The final design has iterated the lattice plane concept and utilizes vertically placed planes with pores running parallel to the water reservoir. This design has reliably shown water uptake and retention and has been successful in growing multiple romaine lettuce plants. Future work should include further growth testing using multiple plant species, compost and reusability testing, food safety testing and microgravity growth testing.

Affan Bhutta↗
DOE OSTI2025

S AP F LOWER : an automated tool for sap flow data preprocessing, gap-filling, and analysis using deep learning

Sap flow, a critical process in plant water use and ecosystem water cycles, is often measured using thermal dissipation probes (TDP) due to their ease of installation and continuous data collection. However, sap flow data frequently include noise, outliers, and gaps, creating challenges for analysis and requiring substantial manual processing. We developed S AP F LOWER , a tool that automates data preprocessing, model training, gap-filling, sapwood area scaling and modeling, and water use analysis. It integrates autocleaning, machine learning and deep learning models (e.g. random forest, Gaussian process regression, long short-term memory (LSTM), bidirectional LSTM (BiLSTM)), and efficient workflows to process sap flow data. S AP F LOWER can remove over 90% of noisy data while preserving legitimate variations and achieve high accuracy in gap-filling based on user-determined parameters. Random forest, LSTM, and BiLSTM models reduced root mean square error to 10% or less for long-term gaps. Model training and prediction can be performed efficiently within seconds. S AP F LOWER significantly enhances the efficiency and accessibility of TDP data analysis by automating complex tasks, enabling researchers without programming expertise to employ advanced techniques. Future improvements will focus on species-specific corrections for TDP and support for additional measurement methods. S AP F LOWER is openly available on GitHub (https://github.com/JiaxinWang123/SapFlower) and Zenodo (doi: 10.5281/zenodo.13665919).

ecosystem water balance↗
DOE OSTI2024

Physics-informed State-space Neural Networks for transport phenomena

This work introduces Physics -informed State -space neural network Models (PSMs), a novel solution to achieving real-time optimization, flexibility, and fault tolerance in autonomous systems, particularly in transportdominated systems such as chemical, biomedical, and power plants. Traditional data -driven methods fall short due to a lack of physical constraints like mass conservation; PSMs address this issue by training deep neural networks with sensor data and physics -informing using components' Partial Differential Equations (PDEs), resulting in a physics -constrained, end -to -end differentiable forward dynamics model. Further, through two in silico experiments - a heated channel and a cooling system loop - we demonstrate that PSMs offer a more accurate approach than a purely data -driven model. In the former experiment, PSMs demonstrated significantly lower average root -mean -square errors across test datasets compared to a purely data -driven neural network, with reductions of 44 %, 48 %, and 94 % in predicting pressure, velocity, and temperature, respectively. Beyond accuracy, PSMs demonstrate a compelling multitask capability, making them highly versatile. In this work, we showcase two: supervisory control of a nonlinear system through a sequentially updated state -space representation and the proposal of a diagnostic algorithm using residuals from each of the PDEs. The former demonstrates PSMs' ability to handle constant and time -dependent constraints, while the latter illustrates their value in system diagnostics and fault detection.

42 ENGINEERING↗
DOE OSTI2021

Detection of Outliers in LiDAR Data Acquired by Multiple Platforms over Sorghum and Maize

High-resolution point cloud data acquired with a laser scanner from any platform contain random noise and outliers. Therefore, outlier detection in LiDAR data is often necessary prior to analysis. Applications in agriculture are particularly challenging, as there is typically no prior knowledge of the statistical distribution of points, plant complexity, and local point densities, which are crop-dependent. The goals of this study were first to investigate approaches to minimize the impact of outliers on LiDAR acquired over agricultural row crops, and specifically for sorghum and maize breeding experiments, by an unmanned aerial vehicle (UAV) and a wheel-based ground platform; second, to evaluate the impact of existing outliers in the datasets on leaf area index (LAI) prediction using LiDAR data. Two methods were investigated to detect and remove the outliers from the plant datasets. The first was based on surface fitting to noisy point cloud data via normal and curvature estimation in a local neighborhood. The second utilized the PointCleanNet deep learning framework. Both methods were applied to individual plants and field-based datasets. To evaluate the method, an F-score was calculated for synthetic data in the controlled conditions, and LAI, the variable being predicted, was computed both before and after outlier removal for both scenarios. Results indicate that the deep learning method for outlier detection is more robust than the geometric approach to changes in point densities, level of noise, and shapes. The prediction of LAI was also improved for the wheel-based vehicle data based on the coefficient of determination (R2) and the root mean squared error (RMSE) of the residuals before and after the removal of outliers.

36 MATERIALS SCIENCE↗
DOE OSTI2024

Using phenology to unravel differential soil water use and productivity in a semiarid savanna

Savannas are water-limited ecosystems characterized by two dominant plant types: trees and an understory primarily made up grass. Different phenology and root structures of these plant types complicate how savanna primary productivity responds to changes in water availability. We tested the hypothesis that productivity in savannas is controlled by the temporal and vertical distribution of soil water content (SWC) and differences in growing season length of understory and tree plant functional types. To quantify the relationship between tree, understory, and savanna-wide phenology and productivity, we used PhenoCam and satellite observations surrounding an eddy covariance tower at a semiarid savanna site in Arizona, USA. We distinguished between SWC across two different depth intervals (shallow, <0–30 cm and deep, >30–100 cm). We found that tree greenness increased with SWC at both depths, while understory greenness was only sensitive to the shallower SWC measurements. Onset of ecosystem dormancy, estimated from satellite observations close to the eddy covariance tower, explained more variability in annual gross primary productivity (GPP) than in other phenometrics. Higher SWC led to an extended growing season, caused by delayed dormancy in trees, but the understory showed no evidence of delayed dormancy in wetter periods. We infer that the timing of ecosystem scale dormancy, driven by trees, is important in understanding changes in a savanna's GPP. These findings highlight the important effects of rainfall during the winter. These findings suggest that savanna GPP is conditional on different responses to moisture availability in each of the dominant vegetation components.

54 ENVIRONMENTAL SCIENCES↗
NASA NTRS2019

Plant Microbiomes May Provide Vital Information to Plant Success

Plant associated microbiomes, the rhizosphere and phyllosphere, are composed of communities of bacteria and fungi that may be mutualistic or pathogenic. These communities have the potential to influence plant health and development and can affect plant growth. Crop plants are being investigated as a fresh and safe supplement to astronauts’ diet and it is critical to understand and characterize these microbial communities. Multi-species crops, Mizuna mustard (Brassica rapa var japonica), ‘Outredgeous’ red romaine lettuce (Lactuca sativa), and Waldman’s Green lettuce (Lactuca sativa) were grown in two Veggie units on the International Space Station (ISS) for three grow outs in various combinations of plant types. Upon harvest, plant and pillow samples were frozen and returned to Earth for analysis. Bacterial and fungal community analyses for plant leaf and root, as well as pillow components, wick and media, were completed using next generation sequencing with the goal of surveying the composition of the entire community and identifying any potential pathogens. Bacteria were identified using the 16S rRNA gene whereas, fungi were identified with the internal transcribed spacer (ITS). The community composition for these three crops was compared between crop types and between plant tissue types. It is vital to mission success for the short term and long term to add nutritious, safe to eat vegetables providing a supplement to the crew members’ dietary requirements as well as to develop planning for deep space missions as we reach for the moon and on to Mars. Veggie technology validation tests were supported by NASA’s Space Biology Program.

Khodadad, Christina L.↗
DOE OSTI2024

Bioenergy sorghum nodal root bud development: morphometric, transcriptomic and gene regulatory network analysis

Bioenergy sorghum’s large and deep nodal root system and associated microbiome enables uptake of water and nutrients from and deposition of soil organic carbon into soil profiles, key contributors to the crop’s resilience and sustainability. The goal of this study was to increase our understanding of bioenergy sorghum nodal root bud development. Sorghum nodal root bud initiation was first observed on the stem node of the 7 th phytomer below the shoot apex. Buds were initiated near the upper end of the stem node pulvinus on the side of the stem opposite the tiller bud, then additional buds were added over the next 6-8 days forming a ring of 10-15 nascent nodal root buds around the stem. Later in plant development, a second ring of nodal root buds began forming on the 17 th stem node immediately above the first ring of buds. Overall, nodal root bud development can take ~40 days from initiation to onset of nodal root outgrowth. Nodal root buds were initiated in close association with vascular bundles in the rind of the pulvinus. Stem tissue forming nascent nodal root buds expressed sorghum homologs of genes associated with root initiation (WOX4), auxin transport (LAX2, PIN4), meristem activation (NGAL2), and genes involved in cell proliferation. Expression of WOX11 and WOX5, genes involved in root stem niche formation, increased early in nodal root bud development followed by genes encoding PLTs, LBDs (LBD29), LRP1, SMB, RGF1 and root cap LEAs later in development. A nodal root bud gene regulatory network module expressed during nodal root bud initiation predicted connections linking PFA5, SPL9 and WOX4 to genes involved in hormone signaling, meristem activation, and cell proliferation. A network module expressed later in development predicted connections among SOMBRERO, a gene involved in root cap formation, and GATA19, BBM, LBD29 and RITF1/RGF1 signaling. Overall, this study provides a detailed description of bioenergy sorghum nodal root bud development and transcriptome information useful for understanding the regulation of sorghum nodal root bud formation and development.

09 BIOMASS FUELS↗
DOE OSTI2021

Deep Analysis Net with Causal Embedding for Coal-fired Power Plant Fault Detection and Diagnosis (DANCE4CFDD)

Fault detection and diagnosis is critical to power plant operation to ensure attaining high reliability while reducing operation cost. As more renewable power is introduced to the power grid, traditional fossil power plants take on the extra burden of excessive load cycling to compensate the generation variability from renewable power. Such load cycling will pose more reliability challenges to power plant operation. There are a number of challenges faced by today’s asset health management system in coal- fired (or gas) power plants: 1) high-dimensional nonlinear interaction among multiple time series measurements; 2) high measurement variance induced by operational conditions/modes; 3) variation among asset types and plant configurations; and 4) a small number of faulty events to learn from. To cope with these challenges, today’s fielded asset health management systems rely heavily on manual efforts from domain experts and hand-crafted features or rules based on domain knowledge. Despite its role in plant reliability, such a practice is costly and hinders its scalability and sustainability, particularly when a plant undergoes modifications. The objective of this project is to develop a novel end-to-end AI learning system that is trainable (i.e., the AI representation of a complex system behavior can be directly learned from properly labeled data) for accurate fault detection and root cause analysis. The ability to create a fault detection model directly from time series could alleviate the efforts associated with today’s asset management solution development. In the course of this project, we have achieved the following: Created an AI model development environment incorporating state-of-the-art neural network architectures for rapid model development and evaluation; Developed novel learning strategies for training of fault detection model; Developed special-purpose neural network architecture embedded with variable association graph aiming for better interpretability; Developed a learning strategy to leverage a small number of faulty events for enhanced fault detection capability; Conducted detailed experimental study based on public benchmark datasets and demonstrated the effectiveness of the proposed solution; and Validated the developed system with data from both a coal-fired plant boiler dynamic simulation model and real-world coal-fired power plant covering multiple asset and fault types. Overall, the project attained a technology readiness level of TRL 5 from TRL 2 at the beginning of the project.

20 FOSSIL-FUELED POWER PLANTS↗
DOE OSTI2020

The effects of different types of mulch on soil properties and tea production and quality

Abstract BACKGROUND Tea is an important economic crop in China. Mulching, a modern agricultural practice, can modify the soil microenvironment and maintain the crop yield. RESULTS To investigate the effect of different mulching modes on tea plant growth, filed experiments were conducted in a Shizipu tea plantation located in Langxi Country (Xuanchen City, Anhui Province, China). Five treatments were carried out in a randomized complete block arrangement: (i) clean tillage (control); (ii) black plastic film; (iii) weed barrier fabric; (iv) rice straw mulch; and (v) intercropping with Vulpia myuros . The effects of different mulch modes on soil temperature, water moisture, soil compactness, root activity, soil enzyme activity and nutrition status on tea yield, quality and economic benefits were compared at the harvest stage. In the present study, compared with other mulch treatments, intercropping with V. myuros significantly reduced the topsoil temperature (to an optimum temperature) and soil compactness, and increased the water holding capacity in the deep soil layer, which contributed to increased tea root activity and respiration. Furthermore, intercropping with V. myuros significantly increased soil enzymes activity, soil organic matter, and the available nitrogen and phosphorus concentrations in the main root zone. Therefore, the stronger tea root activity accelerated nutrition uptake and increased the tea yield and quality‐related components of the tea, thus resulting in a larger average net income. CONCLUSION Intercropping with V. myuros could serve as a profitable agricultural method for tea production. © 2020 Society of Chemical Industry

Xianchen, Zhang↗
DOE OSTI2025

Root exudate lipids: Uncovering chemodiversity and carbon stability potential

Root-derived carbon has been shown to contribute more to soil carbon stocks than aboveground litter. Yet the molecular chemodiversity of root exudates remains poorly understood due to limited characterization and annotation. In this study, we characterized the molecular chemodiversity and production of metabolites and lipids in root exudates from field grown mature tall wheatgrass (Thinopyrum ponticum). We discovered a diversity of lipids, including substantial levels of triacylglycerols (∼19 μg/g fresh root per min), fatty acyls, sphingolipids, sterol lipids, and glycerophospholipids, some of which have not been previously documented in root exudates. By integrating tandem mass spectral library searching and deep learning-based chemical class assignment, our metabo-lipidomics approach significantly expanded the known molecular diversity of root exudates. Rates of lipid derived carbon production were approximately double that of polar metabolites (lipids: 81.52 ± 13.81 vs polar metabolites: 38.41 ± 5.93 μg C g −1 fresh root mass min −1 ) with an order of magnitude higher carbon to nitrogen ratios (lipids: 459 ± 90 vs polar metabolites: 14.40 ± 0.58). Exudate lipids displayed highly negative nominal oxidation state of carbon (−1.182 to −1.909), indicating that these compounds may be less favorable for microbial decomposition. Together our results suggest the potential of root exudate lipids to contribute to stable carbon pools in soil, supporting long-term carbon storage. This work advances understanding of plant-derived lipid inputs to soil and underscores the need for future studies on the functional roles of lipids in shaping root-microbe-soil interactions, microbial activity, soil structure, and nutrient availability – contributing to soil health.

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

NIST: Soil Respiration, Moisture, Temperature, Chemistry; and Fine Root Measurements from a Transect Through a Forest Edge, Gaithersburg, Maryland, 2017-2021

This dataset contains soil respiration, moisture, temperature, and chemistry, as well as fine root measurements from the National Institute of Standards and Technology (NIST) Forested Optical Reference for Evaluating Sensor Technology (FOREST) research facility at Gaithersburg, Maryland. Measurements were taken at an existing transect array that begins in a grassy meadow, crosses a sharp forest edge, then a small stream, and finally extends upwards in the interior of the forest at the top of a ridge. There are 6 different landscape positions replicated across three transects in the array. Soil respiration was measured during growing seasons in 2017-2019 (2017-06-02 to 2020-02-27). Pedons (1 m3) were isolated from surrounding tree roots using trenching and a fabric to inhibit root ingrowth. Flux measurements inside the pedons were thus assumed to represent heterotrophic only respiration in 2019, and these fluxes were paired with nearby fluxes assumed to represent total respiration. Deep vertical probes measured volumetric moisture content and temperature at the same points in the array every 10 cm in depth to either 90 cm or 120 cm total depth, at 15 minute intervals, from 2019-2021 (2019-07-09 to 2021-09-10). Soil core samples were collected from each of the array points for three different months in early- to mid-2019 (2019-03-19 to 2019-07-10), at three depths each. Soils were analyzed for gravimetric moisture content; pH; total carbon, nitrogen, and phosphorus; texture; microbial biomass carbon, nitrogen, and phosphorus; extractable dissolved organic carbon, nitrogen, and phosphorus; extractable nitrate and ammonia; and extracellular hydrolytic enzyme activities. The fine roots were separated from the cores and segregated by plant functional type (grass or tree species) and if they were dead or alive. Fine roots were then measured for length, surface area, diameter, and dry mass. This dataset contains four data files in comma separated (*.csv) format. These data serve to deepen our understanding of root and soil processes at forest edges and in transitional zones.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2021

Soil Water Potentials (1990-2018) from a calibrated ELM-FATES, and rooting depth analyses scripts, PA-BCI, Panama

This dataset has ELM-FATES simulation outputs and scripts that accompany and reproduce the manuscript, Chitra-Tarak et al. (2021) Hydraulically-vulnerable trees survive on deep-water access during droughts in a tropical forest. New Phytologist. https: //doi.org/10.1111/nph.17464”. This dataset is organized into four parts: (1) source code for the model, Energy Exascale Earth System Land Model-Functionally Assembled Advanced Terrestrial simulator (ELM-FATES) (2) R scripts associated with ELM-FATES simulations: to prepare files for parameter ensemble simulation runs, calibrate the model with observed hydrological fluxes observed at BCI, re-run the model with tuned parameters and obtain long-term soil matric potentials by depth (3) ELM-FATES hydrological simulation outputs at PA-BCI, Panama (1990-2018, daily), and (4) R scripts for inverse rooting depth modeling and post-processing: prepare datasets and inversely model tree rooting depths at BCI, test hypotheses about the relationship of rooting depths with plant hydraulic traits and drought-induced mortality, and reproduce the manuscript itself. n.b. 1. Data needed to run R scripts in (2) and (4) and thus to reproduce the manuscript are sourced from various public datasets. Refer to the data availability section of the accompanying manuscript. 2. This dataset version (prepared on May 12, 2021) matches the revised and published version of the accompanying manuscript (published May 16, 2021) replacing a previous version. In the publication, this dataset is cited as follows: Chitra-Tarak R, Xu C, Aguilar S, Anderson-Teixeira K, Chambers J, Detto M, Faybishenko B, Fisher R, Knox R, Koven C et al. 2020. Soil water potentials (1990–2018) from a calibrated ELM-FATES, and rooting depth analyses scripts, PA-BCI, Panama. 2.0. NGEE Tropics Data Collection. doi: 10.15486/ngt/1696806

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2023

Impact of bioenergy feedstock carbon farming on sustainable aviation fuel viability in the United States

Biomass-derived sustainable aviation fuel holds significant potential for decarbonizing the aviation sector. Its long-term viability depends on crop choice, longevity of soil organic carbon (SOC) sequestration, and the biomass-to-biojet fuel conversion efficiency. We explored the impact of fuel price and SOC value on viable biojet fuel production scale by integrating an agroecosystem model with a field-to-biojet fuel production process model for 1,4-dimethylcyclooctane (DMCO), a representative high-performance biojet fuel molecule, from Miscanthus, sorghum, and switchgrass. Assigning monetary value to SOC sequestration results in substantially different outcomes than an increased fuel selling price. If SOC accumulation is valued at $185/ton CO 2 , planting Miscanthus for conversion to DMCO would be economically cost-competitive across 66% of croplands across the continental United States (US) by 2050 if conventional jet fuel remains at $0.74/L (in 2020 US dollars). Cutting the SOC sequestration value in half reduces the viable area to 54% of cropland, and eliminating any payment for SOC shrinks the viable area to 16%. If future biojet fuel prices increase to $1.24/L-Jet A-equivalent, 48 to 58% of the total cultivated land in the United States could support a more diverse set of feedstocks including Miscanthus, sorghum, or switchgrass. Among these options, only 8–14% of the area would be suitable exclusively for Miscanthus cultivation. These findings highlight the intersection of natural solutions for carbon removal and the use of deep-rooted feedstocks for biofuels and biomanufacturing. The results underscore the need to establish clear and consistent values for SOC sequestration to enable the future bioeconomy.

09 BIOMASS FUELS↗
NASA NTRS2008

Experiments Advance Gardening at Home and in Space

Aeroponics, the process of growing plants suspended in air without soil or media, provides clean, efficient, and rapid food production. Crops can be planted and harvested year-round without interruption, and without contamination from soil, pesticides, and residue. Aeroponic systems also reduce water usage by 98 percent, fertilizer usage by 60 percent, and eliminate pesticide usage altogether. Plants grown in aeroponic systems have been shown to absorb more minerals and vitamins, making the plants healthier and potentially more nutritious. The suspended system also has other advantages. Since the growing environment can be kept clean and sterile, the chances of spreading plant diseases and infections commonly found in soil and other growing media are greatly reduced. Also, seedlings do not stretch or wilt while their roots are forming, and once the roots are developed, the plants can be easily moved into any type of growing media without the risk of transplant shock. Lastly, plants tend to grow faster in a regulated aeroponic environment, and the subsequent ease of transplant to a natural medium means a higher annual crop yield. For example, tomatoes are traditionally started in pots and transplanted to the ground at least 28 days later; growers using an aeroponic system can transplant them just 10 days after starting the plants in the growing chamber. This accelerated cycle produces six tomato crops per year, rather than the traditional one to two crop cycles. These benefits, along with the great reduction in weight by eliminating soil and much of the water required for plant growth, illustrate why this technique has found such enthusiastic support from NASA. Successful long-term missions into deep space will require crews to grow some of their own food during flight. Aeroponic crops are also a potential source of fresh oxygen and clean drinking water, and every ounce of food produced and water conserved aboard a spacecraft reduces payload weight, decreasing launch costs and freeing room for other cargo.

Source record↗