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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 271 records · Page 15
All sourcesNASA NTRSDOE OSTI
NASA NTRS2021

NASA's Surface Biology and Geology Designated Observable: A Perspective on Surface Imaging Algorithms

The 2017–2027 National Academies’ Decadal Survey, Thriving on Our Changing Planet, recommended Surface Biology and Geology (SBG) as a “Designated Targeted Observable” (DO). The SBG DO is based on the need for capabilities to acquire global, high spatial resolution, visible to shortwave infrared (VSWIR; 380–2500 nm; ~30 m pixel resolution) hyperspectral (imaging spectroscopy) and multispectral midwave and thermal infrared (MWIR: 3–5 μm; TIR: 8–12 μm; ~60 m pixel resolution) measurements with sub-monthly temporal revisits over terrestrial, freshwater, and coastal marine habitats. To address the various mission design needs, an SBG Algo-rithms Working Group of multidisciplinary researchers has been formed to review and evaluate the algorithms applicable to the SBG DO across a wide range of Earth science disciplines, including terrestrial and aquatic ecology, atmospheric science, geology, and hydrology. Here, we summarize current state-of-the-practice VSWIR and TIR algorithms that use airborne or orbital spectral imaging observations to address the SBG DO priorities identified by the Decadal Survey: (i) terrestrial vegetation physiology, functional traits, and health; (ii) inland and coastal aquatic ecosystems physiology, functional traits, and health; (iii) snow and ice accumulation, melting, and albedo; (iv) active surface composition (eruptions, landslides, evolving landscapes, hazard risks); (v) effects of changing land use on surface energy, water, momentum, and carbon fluxes; and (vi) managing agriculture, natural habitats, water use/quality, and urban development. We review existing algorithms in the following categories: snow/ice, aquatic environments, geology, and terrestrial vegetation, and summarize the community-state-of-practice in each category. This effort synthesizes the findings of more than 130 scientists.

Aquatic↗
NASA NTRS2022

Canopy height and climate dryness parsimoniously explain spatial variation of unstressed stomatal conductance

The spatio-temporal variation of stomatal conductance directly regulates photosynthesis, water partitioning, and biosphere-atmosphere interactions. While many studies have focused on stomatal response to stresses, the spatial variation of unstressed stomatal conductance remains poorly determined, and is usually characterized in land surface models (LSMs) simply based on plant functional type (PFT). Here, we derived unstressed stomatal conductance at the ecosystem-scale using observations from 115 global FLUXNET sites. When aggregated by PFTs, the across-PFT pattern was highly consistent with the parameterizations of LSMs. However, PFTs alone captured only 17% of the variation in unstressed stomatal conductance across sites. Within the same PFT, unstressed stomatal conductance was negatively related to climate dryness and canopy height, which explained 45% of the total spatial variation. Our results highlight the importance of plant environment interactions in shaping stomatal traits. The trait-environment relationship established here provides an empirical approach for improved parameterizations of stomatal conductance in LSMs.

Yanlan Liu↗
NASA NTRSDate not supplied

Autonomous In-space Construction, Maintenance, and Reconfiguration Using Programmable Meta-Material

NASA ARC's Coded Structures Laboratory (CSL) is developing autonomous construction, maintenance, and reconfiguration technologies to meet long-duration and deep space infrastructure needs, in accordance with long-term NASA goals of "in-space reliance" and "mass-less exploration." We seek to achieve these capabilities by utilizing a "programmable meta-material" approach that integrates emerging advances in materials (mechanical meta-materials), manufacturing (cooperative mobile robotics), and autonomy (multi-agent planning algorithms). Through the ARMADAS project, we have shown assembly of high-performance engineered cellular materials using multiple cooperating mobile robotic assemblers. In this paper, we describe how such a programmable meta-material architecture may shift the paradigm of how we design, build, manufacture, and operate future space infrastructure and assets. The core of a programmable meta-material architecture consists of 3 main technology sub-areas: the structure, the assembly agents, and the assembly algorithms. We co-design these systems to ensure an adaptable system that can create and reconfigure structures from a base set of building block components. From this core technology, we can branch out and expand the capability of the system through additional secondary component types and robotic agents to perform activities such as inspections, maintenance, repair, payload installation, or perform power and communications interconnect. As these technologies mature, future designers will be able to utilize the system to rapidly integrate and operate assets in space or on planetary surfaces from a set of well-tested part library, or create their own modules to integrate into the system. A core trait to the development of this system is the automation approach. Because of the modular and functional discrete (pixel-like) nature of the structural system, a diverse set of powerful algorithms for analysis, planning, and simulation can be adapted and leveraged to optimize construction, maintenance, and dynamic reorganization (as hardware with programmable form and function). With an ability to free the design space from launch vehicle constraints and fundamentally shift how a mission is designed and conducted, we discuss the influence of a programmable meta-material architecture on mission design, build, and operations. For the "design phase", we discuss project lifecycle effects, costs, time, and performance. For the "build phase", we discuss reusability, ISRU, manufacturing, material logistics, and scalability. And for "operations", we discuss autonomy, maintenance and upgrades, reliability, and reconfiguration. Autonomy and modularity are the primary enabling traits of this system. Engineering systems that utilize a modular and reconfiguration building block approach such as digital communication and computation systems, currently lead all other areas of technology in size and complexity scalability. NASA is extending the benefits and flexibility of digital systems to hardware systems, to optimize materials lifecycle management and expand our space exploration mission capabilities.

in space assembly↗
NASA NTRSDate not supplied

Quantitative Phenotypic Analysis of Arabidopsis Thaliana Grown in Microgravity Using Soap, an Applied Artificial Intelligence

Phenotypic analysis is an essential step in studying the gravitropic responses and gravitational stress experienced by plants grown in microgravity. Many of the phenotypic traits analyzed in gravitropism studies, such as root length, leaf area, secondary root count, and number of root hairs, currently rely upon manual measurement methods for quantification. However, new advances in data analysis technology using artificial intelligence offer an opportunity for more efficient phenotypic analysis and a reduction of time spent in the data collection phase. In this project, the ability of a new artificially intelligent data collection software, SOAP (Simple Object Access Protocol), to collect and quantify phenotypic traits of Arabidopsis thaliana will be assessed. This project will test the measurements taken by an initial draft of the software. SOAP will take measurements of shoot length, a key phenotype used to assess A. thaliana stress response when grown in microgravity conditions. Shoot length measurements made by SOAP will be compared against a series of manual shoot length measurements. The comparison between the two methods of data measurement will provide valuable insight into the relative accuracy of SOAP and the margin of human error when conducting lab measurements.

Arabidopsis↗
NASA NTRS2022

Effects of Amazonian Flying Rivers on Frog Biodiversity and Populations in the Atlantic Rainforest

Given the speed at which humans are changing the climate, species with high degrees of endemism may not have time to avoid extinction through adaptation. We investigated through teleconnection analysis the origin of rainfall that determines the phylogenetic diversity of rainforest frogs and the effects of microclimate differences in shaping the morphological traits of isolated populations (which contribute to greater phylogenetic diversity and speciation). We also investigated through teleconnection analysis how deforestation in Amazonia can affect ecosystem services that are fundamental to maintaining the climate of the Atlantic rainforest biodiversity hotspot. Seasonal winds known as flying rivers carry water vapor from Amazonia to the Atlantic Forest, and the breaking of this ecosystem service could lead Atlantic Forest species to population decline and extinction in the short term. Our results suggest that the selection of morphological traits that shape Atlantic Forest frog diversity and their population dynamics are influenced by the Amazonian flying rivers. Our results also suggest that the increases of temperature anomalies in the Atlantic Ocean due to global warming and in the Amazon forest due to deforestation are already breaking this cycle and threaten the biodiversity of the Atlantic Forest hotspot.

anurans↗
NASA NTRS2024

Global Nitrogen Needs to Improve Wheat Yield Under Climate Change

Increasing global food demand will require more food production without further exceeding the planetary boundaries, while at the same time adapting to climate change. We used an ensemble of wheat simulation models, with improved yield traits from the highest-yielding wheat genotypes to quantify potential yield gains and associated N requirements. This was explored for current and climate change scenarios across representative sites of major world wheat producing regions. The improved yield traits increased yield by 16% with current N fertilizer applications under both current climate and mid-century climate change scenarios. To achieve the full yield potential, a 52% increase in global average yield under a mid-century high warming climate scenario (RCP8.5), fertilizer use would need to increase fourfold over current use, which would unavoidably lead to higher environmental impacts from wheat production. Our results show the need to improve soil N availability and N use efficiency, along with yield potential.

Nitrogen↗
NASA NTRSDate not supplied

Developing a Genetic Variant Calling Pipeline for Quantifying the Complex Mutagenic Load Accumulated in BioNutrients-1 Production Pack Samples

Microorganisms hold great promise for on demand production of labile nutrients and pharmaceuticals as well recycling and in situ resource utilization. The utilization of microorganisms for such tasks on space missions is hindered by the limited data on how microbes respond to spaceflight. For example, the genetic stability of microorganisms, and the genomic engineered traits added to deliver desired functions, over long-term storage in the spacecraft environment is poorly understood. The BioNutrients-1 (BN-1) mission conducted a 5-year study of desiccated storage in Low Earth Orbit (LEO) to evaluate the suitability of eight synthetic biology chassis organisms for long-duration space missions. We are employing high-depth, whole genome sequencing (WGS) to determine the mutagenic load that accumulated during long-term storage. Mutation analysis pipelines are well established for homogenous culture grown from a single colony, but the mutational landscape of the BN-1 samples present a unique analysis challenge, as every cell in the BN-1 samples had a unique genetic journey of DNA damage and repair. Consequently, sequence variants are expected at low allele frequency within samples. To address this genetic complexity, we apply two distinct computational approaches to identify mutations in pre-existing WGS data collected from populations of Chlamydomonas reinhardtii that were exposed to UV mutagenesis and growth in LEO. For reference genome free mutation detection, we utilized DiscoSNP++, which is a de Bruijn graph approach. For reference genome-based mutation detection we utilize GATK for Microbes, which is a Bayesian probabilistic approach. We will benchmark these approaches against the mutations originally identified using CRISP, a method optimized for pooled samples. Ultimately, quantifying the mutation load imposed by storage or growth on the ISS will help identify chassis organisms with both high levels of genome stability and viability, which are desirable traits for implementation of bioproduction in long-duration missions.

SNP↗
DOE OSTI2024

Shifts in belowground processes along a temperate forest edge

Abstract Context Forests are increasingly fragmented, and as a result most forests in the United States are within one km of an edge. Edges change environmental conditions of the forest—especially radiation, roughness, temperature, and moisture—that can have consequences for plant productivity and ecosystem functions. However, edge effects on aboveground characteristics of plants and the environment are better understood relative to plant roots and soil in the belowground environment. Objectives Our main objectives were to determine if soil C pools and fluxes are higher at the edge relative to other landscape positions, and to understand how specific belowground processes contribute to bulk differences in pools and fluxes. Methods We measured environmental conditions, live and dead fine root traits, soil chemistry, and soil respiration along a 75 m transect from interior forest to meadow in Gaithersburg, MD. Results We observed differences in the soil chemical, biological and hydrological environment between the forest interior, edge and adjacent meadow. In some cases, the forest edge represented a mid-point in environmental or belowground characteristics between the forest interior and meadow ( e.g. , pH, C-to-N ratio [C:N], live fine root biomass, heterotrophic respiration), likely reflecting the change in litter type and quality associated with the transition from grass to woody species. In other cases, neighboring landscape positions were different from the forest edge, which was drier and had higher dead fine root biomass. Although soil C contents were not significantly different across landscape positions, there was a tendency towards higher average soil C content at the edge relative to other landscape positions, suggesting that increased C loss related to root decay and greater soil respiration at the edge relative to the forest interior may have been offset by increased C gain from high plant productivity and subsequent inputs to soil. Conclusions This research provides insight into how forest edge environments may differ from the interior and how concurrent processes above- and belowground may contribute to those differences.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2025

Leveraging hyperspectral phenotyping for accurate, non-destructive prediction of metabolite profiles in poplar under drought stress

Accurately predicting drought tolerance in woody perennial bioenergy crops is critical for sustainable biomass production under fluctuating precipitation. Hyperspectral imaging (HSI) in the visible-near-infrared (VNIR) and shortwave-infrared (SWIR) ranges offers a promising approach for predicting plant biochemical traits, yet its application in metabolite profiling remains underexplored. We integrated VNIR+SWIR HSI with untargeted metabolomics to investigate drought-induced metabolic shifts in Populus leaves from eight Populus genotypes. Metabolite profiling identified 127 compounds, with 73 showing significant drought responses spanning amino acids (AA), carbohydrates (CHO), phenolic glycosides (PG), organic acids (OA), fatty acids and alcohols (FA), terpenes (T), phenolic metabolites (P), and unclassified metabolites. Spectral analysis revealed consistently higher reflectance across VNIR and SWIR wavelengths in drought-stressed plants, corresponding with increased accumulation of AA and reduced CHO and PG levels. Least absolute shrinkage and selection operator (LASSO) regression modeling identified robust spectral predictors of metabolite concentrations, associating VNIR wavelengths (500–700 nm) predominantly with AA and P, whereas SWIR wavelengths (1680–1700 nm) reliably predicted CHO, OA, and T. Several stable spectral-metabolite associations persisted across the two watering regimes (drought vs. well-watered), highlighting their potential as spectral biomarkers for non-destructive stress monitoring. Minimal genotype-specific variation suggests that observed spectral and metabolic responses were driven primarily by environmental factors, likely reflecting limited genetic diversity among the commercial Populus genotypes examined. This work establishes VNIR+SWIR hyperspectral imaging as a powerful, non-destructive phenotyping tool for precision monitoring and targeted improvement of drought resilience in bioenergy crops.

Biochemical trait prediction↗
DOE OSTI2025

Influence of fertilization on the dynamics of energy use in wheat

Plant energy use is fundamental to plant survival and growth. However, we still lack effective means to quantify plant energy use strategies. This study introduced a concept quantifying the light level at which photochemical and non-photochemical energy use in plants are in equilibrium — the photochemical compensation point (PCCP) which can be determined with chlorophyll fluorescence measurements. We used winter wheat as a test case to explore the dynamics of PCCP and its physiological and biochemical regulations. Winter wheat PCCP decreased significantly across growth stages from jointing to grain filling. Long-term nitrogen and phosphate (NP) fertilization significantly increased PCCP, whereas potassium (K) and manure (M) fertilizer supplementation had negligible effects. PCCP exhibited significant positive correlations with leaf thickness, leaf P and sulfur (S), and stomatal conductance (gs) across all growth stages. All manure-amended treatments exhibited positive correlations of PCCP with leaf N, P, K and gs, and negative correlations with leaf calcium (Ca). Random forest analysis revealed that gs was the most significant predictor of PCCP variation, followed by leaf P, iWUE, and leaf thickness across all treatments. We suggest that plant energy use strategies are strongly coupled with plant water use strategies and nutrient availability through a complex interplay of effects on physiological and biochemical traits.

energy allocation↗
DOE OSTI2024

Programmed cell death regulator BAP2 is required for IRE1-mediated unfolded protein response in Arabidopsis

Environmental and physiological situations can challenge the balance between protein synthesis and folding capacity of the endoplasmic reticulum (ER) and cause ER stress, a potentially lethal condition. The unfolded protein response (UPR) restores ER homeostasis or actuates programmed cell death (PCD) when ER stress is unresolved. The cell fate determination mechanisms of the UPR are not well understood, especially in plants. Here, we integrate genetics and ER stress profiling with natural variation and quantitative trait locus analysis of 350 natural accessions of the model species Arabidopsis thaliana . Our analyses implicate a single nucleotide polymorphism to the loss of function of the general PCD regulator BON-ASSOCIATED PROTEIN2 (BAP2) in UPR outcomes. We establish that ER stress-induced BAP2 expression is antagonistically regulated by the UPR master regulator, inositol-requiring enzyme 1 (IRE1), and that BAP2 controls adaptive UPR amplitude in ER stress and ignites pro-death mechanisms in conditions of UPR insufficiency.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2024

CORN (Crop Optimization Realized through Neuralnets)

Traditional models of predicting plant traits are limited because they often rely on linear assumptions that do not fully capture the complexity of biological interactions and DNA-based markers which are static across environments. This project generated a set of RNA-based data from large multi-environment field trials and combined it with advanced machine learning techniques to account for these complex interactions and improve the accuracy of predictions.

59 BASIC BIOLOGICAL SCIENCES↗
NASA NTRS2022

A Conceptual Framework to Integrate Biodiversity, Ecosystem Function, and Ecosystem Service Models

Global biodiversity and ecosystem service models typically operate independently. Ecosystem service projections may therefore be overly optimistic because they do not always account for the role of biodiversity in maintaining ecological functions. We review models used in recent global model intercomparison projects and develop a novel model integration framework to more fully account for the role of biodiversity in ecosystem function, a key gap for linking biodiversity changes to ecosystem services. We propose two integration pathways. The first uses empirical data on biodiversity–ecosystem function relationships to bridge biodiversity and ecosystem function models and could currently be implemented globally for systems and taxa with sufficient data. We also propose a trait-based approach involving greater incorporation of biodiversity into ecosystem function models. Pursuing both approaches will provide greater insight into biodiversity and ecosystem services projections. Integrating biodiversity, ecosystem function, and ecosystem service modeling will enhance policy development to meet global sustainability goals.

biodiversity models↗
DOE OSTI2026

Bioenergy Cropping Reduces the Spatiotemporal Scaling of Soil Bacterial Biodiversity

Widespread bioenergy cropping can transform landscapes, strongly affecting biodiversity. However, the impact of bioenergy cropping on the spatiotemporal scaling of soil biodiversity remains virtually unknown, despite its profound implications for the functioning of the ecological community. Here, we investigated how bioenergy cropping influenced the spatiotemporal scaling of soil bacterial biodiversity in marginal soils (sandy loam and clay loam soils) in Oklahoma, USA. We detected strong, significant species-time-area relationships (STARs) and phylogenetic-time-area relationships (PTARs) in bacterial communities and their lineages, suggesting that STARs and PTARs exist in microbial ecology within the studied system. Also, spatiotemporal scaling rates (the slopes of STAR and PTAR models) varied substantially among bacterial lineages and were positively correlated with their 16S rRNA gene copy numbers, a genomic trait indicative of microbial growth potentials. Strikingly, bioenergy cropping significantly reduced spatiotemporal scaling rates by 6.8%-14.1%, with a more pronounced reduction observed in sandy loam soils, where those rates were significantly lower than in clay loam soils. The heterogeneity of soil phosphorus and carbon resulted in variations in bacterial spatiotemporal scaling rates. Collectively, our findings suggest that bioenergy cropping may alleviate rapid shifts in soil biodiversity across space and time, thereby stabilizing soil biodiversity and supporting its role as part of sustainable land management and climate mitigation strategies.

bacterial diversity↗
DOE OSTI2024

Reformulation of dicamba herbicide: Impacts on offsite transport and soybean damage

Abstract The herbicide dicamba (3,6‐dichloro‐2‐methoxybenzoic acid) is commonly used to control broadleaf weeds in soybeans. Dicamba, however, is susceptible to volatilization and drift, thereby causing significant plant damage to nontarget crops downwind. Dicamba was reformulated to reduce volatility and off‐target movement. The effectiveness of the dicamba reformulation was assessed by quantifying dicamba emissions following spray application and investigated how meteorological factors influenced the off‐target movement. The experiments were conducted at the University of Minnesota Agricultural Experiment Station (UMORE Park) during the growing season of 2018, 2019, 2021, and 2022. Multiple high‐flow polyurethane foam air samplers were used to measure dicamba concentrations downwind from a 4‐ha soybean field sprayed with dicamba. Dicamba emissions were estimated using backward Lagrangian modeling constrained by the air sample observations. The results indicate that dicamba emissions and downwind transport were significant for several days following application. Further, non‐traited soybeans located within 15–45 m showed substantial dicamba‐related damage. In warmer, drier seasons, increased dicamba emissions caused more severe damage to downwind soybeans, likely worsened by drought stress preventing recovery. Favorable atmospheric conditions that reduced potential drift can be difficult to achieve in terms of the typical weather experienced over agricultural sites in the Upper Midwest. These results indicate that the dicamba reformulation has not adequately prevented significant post‐spray volatilization losses and downwind transport.

Agriculture↗
DOE OSTI2024

Acetaminophen production in the edible, filamentous cyanobacterium Arthrospira platensis

Abstract Spirulina is the common name for the edible, nonheterocystous, filamentous cyanobacteriumArthrospira platensisthat is grown industrially as a food supplement, animal feedstock, and pigment source. Although there are many applications for engineering this organism, until recently no genetic tools or reproducible transformation methods have been published. While recent work showed the production of a diversity of proteins inA. platensis, including single‐domain antibodies for oral delivery, there remains a need for a modular, characterized genetic toolkit. Here, we independently establish a reproducible method for the transformation ofA. platensisand engineer this bacterium to produce acetaminophen as proof‐of‐concept for small molecule production in an edible host. This work opensA. platensisto the wider scientific community for future engineering as a functional food for nutritional enhancement, modification of organoleptic traits, and production of pharmaceuticals for oral delivery.

Biotechnology & Applied Microbiology↗
DOE OSTI2024

Oleaginous Yeast Biology Elucidated With Comparative Transcriptomics

ABSTRACT Extremophilic yeasts have favorable metabolic and tolerance traits for biomanufacturing‐ like lipid biosynthesis, flavinogenesis, and halotolerance – yet the connection between these favorable phenotypes and strain genotype is not well understood. To this end, this study compares the phenotypes and gene expression patterns of biotechnologically relevant yeasts Yarrowia lipolytica , Debaryomyces hansenii , and Debaryomyces subglobosus grown under nitrogen starvation, iron starvation, and salt stress. To analyze the large data set across species and conditions, two approaches were used: a “network‐first” approach where a generalized metabolic network serves as a scaffold for mapping genes and a “cluster‐first” approach where unsupervised machine learning co‐expression analysis clusters genes. Both approaches provide insight into strain behavior. The network‐first approach corroborates that Yarrowia upregulates lipid biosynthesis during nitrogen starvation and provides new evidence that riboflavin overproduction in Debaryomyces yeasts is overflow metabolism that is routed to flavin cofactor production under salt stress. The cluster‐first approach does not rely on annotation; therefore, the coexpression analysis can identify known and novel genes involved in stress responses, mainly transcription factors and transporters. Therefore, this work links the genotype to the phenotype of biotechnologically relevant yeasts and demonstrates the utility of complementary computational approaches to gain insight from transcriptomics data across species and conditions.

Weintraub, Sarah J. [Department of Bioinformatics ↗
DOE OSTI2025

Genetically engineered poplar wood effectively enhances the efficiency of deep eutectic solvent-mediated one-pot processing

Although lignocellulosic biomass is a renewable resource with the potential to replace fossil-derived fuels and chemicals, its recalcitrance, largely due to lignin, limits its utilization. Recent advancements in genetic engineering have produced transgenic trees with reduced lignin content and/or modified lignin structure without compromising growth traits. Here, three engineered poplar varieties are evaluated as feedstocks using a biocompatible one-pot deep eutectic solvent-mediated process that integrates biomass fractionation and enzymatic saccharification within a single reactor, eliminating water washing and reconditioning. All transgenic poplars exhibit higher fermentable sugar yields than wild-type (WT) trees. Notably, QsuB poplar, incorporating 3,4-dihydroxybenzoate in lignin, achieves the highest glucose conversion yield of 91.3% (vs. 73.0% from WT). AT5 and MdCHS3 poplars, incorporating ferulate esters and naringenin, also demonstrate improved glucose yields (86.7 and 84.7%, respectively), confirming reduced biomass recalcitrance. Additionally, residual lignins are valorized via hydrogenolysis into phenolic compounds, with comparable alkylphenol production across all lines. These findings demonstrate that the transgenic poplar lines not only serve as superior feedstocks for sugar conversion but also provide a rich resource for phenolic compound production, enhancing the operational and economic viability of integrated biorefinery processes.

biomasses↗