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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
NASA NTRS2003

Adaptation of SUBSTOR for controlled-environment potato production with elevated carbon dioxide

The SUBSTOR crop growth model was adapted for controlled-environment hydroponic production of potato (Solanum tuberosum L. cv. Norland) under elevated atmospheric carbon dioxide concentration. Adaptations included adjustment of input files to account for cultural differences between the field and controlled environments, calibration of genetic coefficients, and adjustment of crop parameters including radiation use efficiency. Source code modifications were also performed to account for the absorption of light reflected from the surface below the crop canopy, an increased leaf senescence rate, a carbon (mass) balance to the model, and to modify the response of crop growth rate to elevated atmospheric carbon dioxide concentration. Adaptations were primarily based on growth and phenological data obtained from growth chamber experiments at Rutgers University (New Brunswick, N.J.) and from the modeling literature. Modified-SUBSTOR predictions were compared with data from Kennedy Space Center's Biomass Production Chamber for verification. Results show that, with further development, modified-SUBSTOR will be a useful tool for analysis and optimization of potato growth in controlled environments.

NASA Discipline Life Support Systems↗
NASA NTRS1990

USSR Space Life Sciences Digest, issue 28

This is the twenty-eighth issue of NASA's Space Life Sciences Digest. It contains abstracts of 60 journal papers or book chapters published in Russian and of 3 Soviet monographs. Selected abstracts are illustrated with figures and tables from the original. The abstracts in this issue have been identified as relevant to 20 areas of space biology and medicine. These areas include: adaptation, aviation medicine, botany, cardiovascular and respiratory systems, developmental biology, endocrinology, enzymology, equipment and instrumentation, hematology, human performance, immunology, life support systems, mathematical modeling, musculoskeletal system, neurophysiology, personnel selection, psychology, radiobiology, reproductive system, and space medicine.

Stone, Lydia Razran↗
NASA NTRS2004

Lunar and Planetary Science XXXV: Impacts on Mars and Earth

The session "Impacts on Mars and Earth" included the following reports:A Timescale for Major Events in Early Mars Crustal Evolution; Impact Craters in Arabia Terra, Mars; Ancient Giant Basin/Aquifer System in the Arabia Region, Mars; Small Crater Morphology Within Gusev Crater and Isidis Planitia: Evidence for Widespread Secondaries on Mars; Martian Surface Properties: Inferences from Resolved Differences in Crater Geometries; Asymmetric Ramparts of Secondary Craters in Cerberus, Mars: Interpretation and Implications for Local Stratigraphy; Depth to Diameter Relationships of Craters in the High Latitudes (70 -80 ) of Mars: Implications for Geologic History of Those Areas; Impact Cratering on Mars: Search for Target Influence on Morphology; Starting Conditions for Hydrothermal Systems Underneath Martian Craters: Hydrocode Modeling; Impact-induced Hydrothermal System at the Sudbury Crater: Duration, Temperatures, Mechanics, and Biological Implications; Major and Trace Element Variations in Impact Crater Clay from Chicxulub, Lonar, and Mistastin, Implications for the Martian Soil; Stable Isotopes and Hydrothermal Fluid Source in the Yaxcopoil-1 Borehole, Chicxulub Impact Structure, Mexico; and Chicxulub Impact Melts: Geochemical Signatures of Target Lithology Mixing and Post-Impact Hydrothermal Fluid Processes.

Source record↗
NASA NTRSDate not supplied

Open Science for Plants in Space: Improvements in NASA's Open Science Data Repository

Upcoming deep space missions will rely on plants for crew and ecosystem health. Open access space biology data enables scientists to examine the biological responses of plants to ionizing radiation, altered gravity, elevated CO2, and many other abiotic stressors. NASA has declared 2023 as the ‘Year of Open Science’ and created a 5-year Transform to Open Science (TOPS) initiative designed to rapidly transform the agency toward an inclusive culture of open science. NASA’s Open Science Data Repository (OSDR) combines two databases, GeneLab and Ames Life Sciences Data Archive (ALSDA) to maximize access to standardized ‘omics (e.g., transcriptomics, proteomics) and phenotypic data (e.g., microscopy, biomass), respectively. Current OSDR standards include the ISA (Investigation-Study-Assay) experiment model, assay metadata configurations, and standardized terminology and ontologies. In 2024 OSDR will include a new suite of features for improved FAIR compliance including downloadable plant metadata templates, data submission tools and overall improved AI-readiness of plant datasets. AI/ML methods can be helpful tools to overcome the inherent challenges of space biology research (small sample size, sparse and heterogeneous data etc.). However these methods are built on an assumption of normalized and well-curated data. OSDR’s new curation tools will improve users ability to leverage ML and AI methods to model space biology data and better understand the complex effects of spaceflight on living systems across hierarchical biological levels. We look forward to sharing our advances with the spaceflight community.

FAIR↗
DOE OSTI2026

Kinetics Modeling and Reactor Design Study of Glucose-to-Terpenes Cell-Free Conversion

Cell-free systems offer many advantages over traditional biological conversion by eliminating biological growth constraints. It also offers easy manipulation and finetuning of the reaction conditions for each individual enzyme. The conversion of cellulosic glucose to Limonene, a terpene, is a promising pathway for producing fuels and chemicals. Recent advances in developing cell-free systems focuses on bench scale optimization of terpene yield and to demonstrate its feasibility towards commercialization [1,2]. There is significant knowledge gap regarding reaction kinetics of these cell-free systems to further study how it will perform at larger scale. We present here, our studies on reaction kinetics and reactor design implications of cell-free glucose to Limonene conversion to facilitate the further development and commercialization of this process. We developed a novel kinetic model based on the metabolic-network structure of the cell-free system with multi-substrate reversible Michaelis-Menten rate law. To estimate kinetic parameters for this system of rate equations, we employed Bayesian optimization to perform global search with the assistance of gaussian processes to balance exploration and exploitation. The model parameters estimated showed good results compared with experimental data. The estimated parameters were used to perform sensitivity analysis. We found that Hexokinase is one of the most critical enzymes that affect the conversion of the glucose. We also observed that abundance of co-factors is also critical to the conversion of glucose to limonene. We investigated packed bed reactors with enzymes immobilized on the surface of particles to convert glucose stream into Limonene for larger scale production. The reactor design such as particle size, enzyme loading, and flow rate are found to be critical for improving yields. [1] Dudley, Q.M., Nash, C.J. and Jewett, M.C., 2019. Synthetic Biology, 4(1), p.ysz003. [2] Korman, T.P., Opgenorth, P.H. and Bowie, J.U., 2017. Nature communications, 8(1), p.15526.

09 BIOMASS FUELS↗
DOE OSTI2020

One Electron Multiple Proton Transfer in Model Organic Donor–Acceptor Systems: Implications for High-Frequency EPR

EPR spectroscopy is an important spectroscopic method for identification and characterization of radical species involved in many biological reactions. The tyrosyl radical is one of the most studied amino acid radical intermediates in biology. Often in conjunction with histidine residues, it is involved in many fundamental biological electron and proton transfer processes, such as in the water oxidation in photosystem II. As biological processes are typically extremely complicated and hard to control, molecular bio-mimetic model complexes are often used to clarify the mechanisms of the biological reactions. In this work, we present theoretical calculations to investigate the sensitivity of magnetic resonance parameters to proton-coupled electron transfer events, as well as conformational substates of the molecular constructs which mimic the tyrosine-histidine (Tyr-His) pairs found in a large variety of proteins. Upon oxidation of the phenol, the Tyr analog, these complexes can perform not only one-electron one-proton transfer (EPT), but also one-electron two-proton transfers (E2PT). It is shown that in aprotic environment the g X -components of the electronicg-tensor are extremely sensitive to the first proton transfer from the phenoxyl oxygen to the imidazole nitrogen (EPT product), leading to a significant increase of the g X -value of up to 0.003, but are not sensitive to the second proton transfer (E2PT). In the latter case, the change of the g X -value is much smaller (ca. 0.0001), which is too small to be distinguished even by high-frequency EPR. The 14 N hyperfine values are also too similar to allow differentiation between the different protonation states in EPT and E2PT. The magnetic resonance parameters were also calculated as a function of the rotation angles around single bonds. It was demonstrated that rotation of the phenoxyl group results in large positive changes (> 0.001) in the g X -values. Analysis of the data reveals that the main source of these changes is related to the strength of the H-bond between phenoxyl oxygen and the proton(s) on N 1 and N 2 positions of the imidazole.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2025

2025 Workshop on Envisioning Frontiers in AI and Computing for Biological Research: Position Papers

This workshop aims to identify key research directions for transforming biology using artificial intelligence (AI), machine learning (ML) and computational methods to facilitate the discovery of new behaviors, mechanisms, and designs of biological processes relevant to DOE missions, underpinning a broader U.S. bioeconomy. By developing novel AI/ML technologies to analyze and interpret complex biological data, researchers can organize and simulate biological processes at various scales as well as advance predictive understanding and manipulation of biological systems. This integration of computation, experimentation, and next-generation experimental technologies can lead to discoveries in new biological behaviors and mechanisms relevant to DOE missions. The focus is on how advanced computational and mathematical methods can impact this mission by exploring digital twins, foundation models, automated laboratory experiments, modeling of complex living systems, and data-driven approaches for the biodesign of plants and microbial systems. While data management is important, it is not the primary focus of this workshop, which will assess the current state, trends, and AI/ML challenges at the interface between biology and computational science to identify opportunities for high-impact research at their intersection. The goal is to define research needs and opportunities that align with biological sciences, computational sciences, and applied mathematics research.

59 BASIC BIOLOGICAL SCIENCES↗
NASA NTRS1974

Space Shuttle food galley design concept

A food galley has been designed for the crew compartment of the NASA Space Shuttle Orbiter. The rationale for the definition of this design was based upon assignment of priorities to each functional element of the total food system. Principle priority categories were assigned in the following order: food quality, nutrition, food packaging, menu acceptance, meal preparation efficiency, total system weight, total system volume, and total power requirements. Hence, the galley was designed using an 'inside-out' approach which first considered the food and related biological functions and subsequently proceeded 'outward' from the food to encompass supporting hardware. The resulting galley is an optimal design incorporating appropriate priorities for trade-offs between biological and engineering constraints. This design approach is offered as a model for the design of life support systems.

Heidelbaugh, N. D.↗
DOE OSTI2023

The impact of physiologically relevant temperatures on physical properties of thylakoid membranes: a molecular dynamics study

Thylakoid membranes are energy-converting membranes with a unique lipid composition. Though the membranes are primarily composed of proteins, their photosynthetic function is strongly influenced by the lipid constituents. Here we characterize, with molecular dynamics (MD) simulations, lipid bilayers with compositions representative of plant thylakoid membranes. We determine, in a wide range of temperatures, the physical parameters of the model membranes which are relevant for the photosynthetic function. We found a marked impact of temperature on membrane permeability due to a combination of increased compressibility and curvature of the membrane at elevated temperatures. With increasing temperatures, we observed increasingly smeared transmembrane density profiles of the membrane forming lipid headgroups predicting increased membrane flexibility. The diffusion coefficient of the lipids increased with temperature without apparent specificity for lipid species. Instead of a comprehensive experimental dataset in the relevant temperature range, we quantitatively compared and validated our MD results with MD simulations on a dipalmitoylphosphatidylcholine model system.

59 BASIC BIOLOGICAL SCIENCES↗
NASA NTRS2009

A Cell Kinetic Model of Granulocytopoiesis Under Radiation Exposure: Extension from Murines to Canines and Humans

Space radiation poses significant challenges to space travel, and it is essential to understand the possible adverse effects from space radiation exposure to the radiosensitive organ systems that are important for immediate survival of human, e.g., the hematopoietic system. In this presentation a biomathematical model of granulocytopoiesis is described and used to analyze the blood granulocyte changes seen in the blood of mammalians under continuous and acute radiation exposure. This is one of a set of hematopoietic models that have been successfully utilized to simulate and interpret the experimental data of acute and chronic radiation on rodents. We discuss the underlying implicit regulation mechanism and the biological relevance of the kinetic parameters estimation method. Extension of the model to predictions in dogs and humans systems indicates that the modeling results are consistent with the cumulative experimental and empirical data from various sources. This implies the potential to integrate the models into one united system for monitoring the hematopoietic response of various species under irradiation. Based on the evidence of threshold responses of dogs to extended periods of low daily dose exposures, we discuss the potential health risks of the space traveler under chronic stress of low-dose irradiation and the possibly encountered Solar Particle Events.

Hu, Shaowen↗
NASA NTRS2023

Broader Impacts and Outreach Videos on Ocean Physics and Biogeochemistry

This is just a preliminary NF1676 in advance of filming for a project....A convergent team of experts in biology, chemistry, physics, engineering, mathematics, and computational modeling examine how dynamic and coupled phytoplankton-pathogen-particle-predator linkages coalesce to explain the observed high spatial variability in the efficiency of the export of particulate organic carbon (POC) to the deep ocean. They elucidate and quantify the linkages between viruses and ballast minerals to increase understanding of carbon cycling in the oceanic biological carbon pump and the impact of viruses within it. By providing knowledge than can be used to improve the parameterization of carbon export in Earth system models, the project will help reduce uncertainty in regional marine biogeochemical projections, potentially improving marine ecosystem and fisheries management on timescales from seasons to decades. The project includes activities that provide teaching resources and hands-on training to educators within a ‘Tools of Science’ program that provides a simple and succinct way to communicate the process of scientific research to students in a way that is useful to teachers. The project couples laboratory-based experiments on model host-virus-grazer systems with extensive field based observational and manipulative studies on natural populations of diatoms and coccolithophores, the two phytoplankton groups that account for most of the estimated particulate organic matter flux to the deep ocean. Experiments and measurements integrate diagnostic biological and chemical controls on infection and particle coagulation theory with microscale physics and grazing to quantify links to each hypothesized export mechanism under field-relevant turbulent conditions. Cutting-edge engineering and analytical tools are used to diagnose and track infection dynamics while characterizing and quantifying particle aggregation and disaggregation, mineral dissolution, sinking dynamics, grazing rates, and fecal pellet production at unprecedented resolution and under well-defined, microscale physical regimes. Field campaigns elucidate the relative efficiency of hypothesized mechanisms in stimulating POC export in natural blooms, while providing bulk and size-resolved estimates of POC flux.

Daniel B Whitt↗
DOE OSTI2023

Genomes to ecosystem function: Targeting critical knowledge gaps in methanogenesis and translation to updated global biogeochemical models

Natural freshwater temperate wetland systems currently represent the largest natural source of atmospheric methane, but are relatively understudied using systems biology tools (e.g. meta-omics) compared to other high producing methane systems (e.g. peat, tropical, or reconstructed wetlands). Using field investigations at the NOAA operated sentinel site on Lake Erie, methane producing activities and responses to geochemical conditions will be determined along seasonal and spatial gradients (Objective 1). Here, a combination of high-throughput activity and gas measurements, combined with high-resolution systems biology and analytical methods, will provide in depth knowledge of the microbiological, chemical, and physical constraints on methane production in wetlands. Using laboratory microcosms, the formation of anoxic microsites and their capacity to facilitate methane production in wetland soils will be simulated (Objective 2). This objective will validate the findings from the field investigations, offering a more controlled environment for teasing out the role of different yet interrelated variables. Lastly, these field and laboratory data will be used for multi-scale, process-level evaluation of an ecosystem biogeochemical model that accommodates these newly identified processes and parameterizes representation of these processes along relevant environmental gradients (Objective 3).This research will identify multiple interacting geochemical, ecological, and metabolic constraints that are poorly understood, oversimplified, or missing in global biogeochemical methane models. This proposal targets the role of oxygen limitation on methane processes in soil domains, to improve reactive transport models of microbial carbon cycling across terrestrial-aquatic soils and generate data on nutrient cycling activities in Great Lake wetlands. This information could provide new insights into the microbial controllers of Lake Erie eutrophication.

59 BASIC BIOLOGICAL SCIENCES↗
NASA NTRS2007

Automated Knowledge Discovery From Simulators

A computational method, SimLearn, has been devised to facilitate efficient knowledge discovery from simulators. Simulators are complex computer programs used in science and engineering to model diverse phenomena such as fluid flow, gravitational interactions, coupled mechanical systems, and nuclear, chemical, and biological processes. SimLearn uses active-learning techniques to efficiently address the "landscape characterization problem." In particular, SimLearn tries to determine which regions in "input space" lead to a given output from the simulator, where "input space" refers to an abstraction of all the variables going into the simulator, e.g., initial conditions, parameters, and interaction equations. Landscape characterization can be viewed as an attempt to invert the forward mapping of the simulator and recover the inputs that produce a particular output. Given that a single simulation run can take days or weeks to complete even on a large computing cluster, SimLearn attempts to reduce costs by reducing the number of simulations needed to effect discoveries. Unlike conventional data-mining methods that are applied to static predefined datasets, SimLearn involves an iterative process in which a most informative dataset is constructed dynamically by using the simulator as an oracle. On each iteration, the algorithm models the knowledge it has gained through previous simulation trials and then chooses which simulation trials to run next. Running these trials through the simulator produces new data in the form of input-output pairs. The overall process is embodied in an algorithm that combines support vector machines (SVMs) with active learning. SVMs use learning from examples (the examples are the input-output pairs generated by running the simulator) and a principle called maximum margin to derive predictors that generalize well to new inputs. In SimLearn, the SVM plays the role of modeling the knowledge that has been gained through previous simulation trials. Active learning is used to determine which new input points would be most informative if their output were known. The selected input points are run through the simulator to generate new information that can be used to refine the SVM. The process is then repeated. SimLearn carefully balances exploration (semi-randomly searching around the input space) versus exploitation (using the current state of knowledge to conduct a tightly focused search). During each iteration, SimLearn uses not one, but an ensemble of SVMs. Each SVM in the ensemble is characterized by different hyper-parameters that control various aspects of the learned predictor - for example, whether the predictor is constrained to be very smooth (nearby points in input space lead to similar output predictions) or whether the predictor is allowed to be "bumpy." The various SVMs will have different preferences about which input points they would like to run through the simulator next. SimLearn includes a formal mechanism for balancing the ensemble SVM preferences so that a single choice can be made for the next set of trials.

Burl, Michael↗
DOE OSTI2023

Deriving spatially explicit direct and indirect interaction networks from animal movement data

Abstract Quantifying spatiotemporally explicit interactions within animal populations facilitates the understanding of social structure and its relationship with ecological processes. Data from animal tracking technologies (Global Positioning Systems [“GPS”]) can circumvent longstanding challenges in the estimation of spatiotemporally explicit interactions, but the discrete nature and coarse temporal resolution of data mean that ephemeral interactions that occur between consecutive GPS locations go undetected. Here, we developed a method to quantify individual and spatial patterns of interaction using continuous‐time movement models (CTMMs) fit to GPS tracking data. We first applied CTMMs to infer the full movement trajectories at an arbitrarily fine temporal scale before estimating interactions, thus allowing inference of interactions occurring between observed GPS locations. Our framework then infers indirect interactions—individuals occurring at the same location, but at different times—while allowing the identification of indirect interactions to vary with ecological context based on CTMM outputs. We assessed the performance of our new method using simulations and illustrated its implementation by deriving disease‐relevant interaction networks for two behaviorally differentiated species, wild pigs ( Sus scrofa ) that can host African Swine Fever and mule deer ( Odocoileus hemionus ) that can host chronic wasting disease. Simulations showed that interactions derived from observed GPS data can be substantially underestimated when temporal resolution of movement data exceeds 30‐min intervals. Empirical application suggested that underestimation occurred in both interaction rates and their spatial distributions. CTMM‐Interaction method, which can introduce uncertainties, recovered majority of true interactions. Our method leverages advances in movement ecology to quantify fine‐scale spatiotemporal interactions between individuals from lower temporal resolution GPS data. It can be leveraged to infer dynamic social networks, transmission potential in disease systems, consumer–resource interactions, information sharing, and beyond. The method also sets the stage for future predictive models linking observed spatiotemporal interaction patterns to environmental drivers.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2020

The matrix product approximation for the dynamic cavity method

Stochastic dynamics of classical degrees of freedom, defined on vertices of locally tree-like graphs, can be studied in the framework of the dynamic cavity method which is exact for tree graphs. Such models correspond for example to spin-glass systems, Boolean networks, neural networks, and other technical, biological, and social networks. The central objects in the cavity method are edge messages—conditional probabilities of two vertex variable trajectories. In this paper, we discuss a rather pedagogical derivation for the dynamic cavity method, give a detailed account of the novel matrix product edge message (MPEM) algorithm for the solution of the dynamic cavity equation as introduced, and present optimizations and extensions. Matrix product approximations of the edge messages are constructed recursively in an iteration over time. Computation costs and precision can be tuned by controlling the matrix dimensions of the MPEM in truncations. Without truncations, the dynamics is exact. Data for Glauber–Ising dynamics shows a linear growth of computation costs in time. In contrast to Monte Carlo simulations, the approach has a much better error scaling. Hence, it gives for example access to low probability events and decaying observables like temporal correlations. Here, we discuss optimized truncation schemes and an extension that allows to capture models which have a continuum time limit.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗
DOE OSTI2026

NGEE Arctic Authorship Guidelines

Authorship Guidelines were developed to help facilitate trust among team members as we span multiple institutions, scientific disciplines, and career stages. NGEE Arctic was built on a foundation of open science, data sharing, and collaboration. In Phase 4 of the project, it was particularly important to keep this foundation in mind as we develop new collaborations across the Arctic. Included in this package is one *.pdf. The Next-Generation Ecosystem Experiments in the Arctic (NGEE Arctic) project is a research effort to reduce uncertainty in the Department of Energy’s Energy Exascale Earth System Model (E3SM) by developing a predictive understanding of Arctic tundra ecosystems underlain by permafrost and to quantify feedbacks from the Arctic tundra to the Earth system. NGEE Arctic is supported by the Department of Energy's Office of Biological and Environmental Research. Over Phases 1–3, observations made by the NGEE Arctic team across a gradient of permafrost landscapes in Arctic Alaska improved the representation of tundra processes in the land surface component of E3SM (the E3SM Land Model, ELM). Model improvements emphasized unique aspects of permafrost environments and explored reductions in model complexity while retaining predictive power. The Arctic-informed ELM developed by NGEE Arctic has been used to make novel predictions on processes ranging from permafrost thaw to soil biogeochemical cycling to Earth system feedbacks associated with the unique characteristics of tundra plants. In Phase 4, the NGEE Arctic team is evaluating our new predictive understanding under novel conditions across the Arctic domain. In collaboration with partners at long-term pan-Arctic research sites we are examining whether an Arctic-informed ELM can faithfully simulate interactions among surface and subsurface processes at site, regional, and pan-Arctic scales. In turn, we are using variety of tools to dynamically extend and evaluate ELM inference, with an emphasis on data synthesis and pan-Arctic model evaluation, reintegration of code with an evolving E3SM, scaling across heterogeneous Arctic landscapes, and the appropriate representation of the impacts of increasingly frequent Arctic disturbances.

Iversen, Colleen [ORNL] (ORCID:0000000182933450)↗
NASA NTRS2011

Evolutionary Computational Methods for Identifying Emergent Behavior in Autonomous Systems

A technique based on Evolutionary Computational Methods (ECMs) was developed that allows for the automated optimization of complex computationally modeled systems, such as autonomous systems. The primary technology, which enables the ECM to find optimal solutions in complex search spaces, derives from evolutionary algorithms such as the genetic algorithm and differential evolution. These methods are based on biological processes, particularly genetics, and define an iterative process that evolves parameter sets into an optimum. Evolutionary computation is a method that operates on a population of existing computational-based engineering models (or simulators) and competes them using biologically inspired genetic operators on large parallel cluster computers. The result is the ability to automatically find design optimizations and trades, and thereby greatly amplify the role of the system engineer.

Terrile, Richard J.↗