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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 145 records · Page 8

Space-Based Ornithology: On the Wings of Migration and Biophysics

Understanding bird migration and avian biodiversity is one of the most compelling and challenging problems of modern biology with major implications for human health and conservation biology. Migration and conservation efforts cross national boundaries and are subject to numerous international agreements and treaties. Public interest is high with concerns about avian flu, Katrina and its impacts, and the recent sightings of the ivory-billed woodpecker. Space based technology gives us new opportunities to shed light on the distribution and movement of organisms on the planet and their sensitivity to human disturbances and environmental changes. Together with the USGS and other agencies, we are creating ecological forecasting tools for science and application users to address the consequences of loss of wetlands, flooding, drought or other natural disasters such as hurricanes on avian biodiversity and bird migration. In our work, we use individual organism biophysical models of energy and water balance and drive these models with satellite measurements of spatio-temporal gradients in climate and habitat. Dynamic state variable modeling provides an additional tool for studying bird migration across multiple scales and can be linked to our mechanistic models describing the time and energy budget states of migrating birds. Such models yield an understanding of how a migratory flyway and its component habitats function as a whole and link stop-over ecology with biological conservation and management. We also build upon the canopy done by the Environmental Laboratory over the past few years.

Smith, James A.↗

Short-term variation in leaf-level water use efficiency in a tropical forest

Here we report the representation of stomatal regulation of transpiration and CO 2 assimilation is key to forecasting terrestrial ecosystem responses to global change. Given its importance in determining the relationship between forest productivity and climate, accurate and mechanistic model representation of the relationship between stomatal conductance (g s ) and assimilation is crucial. We assess possible physiological and mechanistic controls on the estimation of the g 1 (stomatal slope, inversely proportional to water use efficiency) and g 0 (stomatal intercept) parameters, using diurnal gas exchange surveys and leaf level response curves of six tropical broadleaf evergreen tree species. g 1 estimated from ex-situ response curves averaged 50% less than g 1 estimated from survey data. While g 0 and g 1 varied between leaves of different phenological stages, the trend was not consistent among species. We identified a diurnal trend associated with g 1 and g 0 that significantly improved model projections of diurnal trends in transpiration. The accuracy of modelled g s can be improved by accounting for variation in stomatal behavior across diurnal periods, and between measurement approaches, rather than focusing on phenological variation in stomatal behavior. Additional investigation into the primary mechanisms responsible for diurnal variation in g 1 will be required to account for this phenomena in land surface models.

54 ENVIRONMENTAL SCIENCES↗

Directly monitoring the shift in corrosion mechanisms of a model FeCrNi alloy driven by electric potential

Abstract Stainless steels are used in a myriad of engineering applications, including construction, automobiles, and nuclear reactors. Developing accurate, predictive mechanistic models for corrosion and electrochemical corrosion kinetics of stainless steels has been a topic of research studies over many decades. Herein, we quantified the aqueous corrosion kinetics of a model austenitic Fe–18Cr–14Ni (wt%) alloy in the presence and absence of applied potential using systematic in situ electrochemical atomic force microscopy (EC-AFM) and transmission electron microscopy (TEM). Without an applied bias, vertical dissolution of corrosion pits is controlled by the surface kinetics/diffusion hybrid mechanism, whereas lateral dissolution is diffusion controlled. When an electric bias is applied, the increase in corrosion rate is dominated by the nucleation of new pits. These insights gained by the in situ EC-AFM will allow applications of this method for a quantitative understanding of corrosion of a wider class of materials.

36 MATERIALS SCIENCE↗

Climate and hydraulic traits interact to set thresholds for liana viability

Lianas, or woody vines, and trees dominate the canopy of tropical forests and comprise the majority of tropical aboveground carbon storage. These growth forms respond differently to contemporary variation in climate and resource availability, but their responses to future climate change are poorly understood because there are very few predictive ecosystem models representing lianas. We compile a database of liana functional traits (846 species) and use it to parameterize a mechanistic model of liana-tree competition. The substantial difference between liana and tree hydraulic conductivity represents a critical source of inter-growth form variation. Here, we show that lianas are many times more sensitive to drying atmospheric conditions than trees as a result of this trait difference. Further, we use our competition model and projections of tropical hydroclimate based on Representative Concentration Pathway 4.5 to show that lianas are more susceptible to reaching a hydraulic threshold for viability by 2100.

54 ENVIRONMENTAL SCIENCES↗

Multi-Omics Reveals Temporal Scales of Carbon Metabolism in Synechococcus Elongatus PCC 7942 Under Light Disturbance

Central carbon metabolism in model cyanobacteria involves multiple pathways to adapt to energy-light limitations across diel cycles. However, the success in mechanistic modeling for phenotypic prediction of the protein regulators in the metabolic state depends on capturing the vast possibilities emerging from multiple regulatory pathways in complex biological processes. Here, we developed a physics-informed machine learning approach based on energy-landscape concepts to predict regulatory proteins responding to cyclic circadian and unforeseen light perturbations in cyanobacterial metabolic networks. Our approach provides interpretable de novo models for inferring gene expression dynamics from Synechococcus elongatus over diel cycles and using redox proteome analysis to distinguish immediate light-responsive elements from circadian-regulated processes in carbon metabolism pathways. We identified distinct temporal signatures with the analysis of the redox proteome: there was an immediate shift in cysteine redox states accompanied by a limited change in protein abundance under constant illumination and after 2 hours of darkness. This discovery indicates that the generation of reductants coordinates photoinduced electron transport with redox metabolic pathways in two discernable molecular mechanisms: fast redox-based protein modifications occur immediately after the light disturbance, followed by slow transcriptional regulations across networks. This temporal regulation reveals how metabolic networks integrate rapid light responses with programmed circadian rhythms to maintain cellular homeostasis under the light-energy limitations over the diel cycle.

Biomolecular & subcellular processes↗

Predictive Modeling of Fast-Curing Thermosets in Nozzle-Based Extrusion

This work presents an approach to modeling the dynamic spreading and curing behavior of thermosets in nozzle-based extrusions. Thermosets cover a wide range of materials, some of which permit low-temperature processing with subsequent high-temperature and high-strength working properties. Extruding thermosets may overcome the limited working temperatures and strengths of conventional thermoplastic materials used in additive manufacturing. This project aims to produce technology for the fabrication of thermoset-based structures leveraging advances made in nozzle-based extrusion, such as fused deposition modeling (FDM), material jetting, and direct writing. Understanding the synergistic interactions between spreading and fast curing of extruded thermosetting materials will provide essential insights for applications that require accurate dimensional controls, such as additive manufacturing [1], [2] and centrifugal coating/forming [3]. Two types of thermally curing thermosets -- one being a soft silicone (Ecoflex 0050) and the other being a toughened epoxy (G/Flex) -- served as the test materials in this work to obtain models for cure kinetics and viscosity. The developed models align with extensive measurements made with differential scanning calorimetry (DSC) and rheology. DSC monitors the change in the heat of reaction, which reflects the rate and degree of cure at different crosslinking stages. Rheology measures the change in complex viscosity, shear moduli, yield stress, and other properties dictated by chemical composition. By combining DSC and rheological measurements, it is possible to establish a set of models profiling the cure kinetics and chemorheology without prior knowledge of chemical composition, which is usually necessary for sophisticated mechanistic modeling. In this work, we conducted both isothermal and dynamic measurements with both DSC and rheology. With the developed models, numerical simulations yielded predictions of diameter and height of droplets, along with width and height of extruded lines cured at varied temperatures. Experimental results carried out on a goniometric platform and a nozzle-based 3D printer showed agreement with the numerical simulations. Finally, this presentation will show how the models are adaptable to the planning of tool paths and designs in additive manufacturing.

Xie, Jingjin↗

Machine learning-enabled phenotyping for GWAS and TWAS of WUE traits in 869 field-grown sorghum accessions

Abstract Sorghum (Sorghum bicolor) is a model C4 crop made experimentally tractable by extensive genomic and genetic resources. Biomass sorghum is studied as a feedstock for biofuel and forage. Mechanistic modeling suggests that reducing stomatal conductance (gs) could improve sorghum intrinsic water use efficiency (iWUE) and biomass production. Phenotyping to discover genotype-to-phenotype associations remains a bottleneck in understanding the mechanistic basis for natural variation in gs and iWUE. This study addressed multiple methodological limitations. Optical tomography and a machine learning tool were combined to measure stomatal density (SD). This was combined with rapid measurements of leaf photosynthetic gas exchange and specific leaf area (SLA). These traits were the subject of genome-wide association study and transcriptome-wide association study across 869 field-grown biomass sorghum accessions. The ratio of intracellular to ambient CO2 was genetically correlated with SD, SLA, gs, and biomass production. Plasticity in SD and SLA was interrelated with each other and with productivity across wet and dry growing seasons. Moderate-to-high heritability of traits studied across the large mapping population validated associations between DNA sequence variation or RNA transcript abundance and trait variation. A total of 394 unique genes underpinning variation in WUE-related traits are described with higher confidence because they were identified in multiple independent tests. This list was enriched in genes whose Arabidopsis (Arabidopsis thaliana) putative orthologs have functions related to stomatal or leaf development and leaf gas exchange, as well as genes with nonsynonymous/missense variants. These advances in methodology and knowledge will facilitate improving C4 crop WUE.

54 ENVIRONMENTAL SCIENCES↗

Climatic imprint on interfacially-controlled platinum-palladium resources

Abstract Iron oxide-rich laterites, soils, and regolith formed from the weathering of ultramafic rocks represent untapped unconventional resources for the critical minerals platinum and palladium, but the fundamental surficial geochemistry of these elements remains poorly understood. Depletion of Pd relative to Pt occurs in some weathering zones in semi-arid climates. The accepted model attributes this platinum-palladium chemical fractionation to preferential complexation of Pd by dissolved chloride. However, similar fractionation is not observed in laterites of humid equatorial regions despite substantial wet deposition of chloride. The established mechanistic model for Pt and Pd behavior during weathering thus inaccurately predicts the distribution of these critical minerals in many settings, hindering global resource assessment. We show through mineral-fluid partitioning experiments coupled to element-specific spectroscopy that this canonical explanation for platinum-palladium fractionation is invalid: chloride complexation does not differentially mobilize Pd versus Pt. Instead, mineral-specific interfacial reactions control Pd and Pt accumulation. Modeling of platinum-palladium fractionation in representative weathering zone profiles demonstrates sub-equal retention in goethite-rich settings and Pd depletion in hematite-rich zones, accurately predicting trends observed in soils and laterites. Iron oxide mineralogy, reflecting modern and past regional climate conditions, is likely the primary determinant of Pt and Pd endowment in weathering zone resources. This new model for Pt and Pd mobilization and accumulation behavior provides a mechanistic foundation for exploration and recovery of platinum group elements from novel ultramafic regolith deposits.

58 GEOSCIENCES↗

Regulatory utility of physiologically based pharmacokinetic modeling for assessing food impact in bioequivalence studies: A workshop summary report

This workshop report summarizes the presentations and panel discussion related to the use of physiologically based pharmacokinetic (PBPK) modeling approaches for food effect assessment, collected from Session 2 of Day 2 of the workshop titled “Regulatory Utility of Mechanistic Modeling to Support Alternative Bioequivalence Approaches.” The US Food and Drug Administration in collaboration with the Center for Research on Complex Generics organized this workshop where this particular session titled “Oral PBPK for Evaluating the Impact of Food on BE” presented successful cases of PBPK modeling approaches for food effect assessment. Recently, PBPK modeling has started to gain popularity among academia, industries, and regulatory agencies for its potential utility during bioavailability (BA) and/or bioequivalence (BE) studies of new and generic drug products to assess the impact of food on BA/BE. Considering the promises of PBPK modeling in generic drug development, the aim of this workshop session was to facilitate knowledge sharing among academia, industries, and regulatory agencies to understand the knowledge gap and guide the path forward. This report collects and summarizes the information presented and discussed during this session to disseminate the information into a broader audience for further advancement in this area.

60 APPLIED LIFE SCIENCES↗

Development of a generic combustion stability code for liquid propellant rocket engines

The mathematical framework for a combustion stability analysis code is outlined. The goal for the code is to be general enough in problem treatment so that its validity and accuracy extend over a wide range of problem applications and that it lends the convenience for any future model improvement if necessary. An approach for modeling the combustion dynamics is devised to meet both requirements. An open-loop numerical procedure is also developed to mechanistically model various combustion processes for determining the stability parameters.

Fang, J. J.↗

Combining mechanistic and machine learning models for predictive engineering and optimization of tryptophan metabolism

Abstract Through advanced mechanistic modeling and the generation of large high-quality datasets, machine learning is becoming an integral part of understanding and engineering living systems. Here we show that mechanistic and machine learning models can be combined to enable accurate genotype-to-phenotype predictions. We use a genome-scale model to pinpoint engineering targets, efficient library construction of metabolic pathway designs, and high-throughput biosensor-enabled screening for training diverse machine learning algorithms. From a single data-generation cycle, this enables successful forward engineering of complex aromatic amino acid metabolism in yeast, with the best machine learning-guided design recommendations improving tryptophan titer and productivity by up to 74 and 43%, respectively, compared to the best designs used for algorithm training. Thus, this study highlights the power of combining mechanistic and machine learning models to effectively direct metabolic engineering efforts.

59 BASIC BIOLOGICAL SCIENCES↗

Microbiome Adaptation Could Amplify Modeled Projections of Global Soil Carbon Loss With Climate Warming

Warming alters soil microbial traits through ecological and evolutionary processes, directly influencing the decomposition of organic matter, which significantly affects global soil carbon emissions. Yet, soil carbon models largely ignore these processes and their implications for global responses to warming. Here, we incorporate eco-evolutionary theory into a mechanistic model describing microbial soil carbon decomposition to address the question of whether such processes could have consequential effects on climate carbon feedbacks globally. We assume that a key trait of microbes, their resource allocation to production of exoenzymes (which facilitate decomposition of organic matter)—is optimized to environmental temperatures by natural selection. We find that eco-evolutionary optimization results in microbes allocating more resources to enzyme production under warming. When applied at the global scale, eco-evolutionary optimization enhances the biological realism of soil carbon models and significantly amplifies global soil carbon loss by 2100. Our results highlight the significant potential of microbial eco-evolutionary responses to influence carbon cycle feedbacks to climate change, and motivate an urgent need for more comprehensive data to accurately quantify the adaptive potential of microbiomes in response to climate change.

Biodiversity & Conservation↗

Modeling of polymer photodegradation for solar cell modules

It was shown that many of the experimental observations in the photooxidation of hydrocarbon polymers can be accounted for with a computer simulation using an elementary mechanistic model with corresponding rate constants for each reaction. For outdoor applications, however, such as in photovoltaics, the variation of temperature must have important effects on the useful lifetimes of such materials. The data bank necessary to replace the isothermal rate constant values with Arrhenius activation parameters: A (the pre-exponential factor) and E (the activation energy) was searched. The best collection of data assembled to data is summarized. Note, however, that the problem is now considerably enlarged since from a theoretical point of view, with 51 of the input variables replaced with 102 parameters. The sensitivity of the overall scheme is such that even after many computer simulations, a successful photooxidation simulation with the expanded variable set was not completed. Many of the species in the complex process undergo a number of competitive pathways, the relative importance of each being often sensitive to small changes in the calculated rate constant values.

Somersall, A. C.↗

Application of Linear Additive Conditions for Near-Infrared Diffuse Reflectance Absorption Spectroscopy

Determining the homogeneity of material mixing in real time during product processing is critical for quality control. According to the Kubelka–Munk (K-M) function of diffuse reflectance absorption spectrum, absorbance (A) is approximately linear with the content of the components when the sample scattering coefficient (S) is in a certain range. The S is determined by the particle size of powder samples. Therefore, this study determined particle size ranges that satisfy linear additivity in near-infrared diffuse reflectance spectroscopy (NIRDRS). Thus, the proposed NIRDRS analysis technique can be used to determine the homogeneity of material mixes or analyze the percentages of the components in the mixture. In this study, vitamin B3 and vitamin C were used for preparing mixed samples with varying percentages. The experimental results revealed that linear additivity is satisfied when the powder particle size is in the range of less than 280, 280–450, and 450–900 μm. When the confidence level is 0.01, the actual mixed spectra are not significantly different from the “simulated mixed spectra” constructed by linear addition, with their relative deviations less than 1.08%. The absolute errors of the actual and analytic percentages were within 2.98% for each component in the mixtures. The above conclusions also hold for sorghum, which has a complex material composition. Statistical models cannot analyze the percentages of components in the mixture. In contrast, linear addition and direct calibration approach avoids the use of a large number of samples for statistical modeling and analyze the percentages of mixed samples. Meanwhile, it can be used to discriminate and analyze the material mixing uniformity by building a mechanistic model.

Feng, Zhiyue↗

Evaluation of simulated soil carbon dynamics in Arctic-Boreal ecosystems

Given the magnitude of soil carbon stocks in northern ecosystems, and the vulnerability of these stocks to climate warming, land surface models must accurately represent soil carbon dynamics in these regions. We evaluate soil carbon stocks and turnover rates, and the relationship between soil carbon loss with soil temperature and moisture, from an ensemble of eleven global land surface models. We focus on the region of NASA's Arctic-Boreal vulnerability experiment (ABoVE) in North America to inform data collection and model development efforts. Models exhibit an order of magnitude difference in estimates of current total soil carbon stocks, generally under- or overestimating the size of current soil carbon stocks by greater than 50 PgC. We find that a model's soil carbon stock at steady-state in 1901 is the prime driver of its soil carbon stock a hundred years later—overwhelming the effect of environmental forcing factors like climate. The greatest divergence between modeled and observed soil carbon stocks is in regions dominated by peat and permafrost soils, suggesting that models are failing to capture the frozen soil carbon dynamics of permafrost regions. Using a set of functional benchmarks to test the simulated relationship of soil respiration to both soil temperature and moisture, we find that although models capture the observed shape of the soil moisture response of respiration, almost half of the models examined show temperature sensitivities, or Q10 values, that are half of observed. Significantly, models that perform better against observational constraints of respiration or carbon stock size do not necessarily perform well in terms of their functional response to key climatic factors like changing temperature. This suggests that models may be arriving at the right result, but for the wrong reason. The results of this work can help to bridge the gap between data and models by both pointing to the need to constrain initial carbon pool sizes, as well as highlighting the importance of incorporating functional benchmarks into ongoing, mechanistic modeling activities such as those included in ABoVE.

54 ENVIRONMENTAL SCIENCES↗

The QBO and weak external forcing by solar activity: A three dimensional model study

A better understanding is attempted of the physical mechanisms leading to significant correlations between oscillations in the lower and middle stratosphere and solar variability associated with the sun's rotation. A global 3-d mechanistic model of the middle atmosphere is employed to investigate the effects of minor artificially induced perturbations. The aim is to explore the physical mechanisms of the dynamical response especially of the stratosphere to weak external forcing as it may result from UV flux changes due to solar rotation. First results of numerical experiments dealing about the external forcing of the middle atmosphere by solar activity were presented elsewhere. Different numerical studies regarding the excitation and propagation of weak perturbations have been continued since then. The model calculations presented are made to investigate the influence of the quasi-biennial oscillation (QBO) on the dynamical response of the middle atmosphere to weak perturbations by employing different initial wind fields which represent the west and east phase of the QBO.

Dameris, M.↗

Machine Learning in Environmental Chemistry: Application to Surface Complexation Modeling

Environmental chemistry – or biogeochemistry – is the scientific discipline typically invoked when examining and quantifying groundwater or surface water contamination, and nutrient cycling in the environment. Over the last three decades, there have been significant advances in mechanistic model development to describe and predict these complex biogeochemical processes. In particular, surface complexation models (SCMs) have been developed to describe the rock/soil surface reactions of metals and radionuclides, and their partitioning between various mobile species in the aqueous phase or immobile species sorbed on solid surfaces. Often represented by a simplified linear isotherm constant – Kd – in reactive transport models, these reactions play a critical role in many environmental science applications; particularly in contamination risk assessments and nuclear waste disposal performance assessments. In the past several decades, efforts by various institutions across the world have focused on developing SCMs based on datasets from laboratory measurements, including the identification of key parameters such as equilibrium constants.

54 ENVIRONMENTAL SCIENCES↗

In vivo kinetics of SARS-CoV-2 infection and its relationship with a person’s infectiousness

Significance Quantifying the kinetics of SARS-CoV-2 infection and individual infectiousness is important for understanding SARS-CoV-2 transmission and evaluating intervention strategies. Here, we developed within-host models of SARS-CoV-2 infection, and by fitting them to clinical data, we estimated key within-host viral dynamic parameters. We also developed a mechanistic model for viral transmission and show that the logarithm of the viral load in the upper respiratory tract serves as an appropriate surrogate for a person’s infectiousness. Using data on how viral load changes during infection, we further evaluated the effectiveness of PCR and antigen-based testing strategies for averting transmission and identifying infected individuals.

60 APPLIED LIFE SCIENCES↗