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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 289 records · Page 16
All sourcesNASA NTRSDOE OSTI
NASA NTRS2024

Surface EVA Architectural Drivers

Key elements of NASA’s Moon to Mars Objectives for expanding humanity’s presence beyond low-Earth orbit will require surface-based, partial-gravity extravehicular activities (EVAs). Surface EVA needs affect many aspects of the exploration architecture, including EVA suit subsystems, such as suit or pressure garment mobility, the portable life support system, and the informatics system; and external systems, such as habitation modules and surface mobility platforms.

Moon↗
DOE OSTI2026

Modelling the Sensitivity of Yukon River Biogeochemical Dynamics to Environmental and Chemical Drivers: Implications for Dissolved Organic Carbon

Riverine dissolved organic carbon (DOC) is a critical biogeochemical component that transmits information from Arctic soils to the Arctic Ocean, significantly influencing carbon dynamics in this unique ecosystem. As DOC travels downstream, it undergoes transformations that alter its composition and fate. The Yukon River serves as an effective testbed for modelling these dynamics, offering sufficient scale to capture key biogeochemical processes while having a simpler hydrology than other major Arctic rivers, as well as long-term DOC observational data for model validation. To investigate DOC transformations during transit in the Yukon River, we adapted our Arctic Riverine Organic Macromolecular Model by applying regional-specific parameterisations. Our model simulates the transport and transformation of 15 organic macromolecules, including CDOM (coloured dissolved organic matter), proteins, polysaccharides, lipids, lignin phenols, and humic substances. Initial DOC concentrations were derived from observed soil organic carbon stocks in the surrounding watershed, while chemical transformations and hydrological dynamics were modelled along the river's course. Sensitivity and uncertainty analyses were conducted using a Monte Carlo approach under two experimental setups. Results revealed that variability in DOC and CDOM concentrations at the river mouth were predominantly driven by initial DOC concentration (~70% of variability explained) and dilution at confluence points (~10%). The refractory fraction of DOC explained 21%–88% of the variability in 14 macromolecular concentrations and ranked in the top five sensitive parameters for all outputs when a uniform parameter distribution was assumed. However, when a more likely variability was applied to this parameter, its influence on DOC and CDOM decreased. Given that refractory DOC accounts for ~80% of total DOC in Arctic Rivers, this suggests that most DOC resists degradation and retains its chemical composition during transport to the coastal environment. River velocity, which determines residence time, explained 8%–47% of the variability in protein, polysaccharide, lipid, pigments, and lignin phenols at the river mouth. In contrast, chemical turnover times contributed only 1%–5% to output variability. Our findings underscore the need for improved land-specific headwater observations, including seasonal soil moisture and lateral transport dynamics that control the initial tributary-specific DOC inputs. With accelerated permafrost thaw and increasing river discharge, extending our model to other Arctic River systems and seasons will enhance understanding of Arctic riverine carbon fluxes and their contributions to the Arctic Ocean.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2026

Change Agent: Energy Storage as a Driver of Regulatory Evolution

Purpose of Review Dividing the electric grid into the functions of generation, transmission, anddistribution enabled the drawing of jurisdictional lines and the application of the U.S.Constitution’s federalist system to energy regulation. Energy storage technologies,which can be placed throughout the grid to increase flexibility, can provide serviceacross all three of those functions. But the jurisdictional boundaries that have beendrawn around those functions have created barriers that restrict energy storagetechnologies from achieving their full potential. This review analyzes regulatory changes made to reduce barriers to storage deployment and their broader impacts on energy regulation in the U.S. Recent Findings Major energy regulations promulgated at the state and federal levels have generally focused on liberalizing the U.S. electric system through deregulation and increased competition. Paradoxically, however, these efforts have erected strict regulatory barriers that prevent energy storage technologies from providing service across multiple functions. A new wave of regulations in recent years has endeavored to reduce and remove those barriers. Summary Energy regulations adopted in recent years to reduce barriers to energy storage functionality in recent years have had deep and far-reaching impacts on U.S. electric regulation. These impacts go beyond storage and affect all energy technologies. This paper traces the development of energy regulation in U.S., the functional barriers that they created that impede energy storage functionality, recent efforts to remove those barriers, and the broader effects of those efforts. It concludes with a brief discussion of remaining barriers that prevent energy storage from reaching their full potential on the U.S. electric grid.

24 POWER TRANSMISSION AND DISTRIBUTION↗
DOE OSTI2025

Explaining drivers of housing prices with nonlinear hedonic regressions

Housing markets play a critical role in shaping the spatial and demographic evolution of urban areas. Simulating housing price dynamics can enhance projections of future urban development outcomes. However, traditional hedonic regressions for housing prices, which neglect nonlinear interactions among explanatory variables, often exhibit limited predictive performance. While machine learning (ML) methods can provide a more flexible representation of the relationships between predictors, they are often regarded as “black boxes” due to their complexity and lack of transparency. Interpretable ML techniques provide a promising route by combining the flexibility of ML methods with approaches to analyze the relationships between inputs and outputs. In this study, we employ interpretable ML to analyze the patterns driving the housing market in Baltimore, Maryland, USA. We train an Artificial Neural Network (ANN) to predict Baltimore housing prices based on structural characteristics (e.g., home size, number of stories) and locational attributes (e.g., distance to the city center). We then conduct sensitivity and Partial Dependence Plot (PDP) analyses to interpret the fitted ANN model. We find that the ML model achieves higher predictive accuracy and explains 16 % more of housing price variance than a traditional linear regression model. The interpretable ML model also reveals more nuanced and realistic nonlinear relationships between housing sales price and predictors as well as interactive effects underlying Baltimore home price dynamics. For instance, while the linear model indicates a steady housing price increase over time, our interpretable ML model detects a post-2008 decline, with smaller properties experiencing the sharpest drop.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2024

Medium-Range Structural Order as the Driver of Activated Dynamics and Complexity Reduction in Glass-Forming Liquids

Here, we analyze in depth the Elastically Collective Nonlinear Langevin Equation theory of activated dynamics in metastable liquids to establish that the predicted inter-relationships between the alpha relaxation time, local cage and collective elastic barriers, dynamic localization length, and shear modulus are causally related within the theory to the medium range order (MRO) static correlation length. The latter grows exponentially with density for metastable hard sphere fluids and as a nonuniversal inverse power law with temperature for supercooled liquids under isobaric conditions. The physical origin of predicted connections between the alpha time and other metrics of cage order and the thermodynamic inverse dimensionless compressibility is fully established. It is discovered that although kinetic constraints from the real space first coordination shell are important for the alpha time, they are of secondary importance compared to the consequences of the more universal MRO correlations in both the modestly and deeply metastable regimes. This understanding sheds new light on the theoretical basis for, and prior successes of, the predictive mapping of chemically complex thermal liquids to effective hard sphere fluids based on matching their dimensionless compressibilities, a scheme we call “complexity reduction”. In essence, the latter is equivalent to the physical requirement that the thermal liquid MRO correlation equals that of its effective hard sphere analog. The mapping alone is shown to provide a remarkable level of quantitative predictive power for the glass transition temperature T g of 21 molecular and polymer liquids. Predictions for the chemically specific absolute magnitude and growth with cooling of the MRO correlation length are obtained and lie in the window of 2–6 nm at T g . Dynamic heterogeneity, elastic facilitation, and beyond pair structure issues are briefly discussed. Future opportunities to theoretically analyze the equilibrated deep glass regime are outlined.

cancer↗
DOE OSTI2024

Constraints and Drivers of Dissolved Fluxes of Pyrogenic Carbon in Soil and Freshwater Systems: A Global Review and Meta‐Analysis

Abstract Pyrogenic carbon (PyC) is a significant component of the global soil carbon pool due to its longer environmental persistence than other soil organic matter components. Despite PyC's persistence in soil, recent work has indicated that it is susceptible to loss processes such as mineralization and leaching, with the significance and magnitude of these largely unknown at the hillslope and watershed scales. We present a review of the work concerning dissolved PyC transport in soil and freshwater. Our analysis found that the primary environmental controls on dissolved PyC (dPyC) transport are the formation conditions and quality of the PyC itself, with longer and higher temperature charring conditions leading to less transport of dPyC. While correlations between dPyC and dissolved organic carbon in rivers and other pools are frequently reported, the slope of these correlations was pool‐dependent (i.e., soil‐water, precipitation, lakes, streams, rivers), suggesting site‐specific environmental controls. However, the lack of consistency in analytical techniques and sample preparation remains a major challenge to quantifying environmental controls on dPyC fluxes. We propose that future research should focus on the following: (a) consistency in methodological approaches, (b) more quantitative measures of dPyC in pools and fluxes from soils to streams, (c) turnover times of dPyC in soils and aquatic systems, and (d) improved understanding of how mechanisms controlling the fate of dPyC in dynamic post‐fire landscapes interact. With more refined quantitative information about the controls on dPyC transport at the hillslope and landscape scale, we can increase the accuracy and utility of global carbon models.

58 GEOSCIENCES↗
DOE OSTI2024

Climatic Drivers for the Variation of Gross Primary Productivity Across Terrestrial Ecosystems in the United States

Abstract Temperature and water stress are important factors limiting the gross primary productivity (GPP) in terrestrial ecosystems, yet the extent of their influence across ecosystems remains uncertain. This study examines how surface air temperature, soil water availability (SWA) and vapor pressure deficit (VPD) influence ecosystem light use efficiency (LUE), a critical metric for assessing GPP, across different ecosystems and climatic zones at 80 flux tower sites based on in situ measurements and data assimilation products. Results indicate that LUE increases with temperature in spring, with higher correlation coefficients in colder regions (0.79–0.82) than in warmer regions (0.68–0.78). LUE reaches a plateau earlier in the season in warmer regions. LUE variations in summer are mainly driven by SWA, exhibiting a positive correlation indicative of a water‐limited regime. The relationship between the daily LUE and daytime temperature shows a clear seasonal hysteresis at many sites, with a higher LUE in spring than in fall under the same temperature, likely resulting from younger leaves being more efficient in photosynthesis. Drought stress influences LUE through SWA in all ranges of water availability; VPD variation under moderate conditions does not have a clear influence on LUE, but extremely high VPD (exceeding the threshold of 1.6 kPa, often observed during extreme drought‐heat events) causes a dramatic reduction of LUE. Our findings provide insight into how ecosystem productivities respond to climate variability and how they may change under the influence of more frequent and severe heat and drought events projected for the future.

Environmental Sciences & Ecology↗
DOE OSTI2025

The Imprint of Southern Ocean Storms on Modeled Surface Chlorophyll, Their Drivers and Satellite Biases

Southern Ocean (SO) phytoplankton chlorophyll is highly variable on sub-seasonal time scales. Although the SO is the windiest ocean basin globally, it is not conclusively understood how storms impact SO phytoplankton dynamics. Much of our existing knowledge stems from satellites, but biases due to data gaps from cloud cover and low solar angles remain unquantified. Here, we use ocean–sea-ice simulations with the Community Earth System Model to quantify the climatological 1997–2018 imprint of storms on chlorophyll and phytoplankton dynamics in the ice-free SO. Additionally, by comparing the full-field model output to synthetic satellite observations, we quantify sampling biases in satellite-derived estimates. We find that both the sign and the magnitude of the average surface chlorophyll imprint vary substantially across storms but last for at least 4 days after the storm passing. Based on our analysis, more than one third of the storms explain the majority of local non-seasonal chlorophyll variability, but satellite-derived storm imprints are often too large in magnitude. On the day of the storm passing, changes in vertical mixing predominantly cause surface chlorophyll anomalies, and reduced light availability due to enhanced cloud cover outweighs the enhanced nutrient availability due to entrainment. Interestingly, storms imprint differently on total net primary production than on surface chlorophyll, demonstrating the difficulty to derive carbon-cycle impacts from a surface-chlorophyll assessment. With SO future storm activity projected to increase, complementing satellite observations with other observing technologies, for example, profiling floats, is necessary to better constrain how storms impact biological carbon cycling in the SO.

Nissen, Cara [University of Colorado, Boulder, CO ↗
DOE OSTI2025

Drivers and impacts of sediment deposition in Amazonian floodplains

Abstract The Amazon River carries enormous amounts of sediment from the Andes mountains, much of which is deposited in its floodplains. However, accurate quantification of the sediment sink at fine spatiotemporal scales is still challenging. Here, we present a high-resolution hydrodynamic-sediment model to simulate sediment deposition in a representative Amazon/Solimões floodplain. The process is found to be jointly driven by inundation, suspended sediment concentration in the Amazon River, and floodplain hydrodynamics and only weakly correlated with inundation level. By upscaling the sediment deposition rate (1.33 ± 0.24 kg m −2 yr −1 ), we estimate the trapping of 77.3 ± 13.9 Mt (or 6.1 ± 1%) of the Amazon River sediment by the Amazon/Solimões floodplains every year. Widespread deforestation would reduce the trapping efficiency of the floodplains over time, exacerbating downstream river aggradation. Additionally, we show that the deposition of sediment-associated organic carbon plays a minor role in fueling carbon dioxide and methane emissions in the Amazon.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2025

Electric transmission value and its drivers in United States power markets

Electric transmission infrastructure plays a vital role during extreme weather and supply disruptions and can enable low-cost electricity systems. This paper contributes to a more complete understanding of the value and cost-effectiveness of transmission, as well as barriers to its development. By studying wholesale energy market prices in the United States between 2012 and 2022, we find that additional transfer capacity between regions would have been especially valuable, with a median value of $116 million per GW per year. This capacity would often have provided balanced benefits to each region. The market value of transmission was highly influenced by a small fraction of time: 5% of hours typically captured at least 45% of the total value. These peak periods were primarily driven by unforeseen changes in conditions within one day of operations. Annualized transmission infrastructure cost estimates were lower than the average market value for most locations, including all links crossing regional seams, where the value-to-cost ratio was often greater than 4. This suggests that there are barriers to developing valuable grid infrastructure. These results complement forward-looking modeling studies and support efforts to improve modeling practices.

Energy economics↗
DOE OSTI2026

Iron as a driver of organic carbon fate in permafrost regions

Iron (Fe) and its biogeochemical interactions with organic carbon (OC) exert critical controls over carbon storage and water quality across Arctic and sub-Arctic landscapes. Permafrost thaw affects Fe–organic carbon interactions by exposing thawed material and driving shifts in hydrologic regimes. Here we propose a conceptual framework describing how Fe–OC interactions respond to permafrost thaw and the implications for global carbon cycling. The framework builds on current understanding of hydrologically driven processes that are altered by permafrost thaw, including microbial Fe reduction that influences OC decomposition and carbon emissions; Fe cycling through redox transformations and sulfide oxidation; dissolution and reprecipitation of Fe–OC assemblages; and Fe export to river networks. We put forward four testable hypotheses—on topics from Fe supply to its role in soil OC storage and transfer to rivers—to advance understanding of the coupled environmental controls on pore-scale reactions (that is, microscale reactions at mineral–water–microorganism interfaces) that are central to global Fe–OC cycling. Our framework highlights how soil-profile- and landscape-scale processes, such as hydrologic connectivity and redox dynamics, regulate pore-scale Fe–OC reactions.

Herndon, Elizabeth [ORNL] (ORCID:0000000291945493)↗
DOE OSTI2025

Organic molecules are deterministically assembled in variably inundated river sediments, but drivers remain unclear

Dissolved organic matter (DOM) is central to ecosystem function. A fundamental challenge is understanding the processes leading to variation in the chemistry of organic molecules that comprise DOM. Here we study these processes in variably inundated riverbed sediments, as an understudied, yet ubiquitous component of rivers. Using null-model approaches adopted from community ecology, we found that within-site variation in environmental conditions caused non-random (i.e., deterministic) shifts in DOM chemistry. Deterministic shifts were observed across diverse biomes, though the strength of determinism varied substantially. We found that the strength of determinism decreased with increasing sediment moisture, but in the form of a constraint space. Many systems fell below the upper constraint boundary, however. We propose a conceptual model based on our results and other publications in which DOM assemblages are hypothesized to be increasingly deterministic across the continuum from the river water column to saturated sediment pore spaces to unsaturated and dry soils/sediments.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2025

Exploring prokaryotic diversity in permafrost-affected soils of Ladakh’s Changthang region and its geochemical drivers

Global warming due to climate change has substantial impact on high-altitude permafrost affected soils. This raises a serious concern that the microbial degradation of sequestered carbon can result in alteration of the biogeochemical cycles. Therefore, the characterization of permafrost affected soil microbiomes, especially of unexplored high-altitude, low oxygen arid region, is important for predicting their response to climate change. This study presents the first report of the bacterial diversity of permafrost-affected soils in the Changthang region of Ladakh. The relationship between soil pH, organic carbon, electrical conductivity, and available micronutrients with the microbial diversity was investigated. Amplicon sequencing of permafrost affected soil samples from Jukti and Tsokar showed that Proteobacteria and Actinobacteria were the dominant phyla in all samples. The genera Brevitalea, Chthoniobacter, Sphingomonas, Hydrogenispora, Clostridium, Gaiella, Gemmatimonas were relatively abundant in the Jukti samples whereas the genera Thiocapsa, Actinotalea, Syntrophotalea, Antracticibcterium, Luteolibacter, Nitrospirillum dominated the Tsokar sample. Correlation analyses highlighted the influence of soil geochemical parameters on the bacterial community structure. PCoA analyses showed that the bacterial beta diversity varied significantly between the sampling locations (PERMANOVA test (F-value: 2.3316; R 2 = 0.466, p = 0.001) and similar results were also obtained while comparing genus abundance data using the ANOSIM test (R = 0.345, p = 0.007).

16S rRNA↗