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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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Nanoconfinement and Salt Synergistically Suppress Crystallization in Polyethylene Oxide
Suppressing the crystallization of polyether-based solid electrolytes is a widely sought-after strategy to improve ionic conductivity. We report the effects of nanoconfinement on polyethylene oxide electrolytes. We find that neat polyethylene oxide responds to nanoconfinement by adopting a preferred orientation, yet is able to crystallize even in nanoconfinement volumes with widths as small as 8 nm. However the combination of nanoconfinement and salt addition does suppress polymer crystallization at room temperature, even though either factor alone cannot. Such synergistic suppression of crystallization has implications for polymer electrolytes, since amorphous rather than crystalline domains predominantly contribute to ionic conduction. Our results suggest that salts previously discounted due to their inability to suppress crystallinity in bulk materials could be made viable when combined with nanoconfinement, thereby opening new possibilities for high-performance solid polymer electrolytes.
One-Dimensional Lateral Force Anisotropy at the Atomic Scale in Sliding Single Molecules on a Surface
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Structure and Sulfur: Tuning the Viscoelastic and Surface Properties of Natural Keratin Fibers
Natural keratin fibers, such as wool, possess a complex hierarchical structure that governs their mechanical properties and surface energy. However, the extent to which these characteristics are influenced by combined contributions of structural variations (e.g., fiber diameter, intermediate filament (IF) packing) and chemical composition (e.g., disulfide bond density) remains poorly understood. In this study, we investigate wool fibers from five sheep breeds (Merino, Polwarth, Cheviot, Eider, and Devon) to elucidate how these factors influence viscoelasticity and surface interactions. Using a multimodal approach integrating interfacial and bulk characterization methods, including inverse gas chromatography (IGC), atomic force microscopy-infrared spectroscopy (AFM-IR), X-ray photoelectron spectroscopy (XPS), uniaxial tensile testing, and synchrotron small-angle X-ray scattering (SAXS), we show that the nanometer-thick 18-methyleicosanoic acid (18-MEA) layer is consistently present across all wool types and plays a key role in governing hydrophobicity and surface heterogeneity. A controlled isothermal treatment at 200 °C, designed to cleave disulfide bonds, results in a nearly 40% reduction in specific surface area across all fiber types, accompanied by a significant decrease in tensile strength and 80% reduction in elongation at break for Merino and Devon wool, but limited influence on the mechanical properties of Eider fibers. Furthermore, rate-dependent tensile testing within the elastic regime reveals distinct viscoelastic responses among the fiber types, suggesting that the sulfur-rich protein matrix surrounding IFs and its structure contribute actively to stress partitioning. Altogether, when combined with conclusions from SAXS measurements of IF spacing, our work offers compelling insights into the role of the keratin-associated protein (KAP) matrix in shaping wool fiber mechanics. Differences in mechanical behavior among wool types, despite similar IF spacing or sulfur content, highlight the importance of matrix composition and cross-linking density, suggesting that the molecular architecture of the KAP network may be a dominant factor in determining fiber performance.
Artificial Graphene Nanoribbons: A Test Bed for Topology and Low-Dimensional Dirac Physics
Here, we synthesize artificial graphene nanoribbons by positioning carbon monoxide molecules on a copper surface to confine its surface state electrons into artificial atoms positioned to emulate the low-energy electronic structure of graphene derivatives. We demonstrate that the dimensionality of artificial graphene can be reduced to one dimension with proper "edge" passivation, with the emergence of an effectively gapped one-dimensional nanoribbon structure. These one-dimensional structures show evidence of topological effects analogous to graphene nanoribbons. Guided by first-principles calculations, we spatially explore robust, zero-dimensional topological states by altering the topological invariants of quasi-one-dimensional artificial graphene nanostructures. The robustness and flexibility of our platform allow us to toggle the topological invariants between trivial and nontrivial on the same nanostructure. Ultimately, we spatially manipulate the states to understand fundamental coupling between adjacent topological states that are finely engineered and simulate complex Hamiltonians.
Monochromatic X-ray Source Based on Scattering from a Magnetic Nanoundulator
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Bridging the Gap in Carbon Free Iron Making: How Hydrogen Affects the Reduction of Iron Ore between 900 and 1590 °C
Hydrogen-based reduction of iron ore for iron and steel production has emerged as a promising alternative to coal and natural gas. Unlike other hydrogen-based iron ore reduction studies, this research focuses on a wide temperature range across 900–1590 °C, encompassing reduction in solid, mixed, and liquid (slag) phases. For a 20 min exposure to hydrogen, the reduction degree increased monotonically from ∼35% at 900 °C to >90% at 1550 °C, except between 1100 °C and 1400 °C, where it stagnated ∼60%. This experimental work challenges the widely accepted notion that higher temperatures enhance the reduction process. Instead, it reveals an overlooked kinetic bottleneck, suggesting complex thermodynamic and mass transfer limitations influenced by phase transformations, diffusion barriers, and microstructural changes. Density functional theory-based molecular dynamics simulations indicate that oxygen diffusivity in BCC iron is 3.88 × 10 –5 cm 2 /s which is ∼5–10 times higher than that in FCC iron. This study reports that this stagnant reduction degree in the mixed solid–liquid phase is due to competition of multiple mechanisms, such as surface- and bulk-diffusion, pore collapse mechanisms, and crystallographic transitions.
The Future is Garbage: Repurposing of Food Waste to an Integrated Biorefinery
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Rational Design of RNA Editing Guide Strands: Cytidine Analogs at the Orphan Position
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Active Phase on SrCo 1–x Fe x O -δ (0 ≤ x ≤ 0.5) Perovskite for Water Oxidation: Reconstructed Surface versus Remaining Bulk
Perovskite oxides based on earth-abundant transition metals have been extensively explored as promising oxygen evolution reaction (OER) catalysts in alkaline media. The (electro)chemically induced transformation of their initially crystalline surface into an amorphous state has been reported for a few highly active perovskite catalysts. However, little knowledge is available to distinguish the contribution of the amorphized surface from that of the remaining bulk toward the OER. In this work, we utilize the promoting effects of two types of Fe modification, i.e., bulk Fe dopant and Fe ions absorbed from the electrolyte, on the OER activity of SrCoO 3-δ model perovskite to identify the active phase. Transmission electron microscopy and X-ray photoelectron spectroscopy confirmed the surface amorphization of SrCoO 3-δ as well as SrCo 0.8 Fe 0.2 O 3-δ after potential cycling in Fe-free KOH solution. By further cycling in Fe-spiked electrolyte, Fe was incorporated into the amorphized surface of SrCoO 3-δ (SrCoO 3-δ + Fe 3+ ), yielding approximately sixfold increase in activity. Despite the difference in remaining perovskites, SrCoO 3-δ + Fe 3+ and SrCo 0.8 Fe 0.2 O 3-δ exhibited remarkably similar activity. These results reflect that the in situ developed surface species are directly responsible for the measured OER activity, whereas the remaining bulk phases have little impact.
Perspectives on the Future of Land Surface Models and the Challenges of Representing Complex Terrestrial Systems
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The Central Amazon Biomass Sink Under Current and Future Atmospheric CO 2 : Predictions From Big‐Leaf and Demographic Vegetation Models
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ECOSTRESS: NASA's Next Generation Mission to Measure Evapotranspiration From the International Space Station
Abstract The ECOsystem Spaceborne Thermal Radiometer Experiment on Space Station (ECOSTRESS) was launched to the International Space Station on 29 June 2018 by the National Aeronautics and Space Administration (NASA). The primary science focus of ECOSTRESS is centered on evapotranspiration (ET), which is produced as Level‐3 (L3) latent heat flux ( LE ) data products. These data are generated from the Level‐2 land surface temperature and emissivity product (L2_LSTE), in conjunction with ancillary surface and atmospheric data. Here, we provide the first validation (Stage 1, preliminary) of the global ECOSTRESS clear‐sky ET product (L3_ET_PT‐JPL, Version 6.0) against LE measurements at 82 eddy covariance sites around the world. Overall, the ECOSTRESS ET product performs well against the site measurements (clear‐sky instantaneous/time of overpass: r 2 = 0.88; overall bias = 8%; normalized root‐mean‐square error, RMSE = 6%). ET uncertainty was generally consistent across climate zones, biome types, and times of day (ECOSTRESS samples the diurnal cycle), though temperate sites are overrepresented. The 70‐m‐high spatial resolution of ECOSTRESS improved correlations by 85%, and RMSE by 62%, relative to 1‐km pixels. This paper serves as a reference for the ECOSTRESS L3 ET accuracy and Stage 1 validation status for subsequent science that follows using these data.
Midwest US Croplands Determine Model Divergence in North American Carbon Fluxes
Large uncertainties in North American terrestrial carbon fluxes hinder regional climate projections. Terrestrial biosphere models (TBMs), the essential tools for understanding continental-scale carbon cycle, diverge on whether temperate forests or croplands dominate carbon uptake in North America. Evidence from novel photosynthetic proxies, such as those based on chlorophyll fluorescence, has cast doubt on the “weak cropland, strong forest” carbon uptake patterns simulated by most TBMs. However, no systematic evaluation of TBMs has yet been attempted to pin down space-time patterns that are most consistent with regional CO 2 observational constraints. Here, we leverage atmospheric CO 2 observations and satellite-observed photosynthetic proxies to understand emergent space-time patterns in North American carbon fluxes from a large suite of TBMs and data-driven models. To do so, we evaluate how well the atmospheric signals resulting from carbon flux estimates reproduce the space-time variability in atmospheric CO 2 , as is observed by a network of continuous-monitoring towers over North America. Models with gross or net carbon fluxes that are consistent with the observed CO 2 variability share a salient feature of growing-season carbon uptake in Midwest US croplands. Conversely, the remaining models place most growing-season uptake in boreal or temperate forests. Differences in model explanatory power depend mainly on the simulated annual cycles of cropland uptake—especially, the timing of peak uptake—rather than the distribution of annual mean fluxes across biomes. Our results suggest that improved model representation of cropland phenology is crucial to robust, policy-relevant estimation of North American carbon exchange.
Leaf Trait Plasticity Alters Competitive Ability and Functioning of Simulated Tropical Trees in Response to Elevated Carbon Dioxide
The response of tropical ecosystems to elevated carbon dioxide (CO 2 ) remains a critical uncertainty in projections of future climate. Here, we investigate how leaf trait plasticity in response to elevated CO 2 alters projections of tropical forest competitive dynamics and functioning. We use vegetation demographic model simulations to quantify how plasticity in leaf mass per area and leaf carbon to nitrogen ratio alter the responses of carbon uptake, evapotranspiration, and competitive ability to a doubling of CO 2 in a tropical forest. Observationally constrained leaf trait plasticity in response to CO 2 fertilization reduces the degree to which tropical tree carbon uptake is affected by a doubling of CO 2 (up to -14.7% as compared to a case with no plasticity; 95% confidence interval [CI95%] -14.4 to -15.0). It also diminishes evapotranspiration (up to -7.0%, CI95% -6.4 to -7.7), and lowers competitive ability in comparison to a tree with no plasticity. Consideration of leaf trait plasticity to elevated CO 2 lowers tropical ecosystem carbon uptake and evapotranspirative cooling in the absence of changes in plant-type abundance. However, “plastic” responses to high CO 2 which maintain higher levels of plant productivity, many of which fall outside of the observed range of response, are potentially more competitively advantageous, thus, including changes in plant type abundance may mitigate these decreases in ecosystem functioning. Models that explicitly represent competition between plants with alternative leaf trait plasticity in response to elevated CO 2 are needed to capture these influences on tropical forest functioning and large-scale climate.
Satellite Observations of the Tropical Terrestrial Carbon Balance and Interactions With the Water Cycle During the 21st Century
A constellation of satellites are now in orbit providing information about terrestrial carbon and water storage and fluxes. These combined observations show that the tropical biosphere has changed significantly in the last two decades from the combined effects of climate variability and land use. Large areas of forest have been cleared in both wet and dry forests, increasing the source of carbon to the atmosphere. Concomitantly, tropical fire emissions have declined, at least until 2016, from changes in land-use practices and rainfall, increasing the net carbon sink. Measurements of carbon stocks and fluxes from disturbance and recovery and of vegetation photosynthesis show significant regional variability of net biosphere exchange (NBE) and gross primary productivity (GPP) across the tropics and are tied to seasonal and interannual changes in water fluxes and storage. Comparison of satellite based estimates of evapotranspiration (ET), photosynthesis, and the deuterium content of water vapor with patterns of total water storage and rainfall demonstrate the presence of vegetation-atmosphere interactions and feedback mechanisms across tropical forests. However, these observations of stocks, fluxes and inferred interactions between them do not point unambiguously to either positive or negative feedbacks in carbon and water exchanges. These ambiguities highlight the need for assimilation of these new measurements with Earth System models for a consistent assessment of process interactions, along with focused field campaigns that integrate ground, aircraft and satellite measurements, to quantify the controlling carbon and water processes and their feedback mechanisms.
Are Remote Sensing Evapotranspiration Models Reliable Across South American Ecoregions?
Many remote sensing-based evapotranspiration (RSBET) algorithms have been proposed in the past decades and evaluated using flux tower data, mainly over North America and Europe. Model evaluation across South America has been done locally or using only a single algorithm at a time. Here, in this work, we provide the first evaluation of multiple RSBET models, at a daily scale, across a wide variety of biomes, climate zones, and land uses in South America. We used meteorological data from 25 flux towers to force four RSBET models: Priestley–Taylor Jet Propulsion Laboratory (PT-JPL), Global Land Evaporation Amsterdam Model (GLEAM), Penman–Monteith Mu model (PM-MOD), and Penman–Monteith Nagler model (PM-VI). ET was predicted satisfactorily by all four models, with correlations consistently higher (R 2 > 6) for GLEAM and PT-JPL, and PM-MOD and PM-VI presenting overall better responses in terms of percent bias (-10 < PBIAS < 10%). As for PM-VI, this outcome is expected, given that the model requires calibration with local data. Model skill seems to be unrelated to land-use but instead presented some dependency on biome and climate, with the models producing the best results for wet to moderately wet environments. Our findings show the suitability of individual models for a number of combinations of land cover types, biomes, and climates. At the same time, no model outperformed the others for all conditions, which emphasizes the need for adapting individual algorithms to take into account intrinsic characteristics of climates and ecosystems in South America.