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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 739 records · Page 41
All sourcesNASA NTRSDOE OSTI
DOE OSTI2025

How do Hydrological Variability and Human Activities Control the Spatiotemporal Changes of Riverine Nitrogen Export in the Upper Mississippi River Basin?

Excessive nitrogen export from agricultural watersheds remains a critical water quality challenge, with the Upper Mississippi River Basin (UMRB) significantly contributing to downstream eutrophication and hypoxia in the Gulf. This study investigates the spatiotemporal dynamics of riverine nitrate plus nitrite (NO 3 − + NO 2 − -N) export across the UMRB at high spatial resolution (12-digit Hydrologic Unit Codes or HUC12 subwatershed scale) during 2001−2020 and quantifies the effects of anthropogenic activities and hydrological variability on riverine NO 3 − + NO 2 − -N export changes in the region between 2001−2005 and 2016−2020. Our results revealed hotspots of substantial increases in NO 3 − + NO 2 − -N yields across the UMRB, with distinct regional patterns in driving factors. Over the entire UMRB, NO 3 − + NO 2 − -N yields increased by 9.7 kg/ha/yr on average from 2001−2005 to 2016−2020, with anthropogenic activities contributing 4.8 kg/ha/yr and hydrological variability contributing 4.9 kg/ha/yr. The northern and western UMRB had combined influences from both anthropogenic activities and hydrological variability, while the east-central regions had predominantly hydrologically driven changes. Agricultural sources, including fertilizer, manure, and biological nitrogen fixation, collectively contributed over 80% of NO 3 − + NO 2 − -N loading throughout the basin. Furthermore, this framework for disentangling human and hydrological impacts provides critical insights for developing effective and targeted watershed management strategies to reduce nutrient losses and improve water quality.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2023

Ambient Tip-Enhanced Two Photon Photoluminescence from CdSe/ZnS Quantum Dots

Nonlinear nano-optical measurements that combine ultrafast spectroscopy with tools of scanning probe microscopy are scarce. This is particularly the case when high spatial resolution on the order of a few nanometers is sought after in experiments performed under ambient laboratory conditions. In this work, we demonstrate the latter through measurements that track two-photon photoluminescence from aggregates of CdSe/ZnS quantum dots with sub-5 nm spatial resolution. We report our proof-of-principle measurements that only take advantage of a plasmonic probe (as opposed to a gap mode) pave the way for nonlinear photoluminescence-based spectral nano-imaging of realistic/heterogeneous (bio)molecular and (bio)material systems.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2021

Multimodal Tip-Enhanced Nonlinear Optical Nanoimaging of Plasmonic Silver Nanocubes

Optical field localization at plasmonic tip-sample nanojunctions has enabled high spatial resolution chemical analysis through tip-enhanced linear optical spectroscopies, including Raman scattering and photoluminescence. In this work, we illustrate that nonlinear optical processes, including parametric four-wave mixing (4WM), second harmonic/sum-frequency generation (SHG and SFG), and two-photon photoluminescence (TPPL), can be enhanced at plasmonic junctions and spatio-spectrally resolved simultaneously with few-nm spatial resolution under ambient conditions. More importantly, through a detailed analysis of our spectral nano-images, we find that the efficiencies of the local nonlinear signals are determined by sharp tip-sample junction resonances that vary over the few-nanometer length scale because of the corrugated nature of the probe. Namely, plasmon resonances centered at or around the different nonlinear signals are tracked through TPPL, and they are found to selectively enhance nonlinear signals with closely matched optical resonances.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2023

Engineering of Nanoscale Heterogeneous Transition Metal Dichalcogenide–Au Interfaces

Engineering the transition metal dichalcogenide (TMD)–metal interface is critical for the development of two-dimensional semiconductor devices. By directly probing the electronic structures of WS 2 –Au and WSe 2 –Au interfaces with high spatial resolution, we delineate nanoscale heterogeneities in the composite systems that give rise to local Schottky barrier height modulations. Photoelectron spectroscopy reveals large variations (>100 meV) in TMD work function and binding energies for the occupied electronic states. Characterization of the composite systems with electron backscatter diffraction and scanning tunneling microscopy leads us to attribute these heterogeneities to differing crystallite orientations in the Au contact, suggesting an inherent role of the metal microstructure in contact formation. Here we then leverage our understanding to develop straightforward Au processing techniques to form TMD–Au interfaces with reduced heterogeneity. Our findings illustrate the sensitivity of TMDs’ electronic properties to metal contact microstructure and the viability of tuning the interface through contact engineering.

77 NANOSCIENCE AND NANOTECHNOLOGY↗
DOE OSTI2024

Electrified Operando -Freezing of Electrocatalytic CO 2 Reduction Cells for Cryogenic Electron Microscopy

The ability to freeze and stabilize reaction intermediates in their metastable states and obtain their structural and chemical information with high spatial resolution would be very powerful to unravel the fundamentals in many important materials technologies such as catalysis and batteries. Here, we develop an electrified operando-freezing methodology for the first time to preserve these metastable states under electrochemical reaction conditions for cryogenic electron microscopy (cryo-EM) imaging and spectroscopy. Using Cu catalysts for CO 2 reduction as a model system, we observe restructuring of the Cu catalyst in a CO 2 atmosphere while the same catalyst remains intact in an air atmosphere at the nanometer scale. Furthermore, we discover the existence of single valance Cu (1+) state and C-O bonding at the electrified liquid-solid interface of the operando-frozen samples, which are key reaction intermediates that traditional ex situ measurements fail to detect. Finally, this work highlights our novel technique to study the local structure and chemistry of electrified liquid-solid interfaces, which has broad impact for many electrochemical reactions.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2022

Quantifying the Influence of Defects on Selectivity of Electrodes Encapsulated by Nanoscopic Silicon Oxide Overlayers

Encapsulation of electrocatalysts and photocatalysts with semipermeable nanoscopic oxide overlayers that exhibit selective transport properties is an attractive approach to achieve high redox selectivity. However, defects within the overlayers-such as pinholes, cracks, or particle inclusions-may facilitate local high rates of parasitic reactions by creating pathways for facile transport of undesired reactants to exposed active sites. Scanning electrochemical microscopy (SECM) is an attractive method to determine the influence of defects on macroscopic performance metrics thanks to its ability to measure the relative rates of competing electrochemical reactions with high spatial resolution over the electrode. Here, we report the use of SECM to determine the influence of overlayer defects on the selectivity of silicon oxide (SiOx) encapsulated platinum thin-film electrocatalysts operated under conditions where two competing reactions-the hydrogen evolution and Fe(III) reduction reactions-can occur. After an SECM methodology is described to determine spatially resolved selectivity, representative selectivity maps are correlated with the location of defects that are characterized by optical, electron, and atomic force microscopies. This analysis reveals that certain types of defects in the oxide overlayer are responsible for ∼60-90% of the partial current density toward the undesired Fe(III) reduction reaction. By correcting for defect contributions to Fe(III) reduction rates, true Fe(III) permeability values for the SiOxoverlayers were determined to be over an order of magnitude lower than permeabilities determined from analyses that ignore the presence of defects. Lastly, different types of defects were studied revealing that defect morphology can have varying influence on both redox selectivity and calculated permeability. This work highlights the need for spatially resolved measurements to evaluate the performance of oxide-encapsulated catalysts and understand their performance limits.

25 ENERGY STORAGE↗
DOE OSTI2025

Operando Neutron Imaging of Reaction Extent and Particle Swelling Informs Limiting Factors for Salt Hydrate Thermochemical Energy Storage

Salt hydrates are a promising thermochemical energy storage medium that stores heat through the reversible uptake (hydration) and release (dehydration) of water vapor. Our study deploys operando neutron imaging to investigate salt hydrate performance with high spatial resolution (42 μm pixels). For flow over a packed bed with diffusion-driven transport, measurements reveal the formation of a solid diffusion layer due to particle swelling for the pure SrBr2 salt. In contrast, the SrBr2–vermiculite composite exhibits significantly less swelling and more than a 2-fold increase in the apparent water vapor diffusivity. For axial flow through a packed bed, neutron imaging confirms theoretically predicted transitions from a moving reaction front to a homogeneous profile with an increase in humid air flow rate. Our study establishes neutron imaging as a powerful technique to advance fundamental understanding of thermochemical systems and help guide composite material design.

Kinzer, Bryan [ORNL] (ORCID:0000000337804910)↗
DOE OSTI2021

Surface-Enhanced Raman Scattering Optophysiology Nanofibers for the Detection of Heavy Metals in Single Breast Cancer Cells

Mercury(II) ions (Hg2+) and silver ions (Ag+) are two of the most hazardous pollutants causing serious damage to human health. Here, we constructed surface enhanced Raman scattering (SERS)-active nanofibers covered with 4-mercaptopyridine (4-Mpy)-modified gold nanoparticles (AuNPs) to detect Hg2+ and Ag+. Experimental evidence suggests that the observed spectral changes originate from the combined effect of (i) the coordination between the nitrogen on 4-Mpy and the metal ions and (ii) the 4-Mpy molecular orientation (from flatter to more perpendicular with respect to the metal surface). The relative intensity of a pair of characteristic Raman peaks (at ~428 and ~708 cm-1) was used to quantify the metal ion concentration, greatly increasing the reproducibility of the measurement compared to signal-on or signal-off detection based on a single SERS peak. The detection limit of this method for Hg2+ is lower than that for the Ag+ (5 nM vs 100 nM), which can be explained by the stronger interaction energy between Hg2+ and N compared to Ag+ and N, as demonstrated by density functional theory (DFT) calculations. The Hg2+ and Ag+ ions can be masked by adding ethylenediaminetetraacetate (EDTA) and Cl-, respectively, to the Hg2+ and Ag+ samples. The good sensitivity, high reproducibility and excellent selectivity of these nanosensors were also demonstrated. Furthermore, detection of Hg2+ in living breast cancer cells at the subcellular level is possible thanks to the nanometric size of the herein described SERS nanosensors, allowing high spatial resolution and minimal cell damage.

Zhao, Xingjuan↗
DOE OSTI2024

Dynamic Nanocrystal Superlattices with Thermally Triggerable Lubricating Ligands

The size-dependent and collective physical properties of nanocrystals (NCs) and their self-assembled superlattices (SLs) enable the study of mesoscale phenomena and the design of metamaterials for a broad range of applications. However, the limited mobility of NC building blocks in dried NCSLs often hampers the potential for employing postdeposition methods to produce high-quality NCSLs. In this study, we present tailored promesogenic ligands that exhibit a lubricating property akin to thermotropic liquid crystals. The lubricating ability of ligands is thermally triggerable, allowing the dry solid NC aggregates deposited on the substrates with poor ordering to be transformed into NCSLs with high crystallinity and preferred orientations. The interplay between the dynamic behavior of NCSLs and the molecular structure of the ligands is elucidated through a comprehensive analysis of their lubricating efficacy using both experimental and simulation approaches. Coarse-grained molecular dynamic modeling suggests that a shielding layer from mesogens prevents the interdigitation of ligand tails, facilitating the sliding between outer shells and consequently enhancing the mobility of NC building blocks. Further, the dynamic organization of NCSLs can also be triggered with high spatial resolution by laser illumination. The principles, kinetics, and utility of lubricating ligands could be generalized to unlock stimuli-responsive metamaterials from NCSLs and contribute to the fabrication of NCSLs.

77 NANOSCIENCE AND NANOTECHNOLOGY↗
DOE OSTI2025

Depletion of the Protein Hydration Shell with Increasing Temperature Observed by Small-Angle X-ray Scattering and Molecular Simulations

The hydration shell is an integral part of proteins since it plays key roles in conformational transitions, molecular recognition, and enzymatic activity. While the dynamics of the hydration shell have been described by spectroscopic techniques, the structure of the hydration shell remains less understood due to the lack of hydration shell-sensitive structural probes with high spatial resolution. We combined temperature-ramp small-angle X-ray scattering (T-ramp SAXS) from 255 to 335 K with molecular simulations to demonstrate that the hydration shells of the IgG-binding domain of Protein G (GB3) and the villin headpiece are remarkably temperature-sensitive. For proteins in the folded state, T-ramp SAXS data and explicit-solvent SAXS predictions consistently demonstrate decays of protein contrasts and radii of gyration with increasing temperature, which are shown to reflect predominantly temperature-sensitive, depleting hydration shells. The depletion is caused not merely by enhanced disorder within the hydration shells but also by partial displacements of surface-coordinated water molecules. Together, T-ramp SAXS and explicit-solvent SAXS calculations provide a novel structural view of the protein hydration shell, which underlies temperature-dependent processes such as cold denaturation, thermophoresis, or biomolecular phase separation.

electron density↗
DOE OSTI2020

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.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2021

TROPOMI NO 2 in the United States: A Detailed Look at the Annual Averages, Weekly Cycles, Effects of Temperature, and Correlation With Surface NO 2 Concentrations

Abstract Observing the spatial heterogeneities of NO 2 air pollution is an important first step in quantifying NO X emissions and exposures. This study investigates the capabilities of the Tropospheric Monitoring Instrument (TROPOMI) in observing the spatial and temporal patterns of NO 2 pollution in the continental United States. The unprecedented sensitivity of the sensor can differentiate the fine‐scale spatial heterogeneities in urban areas, such as emissions related to airport/shipping operations and high traffic, and the relatively small emission sources in rural areas, such as power plants and mining operations. We then examine NO 2 columns by day‐of‐the‐week and find that Saturday and Sunday concentrations are 16% and 24% lower respectively, than during weekdays. We also analyze the correlation of daily maximum 2‐m temperatures and NO 2 column amounts and find that NO 2 is larger on the hottest days (>32°C) as compared to warm days (26°C–32°C), which is in contrast to a general decrease in NO 2 with increasing temperature at moderate temperatures. Finally, we demonstrate that a linear regression fit of 2019 annual TROPOMI NO 2 data to annual surface‐level concentrations yields relatively strong correlation ( R 2 = 0.66). These new developments make TROPOMI NO 2 satellite data advantageous for policymakers and public health officials, who request information at high spatial resolution and short timescales, in order to assess, devise, and evaluate regulations.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2022

Impacts of Sub‐Grid Topographic Representations on Surface Energy Balance and Boundary Conditions in the E3SM Land Model: A Case Study in Sierra Nevada

Abstract Sub‐grid topographic heterogeneity has large impacts on surface energy balance and land‐atmosphere interactions. However, the impacts of representing sub‐grid topographic effects in land surface models (LSMs) on surface energy balance and boundary conditions remain unclear. This study analyzed and evaluated the impacts of sub‐grid topographic representations on surface energy balance, turbulent heat flux, and scalar (co‐)variances in the Energy Exascale Earth System Model (E3SM) land model (ELM). Three sub‐grid topographic representations in ELM were compared: (a) the default sub‐grid structure (D), (b) the recently developed sub‐grid topographic structure (T), and (c) high spatial resolution (1KM). Additionally, two different solar radiation schemes in ELM were compared: (a) the default plane‐parallel radiative transfer scheme (PP) and (b) the parameterization scheme (TOP) that accounts for sub‐grid topographic effects on solar radiation. A series of offline simulations with the three grid discretization structures (D, T, and 1KM) and two schemes of solar radiation (TOP and PP) were carried out using the Sierra Nevada, California. 1KM simulations with TOP well capture the spatial heterogeneity of surface fluxes compared to Moderate Resolution Imaging Spectroradiometer remote sensing data. There are significant differences between TOP and PP in the 1‐km simulated surface energy balance, but the differences in mean values and standard deviations become small when aggregated to the grid scale (i.e., 0.5°). The T configuration better mimics the 1KM simulations with TOP than the D configuration and better captures the sub‐grid topographic effects on surface energy balance and boundary conditions. These results underline the importance of representing sub‐grid topographic heterogeneities in LSMs and motivate future research to understand the sub‐grid topographic effects on land‐atmosphere interactions over mountainous areas.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗
DOE OSTI2022

Monitoring Seismic Velocity Changes Across the San Jacinto Fault Using Train‐Generated Seismic Tremors

Abstract Microseismic noise has been used for seismic velocity monitoring. However, such signals are dominated by low‐frequency surface waves that are not ideal for detecting changes associated with small tectonic processes. Here we show that it is possible to extract stable, high‐frequency body waves using seismic tremors generated by freight trains. Such body waves allow us to focus on small velocity perturbations in the crust with high spatial resolution. We report on 10 years of seismic velocity temporal changes at the San Jacinto Fault. We observe and map a two‐month‐long episode of velocity changes with complex spatial distribution and interpret the velocity perturbation as produced by a previously undocumented slow‐slip event. We verify the hypothesis through numerical simulations and locate this event along a fault segment believed to be locked. Such a slow‐slip event stresses its surroundings and may trigger a major earthquake on a fault section approaching failure.

58 GEOSCIENCES↗
DOE OSTI2023

High–Resolution Maps of Near–Surface Permafrost for Three Watersheds on the Seward Peninsula, Alaska Derived From Machine Learning

Permafrost soils are a critical component of the global carbon cycle and are locally important because they regulate the hydrologic flux from uplands to rivers. Furthermore, degradation of permafrost soils causes land surface subsidence, damaging infrastructure that is crucial for local communities. Regional and hemispherical maps of permafrost are too coarse to resolve distributions at a scale relevant to assessments of infrastructure stability or to illuminate geomorphic impacts of permafrost thaw. Here we train machine learning models to generate meter–scale maps of near–surface permafrost for three watersheds in the discontinuous permafrost region. The models were trained using ground truth determinations of near–surface permafrost presence from measurements of soil temperature and electrical resistivity. We trained three classifiers: extremely randomized trees (ERTr), support vector machines (SVM), and an artificial neural network (ANN). Model uncertainty was determined using k–fold cross validation, and the modeled extents of near–surface permafrost were compared to the observed extents at each site. At–a–site near–surface permafrost distributions predicted by the ERTr produced the highest accuracy (70%–90%). However, the transferability of the ERTr to the sites outside of the training data set was poor, with accuracies ranging from 50% to 77%. The SVM and ANN models had lower accuracies for at–a–site prediction (70%–83%), yet they had greater accuracy when transferred to the non–training site (62%–78%). These models demonstrate the potential for integrating high–resolution spatial data and machine learning models to develop maps of near–surface permafrost extent at resolutions fine enough to assess infrastructure vulnerability and landscape morphology influenced by permafrost thaw.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2024

Quantifying Seasonal and Diurnal Cycles of Solar‐Induced Fluorescence With a Novel Hyperspectral Imager

Abstract Solar‐induced fluorescence (SIF) is a proxy of ecosystem photosynthesis that often scales linearly with gross primary productivity (GPP) at the canopy scale. However, the mechanistic relationship between GPP and SIF is still uncertain, especially at smaller temporal and spatial scales. We deployed a ultra‐hyperspectral imager over two grassland sites in California throughout a soil moisture dry down. The imager has high spatial resolution that limits mixed pixels, enabling differentiation between plants and leaves within one scene. We find that imager SIF correlates well with diurnal changes in leaf‐level physiology and gross primary productivity under well‐watered conditions. These relationships deteriorate throughout the dry down event. Our results demonstrate an advancement in SIF imaging with new possibilities in remotely sensing plant canopies from the leaf to the ecosystem. These data can be used to resolve outstanding questions regarding SIF's meaning and usefulness in terrestrial ecosystem monitoring.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2024

Temporal Error Correlations in a Terrestrial Carbon Cycle Model Derived by Comparison to Carbon Dioxide Eddy Covariance Flux Tower Measurements

Abstract Atmospheric CO 2 flux inversions require as input an estimate of spatial and temporal correlations of errors in their estimate of the prior mean. Some previous studies have used the differences in CO 2 daily average flux estimates produced by terrestrial carbon cycle models and eddy covariance measurements to constrain the flux error correlations. Since inversions are starting to resolve the daily cycle, we set out to examine the correlations at sub‐daily time scales, as well as the correlations across years. To this end, we examine the autocorrelations in the difference between net ecosystem‐atmosphere exchange measurements from 75 AmeriFlux towers and temporally downscaled high‐spatial‐resolution flux estimates from the Carnegie‐Ames‐Stanford Approach (CASA) terrestrial carbon cycle model. We find that the daily cycle is prominent in these hourly autocorrelations and that these autocorrelations persist across years. We propose a family of functions to model these temporal correlations in atmospheric inversions, and use cross validation to determine which of the correlation functions best fits autocorrelation data from towers not in the training set. Correlation functions with a component that attempts to model the daily cycle in the differences match correlations from other towers better than those without. Those models that reproduce the same correlation structures at 1‐year intervals while modulating the amplitudes of the correlations between those intervals improve the fit still further.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2024

DASEventNet: AI‐Based Microseismic Detection on Distributed Acoustic Sensing Data From the Utah FORGE Well 16A (78)‐32 Hydraulic Stimulation

Abstract Distributed acoustic sensing (DAS) has emerged as a promising seismic technology for monitoring microearthquakes (MEQs) with high spatial resolution. Efficient algorithms are needed for processing large DAS data volumes. This study introduces a deep learning (DL) model based on a Residual Convolutional Neural Network (ResNet) for detecting MEQs using DAS data, named as DASEventNet. The test data were collected from the Utah FORGE 16A (78)‐32 hydraulic stimulation experiments conducted in April 2022. The DASEventNet model achieves a remarkable accuracy of 100% when discriminating MEQs from noise in the raw test set of 260 examples. Surprisingly, the model identified weak MEQ signatures that have been manually categorized as noise. The decision‐making process with the model is decoded by the classic activation map, which illuminates learning features of the DASEventNet model. These features provide clear illustrations of weak MEQs and varied noise types. Finally, we apply the trained model to the entire period (∼7 days) of continuous DAS recordings and find that it discovers >5,700 new MEQs, previously unregistered in the public Silixa DAS catalog. The DASEventNet model significantly outperforms the traditional seismic method Short‐Term Average/Long‐Term Average (STA/LTA), which detected only 1,307 MEQs. The DASEventNet detection threshold is M w −1.80 compared to the minimum magnitude of M w −1.14 detected by STA/LTA. The spatiotemporal distribution of the newly identified MEQs defines an extensive stimulation zone and more accurately characterizes fracture geometry. Our results highlight the potential of DL for long‐term, real‐time microseismic monitoring that can improve enhanced geothermal systems and other activities that include subsurface hydraulic fracturing.

15 GEOTHERMAL ENERGY↗