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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
All sourcesNASA NTRSDOE OSTI
DOE OSTI2020

Establishing structure-sensitivity of ceria reducibility: real-time observations of surface-hydrogen interactions

The first layer of atoms on an oxide catalyst provides the first sites for adsorption of reactants and the last sites before products or oxygen are desorbed. We employ a unique combination of morphological, structural, and chemical analyses of a model ceria catalyst with different surface terminations under an H 2 environment to unequivocally establish the effect of the last layer of atoms on surface reduction. (111) and (100) terminated epitaxial islands of ceria are simultaneously studied in situ allowing for a direct investigation of the structure–reducibility relationship under identical conditions. Kinetic rate constants of Ce 4+ to Ce 3+ transformation and equilibrium concentrations are extracted for both surface terminations. Unlike the kinetic rate constants, which are practically the same for both types of islands, more pronounced oxygen release, and overall higher reducibility were observed for (100) islands compared to (111) ones. The findings are in agreement with coordination-limited oxygen vacancy formation energies calculated by density functional theory. The results point out the important aspect of surface terminations in redox processes, with particular impact on the catalytic reactions of a variety of catalysts.

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

Improving Prediction of Surface Solar Irradiance Variability by Integrating Observed Cloud Characteristics and Machine Learning

A 5-year, 1-minute resolution observational dataset of clouds and solar radiation was produced that includes two metrics of the variability in surface solar irradiance due to cloud type and fractional sky cover. Multiple regression models were trained to fit observations of surface solar irradiance variability from those two cloud property predictors. We found that ensemble tree-based methods, Random Forest and Gradient Boosting Machine, have the least overfitting issues and showed the best performance with an R2 of 0.42. While the observational data trained in this study was only from one site, the U.S. Department of Energy (DOE) Atmospheric Radiation Measurement (ARM) Southern Great Plains (SGP) site in Oklahoma, initial comparisons of the seasonality of the statistics suggest that these results are relatively weather regime independent; the generality of such a finding across sites will be tested in future work. The observational data and developed machine learning model are being used to create a numerical weather prediction model parameterization to enable day-ahead solar variability prediction in a computationally efficient way. This is a first step towards creating a new paradigm of predicting day-ahead variability with the potential to provide a new tool to improve grid operation, planning, and resilience.

Riihimaki, Laura↗
DOE OSTI2023

Observations of Ocean Surface Wave Attenuation in Sea Ice Using Seafloor Cables

Abstract The attenuation of ocean surface waves during seasonal ice cover is an important control on the evolution of Arctic coastlines. The spatial and temporal variations in this process have been challenging to resolve with conventional sampling using sparse arrays of moorings or buoys. We demonstrate a novel method for persistent observation of wave‐ice interactions using distributed acoustic sensing (DAS) along existing seafloor fiber optic telecommunications cables. DAS measurements span a 36‐km cross‐shore cable on the Beaufort Shelf from Oliktok Point, Alaska. DAS optical sensing of fiber strain‐rate provides a proxy for seafloor pressure, which we calibrate with wave buoy measurements during the ice‐free season (August 2022). We apply this calibration during the ice formation season (November 2021) to obtain unprecedented resolution of variable wave attenuation rates in new, partial ice cover. The location and strength of wave attenuation serve as proxies for ice coverage and thickness, especially during rapidly evolving events.

Smith, Madison M.↗
DOE OSTI2022

Observation of the surface layer of lithium metal using in situ spectroscopy

In this work, we have investigated the surface of lithium metal using x-ray photoemission spectroscopy and optical spectroscopic ellipsometry. Even if we prepare the surface of lithium metal rigorously by chemical cleaning and mechanical polishing inside a glovebox, both spectroscopic investigations show the existence of a few tens of nanometer-thick surface layers, consisting of lithium oxides and lithium carbonates. When lithium metal is exposed to room air (~50% moisture), in situ real-time monitoring of optical spectra indicates that the surface layer grows at a rate of approximately 24 nm/min, presumably driven by an interface-controlled process. Our results hint that surface-layer-free lithium metals are formidable to achieve by a simple cleaning/polishing method, suggesting that the initial interface between lithium metal electrodes and solid-state electrolytes in fabricated lithium metal batteries can differ from an ideal lithium/electrolyte contact.

36 MATERIALS SCIENCE↗
DOE OSTI2024

Observation of negative surface and interface energies of quantum dots

Surface energy is a fundamental property of materials and is particularly important in describing nanomaterials where atoms or molecules at the surface constitute a large fraction of the material. Traditionally, surface energy is considered to be a positive quantity, where atoms or molecules at the surface are less thermodynamically stable than their counterparts in the interior of the material because they have fewer bonds or interactions at the surface. Using calorimetric methods, we show that the surface energy is negative in some prototypical colloidal semiconductor nanocrystals, or quantum dots with organic ligand coatings. This implies that the surface atoms are more thermodynamically stable than those on the interior due to the strong bonds between these atoms and surfactant molecules, or ligands, that coat their surface. In addition, we extend this work to core/shell indium phosphide/zinc sulfide nanocrystals and show that the interfacial energy between these materials is highly thermodynamically favorable in spite of their large lattice mismatch. This work challenges many of the assumptions that have guided thinking about colloidal nanomaterial thermodynamics, investigates the fundamental stability of many technologically relevant colloidal nanomaterials, and paves the way for future experimental and theoretical work on nanocrystal thermodynamics.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2024

Photoelectron spectroscopy of CsK2Sb photocathode at Synchrotron Radiation Facility using vacuum transport system

As accelerators and electron microscopes become more advanced, high-performance photocathodes are required. In particular, Cesium potassium antimonide (CsK 2 Sb) photocathode is of interest because of its low emittance, excitability in visible light, and high quantum efficiency (QE). The challenge is its high susceptibility to environment that lead to low operating vacuum pressure and short lifetime/low extraction charge. To resolve these issues, it is necessary to understand the molecular structure of the cathode and its degradation mechanism. In this study, we transported CsK 2 Sb photocathode to a beamline of synchrotron radiation facility using a vacuum transport system for molecular structure analysis. Specifically, the cathode was deposited in an evaporation system at Nagoya University. We transported it to Aichi Synchrotron Radiation Center (Aichi SR) located 15 km away, and analyzed it in the depth direction by X-ray photoelectron spectroscopy (XPS) at BL7U. Based on the results, we quantitatively evaluated the composition ratios and stoichiometry of the cathode elements (Sb, K, Cs). A Cs ex-cess state of surface was observed at the surface, and it is consistent with previous studies. The intended atomic structure of CsK 2 Sb was formed only at a few nanometres of the surface on the Mo substrate. On the other hand, the CsK 2 Sb cathode structure on the graphene substrate was preserved further in the depth direction.

47 OTHER INSTRUMENTATION↗
DOE OSTI2022

Application of a Machine Learning Algorithm in Generating an Evapotranspiration Data Product From Coupled Thermal Infrared and Microwave Satellite Observations

Land surface evapotranspiration (ET) is one of the main energy sources for atmospheric dynamics and a critical component of the local, regional, and global water cycles. Consequently, accurate measurement or estimation of ET is one of the most active topics in hydro-climatology research. With massive and spatially distributed observational data sets of land surface properties and environmental conditions being collected from the ground, airborne or space-borne platforms daily over the past few decades, many research teams have started to use big data science to advance the ET estimation methods. The Geostationary satellite Evapotranspiration and Drought (GET-D) product system was developed at the National Oceanic and Atmospheric Administration (NOAA) in 2016 to generate daily ET and drought maps operationally. The primary inputs of the current GET-D system are the thermal infrared (TIR) observations from NOAA GOES satellite series. Because of the cloud contamination to the TIR observations, the spatial coverage of the daily GET-D ET product has been severely impacted. Based on the most recent advances, we have tested a machine learning algorithm to estimate all-weather land surface temperature (LST) from TIR and microwave (MW) combined satellite observations. With the regression tree machine learning approach, we can combine the high accuracy and high spatial resolution of GOES TIR data with the better spatial coverage of passive microwave observations and LST simulations from a land surface model (LSM). The regression tree model combines the three LST data sources for both clear and cloudy days, which enables the GET-D system to derive an all-weather ET product. This paper reports how the all-weather LST and ET are generated in the upgraded GET-D system and provides an evaluation of these LST and ET estimates with ground measurements. The results demonstrate that the regression tree machine learning method is feasible and effective for generating daily ET under all weather conditions with satisfactory accuracy from the big volume of satellite observations.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2020

Gapless Dirac surface states in the antiferromagnetic topological insulator MnBi 2 Te 4

We used angle-resolved photoemission spectroscopy (ARPES) and density functional theory calculations to study the electronic properties of MnBi 2 Te 4 , a material that was predicted to be an intrinsic antiferromagnetic (AFM) topological insulator. In striking contrast to earlier literature showing a full gap opening between two surface band manifolds on the (0001) surface, we observed a gapless Dirac surface state with a Dirac point sitting at E B = -280 meV . Furthermore, our ARPES data revealed the existence of a second Dirac cone sitting closer to the Fermi level. Surprisingly, these surface states remain intact across the AFM transition. The presence of gapless Dirac states in this material may be caused by different ordering at the surface from the bulk or weaker magnetic coupling between the bulk and surface. Whereas the surface Dirac cones seem to be remarkably insensitive to the AFM ordering most likely due to weak coupling to magnetism, we did observe a splitting of the bulk band accompanying the AFM transition. Finally, with a moderately high ordering temperature and interesting gapless Dirac surface states, MnBi 2 Te 4 provides a unique platform for studying the interplay between magnetic ordering and topology.

75 CONDENSED MATTER PHYSICS, SUPERCONDUCTIVITY AND↗
DOE OSTI2026

Zinc treatment to enhance nickel electrode performance for liquid alkaline water electrolyzers

Raney Ni treatment is introduced to the surface of Ni mesh anode electrodes, enhancing liquid alkaline water electrolyzer performance. The surface of the Ni mesh is alloyed with Zn, deposited by either an aqueous infiltration of Zn salt or direct reaction with Zn foil. The extent of alloying is controlled by a heat treatment step. For both processes, fine porosity and enhanced surface area are obtained after leaching the Zn out of the surface alloy layer. The observed electrode surface structure and performance is quite sensitive to the thermal treatment temperature. The enhanced surface area improves full cell performance by 90 mV for infiltrated Zn treated at 600 °C and by 185 mV for Zn foil reacted at 425 °C (at 2 A cm −2 and 80 °C). In conclusion, the Zn treatment methods produce stable operating performance, exhibiting no decay after 150 h for the infiltrated Zn treated at 600 °C, and a decay rate of 4.6 μV h −1 over 100 h for the Zn foil reacted at 425 °C (at 1 A cm −2 and 80 °C).

LAWE↗
DOE OSTI2023

Molecular Dynamics Simulations of Calcite Fracture in Water

Calcite (CaCO 3 ) is one of the most common minerals in geologic and engineered systems. It is often in contact with aqueous solutions, causing chemically assisted fracture that is critical to understanding the stability of subsurface systems and manmade structures. Calcite fracture was evaluated with reactive molecular dynamics simulations, including the impacts of crack tip geometry (notch), the presence of water, and surface hydroxyl groups. Chemo-mechanical weakening was assessed by comparing the loads where fracture began to propagate. Our analyses show that in the presence of a notch, the load at which crack growth begins is lower, compared to the effect of water or surface hydroxyls. Additionally, the breaking of two adjacent Ca–O bonds is the kinetic limitation for crack initiation, since transiently broken bonds can reform, not resulting in crack growth. In aqueous environments, fresh (not hydroxylated) calcite surfaces exhibited water strengthening. Manual addition of H + and/or OH – species on the (104) calcite surface resulted in chemo-mechanical weakening of calcite by 9%. Achieving full hydroxylation of the calcite surface was thermodynamically and kinetically limited, with only 0.17–0.01 OH/nm 2 surface hydroxylation observed on the (104) surface at the end of the simulations. In conclusion, the limited reactivity of pure water with the calcite surface restricts the chemo-mechanical effects and suggests that reactions between physiosorbed water and localized structural defects may be dominating the chemo-mechanical process in the studies where water weakening has been reported.

Calcite↗
DOE OSTI2021

Discovery of segmented Fermi surface induced by Cooper pair momentum

Imaging a peculiar Fermi surface Running a current through a superconductor can cause the superconducting gap to close on a section of the Fermi surface. However, observing this segmented Fermi surface directly is tricky. To do so, Zhu et al . worked with a thin film of the topological insulator bismuth telluride placed on top of superconducting niobium diselenide. A small applied magnetic field caused a screening current, which in turn led to a segmented Fermi surface in the topological insulator layer. Using a scanning tunneling microscope, the researchers were able to map out this Fermi surface. —JS

Science & Technology - Other Topics↗
DOE OSTI2026

Data for A Hybrid Biophysical-Machine Learning Framework for Diurnal Surface Energy Flux Estimation Using Proximal Sensing

Thermal infrared-based remote sensing of surface energy fluxes has traditionally relied on high spatial resolution satellite data with revisit frequencies on the order of weeks. In this study, we evaluate a biophysics-based analytical surface energy balance model for predicting latent energy (LE) and sensible heat (H) fluxes using proximal sensing observations. The Surface Temperature Initiated Closure (STIC1.2) model has been extensively validated across a wide range of spatial and temporal scales using various satellite-derived thermal infrared data sets. Here we extend this validation by applying STIC at sub-hourly temporal resolution over multiple growing seasons for four distinct agricultural systems. We further develop and evaluate novel STIC variants that incorporate machine learning (ML) techniques to eliminate the need for surface energy balance observations, specifically net radiation and soil heat flux, thereby enhancing model applicability in data-sparse settings. The integration of a ML component to estimate surface available energy is shown to have strong predictive performance for both LE (R2 = 0.81–0.94) and H (R2 = 0.46–0.72) across all agricultural systems examined here, demonstrating the potential of hybrid biophysical-machine learning approaches for surface energy balance modeling with minimal data requirements. This study concludes with a novel application of explainable machine learning (exML) to diagnose sources of model error. This exML framework attributes residual prediction errors to both model input variables and environmental drivers not explicitly included in the simulation experiments. This approach provides a new pathway for improving model design and integrating previously overlooked yet influential variables into future model iterations.

AI/ML↗
DOE OSTI2024

Contributions From Cloud Morphological Changes to the Interannual Shortwave Cloud Feedback Based on MODIS and ISCCP Satellite Observations

The surface temperature-mediated change in cloud properties, referred to as the cloud feedback, continues to dominate the uncertainty in climate projections. A larger number of contemporary global climate models (GCMs) project a higher degree of warming than the previous generation of GCMs. This greater projected warming has been attributed to a less negative cloud feedback in the Southern Ocean. Here, we apply a novel “double decomposition method” that employs the “cloud radiative kernel” and “cloud regime” concepts, to two data sets of satellite observations to decompose the interannual cloud feedback into contributions arising from changes within and shifts between cloud morphologies. Our results show that contributions from the latter to the cloud feedback are large for certain regimes. We then focus on interpreting how both changes within and between cloud morphologies impact the shortwave cloud optical depth feedback over the Southern Ocean in light of additional observations. Results from the former cloud morphological changes reveal the importance of the wind response to warming increases low- and mid-level cloud optical thickness in the same region. Results from the latter cloud morphological changes reveal that a general shift from thick storm-track clouds to thinner oceanic low-level clouds contributes to a positive feedback over the Southern Ocean that is offset by shifts from thinner broken clouds to thicker mid- and low-level clouds. Our novel analysis can be applied to evaluate GCMs and potentially diagnose shortcomings pertaining to their physical parameterizations of particular cloud morphologies.

58 GEOSCIENCES↗