Search NASA⌕ Search

SEARCH · Search NASA

Results for “Low Earth”

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.

Quote a phrase for an exact phrase match. Source license links do not imply unrestricted reuse.

At least 325 records · Page 18
All sourcesNASA NTRSDOE OSTI
DOE OSTI2025

Simulating High-Frequency Seismograms in Realistic Earth Models to Better Understand Source Discrimination Based on Differential Magnitudes ( M L− M c)

Discriminating low-yield underground nuclear explosions from small earthquakes is a key task in monitoring nuclear test ban treaties. P/S amplitude ratios have been an effective discriminant for moderate-sized events recorded at regional distances, but it is unclear if they are as effective in discriminating small seismic events recorded at local distances (<150 km). The difference between local magnitude (M L ) and coda duration magnitude (M c ) has been proposed as a new discriminant that may complement P/S amplitude ratios at local distances. Here, in this work, we calculate high-frequency (up to ∼4 Hz) synthetic seismograms at epicentral distances of 0–30 km in realistic models of the Salt Lake basin (Utah, United States) to better understand how variations in source type and depth affect M L −M c values. The Earth models incorporate simplified 1D and deterministic 3D structures, small-wavelength stochastic velocity perturbations, and surface topography. Coda waves are enhanced for the more complicated models compared to the base 1D model, but still underpredict observed durations by about a factor of two, which results in overprediction of amplitude to duration ratios (i.e., M L −M c values) for a near-surface explosion and a 7 km deep earthquake. For both source types, the predicted M L and M c values decrease as source depth increases, and M L −M c shows only minor variation with depth; however, M L −M c is on average ∼0.5 units smaller for explosions than earthquakes. This finding may imply that M L −M c has sensitivity to source type, in addition to being a depth discriminant, but more modeling is needed given the limitations of the current study. Future modeling should incorporate higher-frequency (≳5 Hz) simulations over a larger distance range (0–150 km), where M L and M c are commonly measured, while honoring low shear velocities (<300 m/s) near the surface and sampling a wider range of earthquake and explosion source mechanisms.

Hutchings, Sean J. [Univ. of Utah, Salt Lake City,↗
DOE OSTI2023

Can Biomass Burning Aerosol Induced Surface Cooling Be Amplified Through Sea Surface Temperature‐Cloud Feedback Over the Southeast Atlantic?

Biomass burning (BB) aerosols exert a strong surface cooling effect over the southeast Atlantic (SEA) via aerosol-radiation and aerosol-cloud interactions. The reduction of the sea surface temperature (SST) can trigger the SST-low cloud feedback. Whether this feedback can amplify the surface cooling effect is examined. The modeling results from the Community Earth System Model version 2 (CESM2) demonstrate that counterintuitively the cloud radiative effect (CRE) caused by the BB aerosols is weaker if SST-low cloud feedback is considered compared to fixed-SST simulation (-2.99 W m -2 vs. -4.79 W m -2 ). This is caused by (a) stronger sea breeze due to larger sea-land temperature contrast causing less smoke transport over SEA and (b) less moisture supply from surface due to colder SST. Changes in SST also lead to counterclockwise rotation of ocean circulation anomalies. Consequently, the excess heat transport from the equator reverses the direction of SST-cloud feedback in this region.

0305↗
DOE OSTI2020

Kinetically Stable Oxide Overlayers on Mo 3 P Nanoparticles Enabling Lithium–Air Batteries with Low Overpotentials and Long Cycle Life

The main drawbacks of today's state-of-the-art lithium–air (Li–air) batteries are their low energy efficiency and limited cycle life due to the lack of earth-abundant cathode catalysts that can drive both oxygen reduction and evolution reactions (ORR and OER) at high rates at thermodynamic potentials. Here, inexpensive trimolybdenum phosphide (Mo 3 P) nanoparticles with an exceptional activity—ORR and OER current densities of 7.21 and 6.85 mA cm –2 at 2.0 and 4.2 V versus Li/Li + , respectively—in an oxygen-saturated non-aqueous electrolyte are reported. The Tafel plots indicate remarkably low charge transfer resistance—Tafel slopes of 35 and 38 mV dec –1 for ORR and OER, respectively—resulting in the lowest ORR overpotential of 4.0 mV and OER overpotential of 5.1 mV reported to date. Using this catalyst, a Li–air battery cell with low discharge and charge overpotentials of 80 and 270 mV, respectively, and high energy efficiency of 90.2% in the first cycle is demonstrated. Furthermore, a long cycle life of 1200 is also achieved for this cell. Density functional theory calculations of ORR and OER on Mo 3 P (110) reveal that an oxide overlayer formed on the surface gives rise to the observed high ORR and OER electrocatalytic activity and small discharge/charge overpotentials.

25 ENERGY STORAGE↗
DOE OSTI2021

Normal and anomalous self-healing mechanism of crystalline calcium silicate hydrates

The origin of different stability of crystalline calcium silicate hydrates was investigated. The tobermorite crystal has been used as an analog of cement hydrate that is being mostly manufactured material on earth. Normal tobermorite is thermally unstable and transforms to amorphous at low pressure. Meanwhile, anomalous tobermorite with high Al content does not significantly transform under high pressure or high temperature. Conducted X-ray absorption spectroscopy explains the weak stability of normal tobermorite which was originally hypothesized by the role of zeolitic Ca ions in the cavities of silicate chains. Atomic simulations reproduced the experimentally observed trend of pressure behavior once the ideal structures were modified to account for the Al content as well as the chain defects. The simulations also suggested that the stability of tobermorite under stress could be rationalized as a self-healing mechanism in which the structural instabilities were accommodated by a global sliding of the CaO layer.

36 MATERIALS SCIENCE↗
DOE OSTI2023

Strong effect of liquid Fe–S on elastic wave velocity of olivine aggregate: Implication for the low velocity anomaly at the base of the lunar mantle

The origin of the seismic low velocity zone (LVZ) observed at the base of the lunar mantle is important for understanding the nature of the lunar mantle. Liquid Fe–S is considered as a possible candidate of the cause of the LVZ. However, effect of liquid Fe–S on elastic wave velocity of mantle rock has been considered to be minor, according to theoretical estimation based on the high dihedral angle of liquid Fe–S in olivine aggregate observed in previous microstructural analysis of polished sample cross sections. Here, in this work, we carry out direct measurements of elastic wave velocities of liquid Fe–S-bearing olivine aggregate and report strong reduction of the elastic wave velocities in the presence of liquid Fe–S. The effects of liquid Fe–S in olivine aggregates are much more pronounced than the theoretical estimation. Three-dimensional X-ray microtomography analysis reveals that the geometry of liquid Fe–S strongly depends on the size, with larger liquid Fe–S blobs having low aspect ratios, which may predominantly affect elastic wave velocity. Since smaller spherical Fe–S blobs are much more abundant than the larger elongated blobs, conventional dihedral angle analyses based on scanning electron microscope images tend to statistically over-sample smaller blobs and under-sample larger blobs. Our results suggest that the LVZ at the base of the lunar mantle can be explained by the presence of 4.3–5.0 vol.% liquid Fe–S. The LVZ may be a hidden reservoir of highly siderophile elements (HSEs), which may explain the low abundance of HSEs in the lunar mantle compared to the Earth.

58 GEOSCIENCES↗
DOE OSTI2023

Overcoming the Entropy Penalty of Direct Air Capture for Efficient Gigatonne Removal of Carbon Dioxide

Atmospheric carbon poses an existential threat to civilization via global climate change. Hundreds of gigatonnes of carbon dioxide must be removed from earth’s atmosphere in the next three decades, necessitating a low-cost, energy-efficient process to extract low concentrations of carbon dioxide for conversion to a stable material permanently stored for thousands of years. In this work, the challenge of removing gigatonnes of CO 2 is described via the scale of effort and the thermodynamics of collecting and reducing this diffuse chemical, the accumulation of which imparts a substantial entropy penalty on any atmospheric carbon capture process. The methods of CO 2 reduction combined with upstream direct air capture (DAC) including absorption, membrane separation, and adsorption are compared with biomass torrefaction and permanent burial (BTB). A Monte Carlo model assesses the mass, energy, and economics of the full process of biomass torrefaction from biomass collection and transport to stable carbon burial to determine that 95% of scenarios could remove carbon for less than $200 per CO 2 -tonne-equivalent. Torrefied carbon is further discussed for its long-term stability and availability at the scale required to substantially mitigate the threat of climate change.

biomass↗
DOE OSTI2021

Gold-like activity copper-like selectivity of heteroatomic transition metal carbides for electrocatalytic carbon dioxide reduction reaction

An overarching challenge of the electrochemical carbon dioxide reduction reaction (eCO 2 RR) is finding an earth-abundant, highly active catalyst that selectively produces hydrocarbons at relatively low overpotentials. Here, we report the eCO 2 RR performance of two-dimensional transition metal carbide class of materials. Our results indicate a maximum methane (CH 4 ) current density of -421.63 mA/cm 2 and a CH 4 faradic efficiency of 82.7% ± 2% for di-tungsten carbide (W 2 C) nanoflakes in a hybrid electrolyte of 3 M potassium hydroxide and 2 M choline-chloride. Powered by a triple junction photovoltaic cell, we demonstrate a flow electrolyzer that uses humidified CO 2 to produce CH 4 in a 700-h process under one sun illumination with a CO 2 RR energy efficiency of about 62.3% and a solar-to-fuel efficiency of 20.7%. Density functional theory calculations reveal that dissociation of water, chemisorption of CO 2 and cleavage of the C-O bond—the most energy consuming elementary steps in other catalysts such as copper—become nearly spontaneous at the W2C surface. This results in instantaneous formation of adsorbed CO—an important reaction intermediate—and an unlimited source of protons near the tungsten surface sites that are the main reasons for the observed superior activity, selectivity, and small potential.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2025

Electrochemically in situ formed rocksalt phase in titanium dioxide determines pseudocapacitive sodium-ion storage

Earth-abundant TiO 2 is a promising negative electrode for low-cost sodium-ion batteries (SIBs) owing to its high capacity, rapid (dis)charging capability, safe operation potential and nonflammability. Crystalline anatase TiO 2 is not suitable for reversible Na + (de)intercalation, but it displays pseudocapacitive response after repeated cycles. Herein, we find and demonstrate that ordered rocksalt (RS) NaTiO 2 nanograins are in situ formed by electrochemically cycling with Na + ions in anatase and amorphous TiO 2 . The in situ formed RS-NaTiO 2 follows a solid-solution reaction with small volume changes of only 2.0%, that determines the pseudocapacitive “mirror-like” cyclic voltammetry curve with a couple of broad redox peaks at 0.75 V vs. Na + /Na, a high capacity of 253 mAh g −1 , high-rate capability and thousands of stable cycles. The multistep crystalline-to-amorphous-to-RS transformations are able to be electrochemically activated during the aging process of assembled full cells. Our finding provides a direction for developing unconventional Ti-based high-performance active materials for SIBs with both high energy and power densities.

Tang, Dafu [Xiamen University (China); Innovation ↗
DOE OSTI2024

Materials descriptors for advanced water dissociation catalysts in bipolar membranes

The voltage penalty driving water dissociation (WD) at high current density is a major obstacle in the commercialization of bipolar membrane (BPM) technology for energy devices. Here, in this study, we show that three materials descriptors, that is, electrical conductivity, microscopic surface area and (nominal) surface-hydroxyl coverage, effectively control the kinetics of WD in BPMs. Using these descriptors and optimizing mass loading, we design new earth-abundant WD catalysts based on nanoparticle SnO 2 synthesized at low temperature with high conductivity and hydroxyl coverage. These catalysts exhibit exceptional performance in a BPM electrolyser with low WD overvoltage (η wd ) of 100 ± 20 mV at 1.0 A cm -2 . The new catalyst works equivalently well with hydrocarbon proton-exchange layers as it does with fluorocarbon-based Nafion, thus providing pathways to commercializing advanced BPMs for a broad array of electrolysis, fuel-cell and electrodialys is applications.

Sasmal, Sayantan↗
DOE OSTI2023

Let’s Unleash the Network Judgment: A Self-Supervised Approach for Cloud Image Analysis

Accurate cloud type identification and coverage analysis are crucial in understanding the Earth’s radiative budget. Traditional computer vision methods rely on low-level visual features of clouds for estimating cloud coverage or sky conditions. Several handcrafted approaches have been proposed; however, scope for improvement still exists. Newer deep neural networks (DNNs) have demonstrated superior performance for cloud segmentation and categorization. These methods, however, need expert engineering intervention in the preprocessing steps—in the traditional methods—or human assistance in assigning cloud or clear sky labels to a pixel for training DNNs. Such human mediation imposes considerable time and labor costs. We present the application of a new self-supervised learning approach to autonomously extract relevant features from sky images captured by ground-based cameras, for the classification and segmentation of clouds. We evaluate a joint embedding architecture that uses self-knowledge distillation plus regularization. We use two datasets to demonstrate the network’s ability to classify and segment sky images—one with ~85,000 images collected from our ground-based camera and another with 400 labeled images from the WSISEG database. We find that this approach can discriminate full-sky images based on cloud coverage, diurnal variation, and cloud base height. Additionally, it semantically segments the cloud areas without labels. The approach shows competitive performance in all tested tasks, suggesting a new alternative for cloud characterization.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2025

Computational Design of Cost-Effective High-Entropy Thermal/Environmental Barrier Coatings

Developing cost-effective thermal/environmental barrier coatings (TEBC) requires balance among various properties including low thermal conductivity, matching coefficient of thermal expansion (CTE), high thermal stability, high fracture toughness, and high recession resistance while being affordable. Low oxygen diffusivity is desirable as it can slow down oxygen transport to reach the underlying bond coating and hence delay oxidation of the bond coating. This project aims to design low-cost high-performance TEBC based on high entropy rare earth disilicates to protect SiC-based ceramic matrix composites from chemical and thermal attack for better performance of components in the hot section of gas turbine engines. To accelerate the TEBC design, we utilize first-principles density functional theory to predict key properties including phase stability, CTE, lattice thermal conductivity, temperature-dependent elastic constants, and oxygen diffusivity. Alloying elements including Yb, Y, Er, Eu, Gd, Lu, La, and Ce are considered, and modeling prediction are compared with available experimental results.

coefficient of thermal expansion↗
DOE OSTI2022

Xenon in the Protoplanetary Disk (PPD), in Two Planets, and a Comet

Isotopic anomalies in several elements, as recently observed in meteorites, are generally interpreted to indicate nonequilibrium environments in the protoplanetary disk (PPD). Here we study reported Xe isotopic compositions on planets Earth and Mars, in a comet, and in meteorites for precursor discrepancies. Abundance variations of inferred presolar nano-diamonds, the carrier phase of the Xe-HL component, appear to be the primary source of nonuniformity of Xe precursors in the PPD, together with mechanisms of mass-dependent fractionation. While planet Mars kept a record of initial solar Xe isotopic abundances, such a record is missing for planet Earth. Xe isotopic abundances in paleo-atmospheres of both planets represent secondary reservoirs that show mass-dependent fractionation effects, but the inferred compositions of their PPD precursors differ: Mars atmospheric precursor Xe had solar isotopic composition, while Earth’s Xe precursor is consistent with a PPD reservoir of low nano-diamond abundance. Strong mass-dependent fractionation effects are observed in Xe components of IAB irons and in Yamato carbonaceous (CY) chondrites, and show that fractionation mechanisms are not restricted to planetary atmospheres. These records show that Xe isotopes in solar system reservoirs are useful tracers of evolutionary processes and of nonequilibrated presolar components in the PPD.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2022

Capturing the interactions between ice sheets, sea level and the solid Earth on a range of timescales: a new “time window” algorithm

Retreat and advance of ice sheets perturb the gravitational field, solid surface and rotation of the Earth, leading to spatially variable sea-level changes over a range of timescales $\textit{O}$ (10 0–6 years), which in turn feed back onto ice-sheet dynamics. Coupled ice-sheet–sea-level models have been developed to capture the interactive processes between ice sheets, sea level and the solid Earth, but it is computationally challenging to capture short-term interactions $\textit{O}$ (10 0–2 years) precisely within longer $\textit{O}$ (10 3–6 years) simulations. The standard forward sea-level modelling algorithm assigns a uniform temporal resolution in the sea-level model, causing a quadratic increase in total CPU time with the total number of input ice history steps, which increases with either the length or temporal resolution of the simulation. In this study, we introduce a new “time window” algorithm for 1D pseudo-spectral sea-level models based on the normal mode method that enables users to define the temporal resolution at which the ice loading history is captured during different time intervals before the current simulation time. Utilizing the time window, we assign a fine temporal resolution $\textit{O}$ (10 0–2 years) for the period of ongoing and recent history of surface ice and ocean loading changes and a coarser temporal resolution $\textit{O}$ (10 3–6 years) for earlier periods in the simulation. This reduces the total CPU time and memory required per model time step while maintaining the precision of the model results. We explore the sensitivity of sea-level model results to the model temporal resolution and show how this sensitivity feeds back onto ice-sheet dynamics in coupled modelling. We apply the new algorithm to simulate sea-level changes in response to global ice-sheet evolution over two glacial cycles and the rapid collapse of marine sectors of the West Antarctic Ice Sheet in the coming centuries and provide appropriate time window profiles for each application. The time window algorithm reduces the total CPU time by ~ 50 % in each of these examples and changes the trend of the total CPU time increase from quadratic to linear. This improvement would increase with longer simulations than those considered here. Our algorithm also allows for coupling time intervals of annual temporal scale for coupled ice-sheet–sea-level modelling of regions such as West Antarctica that are characterized by rapid solid Earth response to ice changes due to the thin lithosphere and low mantle viscosities.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2020

STRUCTURE AND PROPERTIES STUDY ON ENERGY MATERIALS: THERMOELECTRIC MATERIAL TETRAHEDRITE AND LITHIUM ION CONDUCTOR

Development of efficient energy materials is critical in order to ease the energy demand and reduce our dependence of fossil fuel. Thermoelectric materials are promising due to their capability of generating electrical power by recovering waste heat. The performance of thermoelectric materials is quantified by a dimensionless figure of merit zT, which depends on their properties such as electrical conductivity, Seebeck coefficient and thermal conductivity. Tetrahedrites, a copper antimony sulfosalt mineral, typified by Cu 12-x MxSb 4 S 13 , where M is a transition metal element such as Ni, Zn, Fe or Mn, have great potential for thermoelectric application due to their relatively high zT (close to 1 at 700 K), earth-abundance, environmental friendliness, favorable electrical properties, and most importantly intrinsic low lattice thermal conductivity (less than 1 W m -1 K -1 ) in wide temperature. In addition to energy recovery, reliable energy storage devices are also emerging to relieve the energy demand and improve the efficiency of consuming energy resources. Lithium-ion batteries are known to be reliable and successful electrochemical energy storage devices and appliable in various aspects, including laptops, smartphones and electrical vehicles. Lithium phosphorous oxynitride (LiPON) are widely used as thin-film solid-state electrolytes in Li-ion battery, which is the only demonstrated solid-state electrolyte that is quite stable in direct contact with Li metal at potentials from 0-5 V. However, the structure of LiPON, the effects of N doping, and the origin of its good electrochemical stability remains inconclusive. In this thesis, reliable modeling techniques accompanied with experimental tools, are applied to study the thermoelectric material tetrahedrite and the ionic conductor LiPON, in order to study their structural and dynamical properties. Accurate and efficient density-functional theory (DFT) and density-functional tight-binding (DFTB) methods, combined with molecular dynamics (MD) simulations are utilized in order to investigate the structures and properties of these energy materials. The incoherent and coherent atomic dynamics study of tetrahedrite Cu 10.5 NiZn 0.5 Sb 4 S 13 provides the origin of softening upon cooling by investigate the motion of Cu12e at different temperatures. The dynamic structure factors in the longitudinal and transverse direction will also be discussed. The Cu movement of Cu-rich tetrahedrite Cu 14 Sb 4 S 13 is revealed by Cu self-diffusivity, nuclear density map and “nudged elastic band” (NEB). Moreover, we investigate the effect of simulation cell size and basis sets on the DFT-based MD simulation results using tetrahedrite Cu1 0 Zn 2 Sb 4 S 13 thermoelectric as a model material, showing the advantage of larger cell by accessing smaller Q range. In addition, the low-temperature structural properties of Cu 12 Sb 4 S 13 is measured by neutron diffraction, which indicates that no cubic to tetragonal transition occurs at metal-semiconductor transition (MST) temperature. Thermoelectric properties such as Seebeck coefficient, electrical resistivity and electrical thermal conductivity will also be investigated. DFTB method is implemented to study the structure and transport properties of Li 3 PO 4 and LiPON, while the exploration of N doping effect is included. Lastly, the LiPON/Li interphase will be revealed in order to study the origin of electrochemical stability.

Li, Junchao↗
DOE OSTI2020

STRUCTURE AND PROPERTIES STUDY ON ENERGY MATERIALS: THERMOELECTRIC MATERIAL TETRAHEDRITE AND LITHIUM ION CONDUCTOR LiPON

Development of efficient energy materials is critical in order to ease the energy demand and reduce our dependence of fossil fuel. Thermoelectric materials are promising due to their capability of generating electrical power by recovering waste heat. The performance of thermoelectric materials is quantified by a dimensionless figure of merit zT, which depends on their properties such as electrical conductivity, Seebeck coefficient and thermal conductivity. Tetrahedrites, a copper antimony sulfosalt mineral, typified by Cu12-xMxSb4S13, where M is a transition metal element such as Ni, Zn, Fe or Mn, have great potential for thermoelectric application due to their relatively high zT (close to 1 at 700 K), earth-abundance, environmental friendliness, favorable electrical properties, and most importantly intrinsic low lattice thermal conductivity (less than 1 W m-1 K-1) in wide temperature. In addition to energy recovery, reliable energy storage devices are also emerging to relieve the energy demand and improve the efficiency of consuming energy resources. Lithium-ion batteries are known to be reliable and successful electrochemical energy storage devices and appliable in various aspects, including laptops, smartphones and electrical vehicles. Lithium phosphorous oxynitride (LiPON) are widely used as thin-film solid-state electrolytes in Li-ion battery, which is the only demonstrated solid-state electrolyte that is quite stable in direct contact with Li metal at potentials from 0-5 V. However, the structure of LiPON, the effects of N doping, and the origin of its good electrochemical stability remains inconclusive.In this thesis, reliable modeling techniques accompanied with experimental tools, are applied to study the thermoelectric material tetrahedrite and the ionic conductor LiPON, in order to study their structural and dynamical properties. Accurate and efficient density-functional theory (DFT) and density-functional tight-binding (DFTB) methods, combined with molecular dynamics (MD) simulations are utilized in order to investigate the structures and properties of these energy materials. The incoherent and coherent atomic dynamics study of tetrahedrite Cu10.5NiZn0.5Sb4S13 provides the origin of softening upon cooling by investigate the motion of Cu12e at different temperatures. The dynamic structure factors in the longitudinal and transverse direction will also be discussed. The Cu movement of Cu-rich tetrahedrite Cu14Sb4S13 is revealed by Cu self-diffusivity, nuclear density map and "nudged elastic band" (NEB). Moreover, we investigate the effect of simulation cell size and basis sets on the DFT-based MD simulation results using tetrahedrite Cu10Zn2Sb4S13 thermoelectric as a model material, showing the advantage of larger cell by accessing smaller Q range. In addition, the low-temperature structural properties of Cu12Sb4S13 is measured by neutron diffraction, which indicates that no cubic to tetragonal transition occurs at metal-semiconductor transition (MST) temperature. Thermoelectric properties such as Seebeck coefficient, electrical resistivity and electrical thermal conductivity will also be investigated. DFTB method is implemented to study the structure and transport properties of Li3PO4 and LiPON, while the exploration of N doping effect is included. Lastly, the LiPON/Li interphase will be revealed in order to study the origin of electrochemical stability

25 ENERGY STORAGE↗
DOE OSTI2022

Reconstructing High Resolution ESM Data Through a Novel Fast Super Resolution Convolutional Neural Network (FSRCNN)

In this work, we present the first application of a fast super resolution convolutional neural network (FSRCNN) based approach for downscaling earth system model (ESM) simulations. Unlike other SR approaches, FSRCNN uses the same input feature dimensions as the low resolution input. This allows it to have smaller convolution layers, avoiding over-smoothing, and reducing computational costs. We adapt the FSRCNN to improve reconstruction on ESM data, we term the FSRCNN-ESM. We use high-resolution (~0.25°) monthly averaged model output of five surface variables over North America from the US Department of Energy's Energy Exascale Earth System Model's control simulation. These high-resolution and corresponding coarsened low-resolution (~1°) pairs of images are used to train the FSRCNN-ESM and evaluate its use as a downscaling approach. We find that FSRCNN-ESM outperforms FSRCNN and other super-resolution methods in reconstructing high resolution images producing finer spatial scale features with better accuracy for surface temperature, surface radiative fluxes, and precipitation.

58 GEOSCIENCES↗
DOE OSTI2020

Glycolate Analysis in Tank 22: Developing and Testing Analytical Methods for the Savannah River Site Liquid Waste System

Researchers developed and tested a range of techniques to support a low mg/L Limit of Quantitation (LOQ) of glycolate in radioactive Tank 22 waste solution by Ion Chromatography (IC) and by proton NMR. For the IC method, Dionex OnGuard II cartridges were tested as a means of removing alkali earth and transition metals that can interfere during ion chromatography applications especially when analyzing for low-concentration, chelating analytes. Additionally, concentrations of nitrate in the raw Tank 22 sample (5,000 to 10,000 mg/L) were managed by using reasonable levels of sample dilution. The resulting IC performance quality was enhanced by improving the baseline, increasing sensitivity [Limit of Quantitation (LOQ) of 12 mg/L and Limit of Detection (LOD) of 4.0 mg/L], and resolving analytes into well-defined Gaussian peaks when using the Dionex OnGuard II H⁺ cartridges to remove matrix interferences. High concentrations of nitrate limited the performance of the IC method. To achieve an acceptable baseline, samples with high nitrate content require more dilution, resulting in higher detection limits. Tests on samples with higher concentrations of nitrate (Tank 30 and 32 supernate with approximately 150000 mg/L nitrate) suggested that IC detection limits for glycolate in these samples would be > 500 mg/L. Thus, an LOQ of 12 mg/L is not feasible by this IC method on evaporator feed samples. An alternative method of glycolate analysis, using proton nuclear magnetic resonance (H NMR), was developed by the research team. In initial tests, the H NMR technique provided reasonable quantitation of glycolate in Tank 22 conditions by direct observation of the liquid. The H NMR method may provide improved detection limits for solutions with higher nitrate concentrations. We recommend further development of this analysis for high nitrate LWS samples such as evaporator feed and evaporator drop tank content. A method for pretreatment of samples using crystalline silicotitanate (CST) was developed and tested. The objective of the pretreatment was to facilitate analysis of samples with higher levels of radioactivity. The pretreatment did not influence the glycolate concentration in solution, and the pretreatment is projected to reduce Cs 137 activity in a sample by a factor of 16,200. The double strike CST pretreatment was demonstrated and would allow milliliters of higher activity samples to be transferred from the Shielded Cells and handled in a containment unit for glycolate analysis. The various studies validated IC and H NMR methods for glycolate analysis, defined the range of applicability, and demonstrated key supporting analytical protocols. Based on the results, high quality glycolate analysis of Tank 22 is feasible down to approximately 12 mg/L, with the potential for broader applicability of the methods to other conditions in the Savannah River Site Liquid Waste System (LWS).

12 MANAGEMENT OF RADIOACTIVE AND NON-RADIOACTIVE W↗
DOE OSTI2021

Geothermal Deep Direct-Use for Low-Carbon Heating: A Case Study at Cornell University

Cornell University is developing a geothermal deep direct-use system to provide reliable, low carbon, and cost-competitive heating for its main campus in Ithaca, New York. This geothermal system, also referred to as Earth Source Heat, would provide the majority of the campus heating load and represents a key pillar for the university to become carbon neutral by 2035. Over the last 10 years, numerous desktop, seismic, and geophysical studies have been undertaken at Cornell to characterize the local subsurface, predict long-term reservoir performance, design the integration into the existing district heating network, and estimate capital and levelized costs. Recently, the university received funding for drilling an observatory well to obtain in-situ subsurface data (e.g., reservoir temperatures, pressures, fluid chemistry, stress field), significantly lowering subsurface uncertainty and de-risking the project. Drilling of the test well is planned for fall 2021. This paper provides an overview and status of the project, including a summary of past studies and a description of the proposed well and planned testing activities.

Cornell University↗