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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 19 records
All sourcesNASA NTRSDOE OSTI
DOE OSTI2020

Introducing a New Spitzer Master BLISS Map to Remove the Instrument Systematic Phase-curve-parameter Degeneracy, as Demonstrated by a Reanalysis of the 4.5 μm WASP-43b Phase Curve

While Spitzer Infrared Array Camera (IRAC) systematics are generally well understood, each data set can provide its own challenges that continue to teach us about the underlying functional form of these systematics. Multiple groups have analyzed the phase curves of WASP-43b with varying detrending techniques, each obtaining different results. In this work, we take another look at WASP-43b while exploring the degenerate relation between Bilinearly Interpolated Subpixel Sensitivity (BLISS) mapping, point response function (PRF)–FWHM detrending, and phase curve parameters. We find that there is a strong correlation between the detrending parameters in the two models, and best-fit phase curve amplitudes vary strongly when the data are temporally binned. To remove this degeneracy, we present a new Gaussian centroided intrapixel sensitivity map (hereafter fixed sensitivity map), generated using 3,712,830 exposures spanning 5 yr, for a variety of aperture sizes at 4.5 μm. We find evidence for time variability in the sensitivity at 3.6 μm and do not generate a visit-independent map for this channel. With the fixed 4.5 μm intrapixel sensitivity map, the best fits for WASP-43b no longer vary strongly with bin size and PRF–FWHM detrending is no longer required to remove correlated noise. For data sets that do not fall completely within the sweet spot, temporal binning should not be used in the analysis of Spitzer phase curves. We confirm nightside emission for WASP-43b with a disk-integrated nightside temperature of 806 ± 48 K at 4.5 μm. The 4.5 μm maps are available at github.com/kevin218/POET.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2021

Combinations of Single Chain Variable Fragments From HIV Broadly Neutralizing Antibodies Demonstrate High Potency and Breadth

Broadly neutralizing antibodies (bNAbs) are currently being assessed in clinical trials for their ability to prevent HIV infection. Single chain variable fragments (scFv) of bNAbs have advantages over full antibodies as their smaller size permits improved diffusion into mucosal tissues and facilitates vector-driven gene expression. We have previously shown that scFv of bNAbs individually retain significant breadth and potency. Here we tested combinations of five scFv derived from bNAbs CAP256-VRC26.25 (V2-apex), PGT121 (N332-supersite), 3BNC117 (CD4bs), 8ANC195 (gp120-gp41 interface) and 10E8v4 (MPER). Either two or three scFv were combined in equimolar amounts and tested in the TZM-bl neutralization assay against a multiclade panel of 17 viruses. Experimental IC 50 and IC 80 data were compared to predicted neutralization titers based on single scFv titers using the Loewe additive and the Bliss-Hill model. Like full-sized antibodies, combinations of scFv showed significantly improved potency and breadth compared to single scFv. Combinations of two or three scFv generally followed an independent action model for breadth and potency with no significant synergy or antagonism observed overall although some exceptions were noted. The Loewe model underestimated potency for some dual and triple combinations while the Bliss-Hill model was better at predicting IC 80 titers of triple combinations. Given this, we used the Bliss-Hill model to predict the coverage of scFv against a 45-virus panel at concentrations that correlated with protection in the AMP trials. Using IC 80 titers and concentrations of 1μg/mL, there was 93% coverage for one dual scFv combination (3BNC117+10E8v4), and 96% coverage for two of the triple combinations (CAP256.25+3BNC117+10E8v4 and PGT121+3BNC117+10E8v4). Combinations of scFv, therefore, show significantly improved breadth and potency over individual scFv and given their size advantage, have potential for use in passive immunization.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2023

Improving Dark Energy Constraints Using Low-Redshift Large-Scale Structures

The primary goal of this project was to improve constraints on dark energy measurements by improving our ability to extract cosmological information from low redshift large-scale structures. PI Clowe's project's primary aim was to reduce the bias in measurements of the masses of clusters of galaxies to a level where the evolution of the cluster mass function can be used in the Vera Rubin Observatory's Legacy Survey of Space and Time Dark Energy Science Collaboration survey to improve the accuracy of the measurement of dark energy and other cosmological parameters. Co-PI Seo's project studied observational systematics affecting large-scale clustering of galaxies, which will be used to improve dark energy constraints from the Dark Energy Spectroscopic Instrument (DESI). The cluster lensing project employed a series of simulations and observations of clusters of galaxies to test numerous potential systematic errors in cluster mass measurements using weak gravitational lensing as the accuracy of current weak lensing measurements are more than order of magnitude worse than what is required to use clusters of galaxies for accurate determination of dark energy parameters. PI Clowe and group developed and analyzed simulations to test for, and correct biases introduced in, the weak lensing measurement process. Finally, PI Clowe and group developed a method of detecting clusters using galaxy overdensities and applied the method to the BLISS and DES surveys. The success of spectroscopic dark energy mission such as the extended Baryon Oscillation Spectroscopic Survey (eBOSS) and the Dark Energy Spectroscopic Instrument (DESI) will depend on a thorough understanding of various observational systematics in the target density fluctuations that would give rise to spurious, non-cosmological signals. PI Seo and group developed a deep learning, artificial neural network (ANN) technique that modeled and mitigated such effects, aimed at deriving more robust galaxy clustering signals not only for the baryon acoustic oscillation feature and redshift-space distortions but also for primordial non-Gaussianity constraint.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2024

GeoThermalCloud: Cloud Fusion of Big Data and Multi-Physics Models using Machine Learning for Discovery, Exploration, and Development of Hidden Geothermal Resources

The primary goals of this project are exploring hidden geothermal resources in the U.S.A. and designing profitable enhanced geothermal systems (EGS). Many processes and parameters control geothermal exploration and energy production from geothermal fields. Diverse datasets (e.g., geology, geochemistry, geophysics, satellite, airborne geophysics) are available to help characterize subsurface geothermal conditions. Sparse and multi-scale characteristics of these datasets prohibit properly leveraging these datasets for geothermal exploration and profitable EGS design. Recent advancements in machine learning (ML) promise to resolve these issues. The tremendous challenges and risks of geothermal exploration and production bring the demand for novel ML methods and tools that can (1) analyze large field datasets, (2) assimilate model simulations (large inputs and outputs), (3) process sparse datasets, (4) perform transfer learning (between sites with different exploratory levels), (5) extract hidden geothermal signatures in the field and simulation data, (6) label geothermal resources and processes, (7) identify high-value data acquisition targets, and (8) guide geothermal exploration and production by selecting optimal exploration, production, and drilling strategies. To address these necessities, ML-based geothermal resources exploration and enhanced geothermal systems (EGS) design tools have been developed. The exploration tool is called GeoThermalCloud and EGS design tool is called GeoDT-ML. GeoThermalCloud (https://github.com/SmartTensors/GeoThermalCloud.jl) utilizes a LANL unsupervised ML platform called SmartTensors (https://tensors.lanl.gov/) to automate data analyses and interpretations by extracting hidden signatures to identify geothermal prospects. Also, it enables the identification of critical measurements needed to identify geothermal resource signatures. Alternatively, GeoDT-ML (https://github.com/SmartTensors/GeoThermalCloud.jl/tree/master/EGS) is an ML-based alternative to GeoDT (https://github.com/GeoDesignTool/GeoDT.git), a fast, simplified multi-physics solver to evaluate EGS project designs in uncertain geologic systems. GeoDT-ML leverages recent advances in deep learning and high-performance computing. It is a faster and simpler version of GeoDT. To make this project a success, we used capabilities of LANL, PNNL, Google, Stanford, and Julia Computing. We analyzed eight datasets of the U.S.A. using GeothermalCloud and demonstrated potential highly prospective geothermal resources and identified key factors defining highly prospective sites. The first data set includes 44 locations in southwest New Mexico and 18 geological, hydrogeological, geophysical, geothermal, geochemical attributes. We defined low- and medium-temperature hydrothermal systems and discovered a new highly prospective site. The second data set analyzed 18 shallow water chemistry attributes at 14,342 locations in the Great Basin. It demarcated modestly, moderately, and highly prospective sites including key attributes for each type of prospectivity. The third data set analyzed Utah FORGE data including satellite (InSAR), geophysical (gravity, seismic), geochemical, and geothermal attributes. Here, we performed prospectivity analysis to identify future drilling locations using geological, geochemical, and geophysical attributes. Maps of temperature at depth and heat flow are constructed based on the available data. Prospectivity maps were generated, and drilling locations were proposed for future geothermal field exploration. The fourth data set analyzed 21 attributes at 120 locations in Tularosa Basin, New Mexico; data comes from past play fairway analyses in this region. ML analyses identified geothermal signatures associated with modestly, moderately, and highly hydrothermal systems. We also defined dominant attributes and spatial distribution of the geothermal signatures. The fifth, sixth, seventh, and eighth datasets include Tohatchi Springs, New Mexico, Hawaii, Brady site, Nevada, and EGS Collab, respectively. Moreover, we coupled GeothermalCloud and magnetotellurics data to pinpoint drilling locations for developing geothermal projects in the Tularosa Basin, New Mexico. GeothermalCloud found potential prospective locations for geothermal resources near White Sands Missile Range and McGregor Range at Fort Bliss. Magnetotellurics data determined the potential depth (~1800m) of geothermal prospects at McGregor Range based on apparent resistivity structures/layers in the subsurface. The McGregor Range consists of three resistivity layers and two resistivity structures. Magnetotellurics data also helps identify that the western portion of the McGregor Range has thick and low-resistivity earth materials. The low resistivity to the west is most likely for a fault system. Assuming temperature is consistent with a geothermal reservoir, the west-central part of the McGregor Range has the highest geothermal potential because of the increase in porosity and associated permeability attributed to the interpreted fault system. Also, we devised a coupling strategy between a process model and GeothermalCloud to characterize hydrogeological conditions and geothermal conditions, respectively. The process model characterizes hydrogeological and geothermal conditions on highly prospective geothermal sites provided by GeothermalCloud. We developed a physics-informed neural network (PINN) version of the Burns equation that can be easily coupled with GeothermalCloud. Furthermore, we performed an optimal design decision maximizing the economic value of an EGS power plant. This study optimized the range of well spacing between injection and production wells maximizing net present value in dollars (NPV). For this task, we used the GeoDT to simulate the Utah FORGE EGS development cycle from the initial well design to the end of production. Next, we accomplished another crucial task, which is predicting permeability of geothermal reservoirs. Predicting permeability of geothermal reservoirs is a non-trivial task because of huge computational runtime of simulation and lack of measurements. To avoid these limitations, we used easy-to-measure chemical concentrations in the subsurface as measurement data and convolutional neural network based ML model of a high-fidelity model. Next, we predicted permeability using Markov chain Monte Carlo simulation. We found that Markov chain Monte Carlo simulation predicts permeability with a high certainty if the prediction zone in the simulation area has chemical concentration data. Finally, we analyzed the DOE funded INGENIOUS and GeoDAWN projects data. For discovering hidden geothermal systems in the Great Basin, the INGENIOUS project accumulated old data, collected new data, and released them in 2022. The dataset includes a total of 24 geological, geophysical, and geochemical attributes. Data resolution and scale significantly vary prohibiting an appropriate usage. To avoid such limitations, we brought all data in the same resolution and scale by applying the inverse distance weighting interpolation technique for predicting data in unsampled locations. Subsequently, we analyzed LiDAR data of the GeoDAWN project. We received data in tiles format. The DOE’s overarching goal is to use ML on LiDAR data for finding favorable geological structures (e.g., step up faults in Brady, Nevada). To serve the purpose, we need to label favorable geologic structures that correspond to LiDAR data. We wrote an algorithm to label the LiDAR data with the favorable geologic structures.

15 GEOTHERMAL ENERGY↗
DOE OSTI2020

Energy Master Planning for Resilient Public Communities—Best Practices from U.S. Military Installations

Until recently, most planners at military installations addressed energy systems for new facilities on an individual facility basis without consideration of community-wide goals relevant to energy sources, renewables, storage, or future energy generation needs. Building retrofits of public buildings typically do not address energy needs beyond the minimum code requirements making it difficult, if not impossible, to achieve community-level targets on a building-by-building basis. Planning on the basis of cost and general reliability may also fail to deliver community-level resilience. For example, many building code requirements focus on hardening to specific threats, but in a multi-building community, only a few of these buildings may be mission-critical. Over the past two decades, the frequency and duration of regional power outages and water utility disruptions from weather, man-made events, and aging infrastructure have increased. Major disruptions of electric and thermal energy have degraded critical mission capabilities and caused significant economic impacts. In 2016, the U.S. Department of Defense issued guidance that each Service (Army, Navy, Air Force, Marines) complete comprehensive energy plans for the installations that consumed 75% of total building energy. Guidance was updated in 2017 to include metrics for energy resilience, and in some cases, water. This paper describes how community level quantitative and qualitative resilience analysis and metrics have been incorporated into community energy and water planning best practices for military installations in three geographically diverse locations. It is based on research performed under the International Energy Agency’s “Energy in Buildings and Communities Program Annex 73,” focusing on development of guidelines and tools that support the planning of Net Zero Energy Resilient Public Communities as well as research performed under the Department of Defense Environmental Security Technology Certification Program project EW18-D1- 5281, “Technologies Integration to Achieve Resilient, Low- Energy Military Installations.” The first case study reviews progress made on an energy and water planning study conducted at Fort Bliss, Texas. The second and third describes planning conducted at Fort Bragg, North Carolina and the Joint Region Marianas, Guam, respectively, under the updated guidance from 2017 regarding energy and water resilience. Analysis methods, key metrics, and key infrastructure and operational constraints are described, as well as technical, economic and business concepts used during the planning process.

Urban, Angela B.↗
DOE OSTI2021

Designing Modules to Prevent Reverse Bias Degradation in Perovskite Solar Cells when Partial Shading Occurs

When a solar cell in a panel is shaded, the illuminated cells can place a large reverse bias on the shaded cell to attempt to force current through it. Although the panel can continue to produce power, the reverse bias can cause significant problems for the shaded cell. In the case of perovskite solar modules, Joule heating and irreversible electrochemical reactions will degrade the cell. Some photovoltaic technologies use bypass diodes to solve this problem. To prevent both a perovskite cell and an all-perovskite tandem cell from falling into reverse bias breakdown while shaded, no more than two cells per bypass diode are allowed, which is likely to be prohibitively expensive for many applications. Herein, how many solar cells can be protected by one bypass diode in single-junction and multijunction perovskite modules is explored. It is easier to incorporate bypass diodes into modules with singulated cells than with monolithic panels. It is shown that perovskite–silicon tandems can be protected with fewer bypass diodes than with single-junction perovskite modules. Furthermore, it is suggested that if bypass diodes cannot be feasibly incorporated, then the panels should be deployed in utility-scale power plants.

14 SOLAR ENERGY↗
DOE OSTI2020

From Defects to Degradation: A Mechanistic Understanding of Degradation in Perovskite Solar Cell Devices and Modules

Metal halide perovskite solar cells (PSCs) have risen in efficiency from just 3.81% in 2009 to over 25.2% today. While metal halide perovskites have excelled in efficiency, advances in stability are significantly more complex and have progressed more slowly. The advance of efficiency, which is readily measured, over stability, which can require literally thousands of hours to demonstrate, is to be expected given the rapid rate of innovation in the field. In the face of changing absorber composition, synthetic approaches, and device stack components it is necessary to understand basic material properties to rationalize how to enable stability in devices. In this article the aim is to present an in-depth review of the current understanding of metal halide perovskite devices and module stability by focusing on what is known retarding intrinsic and extrinsic degradation mechanisms at the material, device, and module level. Once these considerations are presented the discussion then moves to connecting different degradation mechanisms to stresses anticipated in operation and how they can impact efficiency of cells and ultimately modules over time.

36 MATERIALS SCIENCE↗
DOE OSTI2022

Identification of background limitations to ultra-sensitive $\mathrm{LSC}$ counting through $\mathrm{ICP}$-$\mathrm{MS}$ assay of $\mathrm{LSC}$ cocktails

We report the performance of LSC cocktails in ultra-sensitive applications was evaluated. Backgrounds from radioactive contaminations in commercially available and in-house developed liquid scintillation cocktails were measured and compared to the predicted background levels of the ultra-low background liquid scintillation counter. Through the ICP-MS assay of the cocktails and their constituents, potassium impurities in the surfactant component were identified as a significant source of background, potentially limiting the use of LSC counting in ultra-sensitive applications. This work lays the groundwork for future research towards ultrapure LSC cocktails for ultrasensitive LSC counting

73 NUCLEAR PHYSICS AND RADIATION PHYSICS↗
DOE OSTI2023

Impact of lowering potassium contamination in liquid scintillation cocktails for ultra-sensitive radiation detection

Intrinsic 40 K radioactive backgrounds from impurities of natural K in liquid scintillation cocktails have previously been demonstrated to limit their use in ultra-sensitive applications. Here, this work explores two methodologies in parallel for the reduction of 40 K backgrounds in the cocktails, and lays the groundwork for use in ultra-sensitive applications. In one method, alternative low-K liquid scintillation matrix constituents were identified and in the other, a simple purification method for single components and finished cocktails was developed. Both methods were verified via ICP-MS analysis. Liquid scintillation counting of selected purified cocktails demonstrated background reduction, improved stability, and enhanced performance. The best performing purified cocktail was also counted on a custom-built ultra-low background liquid scintillation counter, with results below the detector background.

38 RADIATION CHEMISTRY, RADIOCHEMISTRY, AND NUCLEA↗
DOE OSTI2023

Bona fide upcycling strategy of anhydride cured epoxy and reutilization of decomposed dual monomers into multipurpose applications

Waste epoxy materials become an enormous problem to society and the environment. The advantages of epoxy resins derive from their rigid and chemically stable networks, but these qualities also make them difficult to dispose of or recycle. In this work, we demonstrated an efficient degradation of anhydride cured epoxy resin by aminolysis in aminoethanol without using any catalysts. The epoxy resin was fully decomposed at 160 °C in 4 h, resulting in two distinct high purity monomers (HHPA-OH and BPA-OH). To fully realize this upcycling approach, The BPA-OH was used to synthesize a polyurethane coating with an excellent glass transition temperature (88.9 °C), scratch hardness (8H), gouge hardness (6H), adhesive strength (5B), and strong solvent resistance. The HHPA-OH with two hydroxyl groups was reacted with methacrylic anhydride to form a dimethacrylate monomer which was then used as a viable crosslinker for photo-curable 3D printing thermosetting polymer with tensile strength as high as 64 MPa and impact strength of 4.86 kJ/m 2 . This work demonstrates a feasible pathway to convert anhydride cured epoxy waste to new monomeric recyclates for superior polymer products.

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

Hemp fiber reinforced dual dynamic network vitrimer biocomposites with direct incorporation of amino silane

Natural fiber composites are inexpensive and renewable alternatives to traditional fiber reinforced polymers (FRPs). However, existing natural fiber composites primarily rely on petrochemical based thermosetting matrices, which are difficult to recycle due to their stable crosslinked network structures. To address this challenge, it is desirable to develop natural fiber composites with inherently recyclable biobased matrices. In this study, we developed a dual dynamic network vitrimer matrix from hempseed oil and limonene derivatives and demonstrated its application in hemp fiber reinforced composites. To improve the weak interface between hemp fiber and the polymer matrix, a common challenge in the realm of natural fiber composites, we directly incorporated amino silane into the vitrimer matrix. The amino silane participated in the polymer network, increasing the crosslink density and toughening the matrix, as evidenced by a significantly improved impact strength from 3.5 kJ/m 2 to 10.3 kJ/m 2 . Moreover, the incorporation of amino silane resulted in a lower water absorption by 11% in 7-day soaking for the composites, demonstrating improved fiber/matrix interfacial interaction. Furthermore, our biobased vitrimer matrix exhibits both imine and hydroxy-ester dynamic bonds, which enable the recycling of the biocomposite through a mild and cost-effective aminolysis process (100°C, 3h, ambient pressure). The decomposed polymer matrix was successfully reused as a polyol for polyurethane adhesives, and the surface morphology of recovered hemp fibers was analyzed and compared with that of the original fibers. Furthermore, these findings will help broaden the use of vitrimers for practical natural fiber composite applications, raise awareness of the problem of recycling natural fiber composite waste, and shed light on the interfacial challenge of natural fiber reinforced vitrimer composites.

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

Unleashing the potential of waste: A supercharged high-performance 3D printing resin from discarded polylactic acid

In additive manufacturing/3D printing, the limitation no longer lies in people’s imagination but in the very materials that one can print with. While the additive manufacturing process can virtually create any geometry, available applications are often limited by factors like parts’ mechanical strength, glass transition temperature, and heat deflection temperature. These factors are especially critical for polymer-based printing. Here we introduce a simple formulation derived from the aminolysis of polylactic acid (PLA) plastic waste, namely the N-lactoyl ethanolamine (N-LEA). The N-LEA is next reacted with excess methacrylic anhydride, forming a photo-crosslinkable resin for MSLA 3D printing. The resulting 3D printed part has a set of impressive properties that is unrivaled amongst engineering grade 3D printing resins on the market and research literature. The 3D printed part has an ultrahigh tensile strength of 131.7 MPa, glass transition at ~190 °C, and heat deflection temperature at 162.6 °C. Furthermore, this work demonstrates a true upcycling approach for turning PLA waste into a value-added product in a simple and efficient manner while also expanding the high-performance material portfolio available for photocuring additive manufacturing.

36 MATERIALS SCIENCE↗
DOE OSTI2022

Mass spectrometric investigations into 3D printed parts to assess radiopurity as ultralow background materials for rare event physics detectors

The mass concentrations of 232 Th and 238 U in several 3D printing filaments and printed polymer parts are reported as measures of their radiopurity. In order to minimize background signals in rare event physics detectors, radiopure polymers are necessary as dielectric and structural materials in their construction. New data are reported for polyvinylidene fluoride (PVDF), polyphenylene sulfide (PPS), and two forms of polyetherimide (PEI, branded ULTEM 1010 and 9085). Data for starting filaments and both simple and complex printed parts are reported. PVDF filaments and simple printed beads, were found to have values of approximately 30 and 50 pg g -1 for 232 Th and 238 U, respectively, while a more complex spring clip part had slightly elevated 232 Th levels of 65 pg g -1 , with 238 U remaining at 50 pg g -1 . PPS filament was found to have concentrations of 270 and 710 pg g -1 for 232 Th and 238 U, respectively, and were not chosen to be printed as those levels were already higher than other material options. ULTEM 1010 filaments and printed complex spring clip parts were found to have concentrations of around 5 and 7 pg g -1 for 232 Th and 238 U, respectively, illustrating no significant contamination from the printing process. ULTEM 9085 filaments were found to have concentrations of around 9 and 5 pg g -1 for 232 Th and 238 U, respectively, while the printed complex spring clip part was found to have slightly elevated concentrations of 25 and 7 pg g -1 for 232 Th and 238 U, respectively. All these results were obtained using a novel dry ashing method in crucibles constructed of ultralow background electroformed copper or, when applicable, microwave assisted wet ashing digestion. Samples and process blanks were spiked with 229 Th and 233 U as internal standards prior to dry/wet ashing and determinations were made by inductively coupled plasma mass spectrometry (ICP-MS). In order to maintain high radiopurity levels, pre-cleaning the filaments before printing and post-cleaning the parts is recommended, although the printing process itself has shown to contribute very minute amounts of radiocontaminants.

46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND ↗