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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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All sourcesNASA NTRSDOE OSTI
DOE OSTI2017

Plasma-Surface Interaction Studies on DIII-D and Their Implications for Next-Step Fusion Experiments

Unique diagnostic and access features of the DIII-D tokamak, including a sample exposure system, have been used to carry out controlled and well-diagnosed plasma-surface interactions (PSI) experiments. An important contribution of the experiments has been the ability to link a given plasma exposure condition to a measured response of the plasma-facing surface and to thus understand the interaction. This has allowed for benchmarking certain aspects of erosion models, particularly near-surface particle transport. DIII-D has empirically quantified some of the PSI effects that will limit the operation availability and lifetime of future fusion devices, namely, net erosion limiting divertor plate lifetime and hydrogenic fuel retention in deposit layers. Cold divertor plasmas obtained with detachment can suppress net carbon divertor erosion, but many low-temperature divertor PSI phenomena remain poorly understood: nondivertor erosion sources, long-range particle transport, global erosion/deposition patterns, the enhancement of carbon erosion with neon impurity seeding, the sputtered carbon velocity distribution, and the apparent suppression of carbon chemical erosion in detachment. Long-term particle and energy fluences have reduced the chemical erosion yield of lower-divertor tiles. Finally, plasma-caused modification of a material’s erosion properties, including material mixing, will occur quickly and be important in long-pulse fusion devices, making prediction of PSI difficult in future devices.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗
DOE OSTI2018

TOPAS-nBio: An Extension to the TOPAS Simulation Toolkit for Cellular and Sub-cellular Radiobiology

The TOPAS Monte Carlo (MC) system is used in radiation therapy and medical imaging research, having played a significant role in making Monte Carlo simulations widely available for proton therapy related research. While TOPAS provides detailed simulations of patient scale properties, the fundamental unit of the biological response to radiation is a cell. Thus, our goal was to develop TOPAS-nBio, an extension of TOPAS dedicated to advance understanding of radiobiological effects at the (sub-)cellular, (i.e., the cellular and sub-cellular) scale. TOPAS-nBio was designed as a set of open source classes that extends TOPAS to model radiobiological experiments. TOPAS-nBio is based on and extends Geant4-DNA, which extends the Geant4 toolkit, the basis of TOPAS, to include very low-energy interactions of particles down to vibrational energies, explicitly simulates every particle interaction (i.e., without using condensed histories) and propagates radiolysis products. To further facilitate the use of TOPAS-nBio, a graphical user interface was developed. TOPAS-nBio offers full track-structure Monte Carlo simulations, integration of chemical reactions within the first millisecond, an extensive catalogue of specialized cell geometries as well as sub-cellular structures such as DNA and mitochondria, and interfaces to mechanistic models of DNA repair kinetics. We compared TOPAS-nBio simulations to measured and published data of energy deposition patterns and chemical reaction rates (G values). Our simulations agreed well within the experimental uncertainties. Additionally, we expanded the chemical reactions and species provided in Geant4-DNA and developed a new method based on independent reaction times (IRT), including a total of 72 reactions classified into 6 types between neutral and charged species. Chemical stage simulations using IRT were a factor of 145 faster than with step-by-step tracking. Finally, we applied the geometric/chemical modeling to obtain initial yields of double-strand breaks (DSBs) in DNA fibers for proton irradiations of 3 and 50 MeV and compared the effect of including chemical reactions on the number and complexity of DSB induction. Over half of the DSBs were found to include chemical reactions with approximately 5% of DSBs caused only by chemical reactions. In conclusion, the TOPAS-nBio extension to the TOPAS MC application offers access to accurate and detailed multiscale simulations, from a macroscopic description of the radiation field to microscopic description of biological outcome for selected cells. TOPAS-nBio offers detailed physics and chemistry simulations of radiobiological experiments on cells simulating the initially induced damage and links to models of DNA repair kinetics.

62 RADIOLOGY AND NUCLEAR MEDICINE↗
DOE OSTI2024

Extraction of Vibration Data with Imaging

To date, the primary sensing technology used to measure the vibration response has been accelerometers and strain gages mounted directly to the structure and using either wired or, more recently, wireless telemetry. Cost issues with these sensors and the associated data acquisition systems typically limit the numbers that are deployed on in situ structures. Although there are a few structures with larger sensing counts that in some cases exceed over 1000 sensors, more typical numbers range from ten to one hundred sensors resulting in low spatial resolution when they are applied to physically large systems. When one considers that nuclear power plant structures usually have complex geometries, material properties, connectivity and boundary conditions, it is clear these current approaches to vibration measurements can only provide limited information about a system’s dynamics response characteristics. As an alternative, many non-contact measurement technologies have emerged, including point wise measurement methods such as Global Positioning System (GPS), microwave interferometry, and laser Doppler vibrometry (LDV), as well as simultaneous full-field measurement methods such as electronic speckle pattern interferometry, holography interferometry, and muon tomography, some of which can provide high spatial resolution measurements. Among these methods, digital video imaging techniques have emerged as a feasible solution for full-field vibration measurements that provide significantly more detailed dynamic response information because every pixel becomes a measurement point. Furthermore, recent advances in image processing and computer vision algorithms have been successfully used to process video data for experimental and operational modal analysis. Such full-field measurements have the potential to significantly improve many current structural assessment procedures including system identification (modal parameter estimation), structural health monitoring, load reconstruction, model validation, and model updating. Furthermore, more recent full-field imaging techniques can be accomplished with relatively low-cost, commercially-available off-the-shelf cameras. However, these measurement procedures have other limitations that must be considered such as the ability to only measure visibly accessible points on a structure and a more limited dynamic range and bandwidth than can be achieved with accelerometers or strain gages.

46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND ↗
NASA NTRS2011

A Hybrid Lyot Coronagraph for the Direct Imaging and Spectroscopy of Exoplanet Systems: Recent Results and Prospects

We report our best laboratory contrast demonstrations achieved to date. We review the design, fabrication, performance, and future prospects of a hybrid focal plane occulter for exoplanet coronagraphy. Composed of thickness-profiled metallic and dielectric thin films vacuum deposited on a fused silica substrate, the hybrid occulter uses two superimposed thin films for control over both the real and imaginary parts of the complex attenuation pattern. Together with a deformable mirror for adjustment of wavefront phase, the hybrid Lyot coronagraph potentially exceeds billion-to one contrast over dark fields extending to within angular separations of 3 lambda/D (3 x the cosmological constant / diameter of the telescope) from the central star, over spectral bandwidths of 20 percent or more, and with throughput efficiencies up to 60 percent. We report laboratory contrasts of 3 x 10 (sup -10) degrees over 2 percent bandwidths, 6 x 10 (sup -10) degrees over 10 percent bandwidths, and 2 x 10 (sup -9) over 20 percent bandwidths, achieved across high contrast fields extending from an inner working angle of 3 lambda/D to a radius of 15 lambda/D. Occulter performance is analyzed in light of recent experiments and optical models, and prospects for further improvements are summarized. The science capabilities of the hybrid Lyot coronagraph are compared with requirements of the ACCESS mission, a representative exoplanet space telescope concept study for the direct imaging and spectroscopy of exoplanet systems. This work has been supported by NASA's Technology Demonstration for Exoplanet Missions (TDEM) program.

coronagraph↗
NASA NTRS2021

St. Joseph Peninsula Disasters: Using NASA Earth Observations to Investigate Land Cover, Shoreline Change, and Sediment Transport in St. Joseph Peninsula after Hurricane Michael

T.H. Stone Memorial St. Joseph Peninsula State Park experienced significant damages from Hurricane Michael in 2018, the first Category 5 hurricane to hit the contiguous United States since 1992. These damages included a 300-meter-wide and 10-meter-deep breach in the peninsula, habitat disruption, and a forced closure of over half of the total park area. These damages, coupled with restricted visitor access, resulted in a significant loss of revenue for the park. NASA DEVELOP partnered with the Florida Department of Environmental Protection (DEP) to determine the overall impact of Hurricane Michael on land cover and shoreline change by using NASA Earth observations including Landsat 7 Enhanced Thematic Mapper Plus (ETM+), Landsat 8 Operational Land Imager (OLI), Aqua Moderate Resolution Imaging Spectroradiometer (MODIS), and the European Space Agency’s Sentinel-2 Multispectral Instrument (MSI) to analyze sediment transport and climatology to further understand the lasting impacts of hurricanes on the ecosystems of the park. The DEVELOP team’s analyses showed that chlorophyll-a concentrations, sea surface temperature, and precipitation are increasing over time. The sediment transport analysis showed dynamic movement across the peninsula, with the greatest erosion occurring within the bay and along the length of the peninsula. These results are supported by evidence of declining seagrass abundances and seasonal turbidity patterns within those areas. Providing these analyses for the partner allows for a greater understanding of how best to proceed with restoration efforts, which may include rebuilding camping services, expanding fishing recreation, and conserving habitats for endangered species.

Erica Kriner↗
DOE OSTI2021

Cross-correlation analysis of X-ray photon correlation spectroscopy to extract rotational diffusion coefficients

Coefficients for translational and rotational diffusion characterize the Brownian motion of particles. Emerging X-ray photon correlation spectroscopy (XPCS) experiments probe a broad range of length scales and time scales and are well-suited for investigation of Brownian motion. While methods for estimating the translational diffusion coefficients from XPCS are well-developed, there are no algorithms for measuring the rotational diffusion coefficients based on XPCS, even though the required raw data are accessible from such experiments. In this paper, we propose angular-temporal cross-correlation analysis of XPCS data and show that this information can be used to design a numerical algorithm (Multi-Tiered Estimation for Correlation Spectroscopy [MTECS]) for predicting the rotational diffusion coefficient utilizing the cross-correlation: This approach is applicable to other wavelengths beyond this regime. We verify the accuracy of this algorithmic approach across a range of simulated data.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2024

Biosphere Futures: a database of social-ecological scenarios

T. Biosphere Futures (https://biospherefutures.net/) is a new online database to collect and discover scenario studies from across the world, with a specific focus on scenarios that explicitly incorporate interdependencies between humans and their supporting ecosystems. It provides access to a globally diverse collection of case studies that includes most ecosystems and regions, enabling exploration of the multifaceted ways in which the future might unfold. Together, the case studies illuminate the diversity and plurality of people’s expectations and aspirations for the future. The objective of Biosphere Futures is to promote the use of scenarios for sustainable development of the biosphere and to foster a community of practice around social-ecological scenarios. We do so by facilitating the assessment, synthesis, and comparative analysis of scenario case studies, pointing to relevant resources, and by helping practitioners and researchers to disseminate and showcase their own work. This article begins by outlining the rationale behind the creation of the database, followed by an introduction to its functionality and the criteria employed for selecting case studies. Subsequently, we present a synthesis of the first 100 case studies included in the scenarios database, highlighting emerging patterns and identifying potential avenues for further research. Finally, given that broader utilization and contributions to the database will enhance the achievement of Biosphere Futures’ objectives, we invite the creators of social-ecological scenarios to contribute additional case studies. By expanding the database’s breadth and depth, we can collectively foster a more nuanced understanding of the possible trajectories of our biosphere and enable better decision making for sustainable development.

54 ENVIRONMENTAL SCIENCES↗
NASA NTRS2022

Vegetation and vantage point influence visibility across diverse ecosystems: Implications for animal ecology

Visual information can influence animal behavior and habitat use in diverse ways. Visibility is the property that relates 3D habitat structure to accessibility of visual information. Despite the importance of visibility in animal ecology, this property remains largely unstudied. Our objective was to assess how habitat structure from diverse environments and animal position within that structure can influence visibility. We gathered terrestrial lidar data (1 cm at 10 m) in four ecosystems (forest, shrub-steppe, prairie, and desert) to characterize viewsheds (i.e., estimates of visibility based on spatially explicit sightlines) from multiple vantage points. Both ecosystem-specific structure and animal position influenced potential viewsheds. Generally, as height of the vantage point above the ground increased, viewshed extent also increased, but the relationships were not linear. In low-structure ecosystems (prairie, shrub-steppe, and desert), variability in viewsheds decreased as vantage points increased to heights above the vegetation canopy. In the forest, however, variation in viewsheds was highest at intermediate heights, and markedly lower at the lowest and highest vantage points. These patterns are likely linked to the amount, heterogeneity, and distribution of vegetation structure occluding sightlines. Our work is the first to apply a new method that can be used to estimate viewshed properties relevant to animals (i.e., viewshed extent and variability). We demonstrate that these properties differ across terrestrial landscapes in complex ways that likely influence many facets of animal ecology and behavior.

Rachel Stein↗
DOE OSTI2025

Semiconductor-compatible topological digital alloys

Recently, GeSn alloys have attracted much interest for direct-gap infrared photonics and as potential topological materials which are compatible with the semiconductor industry. However, for photonics, the high-Sn content required leads to low detectivity, associated with poor material quality, and the (>35 %) Sn required for topological properties have been out of reach experimentally. Here, we demonstrate that by patterning the Sn distribution within Ge, the electronic properties have a far greater tunability than is possible with the random alloy. For the GeSn δ-digital alloy (DA) formed by confining Sn atoms in atomic layer(s) along the [111] direction of Ge, we show that ∼ 10 % Sn can lead to a triple-point semimetal. These findings are understood in terms of Sn ordering causing spatial separation of Sn and Ge band edges, leading to band inversion. This mechanism can also lead to a weak topological insulator, Weyl semimetal, and enables tunable direct bandgaps down to 2 meV, covering the entire infrared range. This DA induced topological properties are also identified in compound semiconductors, such as InAs 1-x Sb x , showing the general applicability of the DA design for realizing topological properties on conventional semiconductor platforms. Furthermore, our findings not only point to a new class of currently unexplored topological systems accessible by epitaxy, but also establish the promise of low-Sn GeSn DAs for application as infrared laser diodes and photodetectors in Si photonic integrated circuits and infrared image sensors.

3D topological semimetal↗
DOE OSTI2022

Implications of different income distributions for future residential energy demand in the U.S.

Abstract Future income distribution will affect energy demand and its interactions with various societal priorities. Most future model simulations assume a single average consumer and thus miss this important demand determinant. We quantify long-term implications of alternative future income distributions for state-level residential energy demand, investment, greenhouse gas, and pollutant emission patterns in the United States (U.S.) by incorporating income quintiles into the residential energy sector of the Global Change Analysis Model with 50-state disaggregation. We find that if the income distribution within each U.S. state becomes more egalitarian than present, what means that the difference on income between the richest and poorest decreases over time, residential energy demand could be 10% (4%–14% across states) higher in 2100. This increase of residential energy demand will directly reduce energy poverty, with a very modest increment on economywide CO 2 emissions (1%–2%). On the other hand, if U.S. states transition to a less equitable income distribution than present, with the difference between richest and poorest increasing over time, residential energy demand could be 19% (12%–26% across states) lower. While this study focuses on a single sector, we conclude that to improve understanding of synergies and tradeoffs across multiple societal goals such as energy access, emissions, and investments, future model simulations should explicitly consider subregional income distribution impacts.

29 ENERGY PLANNING, POLICY, AND ECONOMY↗
DOE OSTI2021

A daily, 250 m and real-time gross primary productivity product (2000–present) covering the contiguous United States

Abstract. Gross primary productivity (GPP) quantifies the amount of carbon dioxide (CO2) fixed by plants through photosynthesis. Although as a key quantity of terrestrial ecosystems, there is a lack of high-spatial-and-temporal-resolution, real-time and observation-based GPP products. To address this critical gap, here we leverage a state-of-the-art vegetation index, near-infrared reflectance of vegetation (NIRV), along with accurate photosynthetically active radiation (PAR), to produce a SatelLite Only Photosynthesis Estimation (SLOPE) GPP product for the contiguous United States (CONUS). Compared to existing GPP products, the proposed SLOPE product is advanced in its spatial resolution (250 m versus >500 m), temporal resolution (daily versus 8 d), instantaneity (latency of 1 d versus >2 weeks) and quantitative uncertainty (on a per-pixel and daily basis versus no uncertainty information available). These characteristics are achieved because of several technical innovations employed in this study: (1) SLOPE couples machine learning models with MODIS atmosphere and land products to accurately estimate PAR. (2) SLOPE couples highly efficient and pragmatic gap-filling and filtering algorithms with surface reflectance acquired by both Terra and Aqua MODIS satellites to derive a soil-adjusted NIRV (SANIRV) dataset. (3) SLOPE couples a temporal pattern recognition approach with a long-term Cropland Data Layer (CDL) product to predict dynamic C4 crop fraction. Through developing a parsimonious model with only two slope parameters, the proposed SLOPE product explains 85 % of the spatial and temporal variations in GPP acquired from 49 AmeriFlux eddy-covariance sites (324 site years), with a root-mean-square error (RMSE) of 1.63 gC m−2 d−1. The median R2 over C3 and C4 crop sites reaches 0.87 and 0.94, respectively, indicating great potentials for monitoring crops, in particular bioenergy crops, at the field level. With such a satisfactory performance and its distinct characteristics in spatiotemporal resolution and instantaneity, the proposed SLOPE GPP product is promising for biological and environmental research, carbon cycle research, and a broad range of real-time applications at the regional scale. The archived dataset is available at https://doi.org/10.3334/ORNLDAAC/1786 (download page: https://daac.ornl.gov/daacdata/cms/SLOPE_GPP_CONUS/data/, last access: 20 January 2021) (Jiang and Guan, 2020), and the real-time dataset is available upon request.

54 ENVIRONMENTAL SCIENCES↗
NASA NTRS2017

Payload Performance of Third Generation TDRS and Future Services

NASA has accepted two of the 3rd generation Tracking and Data Relay Satellites, TDRS K, L, and M, designed and built by Boeing Defense, Space & Security (DSS). TDRS K, L, and M provide S-band Multiple Access (MA) service and S-band, Ku-band and Ka-band Single Access (SA) services to near Earth orbiting satellites. The TDRS KLM satellites offer improved services relative to the 1st generation TDRS spacecraft, such as: an enhanced MA service featuring increased EIRPs and G/T; and Ka-band SA capability which provides a 225 and 650 MHz return service (customer-to-TDRS direction) bandwidth and a 50 MHz forward service (TDRS-to-customer direction) bandwidth. MA services are provided through a 15 element forward phased array that forms up to two beams with onboard active beamforming and a 32 element return phased array supported by ground-based beamforming. SA services are provided through two 4.6m tri-band reflector antennas which support program track pointing and autotrack pointing. Prior to NASAs acceptance of the satellites, payload on-orbit testing was performed on each satellite to determine on-orbit compliance with design requirements. Performance parameters evaluated include: EIRP, G/T, antenna gain patterns, SA antenna autotrack performance, and radiometric tracking performance. On-orbit antenna calibration and pointing optimization was also performed on the MA and SA antennas including 24 hour duration tests to characterize and calibrate out diurnal effects. Bit-Error-Rate (BER) tests were performed to evaluate the end-to-end link BER performance of service through a TDRS K and L spacecraft. The TDRS M is planned to be launched in August 2017. This paper summarizes the results of the TDRS KL communications payload on-orbit performance verification and end-to-end service characterization and compares the results with the performance of the 2nd generation TDRS J. The paper also provides a high-level overview of an optical communications application that will augment the data rates supported by the Space Network.

RF↗
NASA NTRS2017

Payload Performance of TDRS KL and Future Services

NASA has accepted two of the 3nd generation Tracking and Data Relay Satellites, TDRS K, L, and M, designed and built by Boeing Defense, Space Security (DSS). TDRS K, L, and M provide S-band Multiple Access (MA) service and S-band, Ku-band and Ka-band Single Access (SA) services to near Earth orbiting satellites. The TDRS KLM satellites offer improved services relative to the 1st generation TDRS spacecraft, such as: an enhanced MA service featuring increased EIRPs and GT; and Ka-band SA capability which provides a 225 and 650 MHz return service (customer-to-TDRS direction) bandwidth and a 50 MHz forward service (TDRS-to-customer direction) bandwidth. MA services are provided through a 15 element forward phased array that forms up to two beams with onboard active beamforming and a 32 element return phased array supported by ground-based beamforming. SA services are provided through two 4.6m tri-band reflector antennas which support program track pointing and autotrack pointing. Prior to NASAs acceptance of the satellites, payload on-orbit testing was performed on each satellite to determine on-orbit compliance with design requirements. Performance parameters evaluated include: EIRP, GT, antenna gain patterns, SA antenna autotrack performance, and radiometric tracking performance. On-orbit antenna calibration and pointing optimization was also performed on the MA and SA antennas including 24 hour duration tests to characterize and calibrate out diurnal effects. Bit-Error-Rate (BER) tests were performed to evaluate the end-to-end link BER performance of service through a TDRS K and L spacecraft. The TDRS M is planned to be launched in August 2017. This paper summarizes the results of the TDRS KL communications payload on-orbit performance verification and end-to-end service characterization and compares the results with the performance of the 2nd generation TDRS J. The paper also provides a high-level overview of an optical communications application that will augment the data rates supported by the Space Network.

Laser↗
DOE OSTI2016

SciDAC-Data, A Project to Enabling Data Driven Modeling of Exascale Computing

The SciDAC-Data project is a DOE funded initiative to analyze and exploit two decades of information and analytics that have been collected by the Fermilab Data Center on the organization, movement, and consumption of High Energy Physics data. The project will analyze the analysis patterns and data organization that have been used by the NOvA, MicroBooNE, MINERvA and other experiments, to develop realistic models of HEP analysis workflows and data processing. The SciDAC-Data project aims to provide both realistic input vectors and corresponding output data that can be used to optimize and validate simulations of HEP analysis. These simulations are designed to address questions of data handling, cache optimization and workflow structures that are the prerequisites for modern HEP analysis chains to be mapped and optimized to run on the next generation of leadership class exascale computing facilities. We will address the use of the SciDAC-Data distributions acquired from Fermilab Data Center’s analysis workflows and corresponding to around 71,000 HEP jobs, as the input to detailed queuing simulations that model the expected data consumption and caching behaviors of the work running in HPC environments. In particular we describe in detail how the Sequential Access via Metadata (SAM) data handling system in combination with the dCache/Enstore based data archive facilities have been analyzed to develop the radically different models of the analysis of HEP data. We present how the simulation may be used to analyze the impact of design choices in archive facilities.

Mubarak, M.↗
DOE OSTI2023

Foundations of machine learning for low-temperature plasmas: methods and case studies

Abstract Machine learning (ML) and artificial intelligence have proven to be an invaluable tool in tackling a vast array of scientific, engineering, and societal problems. The main drivers behind the recent proliferation of ML in practically all aspects of science and technology can be attributed to: (a) improved data acquisition and inexpensive data storage; (b) exponential growth in computing power; and (c) availability of open-source software and resources that have made the use of state-of-the-art ML algorithms widely accessible. The impact of ML on the field of low-temperature plasmas (LTPs) could be particularly significant in the emerging applications that involve plasma treatment of complex interfaces in areas ranging from the manufacture of microelectronics and processing of quantum materials, to the LTP-driven electrification of the chemical industry, and to medicine and biotechnology. This is primarily due to the complex and poorly-understood nature of the plasma-surface interactions in these applications that pose unique challenges to the modeling, diagnostics, and predictive control of LTPs. As the use of ML is becoming more prevalent, it is increasingly paramount for the LTP community to be able to critically analyze and assess the concepts and techniques behind data-driven approaches. To this end, the goal of this paper is to provide a tutorial overview of some of the widely-used ML methods that can be useful, amongst others, for discovering and correlating patterns in the data that may be otherwise impractical to decipher by human intuition alone, for learning multivariable nonlinear data-driven prediction models that are capable of describing the complex behavior of plasma interacting with interfaces, and for guiding the design of experiments to explore the parameter space of plasma-assisted processes in a systematic and resource-efficient manner. We illustrate the utility of various supervised, unsupervised and active learning methods using LTP datasets consisting of commonly-available, information-rich measurements (e.g. optical emission spectra, current–voltage characteristics, scanning electron microscope images, infrared surface temperature measurements, Fourier transform infrared spectra). All the ML demonstrations presented in this paper are carried out using open-source software; the datasets and codes are made publicly available. The FAIR guiding principles for scientific data management and stewardship can accelerate the adoption and development of ML in the LTP community.

Physics↗
DOE OSTI2014

Combustion synthesis of MgFe{sub 2}O{sub 4}/graphene nanocomposite as a high-performance negative electrode for lithium ion batteries

We present a facile and cost-effective urea-assisted auto-combustion method for synthesizing pure MgFe{sub 2}O{sub 4} nanoparticle and MgFe{sub 2}O{sub 4}/graphene nanocomposite samples followed by annealing at 600 °C for 5 h under N{sub 2} atmosphere. The X-ray diffraction pattern confirmed the single phase formation for both samples. The obtained morphology of the nanocomposite sample shows that the MgFe{sub 2}O{sub 4} nanoparticles are highly dispersed on conductive graphene nanosheets with particle size in the range of 50–100 nm. When applied as an anode material, MgFe{sub 2}O{sub 4}/graphene nanocomposite electrode shows a high reversible charge capacity of 764.4 mAh g{sup −1} at 0.04 C over 60 charge/discharge cycles and in spite of that it also retained a capacity of 219.9 mAh g{sup −1} at high current rate of 4.2 C. The obtained result is much better than the synthesized pure MgFe{sub 2}O{sub 4} nanoparticle electrode. The excellent electrochemical performance of the MgFe{sub 2}O{sub 4}/graphene nanocomposite electrode can be attributed to the strong favorable synergistic interaction between MgFe{sub 2}O{sub 4} and reduced graphene nanosheets, which supplied a large number of accessible active sites for Li{sup +}-ion insertion and short diffusion length for both Li{sup +} ions and electrons. In addition, the graphene nanosheets in the nanocomposite electrode provide high conductivity and accommodate the large volume expansion/contraction during cycling, resulting in high capacity and long cycling stability. - Highlights: • MgFe{sub 2}O{sub 4}/graphene nanocomposite was synthesized by facile urea-assisted method. • Such well-designed structure results in fine and strong interfacial interaction. • Nanocomposite anode shows high rate capability and long cycling stability. • Better performance is due to synergistic effect between MgFe{sub 2}O{sub 4} and graphene. • Simple, low cost and fast synthesis is attractive for large scale applications.

36 MATERIALS SCIENCE↗
DOE OSTI2020

Reexamining supercritical gas adsorption theories in nano-porous shales under geological conditions

Adsorption is an important phenomenon in surface chemistry, especially for nano-porous shales. In the shale gas-in-place, adsorbed gas could contribute up to 85%. However, adsorption is hard to quantitatively characterize due to different adsorption mechanisms, patterns, surfaces, and pore sizes. Moreover, key thermodynamic parameters, such as the enthalpy of adsorption, are challenging to determine due to uncertainties in adsorbed gas densities used in constructing absolute isotherms. In this paper, we revisit the Brunauer, Emmett, and Teller (BET) model, analytically simplify Ono-Kondo (OK) models for subsurface shales, and compare commonly used mono- and multilayer adsorption models (e.g., Langmuir, supercritical Dubinin-Radushkevich (SDR), supercritical BET (SBET), and simplified OK (OKs) models) with recently proposed pressure-dependent adsorption densities to develop a practical and reliable methodology that can be used in the supercritical state, typical for subsurface black shale conditions.Three independent data sets were used for nitrogen and methane adsorption isotherms at different temperatures. Furthermore, we demonstrate that adsorption predicted by the SDR model is comparable or lower than that predicted by the SBET model, but higher than the amounts predicted by Langmuir and OK models. The nitrogen BET method tends to underestimate the accessible SSA for methane. Measurement of isosteric heat of adsorption is suggested instead of the experimental fitting method, due to the significant difference between the calculated results by two commonly used methods. Experimental fitting and simulation methods are also briefly reviewed to guide future research on shale gas adsorption.

58 GEOSCIENCES↗
NASA NTRS2020

Eta Carinae & the Homunculus: Far Infrared Sub-millimeter Spectral Lines detected with the Herschel Space Observatory

The evolved massive binary star η Carinae underwent eruptive mass loss events that formed the complex bi-polar “Homunculus” nebula harboring tens of solar masses of unusually nitrogen-rich gas and dust. Despite expectations for the presence of a significant molecular component to the gas, detections have been observationally challenged by limited access to the far-infrared and the intense thermal continuum. A spectral survey of the atomic and rotational molecular transitions was carried out with theHerschel Space Observatory, revealing a rich spectrum of broad emission lines originating in the ejecta. Velocity profiles of selected PACS lines correlate well with known substructures: Hi in the central core; NH and weak [Cii] within the Homunculus; and [Nii] emissions in fast-moving structures external to the Homunculus.We have identified transitions from [Oi], Hi, and 18 separate light C- and O-bearing molecules including CO, CH, CH+, and OH, and a wide set of N-bearing molecules,NH, NH+, N2H+, NH2, NH3, HCN, HNC, CN, and N2H+. Half of these are new detections unprecedented for any early-type massive star environment. A very low ratio[12C/13C]≤4 is estimated from five molecules and their isotopologues. We demonstrate that non-LTE effects due to the strong continuum are significant. Abundance patterns are consistent with line formation in regions of carbon and oxygen depletions with nitrogen enhancements, reflecting an evolved state of the erupting star with efficient transport of CNO-processed material to the outer layers. The results offer many opportunities for further observational and theoretical investigations of the molecular chemistry under extreme physical and chemical conditions around massive stars in their final stages of evolution.

T. R. Gull↗