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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 37 records · Page 2
All sourcesNASA NTRSDOE OSTI
DOE OSTI2020

Tailoring conductive networks within hollow carbon nanospheres to host phosphorus for advanced sodium ion batteries

The formidable sustainability challenges in advancing energy storage technologies call for game-changing research in battery designs. The previous pursuing of novel cathode materials with high redox potentials impedes the vast applications due to the simultaneous electrolyte decomposition at high potentials, though they are expected to deliver high specific capacities. Eventually, people start thinking in an opposite way, desirable anode materials with low redox potentials can also own high specific capacities. Among all the promising candidates, phosphorus-based anodes in sodium ion batteries (SIBs) have received considerable attention owing to the low cost and relatively high natural abundance of phosphorus. More importantly, phosphorus can store three sodium atoms and enable a high theoretical capacity of 2596 mAh g -1 , which overwhelms any other SIB anode currently available. However, the poor electronic conductivity and large volume change of phosphorus during cycling severely deteriorate battery performance. The most widely used strategy is to confine phosphorus within well-designed carbon hosts. Here, we thereby introduce a new type of porous hollow carbon with conductive-network interior as phosphorus host, which not only improves the electrical conductivity, but also creates enough interior surface for maximizing phosphorus utilization and shortening the ion's diffusion distance, compared to those conventional hollow carbon hosts. Therefore, the as-prepared red phosphorus-carbon spheres composites (RP/CS) exhibit superior rate performance (similar to 1083 mAh g -1 at 4 A g -1 , similar to 837 mAh g -1 even at 8 A g -1 ) and excellent cycle life (1027 mAh g -1 at 4 A g -1 more than 2000 cycles).

25 ENERGY STORAGE↗
DOE OSTI2021

Synergy of Graphene Nanoribbons and Graphene Sheets for High-Rate Lithium-Sulfur Batteries

According to the increasing demands for shortening the battery charging time, high current rate (C-rate) performances become more significant in practical applications. With a higher theoretical capacity, lithium-sulfur batteries are treated as promising candidates for the next-generation batteries. In this work, the utilization of graphene nanoribbons (GNRs) exhibits the benefits in conductivity and other electrochemical performances, especially for high-rate applications. With air-controlled electrospray as the method, carbon encapsulated sulfur particles, poly(acrylic acid), reduced graphene oxide (rGO) sheets, and GNRs are mixed and directly deposited onto the carbon coated aluminum collector, to employ as the cathode. The scanning electron microscopy (SEM) imaging exhibits that the two-dimensional structure of GNRs helps construct inter-connected networks. This improved structure of cathode can increase the electroconductivity, confirmed by the electrochemical impedance spectroscopy (EIS), and modify the porosity, indicated through pore size distribution profiles. In this way, the polysulfides can be more efficiently trapped and utilized, realizing the promising behavior with faster charge transfer. In terms of the cycling performance at 0.2 C, the batteries with GNRs can perform 18% higher in capacity than those without GNRs, without decreasing the charge retention. According to the rate-capability tests, systems with GNRs can achieve enhanced performance compared to batteries with precursor carbon nanotubes (CNTs), especially at high C-rates. At 2 C, with 80 wt % of graphene-based materials as GNRs, batteries can achieve an increase in capacity by 78% and 41% compared with systems without GNRs and those with CNTs, respectively. Accordingly, the results testify the synergy of GNRs and rGO sheets in Li-S batteries.

25 ENERGY STORAGE↗
DOE OSTI2021

Haze Mitigation in High-Resolution Satellite Imagery using Enhanced Style-Transfer Neural Network and Normalization Across Multiple GPUs

Despite recent advances in deep learning approaches, haze mitigation in large satellite images is still a challenging problem. Due to amorphous nature of haze, object detection or image segmentation approaches are not applicable. Also it is practically infeasible to obtain ground truths for training. Bounded memory capacity of GPUs is another constraint that limits the size of image to be processed. In this paper, we propose a style transfer based neural network approach to mitigate haze in a large overhead imagery. The network is trained without paired ground truths; further, perception loss is added to restore vivid colors, enhance contrast and minimize artifacts. The paper also illustrates our use of multiple GPUs in a collective way to produce a single coherent clear image where each GPU dehazes different portions of a large hazy image.

Park, Byung↗
DOE OSTI2022

Accelerated computation of lattice thermal conductivity using neural network interatomic potentials

We report with the development of the density functional theory (DFT) and ever-increasing computational capacity, an accurate prediction of lattice thermal conductivity based on the Boltzmann transport theory becomes computationally feasible, contributing to a fundamental understanding of thermal conductivity as well as a choice of the optimal materials for specific applications. However, steep costs in evaluating third-order force constants limit the theoretical investigation to crystals with high symmetry and few atoms in the unit cell. Currently, machine learning potentials are garnering attention as a computationally efficient high-fidelity model of DFT, and several studies have demonstrated that the lattice thermal conductivity could be computed accurately via the machine learning potentials. However, test materials were mostly crystals with high symmetries, and the applicability of machine learning potentials to a wide range of materials has yet to be demonstrated. Furthermore, establishing a standard training set that provides consistent accuracy and computational efficiencies across a wide range of materials would be useful. To address these issues, herein we compute lattice thermal conductivities at 300 K using neural network interatomic potentials. As test materials, we select 25 materials with diverse symmetries and a wide range of lattice thermal conductivities between 10 -1 and 10 3 Wm -1 K -1 . Among various choices of training sets, we find that molecular dynamics trajectories at 50–700 K consistently provide results at par with DFT for the test materials. In contrast to pure DFT approaches, the computational cost in the present approach is uniform over the test materials, yielding a speed gain of 2–10 folds. When a smaller reduced training set is used, the relative efficiency increases by up to ~50 folds without sacrificing accuracy significantly. The current work will broaden the application scope of machine learning potentials by establishing a robust framework for accurately computing lattice thermal conductivity with machine learning potentials.

36 MATERIALS SCIENCE↗
DOE OSTI2023

New trends in photonic switching and optical networking architectures for data centers and computing systems [Invited]

The rapid increases in data traffic coupled with user preferences are driving the data center and computing system service providers to offer energy-efficient, intelligent, flexible, cost-effective, high-capacity, and low-latency data services without added complexity to the users. Disaggregated heterogeneous reconfigurable computing systems realized by photonic switching and interconnects can enhance throughput and energy efficiency for artificial intelligence/machine learning (AI/ML) workloads, especially when aided by the AI/ML-enhanced control plane. Photonic switching and new optical networking architectures are expected to solve many of these challenging problems. This paper discusses new trends in photonic switching and optical network architectures for future data centers and computing systems summarized as follows: (1) flat reconfigurable disaggregated computing enabled by high-radix photonic switching and interconnects in data centers; (2) chiplet-based computing architectures empowered by embedded photonics toward heterogeneous reconfigurable computing; (3) nanosecond-scale photonic switching in data centers and computing systems; (4) AI/ML in self-driving, application-aware, and situation-aware data centers; (5) the emergence of flexible networking for cloud computing, edge computing, and split computing, as well as flexible networking for 5G/6G RF-optical networks; and (6) the deployment of embedded co-designed silicon photonics being considered for future data centers.

Yoo, S. J. Ben (ORCID:0000000274201871)↗
NASA NTRS1992

File servers, networking, and supercomputers

One of the major tasks of a supercomputer center is managing the massive amount of data generated by application codes. A data flow analysis of the San Diego Supercomputer Center is presented that illustrates the hierarchical data buffering/caching capacity requirements and the associated I/O throughput requirements needed to sustain file service and archival storage. Usage paradigms are examined for both tightly-coupled and loosely-coupled file servers linked to the supercomputer by high-speed networks.

Moore, Reagan W.↗
NASA NTRS1991

File servers, networking, and supercomputers

One of the major tasks of a supercomputer center is managing the massive amount of data generated by application codes. A data flow analysis of the San Diego Supercomputer Center is presented that illustrates the hierarchical data buffering/caching capacity requirements and the associated I/O throughput requirements needed to sustain file service and archival storage. Usage paradigms are examined for both tightly-coupled and loosely-coupled file servers linked to the supercomputer by high-speed networks.

Moore, Reagan W.↗
NASA NTRS2003

Space Communications and Data Systems Technologies for Next Generation Earth Science Measurements

The next generation of Earth observing satellites and sensor networks will face challenges in supporting robust high rate communications links from the increasingly sophisticated onboard instruments. Emerging applications will need data rates forecast to be in the 100's to 1000's of Mbps. As mission designers seek smaller spacecraft, challenges exist in reducing the size and power requirements while increasing the capacity of the spacecraft's communications technologies. To meet these challenges, this work looks at three areas of selected space communications and data services technologies, specifically in the development of reflectarray antennas, demonstration of space Internet concepts, and measurement of atmospheric propagation effects on Ka-band signal transmitted from LEO.

Bauer, Robert A.↗
NASA NTRS2000

Modeling a Wireless Network for International Space Station

This paper describes the application of wireless local area network (LAN) simulation modeling methods to the hybrid LAN architecture designed for supporting crew-computing tools aboard the International Space Station (ISS). These crew-computing tools, such as wearable computers and portable advisory systems, will provide crew members with real-time vehicle and payload status information and access to digital technical and scientific libraries, significantly enhancing human capabilities in space. A wireless network, therefore, will provide wearable computer and remote instruments with the high performance computational power needed by next-generation 'intelligent' software applications. Wireless network performance in such simulated environments is characterized by the sustainable throughput of data under different traffic conditions. This data will be used to help plan the addition of more access points supporting new modules and more nodes for increased network capacity as the ISS grows.

Alena, Richard↗
DOE OSTI2020

Sampling Subjective Polygons for Patch-Based Deep Learning Land-Use Classification in Satellite Images

Model generalization remains a key challenge in the analysis of large amounts of heterogeneous satellite image data. One major limiting factor in developing generalizable models, in the context of supervised learning, is the lack of high quality training datasets. A model's capacity to perform well on new data is often inhibited by imbalance and bias in the data that was used for training. This is especially a problem when using convolutional neural networks to classify urban land-use in satellite images. Notable dataset imbalance issues in this application include land-use type imbalance and image scene imbalance. To begin understanding these dataset imbalance problems in more detail, we develop and test a number of sampling methods for generating training image datasets from subjective training polygons for urban land-use classification. We investigate sampling at different point densities as a means to reduce content repetition and therefore content imbalance and bias in the training image dataset.

Arndt, Jacob↗
DOE OSTI2026

Plasticizer-Free Gradient-Crosslinked Polyurethane Electrolyte for Room-Temperature Solid-State Lithium Batteries

Polymer-based solid-state electrolytes are promising for next-generation lithium metal batteries, yet their limited ionic conductivity and mechanical stability at ambient conditions remain key challenges. Herein, we report a novel gradient crosslinked polymer electrolyte (PU/PUA/PU) synthesized via a sequential in situ UV-curing process that integrates a mechanically robust polyurethane acrylate (PUA) core with soft linear polyurethane (PU) interfaces. This all-solid membrane operates without any liquid plasticizer and the interfacial PU layers ensure low interfacial resistance and intimate electrode contact, while the PUA core provides enhanced dimensional stability and dendrite suppression. As a result, the gradient electrolyte delivers an impressive ∼2.6 × 10 −4 S cm −1 ionic conductivity at 25 °C (two orders of magnitude higher than conventional PEO) and remains electrochemically stable >5 V (vs Li + /Li). Structural analysis confirms the formation of a well-defined crosslinked network with suppressed crystallinity and expanded interchain spacing. Electrochemical impedance spectroscopy (EIS) and linear sweep voltammetry (LSV) further validate these properties. When applied in a Li||NMC811 cell, the PU/PUA/PU electrolyte delivers stable cycling with high capacity at both 60 °C and 25 °C, demonstrating its potential for room-temperature solid-state battery applications without reliance on plasticizers or heating. In conclusion, this gradient design offers a practical path toward ambient-condition operation of solid-state lithium batteries, providing a paradigm for overcoming the traditional trade-off between ionic conductivity and mechanical robustness.

25 ENERGY STORAGE↗
DOE OSTI2024

Mo 3 S 13 Chalcogel: A High-Capacity Electrode for Conversion-Based Li-Ion Batteries

Despite large theoretical energy densities, metal-sulfide electrodes for energy storage systems face several limitations that impact the practical realization. Here, we present the solution-processable, room temperature (RT) synthesis, local structures, and application of a sulfur-rich Mo 3 S 13 chalcogel as a conversion-based electrode for lithium-sulfide batteries (LiSBs). The structure of the amorphous Mo 3 S 13 chalcogel is derived through operando Raman spectroscopy, synchrotron X-ray pair distribution function (PDF), X-ray absorption near edge structure (XANES), and extended X-ray absorption fine structure (EXAFS) analysis, along with ab initio molecular dynamics (AIMD) simulations. A key feature of the three-dimensional (3D) network is the connection of Mo 3 S 13 units through S–S bonds. Li/Mo 3 S 13 half-cells deliver initial capacity of 1013 mAh g –1 during the first discharge. After the activation cycles, the capacity stabilizes and maintains 312 mAh g –1 at a C/3 rate after 140 cycles, demonstrating sustained performance over subsequent cycling. Such high-capacity and stability are attributed to the high density of (poly)sulfide bonds and the stable Mo–S coordination in Mo 3 S 13 chalcogel. Importantly, these findings showcase the potential of Mo 3 S 13 chalcogels as metal-sulfide electrode materials for LiSBs.

25 ENERGY STORAGE↗
DOE OSTI2024

Optimizing the location and configuration of disaster resilience hubs under transportation and electric power network failures

Natural disasters often result in failures of transportation network components and blackouts that imperil the wellbeing of vulnerable populations. In response to these events, resilience hubs have been proposed as a pre-disaster planning strategy to improve access to critical services. This paper introduces an optimization-based approach to locate and configure electric power-generating resilience hubs considering the possibility of failures in transportation and electric power systems. The model's objective is to identify hub locations and configurations that maximize transportation accessibility to the hubs and maximize the satisfaction of basic energy needs through hub-generated electric power. Besides a budget constraint, the model accounts for limits on the levels of hub energy generation vis-à-vis community energy demands, and on the transportation network distance of communities to hubs. Three heuristics are presented for the proposed planning problem. The first heuristic is a genetic algorithm (GA) with problem-specific solution generation procedures. The other two heuristics implement greedy search techniques. Numerical experiments were conducted, using data from rural Puerto Rico, to illustrate the application of the proposed model and heuristics, and examine their performance. In the numerical experiments, the GA heuristic found better solutions than the greedy heuristics. Additionally, design solutions consisting of spatially dispersed hubs with low energy generation capacity were better than solutions with spatially concentrated high-capacity hubs. Lastly, across a wide range of hub demand scenarios, only a small number of candidate hub locations consistently ranked among the best locations for establishing a hub.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗
DOE OSTI2026

Binder-Free Graphite Anodes for Next-Generation High-Performance Lithium-Ion Batteries

High-energy density anodes are crucial for next-generation lithium-ion batteries (LIBs) particularly for electric vehicle (EV) applications. Sluggish lithium-diffusion kinetics coupled with conventional anode fabrication processes containing polymeric binders hinder fast-charging capabilities and high-energy density of graphite. Herein, we introduce a binder-free graphite anode fabrication strategy using the electrospinning technique that contains ~2.41% carbon nanotubes (CNTs). Our strategy relies on the formation of an interconnecting conductive CNT network coupled with an ultrathin N-doped carbon coating on graphite particles from sacrificial binders. This combination enhances both structural integrity and electrical conductivity and, in turn, improves fast-charging capabilities and high energy density of LIBs. The binder-free graphite anode achieves ~335.0 mAh g–1 capacity at C/3 rate over 400 cycles with capacity retention of >95% and average Coulombic efficiencies >99.95%. These promising results suggest that the binder-free anode fabrication with a multifunctional design approach could elevate the energy-density limits of the graphite anodes, solving high-energy density requirements of EVs, and potentially provides a path forward for the development of economically feasible energy storage systems for various applications.

Ozcan, Muca [ORNL] (ORCID:0000000320020474)↗
DOE OSTI2024

K–Co–Mo–S x chalcogel: high-capacity removal of Pb 2+ and Ag + and the underlying mechanisms

Chalcogenide-based aerogels, known as chalcogels, represent a novel class of nanoparticle-based porous amorphous materials characterized by high surface polarizability and Lewis base properties, exhibiting promising applications in clean energy and separation science. This work presents a K–Co–Mo–S x (KCMS) chalcogel as a highly efficient sorbent for heavy metal ions and details its sorption mechanisms. Its incoherent structure comprises Mo 2 V (S 2 ) 6 and Mo 3 IV S(S 6 ) 2 anion-like clusters with four- and six-coordinated Co–S polyhedra, forming a Co–Mo–S covalent network that hosts K + ions through electrostatic attraction. The interactions of KCMS with heavy metal ions, particularly Pb 2+ and Ag + , reveal that KCMS is exceptionally effective in removing these ions from ppm concentrations down to trace levels (≤5 ppb). KCMS rapidly removes Ag + (≈81.7%) and Pb 2+ (≈99.5%) within five minutes, achieving >99.9% removal within an hour, with a distribution constant K d ≥10 8 mL g -1 . KCMS exhibits an impressive removal capacity of 1378 mg g -1 for Ag + and 1146 mg g -1 for Pb 2+ , establishing it as one of the most effective materials known to date for heavy metal removal. This material is also effective for the removal of Ag + and Pb 2+ along with Hg 2+ , Ni 2+ , Cu 2+ , and Cd 2+ from various water sources even in the presence of highly concentrated and chemically diverse cations, anions, and organic species. Analysis of the post-interacted KCMS by synchrotron X-ray pair distribution function (PDF), X-ray photoelectron spectroscopy (XPS) and energy dispersive X-ray spectroscopy (EDS) revealed that the sorption of Pb 2+ , Ag + , and Hg 2+ mainly occurs by the exchange of K + and Co 2+ . Despite being amorphous, this material exhibits unprecedented ion-exchange mechanisms both for the ionically and covalently bound K + and Co 2+ , respectively. In conclusion, this discovery advances our knowledge of amorphous gels and guides material synthesis principles for the highly selective and efficient removal of heavy metal ions from water.

54 ENVIRONMENTAL SCIENCES↗
NASA NTRS1993

Evolution of a high-performance storage system based on magnetic tape instrumentation recorders

In order to provide transparent access to data in network computing environments, high performance storage systems are getting smarter as well as faster. Magnetic tape instrumentation recorders contain an increasing amount of intelligence in the form of software and firmware that manages the processes of capturing input signals and data, putting them on media and then reproducing or playing them back. Such intelligence makes them better recorders, ideally suited for applications requiring the high-speed capture and playback of large streams of signals or data. In order to make recorders better storage systems, intelligence is also being added to provide appropriate computer and network interfaces along with services that enable them to interoperate with host computers or network client and server entities. Thus, recorders are evolving into high-performance storage systems that become an integral part of a shared information system. Data tape has embarked on a program with the Caltech sponsored Concurrent Supercomputer Consortium to develop a smart mass storage system. Working within the framework of the emerging IEEE Mass Storage System Reference Model, a high-performance storage system that works with the STX File Server to provide storage services for the Intel Touchstone Delta Supercomputer is being built. Our objective is to provide the required high storage capacity and transfer rate to support grand challenge applications, such as global climate modeling.

Peters, Bruce↗
NASA NTRS1995

Advanced systems for shuttle launch processing

Four advanced technologies that could be used in a new shuttle launch processing center are described. The latest methods for high capacity data storage technology, disk arrays and magneto optical disks, are described and their advantages and disadvantages compared. A 3-D protein based optical memory, now being researched, is also described as a possible future technology for data storage. An overview of neural network technology is presented together with several commercial software development options now available for neural network applications. The feasibility of Asynchronous Data Transfer technology as the networking technology to integrate video, voice, and data in a new launch processing center is also considered. Different applications of expert system technology at KSC are enumerated together with a number of commercial expert systems development packages presently available.

Perez, Rafael A.↗