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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 1,225 records · Page 68
All sourcesNASA NTRSDOE OSTI
DOE OSTI2022

Resilience Design Patterns: A Structured Approach to Resilience at Extreme Scale (V.2.0)

Reliability is a serious concern for future extreme-scale high-performance computing (HPC) systems. Projections based on the current generation of HPC systems and technology roadmaps suggest the prevalence of very high fault rates in future systems. The errors resulting from these faults will propagate and generate various kinds of failures, which may result in outcomes ranging from result corruptions to catastrophic application crashes. Therefore, the resilience challenge for extreme-scale HPC systems requires coordination between various hardware and software technologies that are capable of handling a broad set of fault models at accelerated fault rates. Also, due to practical limits on power consumption in future HPC systems, they are likely to embrace innovative architectures, increasing the levels of hardware and software complexities. Therefore, the techniques that seek to improve resilience must navigate the complex trade-off space between resilience and the overheads to power consumption and performance. While the HPC community has developed various resilience solutions, application-level techniques as well as system-based solutions, the solution space of HPC resilience techniques remains fragmented. There are no formal methods to integrate the various HPC resilience techniques into composite solutions, nor are there methods to holistically evaluate the adequacy and efficacy of such solutions in terms of their protection coverage, and their performance & power efficiency characteristics. Additionally, few implementations of current resilience solutions are portable to newer architectures and software environments that will be deployed on future systems. We developed a new structured approach to the management of HPC resilience using the concept of resilience-based design patterns. In general, a design pattern is a repeatable solution to a commonly occurring problem. We identified the well-known solutions that are commonly used to deal with faults, errors and failures in HPC systems. In the initial design patterns specification (version 1.0), we described the various solutions, which address specific problems in the design of resilient HPC environments, in the form of patterns. Each pattern describes a problem caused by a fault, error or failure event in an HPC environment, and then describes the core of the solution of the problem in such a way that this solution may be adapted to different systems and implemented at different layers of the system stack. The catalog of these resilience design patterns provides designers with a collection of design elements. To construct complete resilience solutions using combinations of various patterns, we defined a framework that enhances HPC designers' understanding of the important constraints and the opportunities for the design patterns to be implemented and deployed at various layers of the system stack. The design framework is also useful for establishing interfaces and mechanisms to coordinate flexible fault management across hardware and software components, as well as to consider the trade-off between performance, resilience, and power consumption when constructing a solution. The resilience design patterns specification version 1.1 included more detailed explanations of the pattern solutions, the context in which the patterns are applicable, and the implications for hardware or software design. It also provided several additional examples and detailed case studies to demonstrate the use of patterns to build realistic solutions. In version 1.2 of the specification document, we have improved the pattern descriptions, including graphical representations of the pattern components. These improvements are largely based on critical comments, feedback and suggestions received from pattern experts and readers of the previous versions of the specification. The pattern classification has been modified to further clarify the relationships between pattern categories. This version of the specification also introduces a pattern language for resilience design patterns. The pattern language presents the patterns in the catalog as a network, revealing the relations among the resilience patterns. The language provides designers with the means to explore alternative techniques for handling a specific fault model that may have different efficiency and complexity characteristics. Using the pattern language also enables the design and implementation of comprehensive resilience solutions as a set of interconnected resilience patterns that can be instantiated across layers of the system stack. The overall goal of this work is to provide hardware and software designers, as well as the users and operators of HPC systems, a systematic methodology for the design and evaluation of resilience technologies in HPC systems that keep scientific applications running to a correct solution in a timely and cost-efficient manner despite frequent faults, errors, and failures of various types. Version 2.0 expands the resilience design pattern classification and catalog to include self-stabilization patterns and reliability, availability and performance models for each structural pattern.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2023

Multispectral Quantitative Phase Imaging Using a Diffractive Optical Network

As a label‐free imaging technique, quantitative phase imaging (QPI) provides optical path length information of transparent specimens for various applications in biology, materials science, and engineering. Multispectral QPI measures quantitative phase information across multiple spectral bands, permitting the examination of wavelength‐specific phase and dispersion characteristics of samples. Herein, the design of a diffractive processor is presented that can all‐optically perform multispectral quantitative phase imaging of transparent phase‐only objects within a snapshot. The design utilizes spatially engineered diffractive layers, optimized through deep learning, to encode the phase profile of the input object at a predetermined set of wavelengths into spatial intensity variations at the output plane, allowing multispectral QPI using a monochrome focal plane array. Through numerical simulations, diffractive multispectral processors are demonstrated to simultaneously perform quantitative phase imaging at 9 and 16 target spectral bands in the visible spectrum. The generalization of these diffractive processor designs is validated through numerical tests on unseen objects, including thin Pap smear images. Due to its all‐optical processing capability using passive dielectric diffractive materials, this diffractive multispectral QPI processor offers a compact and power‐efficient solution for high‐throughput quantitative phase microscopy and spectroscopy.

36 MATERIALS SCIENCE↗
NASA NTRS2010

Experience with Delay-Tolerant Networking from Orbit

We describe the first use from space of the Bundle Protocol for Delay-Tolerant Networking (DTN) and lessons learned from experiments made and experience gained with this protocol. The Disaster Monitoring Constellation (DMC), constructed by Surrey Satellite Technology Ltd (SSTL), is a multiple-satellite Earth-imaging low-Earth-orbit sensor network in which recorded image swaths are stored onboard each satellite and later downloaded from the satellite payloads to a ground station. Store-and-forward of images with capture and later download gives each satellite the characteristics of a node in a disruption-tolerant network. Originally developed for the Interplanetary Internet, DTNs are now under investigation in an Internet Research Task Force (IRTF) DTN research group (RG), which has developed a bundle architecture and protocol. The DMC is technically advanced in its adoption of the Internet Protocol (IP) for its imaging payloads and for satellite command and control, based around reuse of commercial networking and link protocols. These satellites use of IP has enabled earlier experiments with the Cisco router in Low Earth Orbit (CLEO) onboard the constellation s UK-DMC satellite. Earth images are downloaded from the satellites using a custom IP-based high-speed transfer protocol developed by SSTL, Saratoga, which tolerates unusual link environments. Saratoga has been documented in the Internet Engineering Task Force (IETF) for wider adoption. We experiment with the use of DTNRG bundle concepts onboard the UK-DMC satellite, by examining how Saratoga can be used as a DTN convergence layer to carry the DTNRG Bundle Protocol, so that sensor images can be delivered to ground stations and beyond as bundles. Our practical experience with the first successful use of the DTNRG Bundle Protocol in a space environment gives us insights into the design of the Bundle Protocol and enables us to identify issues that must be addressed before wider deployment of the Bundle Protocol. Published in 2010 by John Wiley & Sons, Ltd. KEY WORDS: Internet; UK-DMC; satellite; Delay-Tolerant Networking (DTN); Bundle Protocol

Ivancic, W.↗
DOE OSTI2021

A deep learning-accelerated data assimilation and forecasting workflow for commercial-scale geologic carbon storage

Fast assimilation of monitoring data to update forecasts of pressure buildup and carbon dioxide (CO 2 ) plume migration under geologic uncertainties is a challenging problem in geologic carbon storage. The high computational cost of data assimilation with a high-dimensional parameter space impedes fast decision-making for commercial-scale reservoir management. We propose to leverage physical understandings of porous medium flow behavior with deep learning techniques to develop a fast data assimilation-reservoir response forecasting workflow. Applying an Ensemble Smoother Multiple Data Assimilation (ES-MDA) framework, the workflow updates geologic properties and predicts reservoir performance with quantified uncertainty from pressure history and CO 2 plumes interpreted through seismic inversion. As the most computationally expensive component in such a workflow is reservoir simulation, we developed surrogate models to predict dynamic pressure and CO 2 plume extents under multi-well injection. The surrogate models employ deep convolutional neural networks, specifically, a wide residual network and a residual U-Net. The workflow is validated against a flat threedimensional reservoir model representative of a clastic shelf depositional environment. Intelligent treatments are applied to bridge between quantities in a true-3D reservoir model and those in a single-layer reservoir model. The workflow can complete history matching and reservoir forecasting with uncertainty quantification in less than one hour on a mainstream personal workstation.

25 ENERGY STORAGE↗
DOE OSTI2020

The Spatiotemporal Evolution of Granular Microslip Precursors to Laboratory Earthquakes

Laboratory earthquake experiments provide important observational constraints for our understanding of earthquake physics. Here we leverage continuous waveform data from a network of piezoceramic sensors to study the spatial and temporal evolution of microslip activity during a shear experiment with synthetic fault gouge. We combine machine learning techniques with ray theoretical seismology to detect, associate, and locate tens of thousands of microslip events within the gouge layer. Microslip activity is concentrated near the center of the system but is highly variable in space and time. While microslip activity rate increases as failure approaches, the spatiotemporal evolution can differ substantially between stick-slip cycles. These results illustrate that even within a single, well-constrained laboratory experiment, the dynamics of earthquake nucleation can be highly complex.

58 GEOSCIENCES↗
DOE OSTI2020

Topologically derived dislocation theory for twist and stretch moiré superlattices in bilayer graphene

In this work, we develop a continuum dislocation description of twist and stretch moiré superlattices in two-dimensional material bilayers. The continuum formulation is based on the topological constraints introduced by the periodic dislocation network associated with the moiré structure. The approach is based on solving analytically for the structural distortion and displacement fields that satisfy the topological constraints and that minimize the total energy. The total energy is described by both the strain energy of each individual distorted layer and a Peierls-Nabarro-like interfacial contribution arising from stacking disregistry. The dislocation core emerges naturally within the formalism as a result of the competition between the two contributions. The approach presented here captures the structure and energetics of twist and stretch moiré superlattices of dislocations with arbitrary direction and character, without assuming an analytical solution a priori and while accounting naturally for dislocation-dislocation image interactions. In comparisons to atomistic simulations using classical potentials, the maximum structure deviation is 6%, while the maximum line energy deviation is 0.019 eV/Å. Several applications of our model are shown, including predicting the variation of structure with twist angle and describing dislocation line tension and junction energies.

36 MATERIALS SCIENCE↗
DOE OSTI2024

Understanding the Interactions of Multiple Pits Under Freely Corroding Conditions

The interactions of two propagating pits on a single cathode surface were evaluated across variations in chloride concentration, water layer (WL), pit sizes, separation distance (x 2 ), and cathode size (L Cath ) under freely corroding conditions using Finite Element Methods (FEM). Calculated FEM current was utilized to predict stability based on the Galvele pit stability product. FEM predictions were utilized to train a neural network machine learning model for rapid stability predictions. Pit one is in the center of a circular cathode while pit two moves radially from the center pit. With two pits, the overall current in each pit is decreased with respect to a single pit, however, the total current is increased. Increasing WL and L Cath generally increased overall current in each pit and increased predicted maximum pit sizes. Increasing x 2 decreased current in pit two due to less cathode being available to support dissolution in proximity to pit two. Increasing chloride concentration from 0.6 to 3 M NaCl increased current, while increasing from 3 to 5.3 M NaCl decreased current. An overall increase in predicted pit size with increase in chloride concentration is predicted. A machine learning model was created to predict current and maximum pit size and captured underlying physics and predicted stability across the multidimensional parameter space.

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

A Co-Registered In-Situ and Ex-Situ Tensile Properties Dataset from a Laser Powder Bed Fusion Additive Manufacturing Process (Peregrine v2023-11)

This release contains a co-registered in-situ and ex-situ Peregrine dataset from five Concept Laser M2 Laser Powder Bed Fusion (L-PBF) stainless steel 316L builds containing 6,299 SS-J3 individually tracked tensile coupons. These data were collected at the Manufacturing Demonstration Facility (MDF) located at Oak Ridge National Laboratory (ORNL). The dataset includes layer-wise visible-light in-situ imaging data, the laser scan paths and parameters, in-situ temporal sensor data, room-temperature static tensile test results, and the target part geometries. Additionally, anomaly detections produced by a modified Dynamic Segmentation Convolutional Neural Network (DSCNN) are provided.

36 MATERIALS SCIENCE↗
NASA NTRS1994

Estimation and classification by sigmoids based on mutual information

An estimate of the probability density function of a random vector is obtained by maximizing the mutual information between the input and the output of a feedforward network of sigmoidal units with respect to the input weights. Classification problems can be solved by selecting the class associated with the maximal estimated density. Newton's s method, applied to an estimated density, yields a recursive maximum likelihood estimator, consisting of a single internal layer of sigmoids, for a random variable or a random sequence. Applications to the diamond classification and to the prediction of a sun-spot process are demonstrated.

Baram, Yoram↗
NASA NTRS2006

MMIC DHBT Common-Base Amplifier for 172 GHz

Figure 1 shows a single-stage monolithic microwave integrated circuit (MMIC) power amplifier in which the gain element is a double-heterojunction bipolar transistor (DHBT) connected in common-base configuration. This amplifier, which has been demonstrated to function well at a frequency of 172 GHz, is part of a continuing effort to develop compact, efficient amplifiers for scientific instrumentation, wide-band communication systems, and radar systems that will operate at frequencies up to and beyond 180 GHz. The transistor is fabricated from a layered structure formed by molecular beam epitaxy in the InP/InGaAs material system. A highly doped InGaAs base layer and a collector layer are fabricated from the layered structure in a triple mesa process. The transistor includes two separate emitter fingers, each having dimensions of 0.8 by 12 m. The common-base configuration was chosen for its high maximum stable gain in the frequency band of interest. The input-matching network is designed for high bandwidth. The output of the transistor is matched to a load line for maximum saturated output power under large-signal conditions, rather than being matched for maximum gain under small-signal conditions. In a test at a frequency of 172 GHz, the amplifier was found to generate an output power of 7.5 mW, with approximately 5 dB of large-signal gain (see Figure 2). Moreover, the amplifier exhibited a peak small-signal gain of 7 dB at a frequency of 176 GHz. This performance of this MMIC single-stage amplifier containing only a single transistor represents a significant advance in the state of the art, in that it rivals the 170-GHz performance of a prior MMIC three-stage, four-transistor amplifier. [The prior amplifier was reported in "MMIC HEMT Power Amplifier for 140 to 170 GHz" (NPO-30127), NASA Tech Briefs, Vol. 27, No. 11 (November 2003), page 49.] This amplifier is the first heterojunction- bipolar-transistor (HBT) amplifier built for medium power operation in this frequency band. The performance of the amplifier as measured in the aforementioned tests suggests that InP/InGaAs HBTs may be superior to high-electron-mobility (HEMT) transistors in that the HBTs may offer more gain per stage and more output power per transistor.

Paidi, Vamsi↗
NASA NTRS2020

Determining Cloud Thermodynamic Phase from the Polarized Micro Pulse Lidar

A method to distinguish cloud thermodynamic phase from polarized Micro Pulse Lidar (MPL) measurements is described. The method employs a simple enumerative approach to classify cloud layers as either liquid water, ice water, or mixed-phase clouds based on the linear volume depolarization ratio and cloud top temperatures derived from Goddard Earth Observing System, version 5 (GEOS-5) assimilated data. Two years of cloud retrievals from the Micro Pulse Lidar Network (MPLNET) site in Greenbelt, MD are used to evaluate the performance of the algorithm. The fraction of supercooled liquid water in the mixed-phase temperature regime (-37 °C – 0 °C) calculated using MPLNET data is compared to similar calculations made using the spaceborne Cloud-Aerosol Lidar with Orthogonal Polarization (CALIOP) instrument on board the Cloud‐Aerosol Lidar and Infrared Pathfinder Satellite Observations (CALIPSO) satellite, with reasonable consistency.

Jasper R Lewis↗
DOE OSTI2024

Control and Optimization of Energy Storage System in Power Distribution System

The widespread adoption of electric vehicles (EVs) and transportation electrification is encumbered by two chief barriers: i) the limited driving range of EVs in the market today and ii) inadequate fast-charging infrastructure for long-distance trips. Extreme fast charging (XFC) technology can recharge EVs in less than 10 minutes for 200 miles range. Firstly, a novel robust optimization-based mixed integer linear programming model is proposed to size a battery energy storage system (BESS) and PV system in an XFCS. In this part, it is assumed that the sizing and location of the XFCS are known. Secondly, the aforesaid assumption is relaxed, and a strategic multi-period coordinated planning model is proposed to optimally site and size BESS-assisted charging stations in a highway transportation network and PV systems in a power distribution network by considering the coupling between both networks. Optimal operation and control of BESS-assisted EV charging stations are vital to alleviate the adverse impact of extreme fast charging of EVs on the host power network. A joint solution is proposed to mitigate the steady state and transient impact of extremefast charging of EVs and ensure grid-friendly integration of XFCSs with the host grid. Lastly, to make the operation of the XFCS cost-effective, a multi-layered energy management framework is proposed for the XFCS by considering forecast uncertainties, monthly demand charges reduction, and BESS degradation.

24 POWER TRANSMISSION AND DISTRIBUTION↗
DOE OSTI2022

Constructing a multi-functional polymer network for ultra-stable and safe Li-metal batteries

We report the practical application of lithium (Li) metal electrodes is impeded by Li dendrite growth and unstable solid electrolyte interphase (SEI). Herein, a multi-grafting polymer network, poly(dimethyl siloxane)-g-[poly(poly(ethylene glycol) methyl ether methacrylate)-r-sodium poly(p-styrene sulfonate)] (PPS), is chemically synthesized from reversible addition-fragmentation chain transfer (RAFT) polymerization. With integrated stretchability, ionic conductivity, and mechanical robustness, it serves a dual role to stabilize the Li electrode. As artificial SEI layer, the PPS enables superior electrochemical performance in half cells, symmetric cells, and full cells (PPS@Li/LiFePO 4 , capacity retention of >70% after 600 cycles). Utilized as solid polymer electrolyte (SPE), the all-solid-state Li/SPE/LiFePO 4 full cell delivers excellent cycling performance with an unprecedented capacity retention of 90% over 1,700 cycles at 0.5 C and 81% over 1,000 cycles at 1.0 C. With high-voltage LiNi 0.8 Mn 0.1 Co 0.1 O 2 (NMC811) as cathode, the Li/SPE/NMC811 cell exhibits an initial discharge capacity of 162.2 mAh g -1 with a capacity retention of 72% after 200 cycles. The assembled solid-state Li/SPE/LiFePO4 pouch cell with SPE exhibits stable cycling performance over 200 cycles with a capacity retention of 75% and still operates well even after curling, folding, and cutting, demonstrating great potential for achieving ultra-safe and high energy density batteries.

25 ENERGY STORAGE↗
DOE OSTI2025

Resistive Switching of Spinel Li 4 Ti 5 O 12 Lithium-Ion Battery Material for Neuromorphic Computing

The rapid rise of AI has exposed significant limitations in conventional Von Neumann computing architecture, particularly in regard to speed and energy efficiency. To address these challenges, researchers are exploring a brain-inspired neuromorphic architecture that mimics biological neural networks, enabling massive parallel processing with reduced power consumption for complex AI computational demands. Recent interest has focused on utilizing battery electrodes and solid electrolyte materials for their resistive switching properties in developing a neuromorphic architecture. These properties are precisely tuned through local- and bulk-level chemical composition modifications via voltage bias stimuli. In this study, we demonstrate fabricating a three-terminal lithium-ion electrochemical transistor based on lithium titanium oxide (Li 4 Ti 5 O 12 ), a popular lithium-ion battery anode material. We deposited and characterized LTO thin films using RF sputtering, demonstrating a 6 orders of magnitude increase in electronic conductivity upon lithiation, with conductivity plateauing after 20% lithiation. Density functional theory calculations revealed transformation from the insulating to conducting state, supported by experimental characterization through X-Ray Photoelectron Spectroscopy (XPS) and Direct Current (DC) polarization analyses. The fabricated transistor consisted of LTO as the channel layer, gold as source/drain terminals, lithium phosphorus oxynitride (LiPON) as the lithium-ion conductor, and copper as the gate terminal. The device exhibited clear hysteresis in transfer characteristics due to lithium insertion/extraction processes. Long-term potentiation (LTP) and long-term depression (LTD) measurements showed an asymmetric ratio of 1.425 and maximum/minimum conductance ratio of 7.83. When implemented in a deep neural network (DNN) for MNIST handwritten digit recognition, the device achieved 92.03% accuracy over 20 training epochs. Detailed transport mechanism analysis revealed the crucial role of oxygen vacancies and interface effects in device operation. Our preliminary findings establish LTO-based lithium-ion electrochemical transistors as promising candidates for energy-efficient neuromorphic computing applications, offering potential solutions to traditional Von Neumann architecture limitations.

25 ENERGY STORAGE↗
DOE OSTI2020

Rheological and microstructural characterization of aqueous suspensions of carbon black and reduced graphene oxide

Carbon black is often used as a conductivity enhancer in battery electrodes. Reduced graphene oxide is another conducting material, with a high aspect ratio sheet-like morphology, and offers the possibility of generating a conducting network at loadings that are small compared to carbon black. Suspensions of conducting carbon, active materials and binders are typically coated on current collectors during the formation of electrodes. The rheology of these suspensions is an important indicator of the connectivity of particles in the suspension and impacts the electrical conductivity as well as the thickness of the coated layer. In this work, the rheology and microstructures of aqueous suspensions of para-aminobenzoic acid terminated carbon black (CB) with and without reduced graphene oxide (RGO) were investigated. The CB loading varied between 0.05 wt.% -10 wt.%, while the samples containing RGO had an RGO loading of 0.05 wt.%. The pH of the suspension was varied from 7.5 to 3. The carboxyl groups on the CB surface are deprotonated at pH 7.5, making the particles hydrophilic. Protonation of the carboxylate groups at pH 3 makes the particles partially hydrophobic. Suspensions of CB showed Newtonian behavior at all loadings for pH 7.5, and at loadings below 1.5 wt.% for pH 3. They are shear thinning at loadings between 1.5–10 wt.%. Interestingly, the low shear viscosity is non-monotonic with CB loading, rising up to a CB loading of 7.5 wt.%, and then dropping at 10 wt.%. Cryogenic scanning electron microscopy (SEM) showed CB particles forming aggregates at pH 3 for CB loadings greater than 1.5 wt.%. At pH 3, addition of 0.05 wt.% RGO resulted in a viscosity increase for suspensions containing 0.5 wt.% CB, as the RGO formed connections with CB particles that enhanced network formation. Adding RGO had little effect on suspensions that had 1.5 wt.% CB. The combination of SEM imaging and suspension rheology provides insight into the particle connectivity for the carbon-based materials studied here. These insights will be useful in determining coating conditions for slurries used in rechargeable battery electrodes.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
NASA NTRS2005

Rapid Temporal Changes of Boundary Layer Winds

The statistical distribution of the magnitude of the vector wind change over 0.25, 0.5, 1 and 2-h periods based on data from November 1999 through August 2001 is presented. The distributions of the 2-h u and v component wind changes are also presented for comparison. The wind changes at altitudes from 500 to 3000 m were measured using the Eastern Range network of five 915 MHz Doppler radar wind profilers. Quality controlled profiles were produced every 15 minutes for up to sixty gates, each representing 101 m in altitude over the range from 130 m to 6089 m. Five levels, each constituting three consecutive gates, were selected for analysis because of their significance to aerodynamic loads during the Space Shuttle ascent roll maneuver. The distribution of the magnitude of the vector wind change is found to be lognormal consistent with earlier work in the mid-troposphere. The parameters of the distribution vary with time lag, season and altitude. The component wind changes are symmetrically distributed with near-zero means, but the kurtosis coefficient is larger than that of a Gaussian distribution.

Merceret, Francis J.↗
DOE OSTI2023

Mechanical properties of heterogeneous metallic glasses: Insights from brick-and-mortar designs

The brick-and-mortar architectural paradigm is commonly applied in structural design to achieve an exceptional combination of strength and plasticity due to its highly tunable mechanical attributes. This study applies molecular dynamics simulations of tensile loading to investigate the mechanical properties and failure mechanisms of brick-and-mortar metallic glasses (BMMGs). The focus is primarily on the implications of the aspect ratio of the bricks and interlayer thickness on the strength, plasticity, and deformation mechanisms. Results indicate that the failure mode is typified by multiple shear bands localized within the softer mortar regions, creating a staggered network under smaller aspect ratios that significantly enhances plastic deformation. However, a transition to a single dominant SB occurs when the aspect ratio exceeds 4.1. In addition, results indicate that a concurrent improvement in strength and plasticity can be achieved by modulating the distance between brick layers in BMMGs. This enhancement originates from the extensive generation and interaction of shear transformation zones. Furthermore, this study highlights that a desirable balance between strength and plasticity can be obtained in BMMGs with appropriate brick aspect ratios and interlayer thicknesses, providing a potential design strategy for the advancement of novel metallic glasses with superior properties.

36 MATERIALS SCIENCE↗
DOE OSTI2020

Surpassing the Organic Cathode Performance for Lithium-Ion Batteries with Robust Fluorinated Covalent Quinazoline Networks

Organic electrode materials have promising application prospects in energy storage, but issues including rapid capacity fading and poor power capacity restrict their practical applications. In this study, nanoporous fluorinated covalent quinazoline networks (F-CQNs) were constructed by condensation of fluorinated aromatic aminonitrile precursors via an ionothermal pathway. Precise control of the reaction parameters afforded F-CQN-1-600 material featuring high surface area, permanent porosity, high nitrogen content (23.49 wt %), extended π-conjugated architecture, layered structure, and bipolar combination of benzene and tricycloquinazoline. Synergy among these unique properties leads to a good performance as a cathode source for lithium-ion batteries (LIBs) in terms of high capacity (250 mA h g –1 at 0.1 A g –1 ), high rate capability (105 mA h g –1 at 5.0 A g –1 ), and impressive cycling stability (95.8% retention rate after 2000 cycles at 2.0 A g –1 together with a high Coulombic efficiency of 99.95%), surpassing most of the previous organic cathode counterparts.

25 ENERGY STORAGE↗