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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 919 records · Page 51
All sourcesNASA NTRSDOE OSTI
DOE OSTI2022

Machine learning prediction on the fractional free volume of polymer membranes

Fractional free volume (FFV) characterizes the microstructural level features of polymers and affects their properties including thermal, mechanical, and separation performance. Experimental measurements and theoretical analyses have been used to quantify the FFV of polymers, but challenges remain because of their limitations. Experimental measurements are laborious and based on semi empirical equations, while Bondi’s group contribution theory involves ambiguities like the determination of van der Waals volume and the choice of factor values in the theoretical equation. To efficiently evaluate the FFV of polymers, this study utilizes high-throughput molecular dynamics (MD) simulations to build a large dataset regarding polymer’s FFV. Based on this large dataset, we further build machine learning (ML) models to establish the composition-structure relation. Inspired by group contribution theory which correlates polymer’s functional groups to FFV, our ML models correlate polymer’s substructures or physico-chemical indexes to FFV. Here, our study first benchmarks the MD simulation protocol to obtain reliable FFV of polymers and then carries out high-throughput MD simulations for more than 6,500 homopolymers and 1,400 polyamides. Such a large and diverse dataset makes the well-trained ML models more generalizable, compared with the group contribution theory. The efficiency of a feed forward neural network model is further demonstrated by applying it to a hypothetical polyimide dataset of more than 8 million chemical structures. The predicted FFVs of hypothetical polyimides are further validated by MD simulations. The obtained FFVs of the 8 million polymers, plus their previously reported gas separation performances, demonstrate the promising capability of ML virtual screening for the discovery of polymer membranes with exceptional permeability/selectivity.

36 MATERIALS SCIENCE↗
DOE OSTI2020

Understanding effects of conductive additives in lithium-sulfur batteries

Since 2009, lithium-sulfur (Li-S) batteries have been attracting much research interest, ascribed to their high energy density and cost-effectiveness. However, their S cathodes are harassed by the poor conductivity, shuttling of lithium polysulfides (LPSs), and large volume change. In improving conductivity of S cathodes, carbon materials are widely used as conductive additives. However, there still lacks a comparative study on their geometric and structural effects in Li-S batteries. To this end, a systematic investigation is conducted on two different typical conductive additives, carbon black (CB) 1 and nitrogen-doped graphene nanosheets (N-GNS) 2 . This work reveals that, compared to the zero-dimensional (0D) CB nanoparticles, the two-dimensional (2D) N-GNS is structurally less defective and has a larger surface area. Furthermore, as a result, the 2D structure of the N-GNS helps achieve higher capacity retention but inhibits Li-ion transportation. In comparison, the 0D structure of the CB facilitates the transportation of Li-ions with a higher initial capacity but exposed to a severer shuttling behavior of LPSs. It is also found that a combination of the CB and N-GNS enables better performance of Li-S batteries, in terms of sustainable capacity, Coulombic efficiency, and rate capability. This study is inspiring for designing better S cathodes for Li-S batteries.

25 ENERGY STORAGE↗
DOE OSTI2022

Enabling a novel approach to a controlled fabrication of 1D crystalline nanowires on suspended microstructures of arbitrary geometries using two direct-writing technologies

Recent progresses in additive manufacturing have inspired new technologies, such as direct laser writing technique, based on two-photon polymerization (2 PP), which complements and further enriches the nanofabrication tools portfolio. Here, we combine 2 PP and our mask-free scanning probe assisted ‘direct-write patterning’ (DWP) method to allow for: a) the fabrication of micro-bridge structures with sub-micrometer resolution, b) selective synthesis of crystalline ZnO nanowires at predefined locations, respectively. This synergistic approach enables cantilever probe patterning of catalysts directly on suspended micro-bridges for in-situ CVD growth of nanoscale material, in a templated manner. The study reported here represents the first proof-of-concept experiments demonstrating versatile and scalable methodology, which can be applied and straightforwardly extended to grow a variety of other nanomaterials, in a controlled and selective fashion, on freestanding micro/nanoscale structures, whose size and geometry can be conveniently varied via templating of sacrificial 2 PP polymeric scaffolds. Furthermore, the demonstration of the possibility to integrate this new approach with the conventional lithography techniques provides a step forward to the development of the novel class of hybrid polymer-silicon-1D or -2D materials, and systems. The quality of the produced ZnO nanowire assemblies was assessed using several physical characterization methods.

36 MATERIALS SCIENCE↗
DOE OSTI2024

Multiparametric AFM Insights into Electron Transport Mechanisms in Biomemristors

Biomemristors have attracted significant attention due to their applications in biodegradable and flexible electronic devices, and their prospective functions in information storage, neural synapses, and neuromorphic computation. Hence, understanding biomaterials' resistive switching (RS) mechanism is essential to design novel biomemristors with enhanced performance. In the last decade, various atomic force microscopy (AFM) modes, such as Kelvin probe force microscopy (KPFM) and conductive atomic force microscopy (cAFM), in situ/operando visualize the electron transport behaviors within biomemristors at the nanoscale. This review provides a comprehensive discussion of multiparametric AFM techniques to characterize biomemristors and inspire new ideas in developing next-generation consumer electronics. We also summarize the progress of revealing the internal mechanism of biomemristors with functional AFM to visualize and understand the switching behavior. Finally, the opportunities and challenges of further utilizing multiparametric AFM to explore the electron transport mechanisms in biomemristors are presented.

Shi, Chenyang↗
DOE OSTI2024

Superconductivity in CH 4 and BH – 4 containing compounds derived from the high-pressure superhydrides

Inspired by the synthesis of the high-pressure Fm3m LaH 10 superconducting superhydride, systematic density functional theory (DFT) calculations are performed to study ternaries that could be derived from it by replacing two of the hydrogen atoms with boron or carbon and varying the identity of the electropositive element. Though many of the resulting alkali-metal and alkaline-earth MC 2 H 8 phases are predicted to be dynamically stable at mild pressures, their superconducting critical temperatures (T c s) are low because their metallicity results from the filling of an electride-like band. Substitution with a trivalent element leads to phases with substantial metal d- character at the Fermi level whose T c s are typically above 40 K. Here, among the MB 2 H 8 phases examined, KB 2 H 8 , RbB 2 H 8 and CsB 2 H 8 are predicted to be dynamically stable at very mild pressures, and their stability is rationalized by a DFT-Chemical Pressure analysis that elucidates the role of the M atom size. Quantum anharmonic effects strongly affect the properties of KB 2 H 8 , the highest predicted T c compound, near 10 GPa, but molecular dynamics simulations reveal it would decompose below its T c at this pressure. Nonetheless, at ca. 50 GPa KB 2 H 8 is predicted to be thermally stable with a superconducting figure of merit surpassing that of the recently synthesized LaBeH 8 .

36 MATERIALS SCIENCE↗
DOE OSTI2020

Potassium Prussian blue-coated Li-rich cathode with enhanced lithium ion storage property

With high specific capacity, the layered Li-rich Mn-based oxide (LRMO) is a promising candidate cathode material for Li-ion batteries. However, the irreversible release of Li-ions during the first charging process, instability of LRMO/electrolyte interface and relatively low ion conductivity of LRMO result in low initial Coulombic efficiency (ICE), poor cycle stability and rate performance, which prohibit its further application. Furthermore, interface engineering via additive coating is expected to effectively address these issues. Herein, we rely on potassium Prussian blue (KPB), a Li + acceptor with good ion conductivity, as a new coating material on LRMO particles. The KPB coating not only forms a protective layer on the surface of LRMO against electrolyte corrosion, but also functions as a host for Li + transport and accommodation, leading to enhanced ion conductivity and ICE of LRMO cathode. Consequently, 2 wt% KPB coated LRMO cathode achieved an initial discharge capacity of up to 281.7 mA h g -1 with an ICE of 85.69% compared to an ICE of 79.52% for the LRMO cathode without coating. The cycling and rate performance are also greatly improved as evidenced by the well maintained capacity of up to 176.8 mA h g -1 after 100 cycles at a current density of 0.5 C, compared to the limited capacity of only 135.3 mA h g -1 for the LRMO cathode without coating. Overall, this work pioneers the use of potassium Prussian blue as additive coating material to enhance performance of LRMO cathode, and we expect it to inspire the battery community with new strategies of material engineering/design toward practical application in high-energy lithium-ion batteries.

25 ENERGY STORAGE↗
DOE OSTI2020

Rooting MnO 2 into protonated g-C 3 N 4 by intermolecular hydrogen bonding for endurable supercapacitance

Composited electrode materials for energy storage typically benefit from the merits of each component but meanwhile largely suffer from the vulnerable structural integrity during repetitive cycling. Realizing the merely physical attachment in most composited structures being too weak to survive harsh cycling, we report here a facile one-step synthesis strategy with generation of chemical bonds, more specifically, intermolecular hydrogen bonds, to tightly combine each component. Here, we demonstrate this concept by designing a composite featuring MnO 2 nanorods chemically rooted into protonated g-C 3 N 4 , where the protonation of 2D g-C 3 N 4 substrate (pg-C 3 N 4 ), the nucleation/growth of MnO 2 and the formation of hydrogen bonding between pg-C 3 N 4 and MnO 2 simultaneously occur. The obtained composite, when applied for supercapacitive energy storage, yields maximum specific capacitances of 348.4 F·g -1 at a current density of 1.0 A·g -1 as well as a high retention of ~85.0% after 10000 cycles at 6.0 A·g -1 , surpassing most previously reported composited electrode materials based on either MnO 2 or g-C 3 N 4 . Finally, we expect this work to inspire the synthesis of composited electrode materials with consolidated 3D architecture for endurable energy storage performance.

25 ENERGY STORAGE↗
DOE OSTI2020

On the irreversible sodiation of tin disulfide

Tin disulfide is considered as a promising electrode material for sodium-ion batteries because of its two-dimensional layered structural characteristics allowing the intercalation of Na ions. Understanding the underlying reaction mechanisms and the decisive step of the reaction reversibility is critical for its applications. Herein, we investigate the sodiation and desodiation processes of SnS 2 by employing in situ transmission electron microscopy (TEM). After the initial intercalation reaction, a rock-salt Na y SnS 2 phase with disordering Na and Sn cations is observed, followed with a conversion reaction and an alloying reaction. Additionally, the disordering reaction occurs along <1-10> direction of pristine SnS 2 phase which is correlated with local bonding rearrangements induced by the exchange of Sn and Na cations. In-situ TEM studies and first-principles calculations indicate that the original 2D SnS 2 structure could not be recovered during desodiation. Instead, the disordered Na y SnS 2 phase is finally formed, which indicates that the irreversible disordering transition is the determining step of irreversible cycling. This work probes the structural evolution of sodiation, providing a fundamental understanding of the electrochemical properties of metal sulfides and inspiring rational designs of high performance electrodes for sodium-ion batteries.

25 ENERGY STORAGE↗
DOE OSTI2023

Fluid learning: Mimicking brain computing with neuromorphic nanofluidic devices

Relentlessly rising energy demands in computing call for rethinking hardware paradigms with energy efficiency in mind. Nature’s example—the brain—raises the question: How can these natural computers achieve remarkable feats with minimal energy compared to supercomputers? Neuromorphic computing mimics the brain’s principles, but current neuromorphic concepts using electronic components face scalability and their own power consumption challenges. A potentially revolutionary approach is emerging: computing with ion transport in water through nanochannels. This field offers energy-efficient possibilities by imitating brain-like information processing with different types of ions as carriers. Finally, the goal is to converge advanced nanoscale architectures with brain-inspired efficiency, heralding a new era of computing.

77 NANOSCIENCE AND NANOTECHNOLOGY↗
DOE OSTI2023

Sensitivity study of parameters important to Molten Salt Reactor Safety

This paper presents a molten salt reactor (MSR) design parameter sensitivity study using a nodal dynamic modelling methodology with explicitly modified point kinetics equation and Mann’s model for heat transfer. Six parameters that can impact MSR safety are evaluated. A MATLAB-Simulink model inspired by Thorcon’s 550MW th MSR is used for parameter evaluations. A safety envelope was formed to encapsulate power, maximum and minimum temperature, and temperature-induced reactivity feedback. The parameters are perturbed by ±30%. The parameters were then ranked by their subsequent impact on the considered safety envelope, which ranks acceptable parameter uncertainty. The model is openly available on GitHub.

22 GENERAL STUDIES OF NUCLEAR REACTORS↗
DOE OSTI2020

Stochasticity and robustness in spiking neural networks

Despite drawing inspiration from biological systems which are inherently noisy and variable, artificial neural networks have been shown to require precise weights to carry out the task which they are trained to accomplish. This creates a challenge when adapting these artificial networks to specialized execution platforms which may encode weights in a manner which restricts their accuracy and/or precision.Reflecting back on the non-idealities which are observed in biological systems, we investigated the effect these properties have on the robustness of spiking neural networks under perturbations to weights. First, we examined techniques extant in conventional neural networks which resemble noisy processes, and postulated they may produce similar beneficial effects in spiking neural networks. Second, we evolved a set of spiking neural networks utilizing biological non-idealities to solve a pole-balancing task, and estimated their robustness. We showed it is higher in networks using noisy neurons, and demonstrated that one of these networks can perform well under the variance expected when a hafnium-oxide based resistive memory is used to encode synaptic weights. Lastly, we trained a series of networks using a surrogate gradient method on the MNIST classification task. We confirmed that these networks demonstrate similar trends in robustness to the evolved networks. We discuss these results and argue that they display empirical evidence supporting the role of noise as a regularizer which can increase network robustness.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2021

Benchmarking the Performance of Neuromorphic and Spiking Neural Network Simulators

Software simulators play a critical role in the development of new algorithms and system architectures in any field of engineering. Neuromorphic computing, which has shown potential in building brain-inspired energy-efficient hardware, suffers a slow-down in the development cycle due to a lack of flexible and easy-to-use simulators of either neuromorphic hardware itself or of spiking neural networks (SNNs), the type of neural network computation executed on most neuromorphic systems. While there are several openly available neuromorphic or SNN simulation packages developed by a variety of research groups, they have mostly targeted computational neuroscience simulations, and only a few have targeted small-scale machine learning tasks with SNNs. Evaluations or comparisons of these simulators have often targeted computational neuroscience-style workloads. In this work, we seek to evaluate the performance of several publicly available SNN simulators with respect to non-computational neuroscience workloads, in terms of speed, flexibility, and scalability. We evaluate the performance of the NEST, Brian2, Brian2GeNN, BindsNET and Nengo packages under a common front-end neuromorphic framework. Our evaluation tasks include a variety of different network architectures and workload types to mimic the computation common in different algorithms, including feed-forward network inference, genetic algorithms, and reservoir computing. We also study the scalability of each of these simulators when running on different computing hardware, from single core CPU workstations to multi-node supercomputers. Our results show that the BindsNET simulator has the best speed and scalability for most of the SNN workloads (sparse, dense, and layered SNN architectures) on a single core CPU. However, when comparing the simulators leveraging the GPU capabilities, Brian2GeNN outperforms the others for these workloads in terms of scalability. NEST performs the best for small sparse networks and is also the most flexible simulator in terms of reconfiguration capability NEST shows a speedup of at least 2x compared to the other packages when running evolutionary algorithms for SNNs. The multi-node and multi-thread capabilities of NEST show at least 2x speedup compared to the rest of the simulators (single core CPU or GPU based simulators) for large and sparse networks. We conclude our work by providing a set of recommendations on the suitability of employing these simulators for different tasks and scales of operations. We also present the characteristics for a future generic ideal SNN simulator for different neuromorphic computing workloads.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2024

Monte–Carlo ray-tracing studies of multiplexed prismatic graphite analyzers for the cold-neutron triple-axis spectrometer at the High Flux Isotope Reactor

A modern cold triple-axis spectrometer to study quantum condensed matter systems is planned for the High Flux Isotope Reactor (HFIR) at Oak Ridge National Laboratory. Here, we describe the conceptual principles and design of a secondary spectrometer using a multiplexed, prismatic analyzer system relying on graphite crystals and inspired by the successful implementation of the Continuous Angle Multiple Energy Analysis (CAMEA) spectrometers at the Paul Scherrer Institute. This project is currently known as MANTA for Multi-Analyzer Neutron Triple-Axis. Here, we report Monte-Carlo ray-tracing simulations on a simple but realistic sample scattering kernel to further illustrate the prismatic analyzer concept’s workings, calibration, and performance. Then, we introduce a new statistical analysis approach based on the prismatic analyzer concept to improve the number of final energies measured on the spectrometer. We also study possible evolutions in the CAMEA design relevant for MANTA.

46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND ↗
DOE OSTI2021

A fitting algorithm for optimizing ion implantation energies and fluences

In this work we describe a method to automatically generate an ion implantation recipe, a set of energies and fluences, to produce a desired defect density profile in a solid using the fewest required energies. We simulate defect density profiles for a range of ion energies, fit them with an appropriate function, and interpolate to yield defect density profiles at arbitrary ion energies. Given Ν energies, we then optimize a set of Ν energy-fluence pairs to match a given target defect density profile. Finally, we find the minimum Ν such that the error between the target defect density profile and the defect density profile generated by the Ν energy-fluence pairs is less than a given threshold. Inspired by quantum sensing applications with nitrogen-vacancy centers in diamond, we apply our technique to calculate optimal ion implantation recipes to create uniform-density 1 μm surface layers of 15 N or vacancies (using 4 He).

46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND ↗
DOE OSTI2021

Feasibility of full-core pin resolved CFD simulations of small modular reactor with momentum sources

Complex flow structure interactions and heat transfer processes take place in nuclear reactor cores. Given the extreme pressure/temperature and radioactive conditions inside the core, numerical simulations offer an attractive and sometimes more feasible approach to study the related flow and heat transfer phenomena in addition to the experiments. Under the Exascale Computing Project, the full-core simulation of a small modular reactor (SMR) has been pursued coupling Computational Fluid Dynamics (CFD) and neutronics. A key aspect of the modeling of SMR fuel assemblies is the presence of spacer grids and the mixing promoted by mixing vanes or the equivalent. A reduced order methodology is adopted based on momentum sources to mimic the mixing of the vanes. The momentum sources have been carefully calibrated with detailed Large Eddy Simulations (LES) of spacer grids performed with Nek5000. Modeling the spacer grid and mixing vanes (SGMV) effect without body-fitted computational grid avoids the excessive costs in resolving the local geometric details, and thus supports the simulation to be scaled up to the full core. Besides the progress on momentum source modeling, this paper also features the first full-core pin resolved CFD simulation ever performed to the authors' knowledge. This represents a significant advancement in capability for the CFD of nuclear reactors, which will hopefully serve as an inspiration for further integrating high-fidelity numerical simulations in actual engineering designs.

22 GENERAL STUDIES OF NUCLEAR REACTORS↗
DOE OSTI2025

A hybrid surrogate modeling framework for the Digital Twin of a Fluoride-salt-cooled High-temperature Reactor (FHR)

While nuclear energy is a non-greenhouse-gas emitting energy source, expensive operational costs due to the high-level of safety requirements decreases their competitiveness in the sustainable energy market. Advanced reactor concepts paired with Digital Twins aim to increase the commercialization gains of nuclear energy by reducing operational costs, increasing reactor reliability and enhancing power generation. To support Digital Twin tasks such as real-time autonomous control, proactive maintenance monitoring or optimizing power demand operations, a fast and accurate virtual representation of the Nuclear Power Plant (NPP) is required. The computational cost of high-fidelity, physics-based models are unsuitable for real-time analysis or scalability. Here, in this work, a hybrid surrogate modeling framework is developed fora Fluoride-salt-cooled High-temperature Reactor (FHR) that leverages physics-inspired models for key reactor components and uses data-driven methods for rapid system state space prediction. The Xenon reactivity feedback model is integrated to inform the surrogate model about the reactor core and the homologous pump theory model is the basis for representing pump degradation. Using a detailed, two dimensional thermal hydraulics model to generate data on the FHR, we train a network of Vectorized Autoregressive Moving-Average with eXogenous input (VARMAX) models to predict the remaining state values. The result is a surrogate model that provides a detailed reactor state representation of 41 system states and a pump degradation analysis. The framework is applied to Load Follows profiles, yielding high accuracy and a speedup that is more than 4000x faster compared to the higher- fidelity thermal hydraulics model, enabling real-time operational intelligence and applications in long horizon predictions. While the surrogate model framework is demonstrated for the particular case of FHR, the hybrid physical/data-driven modeling approach including the network of surrogates and the underlying modularity has the potential to be applied to other physical asset systems.

Digital Twins↗
DOE OSTI2024

A new-old approach to composite scalars with chiral fermion constituents

We develop a dynamical, Lorentz invariant theory of composite scalars in configuration space consisting of chiral fermions, interacting by the perturbative exchange of a massive “gluon” of coupling g 0 and mass M 0 2 (the coloron model). The formalism is inspired by, but goes beyond, old ideas of Yukawa and the Nambu-Jona-Lasinio (NJL) model. It yields a non-pointlike internal wave-function of the bound state, ϕ ( r ) , which satisfies a Schrödinger-Klein-Gordon (SKG) equation with eigenvalue μ 2 . For super-critical coupling, g 0 2 > g 0 c 2 , we have μ 2 < 0 leading to spontaneous symmetry breaking. The binding of chiral fermions is semiclassical, not loop-level as in NJL. The mass scale is determined by the interaction as in NJL. We mainly focus on the short-distance, large M 0 2 limit, yielding an NJL pointlike interaction, but the bound state internal wave-function, ϕ ( r → ) , remains spatially extended and dilutes ϕ ( 0 ) . This leads to power-law suppression of the induced Yukawa and quartic couplings and requires radically less fine-tuning of a hierarchy than does the NJL model. We include a discussion of loop corrections of the theory. A realistic top condensation model appears possible.

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗