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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 235 records · Page 13
All sourcesNASA NTRSDOE OSTI
DOE OSTI2022

Designing interphases for practical aqueous zinc flow batteries with high power density and high areal capacity

Aqueous zinc flow batteries (AZFBs) with high power density and high areal capacity are attractive, both in terms of cost and safety. A number of fundamental challenges associated with out-of-plane growth and undesirable side reactions on the anode side, as well as sluggish reaction kinetics and active material loss on the cathode side, limit practical deployment of these batteries. We investigated artificial interphases created using a simple electrospray methodology as a strategy for addressing each of these challenges. The effectiveness of the electrospray interphases in full cell zinc-iodine flow batteries was evaluated and reported; it is possible to simultaneously achieve high power density [115 milliwatts per square centimeter (mW/cm 2 )] and high areal capacity [25 milliampere hour per square centimeter (mA·hour/cm 2 )]. Last, we extended it to aqueous zinc-bromine and zinc-vanadium flow batteries of contemporary interest. It is again found that high power density (255 and 260 mW/cm 2 , respectively) and high areal capacity (20 mA·hour/cm 2 ) can be simultaneously achieved in AZFBs.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
NASA NTRS1994

The response to gravity is correlated with the number of statoliths in Chara rhizoids

In contrast to higher plants, Chara rhizoids have single membrane-bound compartments that appear to function as statoliths. Rhizoids were generated by germinating zygotes of Chara in either soil water (SW) medium or artificial pond water (APW) medium. Differential-interference-contrast microscopy demonstrated that rhizoids form SW-grown plants typically contain 50 to 60 statoliths per cell, whereas rhizoids from APW-grown plants contain 5 to 10 statoliths per cell. Rhizoids from SW are more responsive to gravity than rhizoids from APW because (a) SW rhizoids were oriented to gravity during vertical growth, whereas APW rhizoids were relatively disoriented, and (b) curvature of SW rhizoids was 3 to 4 times greater throughout the time course of curvature. The growth rate of APW rhizoids was significantly greater than that of SW-grown rhizoids. This latter result suggests that APW rhizoids are not limited in their ability for gravitropic curvature by growth and that these rhizoids are impaired in the early stages of gravitropism (i.e. gravity perception). Plants grown in APW appeared to be healthy because of their growth rate and the vigorous cytoplasmic streaming observed in the rhizoids. This study is comparable to earlier studies of gravitropism in starch-deficient mutants of higher plants and provides support for the role of statoliths in gravity perception.

Non-NASA Center↗
NASA NTRS2019

Evaluating Precipitation Features and Rainfall Characteristics in a Multi-scale Modeling Framework

Cloud and precipitation systems over the tropics and subtropics are simulated with a multi-scale modeling framework (MMF) and compared against the TRMM radar precipitation features (RPFs) product. A methodology, in close analogy to the TRMM RPFs, is developed to analyze simulated cloud precipitating structures from the embedded two-dimensional cloud-resolving models (CRMs) within an MMF. Despite the two-dimensionality of the CRMs, the simulated RPFs population distribution, and horizontal and vertical structure are in good agreement with TRMM observations. However, some deficits are also found in the model simulations. The model tends to overestimate mean convective precipitation rates for RPFs with a size less than 100 km, contributing to the excessive precipitation biases in the warm pool and western Pacific, western and northern India Ocean, and eastern Pacific commonly found in most MMFs. For large features with a size greater than 150 km, both convective and stratiform rain rates are underestimated. The distribution of maximum radar echo top heights as a function of RPF size is well simulated except the model tends to underestimate the occurrence frequency of maximum heights greater than 15 km. The maximum echo top heights for convective cells embedded within large RPFs with a size greater than 150 km are also underestimated. The cyclic lateral boundary with a limited model domain generates artificial occurrences for RPFs with a size close to the model domain size, producing a significant contribution to the total rainfall due to their sizes. This cyclic lateral boundary effect can be easily identified and quantified in both probability and cumulative distribution functions of RPFs. The geophysical distribution of the population of the largest RPFs in the control experiment shows they are mainly located in the Subtropics but also partially contribute to the common MMF biases of excessive precipitation in the Tropics. Sensitivity experiments using CRMs with different domain sizes and different grid spacings show larger domains (higher resolution) tend to shift the RPFs distribution to large (small) sizes. The cyclic lateral boundary biases increase as CRM domain size decreases. The impacts of model horizontal and vertical resolution on simulated convective systems are also investigated.

Chern, Jiun-Dar↗
DOE OSTI2024

Manipulating the diffusion energy barrier at the lithium metal electrolyte interface for dendrite-free long-life batteries

Abstract Constructing an artificial solid electrolyte interphase (SEI) on lithium metal electrodes is a promising approach to address the rampant growth of dangerous lithium morphologies (dendritic and dead Li 0 ) and low Coulombic efficiency that plague development of lithium metal batteries, but how Li + transport behavior in the SEI is coupled with mechanical properties remains unknown. We demonstrate here a facile and scalable solution-processed approach to form a Li 3 N-rich SEI with a phase-pure crystalline structure that minimizes the diffusion energy barrier of Li + across the SEI. Compared with a polycrystalline Li 3 N SEI obtained from conventional practice, the phase-pure/single crystalline Li 3 N-rich SEI constitutes an interphase of high mechanical strength and low Li + diffusion barrier. We elucidate the correlation among Li + transference number, diffusion behavior, concentration gradient, and the stability of the lithium metal electrode by integrating phase field simulations with experiments. We demonstrate improved reversibility and charge/discharge cycling behaviors for both symmetric cells and full lithium-metal batteries constructed with this Li 3 N-rich SEI. These studies may cast new insight into the design and engineering of an ideal artificial SEI for stable and high-performance lithium metal batteries.

25 ENERGY STORAGE↗
DOE OSTI2026

AI-assisted rapid crystal structure generation towards a target local environment

In material design, traditional crystal structure prediction approaches are expensive as they require extensive structural sampling through expensive energy minimization methods. Emerging artificial intelligence (AI) generative models have shown great promise in rapidly generating realistic crystals, but they typically handle only a few tens of atoms per unit cell. To overcome this limitation, we introduce a symmetry-informed approach, the Local Environment Geometry-Oriented Crystal Generator (LEGO-xtal). Our method generates initial structures using AI models trained on an augmented dataset, and then optimizes them using structure descriptors rather than energy-based optimization. We demonstrate its effectiveness by expanding from 25 known low-energy sp2 carbon allotropes to over 1700, all within 0.5 eV/atom of the ground-state energy of graphite. This framework offers a generalizable strategy for the targeted design of materials with modular building blocks, such as metal-organic frameworks and battery materials.

Ridwan, Osman Goni [University of North Carolina a↗
NASA NTRS1981

A development of grid generation procedure for multicomponent aerodynamic configuration

Two approaches for solving the transonic flow in a multi-block grid were explored. The first approach examines a method involving "zonal decomposition" wherein block boundaries are treated as true boundary surfaces separating interfacing grids. The issues investigated involve techniques for matching solutions at a block boundary. A feasibility study was completed and the results are presented. The second approach involves overlapping grids for differencing across a block boundary near an artificially induced coordinate singularity occurring at a fictitious corner. This approach selects a set of neighboring nodes for the fictitious corner such that the resulting physical cells for a node are topologically the same as any other node on the airfoil surface.

Chen, H. C.↗
DOE OSTI2024

A deep learning approach to fast analysis of collective Thomson scattering spectra

Fast analysis of collective Thomson scattering ion acoustic wave features using a deep convolutional neural network model is presented. The network was trained from spectra to predict the plasma parameters, including ion velocities, population fractions, and ion and electron temperatures. A fully kinetic particle-in-cell simulation was used to model a laboratory astrophysics experiment and simulate a diagnostic image of the ion acoustic wave feature. Network predictions were compared with Bayesian inference of the plasma model parameters for both the simulated and experimentally measured images. Both approaches were fairly accurate predicting the simulated image and the network predictions matched a good portion of the Bayesian results for the experimentally measured image. The Bayesian approach is more robust to noise and motivates future work to train deep learning models with realistic noise. The advantage of the deep learning model is making thousands of predictions in a few hundred milliseconds, compared to a few seconds to minutes per prediction for the optimization and Bayesian approaches presented here. The results demonstrate promising capabilities of deep learning models to analyze Thomson data orders of magnitude faster than conventional methods when using the neural network for standalone analysis. If more rigorous analysis is needed, neural network predictions can be used to quickly initialize other optimization methods and increase chances of success. This is especially useful when the dataset becomes very large or highly dimensional and manually refining initial conditions for the entire dataset are no longer tractable.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗
DOE OSTI2024

The importance of cycle-by-cycle data in performing rapid battery technology development and validation

Lithium-ion battery (LiB) technology is playing a crucial role in transforming the predominantly fossil fuel-based transportation and stationary storage sectors to achieve a low-carbon economy. Rapid innovation in the LiB materials to electrode to cell design is happening to satisfy the performance, life, and safety metrics required by those myriads of applications. Lately, advanced analytics, such as machine-learning or artificial intelligence (ML/AI) techniques, are being used more frequently to aid in expedited LiB technology development, performance validation, and life prediction. The success of these techniques often relies on a large volume of well-defined and high-quality battery test data. On the other hand, most battery developers and research and development (R&D) communities are still following a classical approach to develop batteries, which is running calendar- and/or cycle-aging tests, performing reference performance tests (RPTs), and conducting post-mortem analyses periodically without paying attention to the wealth of data often not collected during the calendar or cycle life aging tests. This sparse data collection approach is time- and resource-intensive, requiring data capture and evaluation of months to years of RPT data to diagnose accurate battery state of performance, health, and safety. Even so, the underlying aging modes and mechanisms can be missed. If collected properly, battery test data during cycling or calendaring can be efficiently combined with ML/AI techniques to create powerful tools in the rapid diagnosis of battery state of performance, health, and safety along with insights into underlying aging modes and mechanisms. In this report, we discuss the importance of effective cycle-by-cycle (CBC) data collection with example case studies. Within a reasonable timeframe, RPT data are often inadequate in capturing many of the crucial battery aging dynamics, which often predominantly show up in CBC test data. Finally, we also show examples of ML/AI techniques that use CBC data in rapid diagnosis and projection of LiB state of health (SOH) to motivate the scientific community in collecting and using CBC data to facilitate expeditious technology development and validation.

25 ENERGY STORAGE↗
DOE OSTI2020

Maximized lateral inhibition in paired magnetic domain wall racetracks for neuromorphic computing

Lateral inhibition is an important functionality in neuromorphic computing, modeled after the biological neuron behavior that a firing neuron deactivates its neighbors belonging to the same layer and prevents them from firing. In most neuromorphic hardware platforms lateral inhibition is implemented by external circuitry, thereby decreasing the energy efficiency and increasing the area overhead of such systems. Recently, the domain wall—magnetic tunnel junction (DW-MTJ) artificial neuron is demonstrated in modeling to be intrinsically inhibitory. Without peripheral circuitry, lateral inhibition in DW-MTJ neurons results from magnetostatic interaction between neighboring neuron cells. However, the lateral inhibition mechanism in DW-MTJ neurons has not been studied thoroughly, leading to weak inhibition only in very closely-spaced devices. This work approaches these problems by modeling current- and field- driven DW motion in a pair of adjacent DW-MTJ neurons. We maximize the magnitude of lateral inhibition by tuning the magnetic interaction between the neurons. The results are explained by current-driven DW velocity characteristics in response to an external magnetic field and quantified by an analytical model. Dependence of lateral inhibition strength on device parameters is also studied. Finally, lateral inhibition behavior in an array of 1000 DW-MTJ neurons is demonstrated. Our results provide a guideline for the optimization of lateral inhibition implementation in DW-MTJ neurons. Finally, with strong lateral inhibition achieved, a path towards competitive learning algorithms such as the winner-take-all are made possible on such neuromorphic devices.

77 NANOSCIENCE AND NANOTECHNOLOGY↗
NASA NTRS1991

Entropy-Based Approach To Nonlinear Stability

NASA technical memorandum suggests schemes for numerical solution of differential equations of flow made more accurate and robust by invoking second law of thermodynamics. Proposes instead of using artificial viscosity to suppress such unphysical solutions as spurious numerical oscillations and nonlinear instabilities, one should formulate equations so that rate of production of entropy within each cell of computational grid be nonnegative, as required by second law.

Merriam, Marshal L.↗
NASA NTRS2004

New Gas Polarographic Hydrogen Sensor

Polarography is the measurement of the current that flows in solution as a function of an applied voltage. The actual form of the observed polarographic current depends upon the manner in which the voltage is applied and on the characteristics of the working electrode. The new gas polarographic H2 sensor shows a current level increment with concentration of the gaseous H2 similar to those relating to metal ions in liquid electrolytes in well-known polarography. This phenomenon is caused by the fact that the diffusion of the gaseous H2 through a gas diffusion hole built in the sensor is a rate-determining step in the gaseous-hydrogen sensing mechanism. The diffusion hole artificially limits the diffusion of the gaseous H2 toward the electrode located at the sensor cavity. This gas polarographic H2 sensor. is actually an electrochemical-pumping cell since the gaseous H2 is in fact pumped via the electrochemical driving force generated between the electrodes. Gaseous H2 enters the diffusion hole and reaches the first electrode (anode) located in the sensor cavity to be transformed into an H+ ions or protons; H+ ions pass through the electrolyte and reach the second electrode (cathode) to be reformed to gaseous H2. Gas polarographic 02 sensors are commercially available; a gas polarographic 02 sensor was used to prove the feasibility of building a new gas polarographic H2 sensor.

Dominguez, Jesus A.↗
NASA NTRS2006

H2 Detection via Polarography

Polarography is the measurement of the current that flows in solution as a function of an applied voltage. The actual form of the observed polarographic current depends upon the manner in which the voltage is applied and on the characteristics of the working electrode. The new gas polarographic H2 sensor shows a current level increment with concentration of the gaseous H2 similar to those relating to metal ions in liquid electrolytes in well-known polarography. This phenomenon is caused by the fact that the diffusion of the gaseous H2 through a gas diffusion hole built in the sensor is a rate-determining step in the gaseous-hydrogen sensing mechanism. The diffusion hole artificially limits the diffusion of the gaseous H2 toward the electrode located at the sensor cavity. This gas polarographic H2 sensor is actually an electrochemical-pumping cell since the gaseous H2 is in fact pumped via the electrochemical driving force generated between the electrodes. Gaseous H2 enters the diffusion hole and reaches the first electrode (anode) located in the sensor cavity to be transformed into an H ions or protons; H ions pass through the electrolyte and reach the second electrode (cathode) to be reformed to gaseous H2. Gas polarographic O2 sensors are commercially available; a gas polarographic O2 sensor was used to prove the feasibility of building a new gas polarographic H2 sensor.

Dominquez, Jesus↗
DOE OSTI2022

Understanding the surface wave characteristics using 2D particle-in-cell simulation and deep neural network

Here, the characteristics of the surface waves along the interface between a plasma and a dielectric material have been investigated using kinetic particle-in-cell simulations. A microwave source of GHz frequency has been used to trigger the surface wave in the system. The outcome indicates that the surface wave gets excited along the interface of plasma and the dielectric tube and appears as light and dark patterns in the electric field profiles. The dependency of radiation pressure on the dielectric permittivity and supplied input frequency has been investigated. Further, we assessed the capabilities of neural networks to predict the radiation pressure for a given system. The proposed deep neural network model is aimed at developing accurate and efficient data-driven plasma surface wave devices.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗
DOE OSTI2023

An Interface–Type Memristive Device for Artificial Synapse and Neuromorphic Computing

Interface-type (IT) metal/oxide Schottky memristive devices have attracted considerable attention over filament-type (FT) devices for neuromorphic computing because of their uniform, filament-free, and analog resistive switching (RS) characteristics. The most recent IT devices are based on oxygen ions and vacancies movement to alter interfacial Schottky barrier parameters and thereby control RS properties. However, the reliability and stability of these devices have been significantly affected by the undesired diffusion of ionic species. Herein, a reliable interface-dominated memristive device is demonstrated using a simple Au/Nb-doped SrTiO 3 (Nb:STO) Schottky structure. The Au/Nb:STO Schottky barrier modulation by charge trapping and detrapping is responsible for the analog resistive switching characteristics. Because of its interface-controlled RS, the proposed device shows low device-to-device, cell-to-cell, and cycle-to-cycle variability while maintaining high repeatability and stability during endurance and retention tests. Furthermore, the Au/Nb:STO IT memristive device exhibits versatile synaptic functions with an excellent uniformity, programmability, and reliability. A simulated artificial neural network with Au/Nb:STO synapses achieves a high recognition accuracy of 94.72% for large digit recognition from MNIST database. These results suggest that IT resistive switching can be potentially used for artificial synapses to build next-generation neuromorphic computing.

36 MATERIALS SCIENCE↗
DOE OSTI2020

Potentially adaptive SARS-CoV-2 mutations discovered with novel spatiotemporal and explainable AI models

Abstract Background A mechanistic understanding of the spread of SARS-CoV-2 and diligent tracking of ongoing mutagenesis are of key importance to plan robust strategies for confining its transmission. Large numbers of available sequences and their dates of transmission provide an unprecedented opportunity to analyze evolutionary adaptation in novel ways. Addition of high-resolution structural information can reveal the functional basis of these processes at the molecular level. Integrated systems biology-directed analyses of these data layers afford valuable insights to build a global understanding of the COVID-19 pandemic. Results Here we identify globally distributed haplotypes from 15,789 SARS-CoV-2 genomes and model their success based on their duration, dispersal, and frequency in the host population. Our models identify mutations that are likely compensatory adaptive changes that allowed for rapid expansion of the virus. Functional predictions from structural analyses indicate that, contrary to previous reports, the Asp 614 Gly mutation in the spike glycoprotein (S) likely reduced transmission and the subsequent Pro 323 Leu mutation in the RNA-dependent RNA polymerase led to the precipitous spread of the virus. Our model also suggests that two mutations in the nsp13 helicase allowed for the adaptation of the virus to the Pacific Northwest of the USA. Finally, our explainable artificial intelligence algorithm identified a mutational hotspot in the sequence of S that also displays a signature of positive selection and may have implications for tissue or cell-specific expression of the virus. Conclusions These results provide valuable insights for the development of drugs and surveillance strategies to combat the current and future pandemics.

59 BASIC BIOLOGICAL SCIENCES↗
NASA NTRS1975

L-phase variants of Agromyces ramosus

Earlier results suggested that Agromyces ramosus possibly might exist naturally in soil as a cell-wall-defective form. The purpose of the present study was to test this hypothesis by determining whether the laboratory-adapted strains of A. ramosus could be artificially induced into the L-phase and, if so, to examine some parameters affecting induction and the stability of the L-forms. The hypothesis was also tested by attempting to revert the laboratory L-phase strains by subjecting them to the technique originally used for isolation of the bacterial form from soil. It is shown that A. ramosus is easily induced into the L-phase by growing it on an agar media containing low levels of penicillin or glycine. The L-forms are found to be stable after initial contact with the inducing agent and to be unable to be reverted to the bacterial form. However, this lack of reversion does not completely negate the hypothesis that L-forms might occur in nature, because it is possible that L-forms existing in the natural state are less stable than those found in the laboratory where there is little selective pressure toward reversion.

Horwitz, A. H.↗
DOE OSTI2023

Parasitic Effects in Impedance Spectrum of PEM Water Electrolysis Cells: Case Study of High-Frequency Inductive Effects

Electrochemical impedance spectroscopy (EIS) is a powerful tool to characterize and distinguish electrochemical, electrical, and diffusive processes in an electrolysis cell. The EIS response with small impedance (mΩ) depends on components and materials of the cell, but often also on the electrical setup, cables, and connectors, leading to incorrect conclusions about the performance of the electrolysis cells. These parasitic effects are assessed via a short-circuit measurement and confirmed to be external from the membrane electrode assembly (MEA). In the setup, the inductive characteristic is described by a modified inductive reactance and must be included in the equivalent circuit model (ECM) which is fitting the cell EIS spectra in operation (in situ). If points of the spectra showing inductive characteristic (below x-axis in Nyquist plot) are excluded from the ECM evaluation, an artificially increased ohmic resistance at high frequencies is obtained. In addition, considerations on other components of the ECM can be misleading and significantly incorrect. The only way to avoid that inductive behavior caused by the external setup is incorrectly assigned to the ECM describing properties of the MEA is to include the inductive effects in the ECM analysis of in situ EIS measurements.

30 DIRECT ENERGY CONVERSION↗
NASA NTRS1987

Real time AI expert system for robotic applications

A computer controlled multi-robot process cell to demonstrate advanced technologies for the demilitarization of obsolete chemical munitions was developed. The methods through which the vision system and other sensory inputs were used by the artificial intelligence to provide the information required to direct the robots to complete the desired task are discussed. The mechanisms that the expert system uses to solve problems (goals), the different rule data base, and the methods for adapting this control system to any device that can be controlled or programmed through a high level computer interface are discussed.

Follin, John F.↗