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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 1,405 records · Page 78

All sources NASA NTRS DOE OSTI

DOE OSTI2020

Rate-Dependent Aging Resulting from Fast Charging of Li-Ion Cells

The mass electrification of personal and service vehicles is reliant on the ability of battery packs to undergo extreme fast recharging. A central challenge is that the repeated exposure to high currents can trigger and/or accelerate mechanisms of performance degradation, such as lithium deposition, mechanical damage of active material particles, and impedance rise. Here, we investigate whether constraining high-rate charging to limited state-of-charge (SOC) ranges can mitigate these aging processes. Our experiments map the boundaries of lithium plating conditions, and also indicate that permanent losses of electrode capacity appear to be a function of cycle number, but not charging rate. Interestingly, we show that impedance rise appears to depend on charging rate alone, and not on the amount of charge passed at each cycle. This observation suggests that limiting fast charging to a narrow SOC range would only delay cell aging, but not fully prevent it from happening. We identify oxide-particle cracking as the likely mechanism for this impedance rise, suggesting that particle and electrode design are essential to enable fast charging. The findings of this work can inform the development of fast-charging protocols that are less damaging to cell health.

25 ENERGY STORAGE↗

DOE OSTI2021

Effect of Catalyst and Catalyst Layer Composition on Catalyst Support Durability

Polymer electrolyte membrane fuel cells (PEMFCs) are efficient, zero-emission engines for the automotive sector. However, cost and durability are major barriers for the commercialization of PEMFCs. Degradation of the carbon support in the cathode catalyst layer due to high potential excursions caused by unmitigated events like H 2 starvation, or start-up/shutdown are still a major durability issue in PEMFCs. Catalyst support durability was studied by accelerated stress tests (ASTs) that included repeated potential cycling from 1.0 to 1.5 V. The effect of catalyst layer composition on carbon corrosion was studied using membrane electrode assemblies with different catalysts (Pt, PtCo), catalyst composition (catalyst wt.%, supports), ionomer composition (loading, and equivalent weights). The corrosion of the carbon support is similar between Pt and PtCo catalysts. However, the performance degradation rate is higher for the alloy catalysts due to differences in catalyst particle size and the transition metal’s leaching accelerating the performance degradation. The carbon loss is lower for catalysts with lower initial carbon loading (or higher catalyst wt.%), which is better for durability. However, the cumulative loss of carbon is identical for electrodes with the same catalyst support irrespective of catalyst and ionomer composition in the catalyst layer.

25 ENERGY STORAGE↗

DOE OSTI2023

Electrochemical Deposition with Redox Replacement of Lanthanum with Uranium in Molten LiCl-KCl

Electrochemical recovery of dilute concentrations of actinides from spent nuclear fuel would reduce the longevity of storing high-level nuclear waste. Electrochemical deposition with redox replacement (EDRR) is used in a molten salt medium for the selective electrochemical recovery of uranium in the presence of excess concentrations of lanthanum. In each EDRR cycle, after a short electrodeposition pulse, the deposited lanthanum is spontaneously replaced by uranium at open circuit. After repeated cycles, uranium metal was obtained on a tungsten electrode immersed in LiCl-KCl melt that contained 1 wt.% lanthanum chloride – 0.15 wt.% uranium 4+ chloride. Here, scanning electron microscopy and energy dispersive X-ray spectroscopy analysis revealed uranium particles approximately 0.5 - 1 μm with well-defined rectangular shapes; and with 20 – 60 times more uranium recovered on the surface of the electrode than lanthanum.

38 RADIATION CHEMISTRY, RADIOCHEMISTRY, AND NUCLEA↗

DOE OSTI2023

Application of the Solute-Solvent EMF Cell to Measure Activity of NiF 2 in Molten FLiNaK

Electromotive force (emf) measurements made using a combination of solute- and solvent-based electrodes were used to determine the activity of NiF 2 in molten FLiNaK eutectic at 823 K across a concentration range of x NiF2 = 5.2 × 10 –4 –1.0 × 10 –2 . The solute emf values were measured using electrodes consisting of Ni wires immersed in FLiNaK with dissolved NiF 2 contained in graphite crucibles. The measured emf values were then converted to the FLiNaK Eutectic Potassium Electrode (FEKE) potential and used to quantify the activity of dissolved NiF 2 . This quantification was based upon comparative measurements of a reference solvent electrode consisting of a K-Bi alloy immersed in pure FLiNaK contained in a boron nitride crucible and a solute electrode. Short cell lives were characteristic of the measurements due to the corrosive nature of the fluoride salts. Quantifying the activity of NiF 2 will improve the utility of Ni 2+ /Ni reference electrodes in molten fluoride salts, which are notoriously difficult electrolytes to work with because of their reactivity. In conclusion, this work demonstrates the general nature of the solute-solvent approach as a repeatable, easily employed method for measuring the activity values of electroactive species in a variety of molten salts to improve understanding of the electroactive species behavior in these systems.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗

DOE OSTI2022

Seven days of ischemic preconditioning augments hypoxic exercise ventilation and muscle oxygenation in recreationally trained males

This investigation sought to assess whether single or repeated bouts of ischemic preconditioning (IPC) could improve oxyhemoglobin saturation ([Formula: see text]) and/or attenuate reductions in muscle tissue saturation index (TSI) during submaximal hypoxic exercise. Fifteen healthy young men completed submaximal graded exercise under four experimental conditions: 1) normoxia (NORM), 2) hypoxia (HYP) [oxygen fraction of inspired air ([Formula: see text]) = 0.14, ∼3,200 m], 3) hypoxia preceded by a single session of IPC (IPC1-HYP), and 4) hypoxia preceded by seven sessions of IPC, one a day for 7 consecutive days (IPC7-HYP). IPC7-HYP heightened minute ventilation (V̇e) at 80% HYP peak cycling power output ( W peak ) (+10.47 ± 3.35 L·min −1 , P = 0.006), compared with HYP, as a function of increased breathing frequency. Both IPC1-HYP (+0.17 ± 0.04 L·min −1 , P < 0.001) and IPC7-HYP (+0.16 ± 0.04 L·min −1 , P < 0.001) elicited greater oxygen consumption (V̇o 2 ) across exercise intensities compared with NORM, whereas V̇o 2 was unchanged with HYP alone. [Formula: see text] was unchanged by either IPC condition at any exercise intensity, yet the reduction of muscle TSI during resting hypoxic exposure was attenuated by IPC7-HYP (+9.9 ± 3.6%, P = 0.040) compared with HYP, likely as a function of reduced local oxygen extraction. Considering all exercise intensities, IPC7-HYP attenuated reductions of TSI with HYP (+6.4 ± 1.8%, P = 0.001). Seven days of IPC heightens ventilation, posing a threat to ventilatory efficiency, during high-intensity submaximal hypoxic exercise and attenuates reductions in hypoxic resting and exercise muscle oxygenation in healthy young men. A single session of IPC may be capable of modulating hypoxic ventilation; however, our present population was unable to demonstrate this with certainty.

Physiology↗

DOE OSTI2024

Insights into posttranslational regulation of skeletal muscle contractile function by the acetyltransferases, p300 and CBP

Here, mice with skeletal muscle-specific and inducible double knockout of the lysine acetyltransferases, p300 (E1A binding protein p300) and CBP (cAMP-response element-binding protein binding protein), referred to as i-mPCKO, demonstrate a dramatic loss of contractile function in skeletal muscle and ultimately die within 7 days. Given that many proteins involved in ATP generation and cross-bridge cycling are acetylated, we investigated whether these processes are dysregulated in skeletal muscle from i-mPCKO mice and, thus, whether they could underlie the rapid loss of muscle contractile function. Just 4–5 days after inducing knockout of p300 and CBP in skeletal muscle from adult i-mPCKO mice, there was ~90% reduction in ex vivo contractile function in the extensor digitorum longus (EDL) and a ~65% reduction in in vivo ankle dorsiflexion torque, as compared with wild type (WT; i.e., Cre negative) littermates. Despite this profound loss of contractile force in i-mPCKO mice, there were no genotype-driven differences in fatigability during repeated contractions, nor were there genotype differences in mitochondrial-specific pathway enrichment of the proteome, intermyofibrillar mitochondrial volume, or mitochondrial respiratory function. As it relates to cross-bridge cycling, remarkably, the overt loss of contractile function in i-mPCKO muscle was reversed in permeabilized fibers supplied with exogenous Ca 2+ and ATP, with active tension being similar between i-mPCKO and WT mice, regardless of Ca 2+ concentration. Actin-myosin motility was also similar in skeletal muscle from i-mPCKO and WT mice. In conclusion, neither mitochondrial abundance/function, nor actomyosin cross-bridge cycling, are the underlying driver of contractile dysfunction in i-mPCKO mice.

59 BASIC BIOLOGICAL SCIENCES↗

DOE OSTI2020

Internal Standards for Quantitative Analysis of Chemical Warfare Agents by the GC/MS Method: Nerve Agents

General conditions and requirements for an internal standard useful in the determination of chemical warfare agents (CWAs) by the method of gas chromatography coupled with mass detection (GC/MS) were defined. The determination is based on a GC/MS analysis of a mixture of a CWA with an internal standard, conversion of the TIC chromatogram to a chromatogram extracted at a particular m / z ratio, and calculation of the CWA concentration from the internal standard concentration, response factor, and chromatographic peak areas. Available internal standards were identified, and they were verified for seven organophosphorus nerve-paralysing agents. Corresponding response factors were determined as a ratio of gradients of the linear functions of the peak area and compound concentration. Linearity, repeatability, and accuracy of the measurements were evaluated. The determination can be performed on all GC/MS systems of the Fire Rescue Service of the Czech Republic (FRS), where no CWA standards are available.

Capoun, Tomas↗

DOE OSTI2020

m2p (Monomers to Polymers)

The software is Python-based code that takes monomers, represented as SMILES, and "reacts" them together to form long polymer chains, represented as SMILES, based on known polymer chemistries. The software is used in a machine-learning pipeline where a database and machine-learning approaches are used to predict polymer properties base on molecular structure. This software is necessary to represent the polymer chain, instead of simple repeat units, as the software statistically represents different molecular architectures that occur during reactions (e.g. head-to-head, head-to-tail, and tail-to-tail). It is capable of building polymer chains with five different chemistries: vinyl/olefin, polyamide, polycarbonate, polyester, and polyimides. The user is able to specify different monomer pairings and degree of polymerization for different polymer chains.

Wilson, Nolan↗

DOE OSTI2020

Hydra Network Automatic Configuration (Hydra Autoconfig) v1

Hydra Autoconfig is an algorithm and software program to automatically configure a resilient network of independent ASICs that are capable of being connected as a Hydra network. The Hydra network itself is described in an invention disclosure titled: "Ad-Hoc Networks of Readout ASICs for Reliable Detector Instrumentation" (2020-091). The Hydra Autoconfig software works by first configuring a Hydra node. Then it recursively builds a Hydra network by attempting to link to each available upstream node in succession, keeping links that are verified. It then repeats on each newly generated node until no possible links remain. Once it is so configured, a reliable Hydra network of ASICs remains that can be reconfigured later to respond to damage or malfunction of one or more of the constituent ASICs.

Grace, Carl↗

DOE OSTI2021

DEPRECATED AI-Batt-OS (Autonomous Identification of Battery Life Models - Open Source) [SWR 21-17]

DEPRECATED. This repository was archived by the owner on Jun 30, 2026. It is now read-only. Open source implementation of some of the methods utilized by AI-Batt, a battery lifetime modeling and analysis toolkit provided by the National Laboratory of the Rockies (NLR). This software demonstrates the use of bi-level optimization and symbolic regression techniques to semi-autonomously identify algebraic models predicting the capacity fade of lithium-ion batteries during calendar aging. Modeling the degradation of batteries is a complex task, due to the difficulty in separating the time-dependent and time-independent factors impacting cell level degradation, across multiple data series with different numbers of measurements and/or data quality. Bi-level optimization enables model parameters to be optimized to either the entire data set or to individual data series, allowing statistical disambiguation of global behaviors (data series independent) and local behaviors (data series dependent). Symbolic regression is used to automatically search for optimal low-dimesional models predicting the variation of locally optimized parameters versus time-independent experimental variables from millions of possible models, resulting in a more accurate and repeatable model identification process than is possible by a manual search. The provided tools also implement cross-validation and bootstrap resampling schemes, empowering statistical model comparison/selection and quantification of model uncertainties. An example script replicates the results from the manuscript "Challenging Practices of Algebraic Battery Life Models through Statistical Validation and Model Identification via Machine-Learning", submitted to ECS. All code is written in MATLAB. Requires the Statistics and Machine Learning Toolbox. Contact Dr. Paul Gasper at Paul.Gasper@nlr.gov for any questions.

Gasper, Paul↗

DOE OSTI2021

HyPerPy (Hydrogen Extraction and Parabolic Trough Plant Performance Models using Python) [SWR-21-53]

The HyPerPy package consists of three scripts: 1) Model for Hydrogen Tracking in Parabolic Trough Power Plants, 2) Model for Receiver Performance, and 3) Model for Hydrogen Extraction Process. The power plant model (1) tracks hydrogen generation and transport within the circulating heat transfer fluid (HTF) of the power plant. This script is a transient, initial value simulation, in which the hydrogen concentration in the circulating HTF is 0 moles per cubic meter everywhere at time 0 seconds. Hydrogen concentration is calculated at discrete locations within the circulating HTF with 4-second time resolution. During each time step, the change in hydrogen concentration due to hydrogen generation and permeation is calculated for each location according to the local HTF temperature, vessel or piping properties, hydrogen concentration and partial pressure. This model predicts hourly hydrogen concentrations for typical operating days in the spring, summer, fall, and winter seasons. Data is used to create hourly mappings of hydrogen concentrations for a typical operating year. The receiver model (2) uses a 1-hour time step to estimate getter loading and annulus pressure. For each time step, the model uses HTF and ambient temperature data to estimate absorber tube, bellows, and getter temperatures for the time step. In addition, the model uses hourly HTF hydrogen concentrations that are generated by the power plant model (1), and annulus hydrogen pressure from the previous time step. With these data, the model calculates the moles of hydrogen permeating across the absorber tube and bellows during the time step. The net change in moles hydrogen is added or subtracted from the getter loading for the previous time step, and the hydrogen pressure is re-calculated based on getter loading and temperature. The model uses this algorithm to simulate hydrogen permeation and loading 24 hours per day, 365 days per year using seasonal temperature data. The model repeats these calculations for 25 years to create a mapping of receiver getter loading and annulus hydrogen pressure for four seasons of each year. The model for hydrogen extraction (3) estimates hydrogen extraction rates for a specific separation module configuration. The rate depends primarily on membrane area, vacuum pump performance, headspace gas flowrate to the membrane, and headspace gas hydrogen partial pressure. This model has two versions. The steady-state version predicts hydrogen extraction rates when the module is operating in separation mode. The dynamic version predicts hydrogen transfer through the membrane when the module is operating in sensor mode. The steady-state version is used with the plant model (1) to predict hydrogen partial pressures in the power plant when the extraction process is operating.

Glatzmaier, Gregory↗

DOE OSTI2022

VizBrick

Brick (https://brickschema.org/) is a unified metadata schema to address the problem of building data standardization. Creating Brick models for building datasets means that the contents of the datasets are semantically described using the standard terms defined in the Brick ontology, and it will enable the benefits of data standardization, without having to recollect or reorganize the data. The challenge is that building brick models for building datasets leads to repeated manual trial and error processes, which can be time-consuming. VizBrick is a tool with a graphic/Web-based user interface that can assist users to create Brick models visually and interactively without having to understand the Resource Description Framework (RDF) syntax. VizBrick contains a web server that renders VizBrick web interface pages for browsers. The web server utilizes software components that (1) provide Brick ontology entity mapping to data column suggestions to users so that they can efficiently create their model; (2) provide keyword/Metadata-based search capability for easy find of relevant brick concepts and relations to their data columns

Lee, Sangkeun [Oak Ridge National Lab. (ORNL), Oak↗

DOE OSTI2023

Training Data Regarding Optimal Power Flow For Efficient Machine Learning

The code is intended to take the data outputted by the MATPOWER OPF solver tools and restructures the data in a way that is best suited for machine learning. The variables are read and the desired values are taken and added to an array in the correct format. This array is then converted into a python array for future use. The code also utilizes MATPOWER's ability to construct different power flow scenarios and repeat them a chosen number of times. Each iteration will add a new line to the formatted array so that the final output is a matrix has a height equal to the number of repetition used.

Seidel, RachaelI↗

DOE OSTI2024

ORNL-QCI/TISCC

We introduce the Trapped-Ion Surface Code Compiler (TISCC), a software tool that generates circuits for a universal set of surface code patch operations in terms of a native trapped-ion gate set. To accomplish this, TISCC manages an internal representation of a trapped-ion system where a repeating pattern of trapping zones and junctions is arranged in an arbitrarily large rectangular grid. Surface code operations are compiled by instantiating surface code patches on the grid and using methods to generate transversal operations over data qubits, rounds of error correction over stabilizer plaquettes, and/or lattice surgery operations between neighboring patches. Beyond the implementation of a basic surface code instruction set, TISCC contains corner movement functionality and a patch translation that is implemented using ion movement alone. Except in the latter case, all TISCC functionality is extensible to alternative grid-like hardware architectures. TISCC output has been verified using the Oak Ridge Quasi-Clifford Simulator (ORQCS).

LeBlond, Tyler↗

DOE OSTI2024

HiFiPDV2 v. 2

SAND2024-01061O The HiFiPDV-2 software is similar to other photonic Doppler velocimetry (PDV) analysis codes in that it converts signals from intensity-time space to frequency-time space using a short-time Fourier transform (STFT). It then analyzes peaks of the resulting power spectral density information to extract the velocity-time history. The unique aspects of HiFiPDV and HiFiPDV-2 are that this reduction process is repeated for an array of reasonable STFT input variables. The resulting population distribution of output velocity-time histories is evaluated to determine the most likely velocity history and its associated uncertainty. This uncertainty is defined as the systematic uncertainty of the PDV signal as it is a function of input values to the reduction process. The HiFiPDV-2 program brings new functionality and efficiency to these calculations—most notably significant reductions in memory usage and calculation times, the ability to evaluate frequency-shifted signals, and the ability to filter out baseline signals. Developers recommend that users have at least 2 GB of RAM per CPU core in their system. Sandia National Laboratories is a multimission laboratory managed and operated by National Technology & Engineering Solutions of Sandia, LLC, a wholly owned subsidiary of Honeywell International Inc., for the U.S. Department of Energy’s National Nuclear Security Administration under contract DE-NA0003525.

Voorhees, Travis↗

DOE OSTI2023

phenix v. 7.0

SAND2021-6741 O phenix is Sandia's orchestration tool that allows users to quickly deploy, un-deploy, and interact with SCEPTRE ICS environments. phenix is an orchestration tool used for managing the creation, configuration, and deployment of modeling and simulation environments. As an abstraction layer on top of an underlying virtual machine manager called minimega, phenix organizes the network, application, and scenario information for a given deployment. It allows users to create, configure, and deploy experiments in a repeatable and rapid fashion. An application framework provides the flexibility to manipulate an experiment to suit various needs and requirements. phenix also includes a web-based graphical user interface (GUI) where experiments can be created, configured, and interacted with.

Sahakian, Meghan↗

DOE OSTI2023

GNATFinder

SAND2023-05510O GNATFinder is a software tool for extracting graphical neural activity threads (GNATs) from spiking neural networks. GNATFinder efficiently extracts GNATs from spiking neural network data. A GNAT is a graph that represents the causal relationships between individual spikes in a spiking network using a combination of the network connectivity structure and the timing of individual spikes. The software allows for identifying similar, repeating threads that often reappear in a dataset. Using quadtree data structures to recursively subdivide the time interval over which spikes were produced by the spiking network, GNATFinder allows for efficient pairwise comparisons of spike times to determine the degree of causal relatedness among all pairs of spikes in a dataset. Sandia National Laboratories is a multimission laboratory managed and operated by National Technology & Engineering Solutions of Sandia, LLC, a wholly owned subsidiary of Honeywell International Inc., for the U.S. Department of Energy’s National Nuclear Security Administration under contract DE-NA0003525.

SciDAC↗

DOE OSTI2024

cjohnson-LANL/GRL_Kilauea

The python routines are outlined in detail to perform the methods and results in the manuscript under review in the journal Geophysical Research Letters titled “Seismic features predict ground motions during repeating caldera collapse sequence” with LA-UR-23-33345. All routines are written in open source python and were applied to publicly available data sets. The codes formats the data into the appropriate structure required to train a boosted tree regression model. Other codes produce figure results.

Johnson, Christopher W↗