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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 145 records · Page 8
All sourcesNASA NTRSDOE OSTI
DOE OSTI2023

Quantifying Uncertainty in Ice Particle Velocity–Dimension Relationships Using MC3E Observations

Abstract Ice particle terminal fall velocity ( V t ) is fundamental for determining microphysical processes, yet remains extremely challenging to measure. Current theoretical best estimates of V t are functions of Reynolds number. The Reynolds number is related to the Best number, which is a function of ice particle mass, area ratio ( A r ), and maximum dimension ( D max ). These estimates are not conducive for use in most models since model parameterizations often take the form , where ( α , β ) depend on habit and D max . A previously developed framework is used to determine surfaces of equally plausible ( α , β ) coefficients whereby ice particle size/shape distributions are combined with V t best estimates to determine mass- ( V M ) or reflectivity-weighted ( V Z ) velocities that closely match parameterized V M ,SD or V Z ,SD calculated using the ( α , β ) coefficients using two approaches. The first uses surfaces of equally plausible ( a , b ) coefficients describing mass ( M )–dimension relationships (i.e., ) to calculate mass- or reflectivity-weighted velocity from size/shape distributions that are then used to determine ( α , β ) coefficients. The second investigates how uncertainties in A r , D max , and size distribution N ( D ) affect V M or V Z . For seven of nine flight legs flown on 20 and 23 May 2011 during the Mesoscale Continental Convective Clouds Experiment (MC3E), uncertainty from natural parameter variability—namely, the variability in ice particle parameters in similar meteorological conditions—exceeds uncertainties arising from different A r assumptions or D max estimates. The combined uncertainty between A r , D max , and N ( D ) produced smaller variability in ( α , β ) compared to varying M ( D ), demonstrating M ( D ) must be accurately quantified for model fall velocities. Primary sources of uncertainty vary considerably depending on environmental conditions. Significance Statement Ice particle fall velocity is fundamental for numerous processes within clouds, and hence is a critical property that must be accurately represented in weather and climate models. Using aircraft observations of ice particle shapes and sizes obtained in clouds behind midlatitude thunderstorms, this work develops a new framework for estimating ice particle fall velocities and their uncertainty, including quantifying the importance of different uncertainty sources from cloud microphysics measurements. Natural parameter variability contributes the most uncertainty in ice particle fall velocity estimates, although other sources can also be important contributors to uncertainty in certain conditions. Additional work examining ice particle data is needed to further understand how dependent uncertainty in certain ice particle properties are to local environmental conditions.

Meteorology & Atmospheric Sciences↗
DOE OSTI2022

Eddy Current Flow Meter Measurements and Modeling Validation

The goal of the eddy current flow meter (ECFM) is to monitor velocities and temperatures of conductive liquid metals. The ECFM can also detect voids in two-phase liquid metal flows. Measurement of these flows can greatly improve the accuracy of the empirical formulas used for fluid dynamics near the reactor core and aid in mapping any non-uniformities in fluid flow such as estimates of temperature and phase. While the eventual goal is to develop the ECFM for the liquid sodium surrounding the Versatile Test Reactor (VTR) core, this report focuses on modeling and experimental validation of ECFM for a moving solid test rod and for liquid mercury in the Target Test Facility (TTF) flow loop to better understand and further develop ECFM measurement capabilities by validating model predictions with benchmark experiments. Eventually the models could be used to design and optimize future ECFMs for specific applications such as monitoring flow within the VTR pool.

22 GENERAL STUDIES OF NUCLEAR REACTORS↗
DOE OSTI2023

A Quenched Disorder in the Quantum-Critical Superconductor CeCoIn 5

Emergent inhomogeneous electronic phases in metallic quantum systems are crucial for understanding high- T c superconductivity and other novel quantum states. In particular, spin droplets introduced by nonmagnetic dopants in quantum-critical superconductors (QCSs) can lead to a novel magnetic state in superconducting phases. However, the role of disorders caused by nonmagnetic dopants in quantum-critical regimes and their precise relation with superconductivity remain unclear. Here, the systematic evolution of a strong correlation between superconductive intertwined electronic phases and antiferromagnetism in Cd-doped CeCoIn 5 is presented by measuring current–voltage characteristics under an external pressure. In the low-pressure coexisting regime where antiferromagnetic (AFM) and superconducting (SC) orders coexist, the critical current ( I c ) is gradually suppressed by the increasing magnetic field, as in conventional type-II superconductors. At pressures higher than the critical pressure where the AFM order disappears, I c remarkably shows a sudden spike near the irreversible magnetic field. In addition, at high pressures far from the critical pressure point, the peak effect is not suppressed, but remains robust over the whole superconducting region. These results indicate that magnetic islands are protected around dopant sites despite being suppressed by the increasingly correlated effects under pressure, providing a new perspective on the role of quenched disorders in QCSs.

75 CONDENSED MATTER PHYSICS, SUPERCONDUCTIVITY AND↗
DOE OSTI2023

Characterizing Ion Transport in Electrolytes via Concentration and Velocity Profiles

Abstract Current flowing through an electrolyte is accompanied by continuum motion of ions and solvent, species concentration profiles, and the electric field. While, historically, the understanding of electrolyte transport has predominantly relied on interpreting macroscopic voltage (or current) measurements, recent advances in imaging and spectroscopic techniques allow velocity and concentration profiles to be probed directly. Such detailed information creates exciting opportunities—ranging from verifying theories describing transport in previously studied Li‐ion electrolytes, to new approaches for measuring properties as well as accurately and precisely quantifying transport in more complex electrolytes. Such emerging directions are herein discussed.

25 ENERGY STORAGE↗
DOE OSTI2024

A semi–automatic analytical methodology for characterizing the energy consumption of MRI systems using load duration curves

Background and purpose: Magnetic resonance imaging (MRI) scanners are a major contributor to greenhouse gas emissions from the healthcare sector, and efforts to improve energy efficiency and reduce energy consumption rely on quantification of the characteristics of energy consumption. The purpose of this work was to develop a semi-automatic analytical methodology for the characterization of the energy consumption of MRI systems using only the load duration curve (LDC). LDCs are a fundamental tool used across various fields to analyze and understand the behavior of loads over time. Methods: An electric current transformer sensor and data logger were installed on two 3T MRI scanners from two vendors, termed M1 (outpatient scanner) and M2 (inpatient/emergency scanner). Data was collected for 1 month (7/11/2023 to 8/11/2023). Active power was calculated, assuming a balanced three-phase system, using the average current measured across all three phases, a 480 V reference voltage for both machines, and vendor-provided power factors. An LDC was constructed for each system by sorting the active power values in descending order and computing the cumulative time (in units of percentage) for each data point. The first derivative of the LDC was then computed (LDC’), smoothed by convolution with a window function (sLDC’), and used to detect transitions between different system modes including (in descending power levels): scan, prepared-to-scan, idle, low-power, and off. The final, segmented LDC was used to measure time (% total time), total energy (kWh), and mean power (kW) for each system mode on both scanners. The method was validated by comparing mean power values, computed using the segmented 1-month LDC, for each nonproductive system mode (i.e., prepared-to-scan, idle, lower-power, and off) against power levels measured after a deliberate system shutdown was performed for each scanner (1 day worth of data). Results: The validation revealed differences in mean power values <1.4% for all nonproductive modes and both scanners. In the scan system mode, the mean power values ranged from 29.8 to 37.2 kW and the total energy consumed for 1 month ranged from 11 106 to 14 466 kWh depending on the scanner. Over the course of 1 month, the portion of time the scanners were in nonproductive modes ranged from 76% to 80% across scanners and the nonproductive energy consumption ranged from 8010 to 6722 kWh depending on the scanner. The M1 (outpatient) scanner consumed 99.9 and 183.9 kWh/day in idle mode for weekdays and weekends, respectively, because the scanner spent 23% more time proportionally in idle mode on the weekends. Conclusions: A semi-automatic method for quantifying energy consumption characteristics of MRI scanners was introduced and validated. This method is relatively simple to implement as it requires only power data from the scanners and avoids the technical challenges associated with extracting and processing scanner log files. Finally, the methodology enables quantitative evaluation of the power, time, and energy characteristics of MRI scanners in scan and nonproductive system modes, providing baseline data and the capability of identifying potential opportunities for enhancing the energy efficiency of MRI scanners.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗
DOE OSTI2021

Cosmological particle production and pairwise hotspots on the CMB

Heavy particles with masses much bigger than the inflationary Hubble scale H * , can get non-adiabatically pair produced during inflation through their couplings to the inflaton. If such couplings give rise to time-dependent masses for the heavy particles, then following their production, the heavy particles modify the curvature perturbation around their locations in a time-dependent and scale non-invariant manner. This results into a non-trivial spatial profile of the curvature perturbation that is preserved on superhorizon scales and eventually generates localized hot or cold spots on the CMB. We explore this phenomenon by studying the inflationary production of heavy scalars and derive the final temperature profile of the spots on the CMB by taking into account the subhorizon evolution, focusing in particular on the parameter space where pairwise hot spots (PHS) arise. When the heavy scalar has an $\mathscr{O}$(1) coupling to the inflaton, we show that for an idealized situation where the dominant background to the PHS signal comes from the standard CMB fluctuations themselves, a simple position space search based on applying a temperature cut, can be sensitive to heavy particle masses M o /H * ~$\mathscr{O}$(100). The corresponding PHS signal also modifies the CMB power spectra and bispectra, although the corrections are below (outside) the sensitivity of current measurements (searches).

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2023

Quantitatively controlled electrophoretic deposition of nanocrystal films from non-aqueous suspensions

This study presents a novel method to correlate the mass and charge transfer kinetics during the electrophoretic deposition of nanocrystal films by using a purpose-built double quartz crystal microbalance combined with simultaneous current-measurement. Our data support a multistep process for film formation: generation of charged nanocrystal flux, charge transfer at the electrode, and polarization of neutral nanocrystals near the electrode surface. The polarized particles are then subject to dielectrophoretic forces that reduce diffusion away from the interface, generating a sufficiently high neutral particle concentration at the interface to form a film. The correlation of mass and charge transfer enables quantification of the nanocrystal charge, the fraction of charged nanocrystals, and the initial sticking coefficient of the particles. These quantities permit calculation of the film thickness, providing a theoretical basis for using concentration and voltage as process parameters to grow films of targeted thicknesses.

77 NANOSCIENCE AND NANOTECHNOLOGY↗
DOE OSTI2025

The detrimental ratio ( ρ ): A critical metric complementing coulombic loss for long calendar-life silicon-based lithium-ion batteries

Silicon (Si) is a promising high-capacity anode in lithium-ion batteries but suffers from chronic chemical degradation and capacity fading during calendar aging, greatly hindering its automobile applications. Electrolyte engineering currently relies on conventional evaluation criteria of reducing coulombic consumption, which implicitly presume its equivalence to irreversible capacity loss and complicates battery development. Here, we introduce the detrimental ratio p to quantify the fraction of parasitic species that permanently degrades active material. This metric is independent and crucially complements total coulombic consumption for accurate performance evaluation. We systematically investigate multiple electrolyte formulations using high-precision leakage current measurements, open-circuit-voltage experiments, and post-mortem characterizations. Although some electrolytes exhibit similarly low coulombic consumption, they diverge significantly incapacity retention and p. Especially, dimethyl-carbonate-based localized-high concentration electrolyte can synergically achieve low coulombic consumption and detrimental ratio p during calendar aging, owing to its chemically inert and structurally resilient solidelectrolyte interface with minimal isolated Si material. By contrast, increasing fluoroethylene carbonate (FEC) additive content suppresses electrolyte breakdown but suffers aggravated chemical degradation of more LixSi isolation for irreversible capacity loss with arising p. This study critically reveals that the chemistry-characteristic detrimental ratio p establishes physically informed performance evaluation to pave the way for accelerating battery development.

Calendar aging↗
DOE OSTI2020

Improving the bulk gas transport of Fe-N-C platinum group metal-free nanofiber electrodes via electrospinning for fuel cell applications

To overcome transport limitations associated with thicker platinum group metal-free (PGM-free) electrodes, it's essential to investigate and tailor alternative electrode architectures to maximize the bulk electrode transport properties, whilst not significantly impeding electrocatalyst active site accessibility and electrode proton resistance. In this work, PGM-free nanofiber electrode mats, prepared by electrospinning a mixture of pyrolyzed Fe-N-C catalyst, Nafion ionomer and a carrier polymer Poly Acrylic acid (PAA), were compared to traditionally prepared electrodes. The morphological properties and elemental distribution of the fabricated nanofiber electrodes showed that the exterior surface of the PGM-free nanofibers was conformally covered with a thin ionomer film. Electrochemical diagnostics performed utilizing cyclic voltammetry, electrochemical impedance spectroscopy (EIS) and H 2 -limiting current measurements, revealed an increase in electric double layer capacitance, reduction in electrode proton transport and a significant improvement in bulk-electrode gas transport properties for the nanofiber electrodes, supporting the observed performance increase from electrochemical polarization data obtained in H 2 -O 2 /Air fuel cells. At 100% RH in H2/Air, the power density of the nanofiber electrodes increased ca. 50% vs. the traditionally prepared electrodes (ca. 260 vs 175 mW cm -2 ), which was attributed to a less tortuous molecular diffusion pathway and an associated reduction in the pressure dependent and independent gas transport resistances in the nanofiber electrodes.

25 ENERGY STORAGE↗
DOE OSTI2021

Development of a versatile, high-temperature, high-throughput ion irradiation system

We report ion irradiation has long been used as a surrogate for neutron irradiation experiments which are comparatively slower, more expensive, and often can leave materials activated and difficult to handle post-irradiation. However, with the growing use of combinatorial synthesis methods and high-throughput, automated characterization techniques, ion irradiation facilities will need to upgrade their capabilities to be able to accommodate these new material platforms. To address this challenge, a high-throughput ion irradiation system has been developed at the University of Wisconsin-Madison Ion Beam Laboratory (IBL). The system is capable of accommodating large sample arrays of arbitrary geometries and heat samples individually using an infrared laser to enable high-temperature, high-throughput ion irradiation while mitigating the risk of annealing the irradiation damage in nearby samples. To control stage motion, laser power, and beam current measurement, among other tasks, the program Chronos was developed which enables automated high-throughput irradiation experiments.

46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND ↗
DOE OSTI2026

Mu2e straw tube tracker gas flow quality control

Here, we present a tracker gas flow quality control method developed for the Mu2e straw tube tracker. Using time-dependent current measurements, we quantify the onset time of ionization gain induced by an 55 F source during gas exchange, which is correlated to the gas conductance in the straw. This allows for the identification of channels with inadequate flow. This approach is broadly applicable to other gaseous detectors that require high-channel-count screening.

Flow↗
DOE OSTI2022

Controlling extrapolations of nuclear properties with feature selection

Predictions of nuclear properties far from measured data are inherently inaccurate because of uncertainties in our knowledge of nuclear forces and in our treatment of quantum many-body effects in strongly-interacting systems. While the model bias can be directly calculated when experimental data is available, only an estimate can be made in the absence of such measurements. Current approaches to compute the estimated bias quickly lose predictive power when their input variables are taken far from the training region, resulting in uncontrolled uncertainties in applications such as nucleosynthesis simulations. In this letter, we present a novel technique to identify the input variables of machine learning algorithms that can provide robust estimates of model bias. Our process is based on selecting input variables, or features, based on their probability distribution functions across the entire nuclear chart. We illustrate our approach on the problem of quantifying the model bias in nuclear binding energies calculated with Density Functional Theory (DFT). We prove that building model biases with only Z and N as features leads to highly unreliable extrapolations. Conversely, we show that proper feature selection can systematically improve theoretical predictions without increasing uncertainties.

73 NUCLEAR PHYSICS AND RADIATION PHYSICS↗
DOE OSTI2023

Ensemble models for circuit topology estimation, fault detection and classification in distribution systems

This paper presents a methodology for simultaneous fault detection, classification, and topology estimation for adaptive protection of distribution systems. The methodology estimates the probability of the occurrence of each one of these events by using a hybrid structure that combines three sub-systems, a convolutional neural network for topology estimation, a fault detection based on predictive residual analysis, and a standard support vector machine with probabilistic output for fault classification. The input to all these sub-systems is the local voltage and current measurements. A convolutional neural network uses these local measurements in the form of sequential data to extract features and estimate the topology conditions. The fault detector is constructed with a Bayesian stage (a multitask Gaussian process) that computes a predictive distribution (assumed to be Gaussian) of the residuals using the input. Since the distribution is known, these residuals can be transformed into a Standard distribution, whose values are then introduced into a one-class support vector machine. The structure allows using a one-class support vector machine without parameter cross-validation, so the fault detector is fully unsupervised. Finally, a support vector machine uses the input to perform the classification of the fault types. All three sub-systems can work in a parallel setup for both performance and computation efficiency. In conclusion, we test all three sub-systems included in the structure on a modified IEEE123 bus system, and we compare and evaluate the results with standard approaches.

24 POWER TRANSMISSION AND DISTRIBUTION↗
DOE OSTI2021

Brain-implantable multifunctional probe for simultaneous detection of glutamate and GABA neurotransmitters

We report Glutamate (GLU) and gamma-aminobutyric acid (GABA) are neurotransmitters (NTs) with an essential role in signal transmission in the brain. Brain disorders, such as epilepsy, Alzheimer's and Parkinson's diseases, and traumatic brain injury can be linked to imbalances in the GLU-GABA homeostasis that occurs in sub-second to seconds time frames. Current measurement techniques can detect these two NT concentrations simultaneously only in vitro. The present work reports on the fabrication of a silicon multifunctional biosensor microarray probe for sub-second simultaneous GLU-GABA detection in real-time, with excellent analyte sensitivity and selectivity and in vivo capabilities. The novel Si probes feature four surface-functionalized platinum ultramicroelectrodes (UMEs) for simultaneous amperometric detection of GLU and GABA with a sentinel, and a built-in microfluidic channel for the introduction of neurochemicals in the proximity of the UMEs. The microchannel also allows functioning of an On-Demand In-situ Calibrator that runs in-situ biosensor calibration. The probe exhibited excellent robustness at insertion in agarose-gel brain-tissue-mimicking test, and remarkably high hydrogen peroxide sensitivity (a by-product of GLU-GABA enzyme biosensor) with values on the order of 5000 nA μM -1 cm -2 and maximum sensitivities of 204 ± 15 nA μM -1 cm -2 and 37 ± 7 nA μM -1 cm -2 for GLU and GABA, respectively. Furthermore, the limit of detection of the biosensors reached as low as 7 nM, 165 nM and 750 nM for H 2 O 2 , GLU and GABA, respectively and a temporal resolution of hundreds of milliseconds during in vivo studies using freely moving rats.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2021

Voltage-Dependent Barrier Height of Electron Transport through Iron Porphyrin Molecular Junctions

Electron transport through iron porphyrin (FeP) molecules self-assembled on a gold (Au) substrate was investigated using conductive atomic force microscopy (AFM) to measure current–voltage (I–V) characteristics. In the direct tunneling region (|V| ≤ 0.1 V), the Simmons model was used to characterize the electron transport. Furthermore, the energy barrier between the Fermi energy level of Au and the highest occupied molecular orbital (HOMO) level of the FeP molecule was determined to be between 0.3 and 0.6 eV; the range of the electron attenuation coefficient was 0.6–0.8 Å–1. Instead of a constant barrier height, a voltage-dependent barrier height was adapted to simulate the experimental I–V curves over the entire voltage range (|V| ≤ 2 V) using the Simmons model for the intermediate case. The voltage-dependent barrier height is supported by a previously predicted response of molecular-projected self-consistent Hamiltonian orbitals. The dependence showed that the HOMO level relative to the Fermi energy level of the Au electrode decreased as the bias voltage increased. To verify the deposition of the FeP on the Au substrate, Raman spectroscopy and AFM analysis were performed.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2022

In Situ Ion Counting for Improved Implanted Ion Error Rate and Silicon Vacancy Yield Uncertainty

An in situ counted ion implantation experiment improving the error on the number of ions required to form a single optically active silicon vacancy (SiV) defect in diamond 7-fold compared to timed implantation is presented. Here, traditional timed implantation relies on a beam current measurement followed by implantation with a preset pulse duration. It is dominated by Poisson statistics, resulting in large errors for low ion numbers. Instead, our in situ detection, measuring the ion number arriving at the substrate, results in a 2-fold improvement of the error on the ion number required to generate a single SiV compared to timed implantation. Through postimplantation analysis, the error is improved 7-fold compared to timed implantation. SiVs are detected by photoluminescence spectroscopy, and the yield of 2.98% is calculated through the photoluminescence count rate. Hanbury–Brown–Twiss interferometry is performed on locations potentially hosting single-photon emitters, confirming that 82% of the locations exhibit single photon emission statistics.

77 NANOSCIENCE AND NANOTECHNOLOGY↗
DOE OSTI2025

Stability Enhancement in Na 0.67 Fe 0.2 Mn 0.8 O 2 Positive Electrodes via Spherical Coprecipitated Hydroxide Precursor Synthesis for Na-Ion Batteries

Mn/Fe-based layered transition metal oxides (LTMOs) are promising positive electrode materials for sodium-ion batteries (SIBs) due to their high abundance, low cost, and stable price fluctuations. At commercial scale, the fabrication of these materials commonly employs coprecipitation of hydroxide precursors, which allows for the scalable synthesis of uniform, dense particles with a tunable morphology. However, the common chelating agent (ammonia) forms unstable complexes with Fe 2+ ions, resulting in uncontrollable particle morphology and poor electrochemical properties. Here, in this study, three chelation strategies (no chelation, ammonia, oxalate) for Fe/Mn-based hydroxides are evaluated. It was found that oxalate chelation produced uniform, dense spherical hydroxide particles while particles via ammonia / no chelate routes exhibited no morphological control. The LTMOs synthesized from the oxalate-chelated hydroxide precursor formed uniform spherical particles, while the other two LTMOs showed greater variation in particle morphology. The oxalate-chelated LTMO electrode exhibited increased cycling stability due to reduced parasitic reactions with the electrolyte, as characterized by static leakage current measurements and electrochemical impedance spectroscopy.

25 ENERGY STORAGE↗
DOE OSTI2024

Surface and Bulk Stabilization of Silicon Anodes with Mixed-Multivalent Additives: Ca(TFSI) 2 and Mg(TFSI) 2

Here, silicon is drawing attention as the upcoming anode material for the next generation of lithium-ion batteries due to its higher capacity compared to commercial graphite. However, silicon anions formed during lithiation are highly reactive with binder and electrolyte components creating an unstable SEI layer and limiting the calendar life of silicon anodes. The reactivity of lithium silicide and the formation of an unstable SEI layer is mitigated by utilizing the use of a mixture of Ca and Mg multivalent cations as an electrolyte additive for Si anodes to improve their calendar life. The effect of mixed salts on the bulk and surface of silicon anodes was studied by multiple structural characterization techniques. Ca and Mg ions in the electrolyte formed relatively thermodynamically stable quaternary Li-Ca-Mg-Si Zintl phases in an in-situ fashion and more stable and denser SEI layer on the Si particles. These in turn protect silicon particles against side reactions with electrolytes in a coin cell. The full cell with the mixed cation electrolyte demonstrates enhanced calendar life performance with lower measured current and current leakage than that of the baseline electrolyte due to reduced side reactions. Electron Microscopy, HRXRD, and solid-state NMR results showed that electrodes with mixed cations tended to have less cracking on the electrode surface compared to Si electrodes with Gen2 + FEC and the presence of mixed cations enhances cation migration and formation of quaternary Zintl phases stabilizing bulk and forming a more stable SEI.

25 ENERGY STORAGE↗