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At least 1,027 records · Page 57
All sourcesNASA NTRSDOE OSTI
DOE OSTI2020

Unified bulk semiclassical theory for intrinsic thermal transport and magnetization currents

In this work, we reveal the unexpected role of the material inhomogeneity in unifying the formulation of intrinsic thermal and thermoelectric transport as well as magnetization currents. The smooth inhomogeneity leads to the position dependent local band dispersion and phase-space Berry curvature, enabling a general and rapid access to transport and magnetization currents displaying the momentum-space Berry curvature physics. Our theory does not invoke the boundary current, the thermodynamic approach to magnetization or any mechanical counterpart of statistical forces. By introducing a fictitious inhomogeneity, it applies to homogeneous samples as well, promoting the inhomogeneity to be a basic trick in semiclassical transport theories. Such a trick works regardless of the driving force of transport, e.g., temperature gradient, in contrast to the trick of fictitious gravitational field in quantum transport theories. We thus include more general mechanical driving forces and establish the Mott relation between the resulting transport thermal and electric currents, whereas this relation for these two currents was previously only known when an electric field is the driving force.

75 CONDENSED MATTER PHYSICS, SUPERCONDUCTIVITY AND↗
DOE OSTI2024

Using data-science approaches to unravel insights for enhanced transport of lithium ions in single-ion conducting polymer electrolyte

Solid polymer electrolytes have yet to achieve the an ionic conductivity > 1 mS/cm at room temperature for realistic applications. This target implies the need to reduce the effective energy barriers of ion transport in polymer electrolytes to around 20 kJ/mol. In this work, we combine information extracted from existing experimental results with theoretical calculations to provide insights into ion transport in single-ion conductors (SICs) with a focus on lithium ion SICs. Through the analysis of temperature-dependent ionic conductivity data obtained from the literature, we evaluate different methods of extracting energy barriers for lithium transport. The traditional Arrhenius fit to the temperature-dependent ionic conductivity data indicates that the Meyer-Neldel rule holds for SICs. However, the values of the fitting parameters remain unphysical. Our modified approach based on recent work (Macromolecules, 56, 15, 6051(2023)), which incorporates a fixed pre-exponential factor, reveals that the energy barriers exhibit temperature dependence over a wide range of temperatures. Using this approach, we identify a series of anions leading to the energy barriers less than 30 kJ/mol, which include trifluoromethane sulfonimide (TFSI), fluoromethane sulfonimide (FSI), and boron-based organic anions. In our efforts to design the next generation of anions, which can exhibit the energy barriers less than 20 kJ/mol, we focused on boron-containing SICs, and performed density functional theory (DFT) based calculations to connect the chemical structures via the binding energy of cation (lithium)-anion pairs with the experimentally derived effective energy barriers for ion transport. Not only have we identified a correlation between the binding energy and the energy barriers, but we also propose a strategy to design new boron-based anions by using the correlation. This combined approach involving experiments and theoretical calculations is capable of facilitating the identification of promising new anions, which can exhibit ionic conductivity $> 1$ mS/cm near room temperature, thereby expediting the development of novel superionic single-ion conducting polymer electrolytes. The published datasets include all the temperature-dependent ionic conductivity collected from the literature with literature DOIs, DFT calculated binding energies, and python scripts to analyze data, construct statistical models, and generate plots.

36 MATERIALS SCIENCE↗
DOE OSTI2022

Bayesian Estimation of Oscillator Parameters: Toward Anomaly Detection and Cyber-Physical System Security

Cyber-physical system security presents unique challenges to conventional measurement science and technology. Anomaly detection in software-assisted physical systems, such as those employed in additive manufacturing or in DNA synthesis, is often hampered by the limited available parameter space of the underlying mechanism that is transducing the anomaly. As a result, the formulation of anomaly detection for such systems often leads to inverse or ill-posed problems, requiring statistical treatments. Here, we present Bayesian inference of unknown parameters associated with a generic actuator considered as a representative vital element of a cyber-physical system. Via a series of experimental input-output measurements, a transfer function for the actuator is obtained numerically, which serves as our model for the proposed method. Linear, nonlinear, and delayed dynamics may be assumed for the actuator response. By devising a code-based malicious signal, we study the efficacy of Bayesian inference for its potential to produce a detection, including uncertainty quantification, with a remarkably small number of input data points. Our approach should be adaptable to a variety of real-time cyber-physical anomaly detection scenarios.

47 OTHER INSTRUMENTATION↗
DOE OSTI2025

Estimating Large Global Significances with a New Monte Carlo Extrapolation Method

In particle physics, it is needed to evaluate the possibility that excesses of events in mass spectra are due to statistical fluctuations as quantified by the standards of local and global significances. Without prior knowledge of a particle’s mass, it is especially critical to estimate its global significance. The usual approach is to count the number of times a significance limit is exceeded in a collection of simulated Monte Carlo (MC) “toy experiments”. To demonstrate this conventional method for global significance, we performed simulation studies according to a recent Compact Muon Solenoid (CMS) result to show its effectiveness. However, this counting method is not practical for computing large global significances. To address this problem, we developed a new “extrapolation” method to evaluate the global significance. We compared the global significance estimated by our new method with that of the conventional approach, and verified its feasibility and effectiveness. This method is also applicable for cases where only small toy MC samples are available. In this approach, the significance is calculated based on p-values, assuming symmetrical Gaussian distributions.

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗
DOE OSTI2025

Organic Electrochemical Transistor Channel Materials: Copolymerization Versus Physical Mixing of Glycolated and Alkoxylated Polymers

Organic electrochemical transistors (OECTs) feature a polymer channel capable of conducting both ions and electronic charges. The choice of the channel material is critical for OECT performance. Many efforts have focused on improving performance via the chemical tunability of conjugated polymers – through backbone, side chain, and molar mass engineering – leading to useful design principles for accumulation-mode OECT materials. However, tuning the chemical structure of conjugated polymers often requires time-consuming optimization of the synthesis route. Meanwhile, variations in molar mass, dispersity, structural defects, and metal content present challenges when attempting to analyze the detailed effects of structural modifications, as multiple performance-determining factors are often (unintentionally) changed at the same time. Therefore, this study explores blended channel materials obtained by physically mixing glycolated and alkoxylated polymers in different ratios, and compares their OECT performance with the corresponding statistical copolymers. It is shown that mixing two well-performing materials creates blends that enable rational tuning of the transistor properties without compromising on performance. Thus, channels based on blends of alkoxylated and glycolated polymers hold promise for OECT technology with tailored response, as only two materials are needed to achieve any desired side chain ratio, simplifying the optimization of OECT characteristics.

copolymerization↗
DOE OSTI2024

Angular analysis of B → K * e + e − in the low- q 2 region with new electron identification at Belle

We perform an angular analysis of the B → K * e + e − decay for the dielectron mass squared, q 2 , range of 0.0008 – 1.1200 GeV 2 / c 4 using the full Belle dataset in the K * 0 → K + π − and K * + → K S 0 π + channels, incorporating new methods of electron identification to improve the statistical power of the dataset. This analysis is sensitive to contributions from right-handed currents from physics beyond the Standard Model by constraining the Wilson coefficients C 7 ( ′ ) . We perform a fit to the B → K * e + e − differential decay rate and measure the imaginary component of the transversality amplitude to be A T Im = − 1.27 ± 0.52 ± 0.12 , and the K * transverse asymmetry to be A T ( 2 ) = 0.52 ± 0.53 ± 0.11 , with F L and A T Re fixed to the Standard Model values. The resulting constraints on the value of C 7 ′ are consistent with the Standard Model within a 2 σ confidence interval. Published by the American Physical Society 2024

Ferlewicz, D. (ORCID:0000000243741234)↗
DOE OSTI2024

Advanced EXAFS analysis techniques applied to the L -edges of the lanthanide oxides

The unique properties of the lanthanide (Ln) elements make them critical components of modern technologies, such as lasers, anti-corrosive films and catalysts. Thus, there is significant interest in establishing structure–property relationships for Ln-containing materials to advance these technologies. Extended X-ray absorption fine structure (EXAFS) is an excellent technique for this task considering its ability to determine the average local structure around the Ln atoms for both crystalline and amorphous materials. However, the limited availability of EXAFS reference spectra of the Ln oxides and challenges in the EXAFS analysis have hindered the application of this technique to these elements. The challenges include the limited k-range available for the analysis due to the superposition of L-edges on the EXAFS, multielectron excitations (MEEs) creating erroneous peaks in the EXAFS and the presence of inequivalent absorption sites. Herein, we removed MEEs to model the local atomic environment more accurately for light Ln oxides. Further, we investigated the use of cubic and non-cubic lattice expansion to minimize the fitting parameters needed and connect the fitting parameters to physically meaningful crystal parameters. The cubic expansion reduced the number of fitting parameters but resulted in a statistically worse fit. The non-cubic expansion resulted in a similar quality fit and showed non-isotropic expansion in the crystal lattice of Nd 2 O 3 . In total, the EXAFS spectra and the fits for the entire set of Ln oxides (excluding promethium) are included. The knowledge developed here can assist in the structural determination of a wide variety of Ln compounds and can further studies on their structure–property relationships.

36 MATERIALS SCIENCE↗
DOE OSTI2022

The Multi-scenario Extreme Weather Simulator: Energy Resilience for Mission Assurance

The Multi-scenario extreme weather simulator (MEWS) is a stochastic weather generation tool. The MEWS algorithm uses 50 or more years of National Oceanic and Atmospheric Association (NOAA) daily summaries [1] for maximum and minimum temperature and NOAA climate norms [2] to calculate historical heat wave and cold snap statistics. The algorithm takes these statistics and shifts them according to multiplication factors provided in the Intergovernmental Panel on Climate Change (IPCC) physical basis technical summary [3] for heat waves.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2025

Status of the Short-Baseline Near Detector at Fermilab

The Short-Baseline Near Detector (SBND) is one of the Liquid Argon Time Projection Chamber (LArTPC) neutrino detectors positioned along the axis of the Booster Neutrino Beam (BNB) at Fermilab, and is the near detector in the Short-Baseline Neutrino (SBN) Program. The detector completed commissioning and began taking neutrino data in the summer of 2024. SBND is characterized by superb imaging capabilities and will record around 2 million neutrino interactions per year. Thanks to its unique combination of measurement resolution and statistics, SBND will soon carry out a rich program of neutrino interaction measurements and novel searches for physics beyond the Standard Model (BSM). As the near detector, it will enable the full potential of the SBN sterile neutrino program by performing a precise characterization of the unoscillated event rate and constraining BNB flux and neutrino-argon cross-section systematic uncertainties. In this talk, the physics reach, current status, and future prospects of SBND are discussed.

Paton, Josephine [Fermilab]↗
DOE OSTI2026

Status of the Short-Baseline Near Detector at Fermilab

The Short-Baseline Near Detector (SBND) is one of the Liquid Argon Time Projection Chamber (LArTPC) neutrino detectors positioned along the axis of the Booster Neutrino Beam (BNB) at Fermilab, and is the near detector in the Short-Baseline Neutrino (SBN) Program. The detector has completed Run 1, successfully recording approximately 3 million neutrino interactions, and started Run 2 in the autumn of 2025. SBND is characterized by superb imaging capabilities and will record around 3 million neutrino interactions per year. Thanks to its unique combination of measurement resolution and statistics, SBND will soon carry out a rich program of neutrino interaction measurements and novel searches for physics beyond the Standard Model (BSM). As the near detector, it will enable the full potential of the SBN sterile neutrino program by performing a precise characterization of the unoscillated event rate and constraining the BNB flux and neutrino-argon cross-section systematic uncertainties. In this talk, the physics reach, current status, and future prospects of SBND are discussed.

Del Tutto, Marco [Fermilab]↗
DOE OSTI2026

Status of the Short-Baseline Near Detector at Fermilab

The Short-Baseline Near Detector (SBND) is one of the Liquid Argon Time Projection Chamber (LArTPC) neutrino detectors positioned along the axis of the Booster Neutrino Beam (BNB) at Fermilab, and is the near detector in the Short-Baseline Neutrino (SBN) Program. The detector has completed Run 1, successfully recording approximately 3 million neutrino interactions, and started Run 2 in the autumn of 2025. SBND is characterized by superb imaging capabilities and will record around 3 million neutrino interactions per year. Thanks to its unique combination of measurement resolution and statistics, SBND will soon carry out a rich program of neutrino interaction measurements and novel searches for physics beyond the Standard Model (BSM). As the near detector, it will enable the full potential of the SBN sterile neutrino program by performing a precise characterization of the unoscillated event rate and constraining the BNB flux and neutrino-argon cross-section systematic uncertainties. In this talk, the physics reach, current status, and future prospects of SBND are discussed.

Del Tutto, Marco [Fermilab]↗
DOE OSTI2023

Reconstructing Ly$α$ Fields from Low-resolution Hydrodynamical Simulations with Deep Learning

Hydrodynamical cosmological simulations are a powerful tool for accurately predicting the properties of the intergalactic medium (IGM) and for producing mock skies that can be compared against observational data. However, the need to resolve density fluctuation in the IGM puts a stringent requirement on the resolution of such simulations, which in turn limits the volumes that can be modeled, even on the most powerful supercomputers. In this work, we present a novel modeling method that combines physics-driven simulations with data-driven generative neural networks to produce outputs that are qualitatively and statistically close to the outputs of hydrodynamical simulations employing eight times higher resolution. We show that the Ly$α$ flux field, as well as the underlying hydrodynamic fields, have greatly improved statistical fidelity over a low-resolution simulation. Importantly, the design of our neural network allows for sampling multiple realizations from a given input, enabling us to quantify the model uncertainty. Using test data, we demonstrate that this model uncertainty correlates well with the true error of the Ly$α$ flux prediction. Ultimately, our approach allows for training on small simulation volumes and applying it to much larger ones, opening the door to producing accurate Ly$α$ mock skies in volumes of Hubble size, as will be probed with DESI and future spectroscopic sky surveys.

79 ASTRONOMY AND ASTROPHYSICS↗
NASA NTRS1987

Estimating the distribution of fault latency in a digital processor

Presented is a statistical approach to measuring fault latency in a digital processor. The method relies on the use of physical fault injection where the duration of the fault injection can be controlled. Although a specific fault's latency period is never directly measured, the method indirectly determines the distribution of fault latency.

Ellis, Erik L.↗
NASA NTRS1993

A new approach to the study of the turbulence layer

Turbulent thermal convection is of considerable importance in fluid dynamical transport phenomena occurring, for example, in the planetary boundary layer of the Earth, the interiors of stars, and accretion disks. In particular, during a significant portion of the evolutionary phase of many stars having convectively unstable cores or outer envelopes, a substantial fraction of energy is transported from the central layers to the outer layers by thermal convection. Moreover, as much of the interior of a star is in highly turbulent motion, a complete theory of stellar structure and evolution requires the explicit consideration of turbulence in order to have expressions for the turbulent quantities arising in the stellar structure equations, and particularly, the turbulent fluxes that appear in the total flux conservation equation, such as the convective flux, kinetic energy flux, etc. A reliable quantification of these fluxes continues to present a challenge in astrophysical fluid dynamics, primarily because astrophysical turbulence is almost always fully-developed and nearly inviscid, and therefore governed by strong nonlinear interactions that distribute the energy among a very wide spectrum of eddies with scales ranging from the characteristic dimension of the flow to those sufficiently small to be affected by viscous dissipation. Furthermore, astrophysical flows are invariably compressible, anisotropic, and inhomogeneous, which requires the consideration of the dynamics of longitudinal modes and their interaction with the transverse modes, as well as complicated boundary conditions. In order to reach a compromise between analytical and numerical tractability and the basic physics of turbulent convection, we have constructed a model of stationary turbulent convection that yields various turbulence statistics, including the convective flux, that are required in stellar evolution models.

Levenson, Ruth A.↗
NASA NTRSDate not supplied

A Novel Machine Learning Method for Surface PM2.5 Estimations from Geostationary Satellites

Particulate matter (PM) with a diameter of less or equal to 2.5 μm, known as PM , affects human health as it penetrates the respiratory system. The Environmental Protection Agency (EPA) measures the atmospheric concentration of PM using air quality monitors stationed throughout the Continental United States (CONUS). Such measurements are points on a spatial domain and therefore, might not be representative of the air quality at nearby areas considering that the composition of the atmosphere is highly variable from place to place. Satellite based AOD permits a spatially uniform means of estimating PM and new geostationary satellites provide high temporal and spatial resolution estimation of AOD. However, the concentration of PM is non-linearly dependent on other atmospheric parameters that include relative humidity, temperature, and height of the planetary boundary layer. This information may be estimated at similar spatial and temporal resolutions as AOD from numerical modeling such as from the National Oceanic and Atmospheric Administration’s (NOAA) High Resolution Rapid Refresh (HRRR) model which resolves near real-time atmospheric conditions over the CONUS. The estimation of PM concentration is a multi-parametric problem that considers the effect of temporal dependencies among the different parameters. Deep learning approaches are appropriate for such complex estimation problems as they intrinsically capture relations among multiple non-linear parameters. This study compares deep-learning methods to traditional regression analysis to demonstrate the capabilities of these methods in predicting PM2.5 concentrations. Additionally, a novel ensemble learning approach is employed to identify scientific processes that could further improve the estimation of PM concentration. Utilizing Long Short-Term Memory (LSTM) neural networks, which are suitable for multivariate time series estimation problems as they are capable of learning long-term dependencies, individual models are created for each EPA station and trained on the aforementioned dataset collocated over each station. Individual station models are merged if the model's performance is improved by reducing the root mean squared error (RMSE) metric. This ensemble training method ultimately reduces the RMSE value. Evaluation of these results provide insights into physical processes and related observable parameters that may contribute to PM concentrations. Identified parameters evaluated to be statistically different between the merged and unmerged models are expected to improve overall performance. These new parameters are then utilized for reevaluation of the deep learning methods with an extreme gradient boosting model with an RMSE of 5.5 providing the best results.

George Priftis↗
DOE OSTI2021

Search for CPT and Lorentz invariance violation in the muon $g -2$ experiment at Fermilab

The Muon g-2 experiment at Fermilab (E989) aims to measure the anomalous magnetic moment of the muon, $a_{\mu}= (g-2)/2$, to a groundbreaking precision of $140$ ppb, obtaining a near four-fold increase in precision over the previous experiment, E821, at the Brookhaven National Laboratory (BNL). The value of $a_{\mu}$ from BNL currently differs from the Standard Model prediction by $\sim 3.7$ standard deviations, suggesting the potential for new physics and therefore, motivating a new experiment.Because the theory predicts this number with high precision, testing the g-factor through experiment provides a stringent test of the SM and can suggest physics beyond the Standard Model. The goal of the Fermilab Muon $g-2$ experiment is to increase the statistical precision by more than a factor of 20 and reduce systematic errors by a factor of 3. By measuring muon precession rate ($\omega_a$) in an external magnetic field, the anomalous magnetic moment will be calculated. This is an incredibly challenging experiment with a unique opportunity to provide new insight into nature. \\ The $g-2$ data also provides a great opportunity for setting the most stringent limits on some of the Standard Model Extension CPT Lorentz violating (LV) parameters in the muon sector. One of the CPT and Lorentz violating signatures that we can look for using $g-2$ data is a sidereal variation of $\omega_a(t)$. Extensive simulation studies confirm that the sensitivity regarding the sidereal varation roughly scales with $\omega_a$ uncertainty. Hence, the $g-2$ experiment at FNAL should be able to reach limits of $\sim 5\times10^{-25}$ GeV. Because the CPT and LV analyses are essentially studies of variations in $\omega_a$ as a function of time and charge, performing an $\omega_a$ analysis sets the stage for the CPT and LV measurement. This dissertation focuses on the methodology of a fully functioning framework and analyzing the Fermilab Muon $g - 2$ Run 2 data containing $\sim 11$ billion events above an energy threshold of $1.7$~GeV.

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗
DOE OSTI2025

Status and prospects of Muon g-2 experiment

This article reviews the muon g-2 experiment, a cornerstone in precision tests of the Standard Model of particle physics. The experiment measures the anomalous magnetic moment of the muon with unprecedented accuracy, seeking potential discrepancies between theoretical predictions and experimental results that might indicate physics beyond the Standard Model. We trace the evolution of this measurement from its beginnings at CERN in the 1960s to the current state-of-the-art experiment at Fermilab, highlighting the remarkable engineering achievements required to achieve parts-per-billion precision. Recent results from Runs 1-3 have achieved a systematic uncertainty of 70 ppb, exceeding design goals, while ongoing theoretical calculations continue to refine predictions. Despite these advances, the analysis remains statistics-limited, with continued data collection and novel experimental approaches at MUonE and J-PARC promising further insights into this fundamental physical quantity.

Karuza, Marin [INFN, Trieste; Rijeka U.] (ORCID:00↗
DOE OSTI2022

Model comparisons for two-temperature plasma equations of state

When a plasma is generated in the laboratory, energy is often deposited preferentially into either the electrons or the ions, giving rise to a quasiequilibrium state in which the two species, electrons and ions, are well described by two effective temperatures, T e and T i . Accurate hydrodynamic modeling of such a two-temperature plasma requires an equation of state that captures the relevant many-body physics without assuming a strict local thermodynamic equilibrium. Several models have been proposed within the literature, which extend conventional statistical approaches, each employing a different combination of assumptions for modifying the equilibrium equations. Here, in this work, we compare the predictions for several models, presenting derivations of the internal energy and pressure for each microscopic model within a unified framework so that the assumptions of each model may be more easily compared to one another. We find that for sufficiently weak coupling, all models agree with one another. However, as the coupling strength is increased, the disagreement between the models becomes more pronounced. Moreover, the relative sizes of the corrections predicted by each model depend on which species has the higher temperature, T e >T i vs T e < T i .

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗