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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 397 records · Page 22
All sourcesNASA NTRSDOE OSTI
DOE OSTI2023

Neutron Noise Analysis of the Neptunium Subcritical Observation (NESO) Experiment

The Neptunium Subcritical Observation (NeSO) experiment was a subcritical integral benchmark experiment. This experiment was designed in 2017-2018 and was performed at the National Criticality Experiments Research Center (NCERC) in 2019. Eight different subcritical configurations were measured with varying Nickel reflector thickness. For all configurations, a 3 He detection system was used to record neutron events. The focus of this work is to share neutron noise results of the measured data.

46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND ↗
DOE OSTI2025

Hybrid Plasmonic Coupled‐Mode Metasurface Design for Shot‐Noise Limited Ppb‐Level Hydrogen Detection

Plasmonic hydrogen sensors have enabled hydrogen detection below parts-per-million (ppm) range by boosting the sensitivity using localized surface plasmonic resonant (LSPR) structures. However, the intrinsic optical losses of Palladium (Pd), the primary plasmonic metal used for hydrogen detection, result in a low quality (Q) factor LSPR, which fundamentally hinders further improvement. In this work, a hybrid plasmonic metasurface is proposed that couples Pd-based LSPR structure with an Au film supporting surface plasmon polariton mode (Au-SPP). The coupled near-perfect absorber resonance yields a spectrally narrow, high Q response that retains strong sensitivity to hydrogen while improving resonance localization. Numerical analysis shows that, under shot-noise-limited conditions, the limit of detection (LoD) can be improved by over threefold compared to the state-of-the-art designs. Furthermore, this hybrid plasmonic coupled-mode metasurface thus presents a promising pathway to achieve parts-per-billion-level (ppb-level) hydrogen detection with enhanced spectral precision and robustness.

coupled modes↗
DOE OSTI2020

Molybdenum Contacts to MoS 2 Field‐Effect Transistors: Schottky Barrier Extraction, Electrical Transport, and Low‐Frequency Noise

MoS 2 has great interest for nanoscale electronic devices, including transistors and sensors. Defect‐free structure, clean interface with the gate dielectric, and metals with suitable metal–semiconductor junctions are key for reliable devices. Molybdenum is shown as an excellent electrode for MoS 2 field‐effect transistors (FETs), but the correlation among device current, field‐effect mobility, and the low‐frequency noise (LFN) with number of atomic layers is not studied. Furthermore, the impact of Mo/MoS 2 Schottky barrier on electronic injection and scattering in the device needs to be understood. Herein, by studying the FETs with channels of different numbers of atomic‐layer thicknesses, it is shown that a molybdenum metal contact to MoS 2 provides reliable current injection with low Schottky barrier height, low contact resistance, low electron–phonon scattering, and negligible impurity scattering. The mobility and LFN are optimal at a channel thickness of ≈12 atomic layers, which is consistent with the “mobility maximum window” in MoS 2 transistors with prior studies using other contact metals.

Kwon, Jiseok↗
DOE OSTI2023

Analytical estimation of the signal to noise ratio efficiency in axion dark matter searches using a Savitzky-Golay filter

The signal to noise ratio efficiency ϵ SNR in axion dark matter searches has been estimated using large-statistic simulation data reflecting the background information and the expected axion signal power obtained from a real experiment. This usually requires a lot of computing time even with the assistance of powerful computing resources. Employing a Savitzky-Golay filter for background subtraction, in this work, we estimated a fully analytical ϵ SNR without relying on large-statistic simulation data, but only with an arbitrary axion mass and the relevant signal shape information. Hence, our work can provide ϵ SNR using minimal computing time and resources prior to the acquisition of experimental data, without the detailed information that has to be obtained from real experiments. Axion haloscope searches have been observing the coincidence that the frequency independent scale factor ξ is approximately consistent with the ϵ SNR . This was confirmed analytically in this work, when the window length of the Savitzky-Golay filter is reasonably wide enough, i.e., at least 5 times the signal window.

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗
DOE OSTI2023

Monitoring Terrestrial Water Storage, Drought and Seasonal Changes in Central Oklahoma With Ambient Seismic Noise

Abstract Significant imbalances in terrestrial water storage (TWS) and severe drought have been observed around the world as a consequence of climate changes. Improving our ability to monitor TWS and drought is critical for water‐resource management and water‐deficit estimation. We use continuous seismic ambient noise to monitor temporal evolution of near‐surface seismic velocity, dv / v , in central Oklahoma from 2013 to 2022. The derived dv / v is found to be negatively correlated with gravitational measurements and groundwater depths, showing the impact of groundwater storage on seismic velocities. The hydrological effects involving droughts and recharge of groundwater occur on a multi‐year time scale and dominate the overall derived velocity changes. The thermoelastic response to atmospheric temperature variations occurs primarily on a yearly timescale and dominates the superposed seasonal velocity changes in this study. The occurrences of droughts appear simultaneously with local peaks of dv / v , demonstrating the sensitivity of near‐surface seismic velocities to droughts.

58 GEOSCIENCES↗
DOE OSTI2025

Navigating the Noise: Bringing Clarity to ML Parameterization Design With O $\boldsymbol{\mathcal{O}}$(100) Ensembles

Abstract Machine‐learning (ML) parameterizations of subgrid processes (here of turbulence, convection, and radiation) may one day replace conventional parameterizations by emulating high‐resolution physics without the cost of explicit simulation. However, uncertainty about the relationship between offline and online performance (i.e., when integrated with a large‐scale general circulation model) hinders their development. Much of this uncertainty stems from limited sampling of the noisy, emergent effects of upstream ML design decisions on downstream online hybrid simulation. Our work rectifies the sampling issue via the construction of a semi‐automated, end‐to‐end pipeline for size ensembles of hybrid simulations, revealing important nuances in how systematic reductions in offline error manifest in changes to online error and online stability. For example, removing dropout and switching from a Mean Squared Error to a Mean Absolute Error loss both reduce offline error, but they have opposite effects on online error and online stability. Other design decisions, like incorporating memory, converting moisture input from specific humidity to relative humidity, using batch normalization, and training on multiple climates do not come with any such compromises. Finally, we show that ensemble sizes of may be necessary to reliably detect causally relevant differences online. By enabling rapid online experimentation at scale, we can empirically settle debates regarding subgrid ML parameterization design that would have otherwise remained unresolved in the noise.

Lin, Jerry [Department of Earth System Sciences Un↗
DOE OSTI2021

Intrinsic and induced quantum quenches for enhancing qubit-based quantum noise spectroscopy

Quantum sensing protocols that exploit the dephasing of a probe qubit are powerful and ubiquitous methods for interrogating an unknown environment. They have a variety of applications, ranging from noise mitigation in quantum processors, to the study of correlated electron states. Here, we discuss a simple strategy for enhancing these methods, based on the fact that they often give rise to an inadvertent quench of the probed system: there is an effective sudden change in the environmental Hamiltonian at the start of the sensing protocol. These quenches are extremely sensitive to the initial environmental state, and lead to observable changes in the sensor qubit evolution. We show how these new features give access to environmental response properties. This enables methods for direct measurement of bath temperature, and for detecting non-thermal equilibrium states. We also discuss how to deliberately control and modulate this quench physics, which enables reconstruction of the bath spectral function. Extensions to non-Gaussian quantum baths are also discussed, as is the application of our ideas to a range of sensing platforms (e.g., nitrogen-vacancy (NV) centers in diamond, semiconductor quantum dots, and superconducting circuits).

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2025

Exploring single-cell biosynthetic noise and dynamics for enhanced betaxanthin production in Escherichia coli

Cell-to-cell variability often limits the efficiency of microbial bioproduction, yet how individual cells fluctuate over time and how these fluctuations shape population-level output remain unclear. To address this issue, we tracked a heterologous betaxanthin pathway in Escherichia coli using microfluidics-assisted time-lapse microscopy, allowing simultaneous measurement of fluctuations in betaxanthin, its biosynthetic enzyme DOD and growth across generations. Here we show that over 50% of high betaxanthin producers become medium or low producers after two divisions. Betaxanthin variation primarily originates from DOD noise, with a smaller contribution from growth rate fluctuations. We further develop a stochastic model to explore various control circuits and find that pathway enzyme or metabolite-based growth selection strategies are most effective in enhancing production. We experimentally validate the model by coupling enzyme expression to nutrient availability, which enriches high producers and boosts titer by 4.4-fold. Our results highlight key sources of metabolic heterogeneity and provide a framework for designing robust microbial processes.

Bacterial systems biology↗
DOE OSTI2021

Electronic thermal transport measurement in low-dimensional materials with graphene non-local noise thermometry

In low-dimensional systems, the combination of reduced dimensionality, strong interactions and topology has led to a growing number of many-body quantum phenomena. Thermal transport, which is sensitive to all energy-carrying degrees of freedom, provides a discriminating probe of emergent excitations in quantum materials and devices. However, thermal transport measurements in low dimensions are dominated by the phonon contribution of the lattice, requiring an experimental approach to isolate the electronic thermal conductance. In this work we measured non-local voltage fluctuations in a multi-terminal device to reveal the electronic heat transported across a mesoscopic bridge made of low-dimensional materials. Using two-dimensional graphene as a noise thermometer, we measured the quantitative electronic thermal conductance of graphene and carbon nanotubes up to 70 K, achieving a precision of ~1% of the thermal conductance quantum at 5 K. Employing linear and nonlinear thermal transport, we observed signatures of energy transport mediated by long-range interactions in one-dimensional electron systems, in agreement with a theoretical model.

36 MATERIALS SCIENCE↗
DOE OSTI2020

Autonomous materials discovery driven by Gaussian process regression with inhomogeneous measurement noise and anisotropic kernels

Abstract A majority of experimental disciplines face the challenge of exploring large and high-dimensional parameter spaces in search of new scientific discoveries. Materials science is no exception; the wide variety of synthesis, processing, and environmental conditions that influence material properties gives rise to particularly vast parameter spaces. Recent advances have led to an increase in the efficiency of materials discovery by increasingly automating the exploration processes. Methods for autonomous experimentation have become more sophisticated recently, allowing for multi-dimensional parameter spaces to be explored efficiently and with minimal human intervention, thereby liberating the scientists to focus on interpretations and big-picture decisions. Gaussian process regression (GPR) techniques have emerged as the method of choice for steering many classes of experiments. We have recently demonstrated the positive impact of GPR-driven decision-making algorithms on autonomously-steered experiments at a synchrotron beamline. However, due to the complexity of the experiments, GPR often cannot be used in its most basic form, but rather has to be tuned to account for the special requirements of the experiments. Two requirements seem to be of particular importance, namely inhomogeneous measurement noise (input-dependent or non-i.i.d.) and anisotropic kernel functions, which are the two concepts that we tackle in this paper. Our synthetic and experimental tests demonstrate the importance of both concepts for experiments in materials science and the benefits that result from including them in the autonomous decision-making process.

36 MATERIALS SCIENCE↗
DOE OSTI2021

Five-second STEM dislocation tomography for 300 nm thick specimen assisted by deep-learning-based noise filtering

Scanning transmission electron microscopy (STEM) is suitable for visualizing the inside of a relatively thick specimen than the conventional transmission electron microscopy, whose resolution is limited by the chromatic aberration of image forming lenses, and thus, the STEM mode has been employed frequently for computed electron tomography based three-dimensional (3D) structural characterization and combined with analytical methods such as annular dark field imaging or spectroscopies. However, the image quality of STEM is severely suffered by noise or artifacts especially when rapid imaging, in the order of millisecond per frame or faster, is pursued. Here we demonstrate a deep-learning-assisted rapid STEM tomography, which visualizes 3D dislocation arrangement only within five-second acquisition of all the tilt-series images even in a 300 nm thick steel specimen. The developed method offers a new platform for various in situ or operando 3D microanalyses in which dealing with relatively thick specimens or covering media like liquid cells are required.

47 OTHER INSTRUMENTATION↗
DOE OSTI2023

Steam caps in geothermal reservoirs can be monitored using seismic noise interferometry

Harvesting geothermal energy often leads to a pressure drop in reservoirs, decreasing their profitability and promoting the formation of steam caps. While steam caps are valuable energy resources, they also alter the reservoir thermodynamics. Accurately measuring the steam fraction in reservoirs is essential for both operational and economic perspectives. However, steam content estimations are very limited both in space and time since current methods rely on direct measurements within production wells. Besides, these estimations normally present large uncertainties. Here, we present a pioneering method for indirectly sampling the steam content in the subsurface using the ever-present seismic background noise. We observe a consistent annual velocity drop in the Hengill geothermal field (Iceland) and establish a correlation between the velocity drop and steam buildup using in-situ borehole data. This application opens new avenues to track the evolution of any gas reservoir in the crust with a surface-based and cost-effective method.

58 GEOSCIENCES↗
DOE OSTI2024

An RC snubber design method to achieve optimized switching noise‐loss trade‐off of cascode GaN HEMTs

Abstract The cascode gallium nitride high electron mobility transistors (GaN HEMTs) are very vulnerable to self‐sustained turn‐off oscillation due to their cascode configuration. This paper presents a design approach for the RC snubber of cascode GaN HEMTs to achieve the optimized noise‐loss trade‐off. At first, an analytical model is proposed to describe the instability of cascode GaN HEMTs‐based test circuits utilizing RC snubber. Based on the model, an analytical approach is proposed to achieve two optimum RC snubber designs S1 and S2. The design S1 can satisfactorily dampen the oscillation with minimum switching losses. The design S2 achieves maximum effective damping on the oscillation at a minimized cost of additional power losses. In the end, the accuracy of the proposed model is validated by the double‐pulse test and good agreement is obtained.

Xue, Peng↗
DOE OSTI2024

Upgrade of DIII-D radial interferometer–polarimeter for large bandwidth, low noise, and toroidal mode number measurements

Near ion-cyclotron frequency (f ci ) fluctuations, such as those originating from Global and Compressional Alfvén Eigenmodes (GAEs/CAEs), are expected to be present in future fusion reactors but are not well understood due to the limited availability of core measurements in present-day tokamaks. The measurement bandwidth of the Radial Interferometer–Polarimeter (RIP) diagnostic has been upgraded from 1 to 5 MHz to detect these fluctuations in DIII-D. RIP adopts the three-wave technique for simultaneous polarimetric and interferometric measurements. Solid-state microwave sources operating at 650 GHz are used as probe beams and provide 5 MHz bandwidth for both polarimetric and interferometric measurements. Bandwidths of related hardware, including mixer amplifier, signal cable, and digital phase demodulator, are increased correspondingly. Measurement noise is minimized by reducing the time delay between reference and probe signals to nanosecond level and employing correlation-based techniques. Using the upgraded diagnostic, CAE/GAE-like bursting fluctuations are observed in neutral-beam heated plasmas with toroidal magnetic field B φ ≈ 1 T. Current upgrades being undertaken would enable the evaluation of toroidal mode number for these modes. Furthermore, this work opens the possibility of better understanding near ion-cyclotron frequency fluctuations in fusion relevant plasmas.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗
DOE OSTI2026

Automated scanning probe microscopy of combinatorial ferroelectric libraries: Gaussian-process-guided exploration and noise-aware experiment planning

Combinatorial materials libraries provide an efficient route for mapping composition–property relationships, but their broader impact depends on rapid, quantitative, and functionally relevant characterization. Scanning Probe Microscopy (SPM), including piezoresponse force microscopy (PFM), offers significant potential for quantitative, functionally relevant combi-library readouts. Here, we implement a fully automated SPM workflow for ferroelectric combinatorial libraries and benchmark Gaussian-process-based Bayesian optimization strategies for autonomous experiment planning. The workflow integrates automated probe motion, contact optimization, imaging, and dual amplitude resonance tracking-PFM spectroscopy, and uses scalarized spectroscopic observables to guide subsequent measurements. Stage motion, probe engagement, in-contact tuning, imaging, spectroscopy, and the choice of the next measurement location all proceed without human input. We demonstrate the approach on Sm-doped BiFeO 3 and Zn x Mg 1−x O libraries. By comparing vanilla Bayesian optimization with a measured-noise variant, we show that explicit treatment of local reproducibility can improve modeling of composition-dependent response when the measured variance is physically meaningful, but can also reduce robustness when variability is dominated by outliers or topographic artifacts. Furthermore, these results establish automated SPM as a bridge between combinatorial synthesis and quantitative functional characterization.

Liu, Yu [University of Tennessee, Knoxville, TN (U↗
DOE OSTI2023

First results from a multiplexed and massive instrument with sub-electron noise Skipper-CCDs

Here, we present a new instrument composed of a large number of sub-electron noise Skipper-CCDs operated with a two stage analog multiplexed readout scheme suitable for scaling to thousands of channels. New, thick, 1.35 Mpix sensors, from a new foundry, are glued into a Multi-Chip Module (MCM) printed circuit board on a ceramic substrate which has 16 sensors each. The instrument, that can hold up-to 16 MCMs, a total of 256 Skipper-CCD sensors (called a Super-Module with ≈ 130 grams of active mass and 346 Mpix), is part of the R&D effort of the OSCURA experiment which will have ≈ 94 super-modules. Experimental results with 10 MCMs and 160 Skipper-CCDs sensors are presented in this paper. This is already the largest ever built instrument with single electron sensitivity CCDs using nondestructive readout, both, in terms of active mass and number of channels.

46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND ↗
DOE OSTI2025

Calibration and simulation of ionization signal and electronics noise in the ICARUS liquid argon time projection chamber

The ICARUS liquid argon time projection chamber (LArTPC) neutrino detector has been taking physics data since 2022 as part of the Short-Baseline Neutrino (SBN) Program. This paper details the equalization of the response to charge in the ICARUS time projection chamber (TPC), as well as data-driven tuning of the simulation of ionization charge signals and electronics noise. The equalization procedure removes non-uniformities in the ICARUS TPC response to charge in space and time. This work leverages the copious number of cosmic ray muons available to ICARUS at the surface. The ionization signal shape simulation applies a novel procedure that tunes the simulation to match what is measured in data. The end result of the equalization procedure and simulation tuning allows for a comparison of charge measurements in ICARUS between Monte Carlo simulation and data, showing good performance with minimal residual bias between the two.

46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND ↗
DOE OSTI2025

Parameter uncertainties for imperfect surrogate models in the low-noise regime

Abstract Bayesian regression determines model parameters by minimizing the expected loss, an upper bound to the true generalization error. However, this loss ignores model form error, or misspecification, meaning parameter uncertainties are significantly underestimated and vanish in the large data limit. As misspecification is the main source of uncertainty for surrogate models of low-noise calculations, such as those arising in atomistic simulation, predictive uncertainties are systematically underestimated. We analyze the true generalization error of misspecified, near-deterministic surrogate models, a regime of broad relevance in science and engineering. We show that posterior parameter distributions must cover every training point to avoid a divergence in the generalization error and design a compatible ansatz which incurs minimal overhead for linear models. The approach is demonstrated on model problems before application to thousand-dimensional datasets in atomistic machine learning. Our efficient misspecification-aware scheme gives accurate prediction and bounding of test errors in terms of parameter uncertainties, allowing this important source of uncertainty to be incorporated in multi-scale computational workflows.

Swinburne, Thomas D. (ORCID:0000000232554257)↗