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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 1,441 records · Page 80

Alkaline earth stannates: The next silicon?

Semiconductor materials are being used in an increasingly diverse array of applications, with new device concepts being proposed each year for solar cells, flat-panel displays, sensors, memory, and spin transport. This rapid progress of invention outpaces the development of new semiconductor materials with the required properties and performance. In many applications, high carrier mobility at room temperature is required in addition to specific functional properties critical to the device concept. We review recent developments on high mobility stannate perovskite oxide materials and devices.

36 MATERIALS SCIENCE↗

Integrated titanium dioxide (TiO{sub 2}) nanoparticles on interdigitated device electrodes (IDEs) for pH analysis

Titanium dioxide (TiO{sub 2}) nanoparticles based Interdigitated Device Electrodes (IDEs) Nanobiosensor device was developed for intracellular biochemical detection. Fabrication and characterization of pH sensors using IDE nanocoated with TiO{sub 2} was studied in this paper. In this paper, a preliminary assessment of this intracellular sensor with electrical measurement under different pH levels. 3-aminopropyltriethoxysilane (APTES) was used to enhance the sensitivity of titanium dioxide layer as well as able to provide surface modification by undergoing protonation and deprotonation process. Different types of pH solution provide different resistivity and conductivity towards the surface. Base solution has the higher current compared to an acid solution. Amine and oxide functionalized TiO{sub 2} based IDE exhibit pH-dependent could be understood in terms of the change in surface charge during protonation and deprotonation. The simple fabrication process, high sensitivity, and fast response of the TiO{sub 2} based IDEs facilitate their applications in a wide range of areas. The small size of semiconductor TiO{sub 2} based IDE for sensitive, label-free, real time detection of a wide range of biological species could be explored in vivo diagnostics and array-based screening.

77 NANOSCIENCE AND NANOTECHNOLOGY↗

Ecological Insights from Transferable Plant Biomass Mapping across the Arctic using High-resolution Structure-from-Motion and LiDAR Data

Warmer temperatures, permafrost thaw, and increased wildfire activity are driving rapid ecological change across the Arctic, significantly altering plant productivity and aboveground biomass (AGB). These rapid changes highlight the urgent need to improve monitoring of vegetation dynamics in the Earth’s northern ecosystems, where high spatiotemporal heterogeneity occurs at scales finer than those captured by traditional satellite observations. The growing use of Unoccupied Aerial Systems (UASs) presents an opportunity to overcome this limitation. Yet, the diversity of UAS platforms, sensors, and data collection and processing workflows presents challenges for developing standardized, generalizable approaches. To address this challenge, we compiled 672 AGB plots co-located with 183 UAS-based Structure-from-Motion (SfM) or Light Detection and Ranging (LiDAR) surveys collected across the Arctic. Here, we: (1) evaluated the generalizability of UAS-derived canopy structure derived from high-resolution SfM and LiDAR for estimating AGB, (2) assessed scaling errors and their sources in two recent satellite-based AGB products derived from Landsat and MODIS, and (3) demonstrated the use of high-resolution AGB maps to quantify biomass variation across tundra plant functional types (PFTs) and to monitor post-fire recovery. Our results show that both SfM and LiDAR accurately captured AGB and its variability across tundra PFTs using a Random Forest (RF) model (overall RMSE: 0.336 kg/m2), with mapping performance varying slightly by region and data source. Using UAS-derived AGB maps as a benchmark, we identified systematic biases in satellite-derived AGB products, largely attributable to the magnitude of AGB and structural heterogeneity within coarse-resolution pixels. Applying our model to repeat UAS surveys following a tundra fire on Seward Peninsula, we observed rapid AGB recovery in non-shrub patches, with biomass recovering to pre-fire levels within 2 years. In contrast, shrub patches recovered more slowly, with AGB gains continuing over 2–4 years through both in-patch growth and lateral expansion (via dispersal) into remaining burned areas. Overall, these findings demonstrate the generalizability of UAS-based SfM and LiDAR data for estimating tundra AGB and highlight the potential of our approach to be broadly applied to generate high-quality AGB data for ecological monitoring and model benchmarking across the Arctic.

Yang, Daryl [ORNL] (ORCID:0000000317057823)↗

Strong quantum squeezing near the pull-in instability of a nonlinear beam

Microscopic silicon-based suspended mechanical oscillators, constituting an extremely sensitive force probe, transducer, and actuator, are being increasingly employed in many developing microscopies, spectroscopies, and emerging optomechanical and chem-bio sensors. Here, we predict a significant squeezing in the quantum state of motion of an oscillator constrained as a beam and subject to an electrically induced nonlinearity. When we take into account the quantum noise, the underlying nonlinear dynamics is investigated in both the transient and stationary regimes of the driving force leading to the finding that strongly squeezed states are accessible in the vicinity of the pull-in instability of the oscillator. We discuss a possible application of this strong quantum squeezing as an optomechanical method for detecting broad-spectrum single or low-count photons, and further suggest other novel sensing actions.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗

Correlated Noise Estimation with Quantum Sensor Networks

We address the metrological problem of estimating collective stochastic properties imprinted on a network of quantum sensors. Canonical examples include center-of-mass quadrature fluctuations in a system of bosonic modes and correlated dephasing in an ensemble of qubits (e.g., spins), bosons, or fermions. We develop a theoretical framework to determine the limits of correlated (weak) noise estimation with quantum sensor networks and reveal the requirements for entanglement advantage. Notably, an advantage emerges from the synergistic interplay between quantum correlations of the sensors and “classical” correlations of the noises. Here, we determine optimal entangled probe states and identify a sensing protocol—reminiscent of a many-body echo—that achieves the fundamental limits of measurement sensitivity for a broad class of problems, unveiling a route toward entanglement-enhanced metrology of correlated many-body phenomena.

Quantum metrology↗

Joint tests at INL and CEA of a transient hot wire needle probe for in-pile thermal conductivity measurement

Thermal conductivity is a key property that must be known for proper design, testing, and deployment of new fuels and structural materials in nuclear reactors. Thermal conductivity is highly dependent on the physical structure, chemical composition, and the state of the material. Typically, thermal conductivity changes that occur during irradiation are currently measured out-of-pile using a 'cook and look' approach. But repeatedly removing samples from a test reactor to make measurements is expensive, has the potential to disturb phenomena of interest, and only provides understanding of the sample's end state when each measurement is made. There are also limited thermo-physical property data available for advanced fuels; and such data are needed for simulation codes, the development of next generation reactors, and advanced fuels for existing nuclear plants. Being able to quickly characterize fuel thermal conductivity during irradiation can improve the fidelity of data, reduce costs of post-irradiation examinations, increase understanding of how fuels behave under irradiation, and confirm or improve existing thermal conductivity measurement techniques. This paper discusses efforts to develop and evaluate an innovative in-pile thermal conductivity sensor based on the transient hot wire thermal conductivity method (THWM), using a single needle probe (NP) containing a line heat source and thermocouple embedded in the fuel. The sensor that has been designed and manufactured by the Idaho National Laboratory (INL) includes a unique combination of materials, geometry, and fabrication techniques that make the hot wire method suitable for in-pile applications. In particular, efforts were made to minimize the influence of the sensor and maximize fuel hot-wire heating. The probe has a thermocouple-like construction with high temperature resistant materials that remain ductile while resisting transmutation and materials interactions. THWM-NP prototypes were fabricated for both room temperature proof-of-concept evaluations and high temperature testing. Evaluations have been performed jointly by the INL and the French Alternative Energies and Atomic Energy Commission (CEA), both in Idaho Falls (USA) and in Cadarache (France), in the framework of a collaborative program for instrumentation of Material Testing Reactors. Initial tests were conducted on samples with a large range of thermal conductivities and temperatures ranging from 20 deg. C to 600 deg. C. Particularly, tests were recently performed on a sample having thermal conductivity and dimensions similar to UO{sub 2} and MOX nuclear fuels, in order to validate the ability of this sensor to operate for in-pile characterization of Light Water Reactors fuels. The results of the tests already completed at INL and CEA indicate that the Transient Hot Wire Needle Probe offers an enhanced method for in-pile detection of thermal conductivity. (authors)

21 SPECIFIC NUCLEAR REACTORS AND ASSOCIATED PLANTS↗

An Analytic Model for Skin Modulus Measurement Via Conformal Piezoelectric Systems

The Young's modulus of human skin is of great interests to dermatology, cutaneous pathology, and cosmetic industry. A wearable, ultrathin, and stretchable device provides a noninvasive approach to measure the Young's modulus of human skin at any location, and in a way that is mechanically invisible to the subject. Here, a mechanics model is developed in this paper to establish the relation between the sensor voltage and the skin modulus, which, together with the experiments, provides a robust way to determine the Young's modulus of the human skin.

36 MATERIALS SCIENCE↗

Quantum entanglement at ambient conditions in a macroscopic solid-state spin ensemble

Entanglement is a key resource for quantum computers, quantum-communication networks, and high-precision sensors. Macroscopic spin ensembles have been historically important in the development of quantum algorithms for these prospective technologies and remain strong candidates for implementing them today. This strength derives from their long-lived quantum coherence, strong signal, and ability to couple collectively to external degrees of freedom. Nonetheless, preparing ensembles of genuinely entangled spin states has required high magnetic fields and cryogenic temperatures or photochemical reactions. We demonstrate that entanglement can be realized in solid-state spin ensembles at ambient conditions. We use hybrid registers comprising of electron-nuclear spin pairs that are localized at color-center defects in a commercial SiC wafer. We optically initialize 10 3 identical registers in a 40-μm 3 volume (with 0.95$_{-0.07}^{+0.05}$ fidelity) and deterministically prepare them into the maximally entangled Bell states (with 0.88 ± 0.07 fidelity). To verify entanglement, we develop a register-specific quantum-state tomography protocol. The entanglement of a macroscopic solid-state spin ensemble at ambient conditions represents an important step toward practical quantum technology.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗

Multi Infrastructure Control and Optimization Toolkit, Resilient Design Module (MICOT-RDT), version 2.X

MICOT is a tool for optimizing and controlling infrastructure systems. In includes modules for optimizing the operations of an infrastructure structure (for example optimal dispatch), designing infrastructure systems, restoring infrastructures systems, resiliency, preparing for natural disasters, interdicting networks, state estimation, sensor placement, and simulation of infrastructure systems. It implements algorithms developed at LANL that have been published in the academic community. This is a release of the of resilient design module of the MICOT.

Bent, Russell↗

Evaluation of Routine Atmospheric Sounding Measurements using Unmanned Systems (ERASMUS)

The use of small unmanned aircraft systems (sUAS) with miniature sensor systems for atmospheric research is an important capability to develop. The Evaluation of Routine Atmospheric Sounding Measurements using Unmanned Systems (ERASMUS) project, lead by Dr. Gijs de Boer of the Cooperative Institute for Research in Environmental Sciences (CIRES- a partnership of NOAA and CU-Boulder), is a significant milestone in realizing this new potential. This project has clearly demonstrated that the concept of sUAS utilization is valid, and miniature instrumentation can be used to further our understanding of the atmospheric boundary layer in the arctic.

54 ENVIRONMENTAL SCIENCES↗

Remote Sensing System (Engineering Change Proposal (ECP) 2)

The primary technical objective of this CRADA was for Sandia National Laboratories (SNL) and Northrop Grumman Systems Corporation, acting through Northrop Grumman Aerospace Systems (NGAS), to collaborate, as needed, to inform a Remote Sensing System (RSS) development, led by the NGAS CRADA partner, through its System Design Review (SDR) and subsequent Program Design Review (PDR) milestones. More specifically, tasks and deliverables involved SNL and NGAS collaborating in seven broad areas: System Engineering, System Modeling and Simulation, System and Component Design, Assessment and Implementation, System Integration and Testing, Data Path Design and Engineering and Collection Segment Processing Engineering and Implementation. The collaboration between SNL and NGAS has resulted in a benefit to both organizations, and the public/taxpayers, by ensuring that legacy knowledge is not lost to future developments and will be incorporated into ongoing and future U.S. sensor systems.

47 OTHER INSTRUMENTATION↗

Signal Processsing for Sounding Rocket Flights: Campaign No. 2 Results

Sounding rockets provide both a viable and convenient alternative to more costly missile flights with one of the major objectives being to test various types of sensors for monitoring components of high interest as well as developing real-time signal processing techniques. Signal processing, especially in real-time, presents an extreme challenge in this noisy flight environment. The estimation and tracking of modal frequencies from vibrating structures is the an important set of features that can provide information about the components under test; therefore, high resolution multichannel spectral processing is required. In this report we discuss the application of both single/multichannel techniques that can be applied to solve this challenging problem in the case of noisy, uncertain accelerometer measurements. The approach involves the selection of methods capable of producing reliable spectral estimates corresponding to the modal frequencies of a vibrating structure.

42 ENGINEERING↗

On-Line Lead/Water Heat Exchanger Sensor/System Feasibility: CRADA 462 [Abstract Only]

The purpose of this project is to develop and demonstrate the feasibility of a high-temperature sensor for on-line structural health monitoring (OLSHM) of the novel heat exchanger tubes in the Hydromine lead cooled reactor. The high temperature sensor takes advantage of the Hydromine heat exchanger tube design that has a relatively cool inlet temperature (~ 350 °C) and the tube ends are in an Argon gas blanket rather than being submerged in a corrosive fluid and the tube ends are conveniently accessible from the periphery of the heat exchanger shell.

21 SPECIFIC NUCLEAR REACTORS AND ASSOCIATED PLANTS↗

Cyber-Physical System: Design for Sustainability and Resilience

When considering the design tools needed in the transition from numeric models to pilot plant, cyber-physical systems (CPS) come to the forefront as a method to model complex integrated energy systems. CPS approach has proven to be valuable to identify opportunities for economically viable early adoption of integrated energy technologies. This tutorial will introduce the concepts and the roles of CPS in co-design to minimize risks for pilot plant and technology deployment. This tutorial will also layout basic requirements for the CPS development, which requires a highly interdisciplinary effort with expertise in sensors, hardware testing, real-time modeling, controls, and system integration.

Harun, Nor Farida↗

Listening for Neutrinos with Quantum Acoustics

Many low-threshold superconducting detectors rely on athermal phonon sensing, in which meV-scale phonons from a DM scatter are sensed via their ability to break Cooper pairs in superconducting films. While detectors based on such pairbreaking sensors are highly advantageous and scalable, their sensor threshold is ultimately limited by the energy needed to break a Cooper pair, 2Δ. We present a novel detector architecture for single phonon detection at the O(10μeV) scale, the qubit-coupled hBAR, that does not share this limitation. This architecture, composed of a superconducting qubit piezoelectrically coupled to a high-overtone bulk acoustic resonator, was originally developed in the context of the quantum acoustics community and has a sensor threshold ultimately limited by thermal noise. Such ultra-low-threshold detectors have a wide range of fundamental science applications, including reactor monitoring via neutrinos.

Baxter, Daniel Stephen [Fermilab] (ORCID:000000030↗

Multi-Temporal Predictive Modelling of Sorghum Biomass Using UAV-Based Hyperspectral and LiDAR Data

High-throughput phenotyping using high spatial, spectral, and temporal resolution remote sensing (RS) data has become a critical part of the plant breeding chain focused on reducing the time and cost of the selection process for the “best” genotypes with respect to the trait(s) of interest. In this paper, the potential of accurate and reliable sorghum biomass prediction using visible and near infrared (VNIR) and short-wave infrared (SWIR) hyperspectral data as well as light detection and ranging (LiDAR) data acquired by sensors mounted on UAV platforms is investigated. Predictive models are developed using classical regression-based machine learning methods for nine experiments conducted during the 2017 and 2018 growing seasons at the Agronomy Center for Research and Education (ACRE) at Purdue University, Indiana, USA. The impact of the regression method, data source, timing of RS and field-based biomass reference data acquisition, and the number of samples on the prediction results are investigated. R2 values for end-of-season biomass ranged from 0.64 to 0.89 for different experiments when features from all the data sources were included. Geometry-based features derived from the LiDAR point cloud to characterize plant structure and chemistry-based features extracted from hyperspectral data provided the most accurate predictions. Evaluation of the impact of the time of data acquisition during the growing season on the prediction results indicated that although the most accurate and reliable predictions of final biomass were achieved using remotely sensed data from mid-season to end-of-season, predictions in mid-season provided adequate results to differentiate between promising varieties for selection. The analysis of variance (ANOVA) of the accuracies of the predictive models showed that both the data source and regression method are important factors for a reliable prediction; however, the data source was more important with 69% significance, versus 28% significance for the regression method.

09 BIOMASS FUELS↗

Digital Twin Enabling Technologies for Online Condition Monitoring of Nuclear Power Plant Components

Online condition monitoring is an area of active research that may enable improved scheduling, maintenance, and safety of nuclear power plant components, reducing unnecessary derate while simultaneously improving operational capacity. Digital twins are one avenue to conduct online condition monitoring and are currently being explored by national laboratories and universities alike. Digital twins for online condition monitoring are, in essence, state concurrent models that emulate a physical process which predicts a parameter and compares it against a measured value. However, digital twins may also provide additional insights by combining and interpreting various sources of information. These insights may be used for preventative maintenance scheduling optimization or early fault detection and are projected to be valuable for meeting requirements under 10 CFR 50.55a. However, digital twin technologies are still under significant development; quantifying model uncertainties, improving unique fault identification, and multimodal sensor fusion are some areas under investigation. Therefore, in this work, we discuss and review the various enabling technologies, in the form of advanced sensors, instrumentation, and modelling methods, that may be used to implement and enhance digital twins for online condition monitoring. A potential use case for pump-motors is presented to demonstrate how these various pieces of enabling digital twin technologies may integrated together for online condition monitoring. Challenges and opportunities associated with the pump-motor digital twin enabling technologies are also identified and discussed.

22 GENERAL STUDIES OF NUCLEAR REACTORS↗

Digital-Twin-Enabling Technologies for Online Condition Monitoring of Nuclear Power Plant Components

Online condition monitoring is an area of active research that may enable improved scheduling, maintenance, and safety of nuclear power plant components, reducing unnecessary derates while simultaneously improving operational capacity. Digital twins are one avenue to conduct online condition monitoring and are currently being explored by national laboratories and universities alike. Digital twins for online condition monitoring are, in essence, state concurrent models that emulate a physical process which predicts a parameter and compares it against a measured value. However, digital twins may also provide additional insights by combining and interpreting various sources of information. These insights may be used for preventative maintenance scheduling optimization or early fault detection and are projected to be valuable for meeting requirements under 10 CFR 50.55a. However, digital twin technologies are still under significant development; quantifying model uncertainties, improving unique fault identification, and multimodal sensor fusion are some areas under investigation. Therefore, in this work, we discuss and review the various enabling technologies, in the form of advanced sensors, instrumentation, and modelling methods, that may be used to implement and enhance digital twins for online condition monitoring. A potential use case for pump-motors is presented to demonstrate how these various pieces of enabling digital twin technologies may integrated together for online condition monitoring. Challenges and opportunities associated with the pump-motor digital twin enabling technologies are also identified and discussed.

22 - GENERAL STUDIES OF NUCLEAR REACTORS↗