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At least 253 records · Page 14

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DOE OSTI2020

Calibration of the Falcon Solid-state Energetic Electron Detector (SEED)

The Falcon Solid-state Energetic Electron Detector (FalconSEED) is an energetic charged particle sensor that has been developed and tested at the United States Air Force Academy in an effort to monitor electron flux across the energy range of 14 to 145 keV in geosynchronous orbit. This sensor has been developed to complement ongoing efforts by the Air Force Research Laboratory to advance a comprehensive space environment sensor suite, Compact Environmental Anomaly Sensor (CEASE3), for anomaly resolution. In addition, FalconSEED is intended to demonstrate the ability to operate a radiation sensor based on predominantly commercial off the shelf components in the geosynchronous environment. This paper describes the design, development, and calibration of FalconSEED. The electron energy spectrum of interest for the FalconSEED sensor is 14 to 145 keV, and is intended to supplement data acquired from the CEASE3 instrument for anomaly resolution. The calibrations at the Space Atmospheric Research Center at the United States Air Force Academy and Kirtland Air Force Base were used to first establish the conversion between histogram bin number and deposited energy in the sensor, and second have been used to determine the geometric factor as a function of energy. The FalconSEED will be integrated as a science payload on the Space Test Program Satellite-6 which also includes the first flight of the CEASE3 instrument and is scheduled to launch in 2021.

47 OTHER INSTRUMENTATION↗

DOE OSTI2023

Design and Characterization of a Burst Mode 20 Mfps Low Noise CMOS Image Sensor

This paper presents a novel ultra-high speed, high conversion-gain, low noise CMOS image sensor (CIS) based on charge-sweep transfer gates implemented in a standard 180 nm CIS process. Through the optimization of the photodiode geometry and the utilization of charge-sweep transfer gates, the proposed pixels achieve a charge transfer time of less than 10 ns without requiring any process modifications. Moreover, the gate structure significantly reduces the floating diffusion capacitance, resulting in an increased conversion gain of 183 µV/e−. This advancement enables the image sensor to achieve the lowest reported noise of 5.1 e− rms. To demonstrate the effectiveness of both optimizations, a proof-of-concept CMOS image sensor is designed, taped-out and characterized.

47 OTHER INSTRUMENTATION↗

DOE OSTI2020

Advanced Manufacturing of Printed Melt Wire Chips for Cheap, Compact Passive In-Pile Temperature Sensors

Melt wires are a passive sensor used to determine peak temperatures during a test. Traditional melt wires are commonly used in test reactor experiments such as in the Advanced Test Reactor (ATR). However, the conditions within a reactor present significant challenges towards test design due to space limitations and the harsh environment. For example, some test capsules have only a couple millimeters in diameter available for instrumentation, which is too small to accommodate a traditional melt wire package, and they are ultimately filled to capacity when they are immersed in molten metal. To enable instrumentation for space limited applications, peak temperature sensing capabilities paired with additive manufacturing options have been utilized to develop printed melt wires for peak irradiation temperature detection. Here, we report on the fabrication of miniaturized melt wire chips with a melting temperature ~960 °C with printed silver nanoparticle ink. This study will advance the development of unique temperature sensors capable of sensing user specified temperature ranges within the harsh environment of irradiation testing.

46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND ↗

DOE OSTI2021

Distributed, Intelligent Edge-Sensing for a Smarter Grid

The electric grid is undergoing major transformations and developments resulting in unprecedented levels of volatility, uncertainty, and stress on grid infrastructure. Smart sensors and methods aiding in advanced visibility and situational awareness are key for tackling these issues. In this work, a decentralized architecture is proposed, where sensing, local computation and control capability are embedded in the edge devices, communicating with a set of trusted 'data mules' in a 'delay-tolerant' manner, while functioning autonomously. This system has been designed and implemented as an overall platform – called Global Asset Monitoring, Management and Analytics (GAMMA) Platform intended to provide the backbone for a global array of sensors and actuators. Further, as a building block for advanced current sensing solutions, a smart, low-cost ‘clip-on’ current sensor based on PCB-embedded Rogowski coil has been developed. The sensor hosts a novel signal conditioning stage allowing an 'auto-tuning' feature, resulting in a universal current sensor design for measuring a wide range of currents, including faults for smart grid applications. Finally, the research proposes a method to instrument and monitor key parameters for the most common electric utility asset – the pole-top distribution transformer. The work done in this research enables scalable, edge-intelligent sensing solutions for monitoring grid infrastructure, allowing utility operators to gain advanced visibility in an economical way.

Kulkarni, Shreyas Bhalchandra↗

DOE OSTI2021

Conductive Metallophthalocyanine Framework Films with High Carrier Mobility as Efficient Chemiresistors

Abstract The poor electrical conductivity of two‐dimensional (2D) crystalline frameworks greatly limits their utilization in optoelectronics and sensor technology. Herein, we describe a conductive metallophthalocyanine‐based NiPc‐CoTAA framework with cobalt(II) tetraaza[14]annulene linkages. The high conjugation across the whole network combined with densely stacked metallophthalocyanine units endows this material with high electrical conductivity, which can be greatly enhanced by doping with iodine. The NiPc‐CoTAA framework was also fabricated as thin films with different thicknesses from 100 to 1000 nm by the steam‐assisted conversion method. These films enabled the detection of low‐concentration gases and exhibited remarkable sensitivity and stability. This study indicates the enormous potential of metallophthalocyanine‐based conductive frameworks in advanced stand‐off chemical sensors and provides a general strategy through tailor‐make molecular design to develop sensitive and stable chemical sensors for the detection of low‐concentration gases.

Yue, Yan↗

DOE OSTI2021

Conductive Metallophthalocyanine Framework Films with High Carrier Mobility as Efficient Chemiresistors

Yue, Yan↗

DOE OSTI2022

Performance of Microreactor Test Article with Embedded Sensors During Testing in The Single Primary Heat Extraction and Removal Emulator

The nuclear industry is pursuing microreactors that can be factory assembled and deployed to remote regions for reliable power generation. One class of microreactors uses a monolithic metal core block coupled to heat pipes that use passive heat flow, increasing the surface area for heat transfer without requiring active coolant flow through the reactor core. Additional experimental testing is required to understand the heat rejection limitations of heat pipes and thermal stresses in the monolithic core block due to significant temperature gradients. This report describes the initial characterization and testing of a stainless-steel test article that was fabricated with embedded sensors to measure heat pipe performance limits, as well as spatially distributed temperatures and strains during electrically heated thermal testing to simulate nuclear heating. The electrically heated testing was performed in the Single Primary Heat Extraction and Removal Emulator facility located at Idaho National Laboratory. The ultimate goals of this work are to (1) accurately monitor temperature and strain distributions that result from differential thermal expansion in the test articles and (2) quantify the heat rejection limits of heat pipes as a function of operating temperature and working fluid during steady-state and transient operations. Initial tests focused on the feasibility of using advanced fiber-optic sensors and other sensor technologies to improve the understanding of temperature and strain distributions within the electrically heated experiments. However, these sensing capabilities could benefit the broader microreactor community if the sensors could be used to monitor component and system health during nuclear operations to inform a limited number of microreactor operators, ultimately reducing operation and maintenance costs and moving toward semiautonomous operation.

22 GENERAL STUDIES OF NUCLEAR REACTORS↗

DOE OSTI2025

Nucleation and Growth of Blue Phase 3D Cubic Structure Under Continuously Changing Curved Boundary Conditions

The blue phase of liquid crystals (BPLCs) with a cubic lattice of disclination lines and 3D nanostructure enables the modulation of photonic bandgap thus casting them in the category of photonic crystals. Its unique nature promises applications in display technologies, electro‐optics, and sensors. To integrate these ordered materials into wearable devices a fundamental understanding of curvature, and spatial confinement is necessary. Although continuous confinement in flat geometries have been studied, confining curvature has shown to induce strong destabilization effects on the cubic structure and formation of topological defects, thereby deteriorating their optical performance. Moreover, limitations in controlling the curvature of droplets further hinder studies of nucleation and growth of BPLCs. Here, to address these challenges, micro‐scale patterned surfaces of concentric cylinders are exploited to systematically control curvatures. The impact of curvature on the confined BPLCs is revealed in terms of phase transition temperature, nucleation and growth, morphology, as well as phase transformation. This research offers valuable insights into the stability, and structural characteristics of BPLCs in adaptive photonic devices, paving the way for future advancements in flexible displays, sensors, and other technologies leveraging liquid crystal (LC) materials.

3D Cubic Structure↗

DOE OSTI2025

xGFabric: Coupling Sensor Networks and HPC Facilities with Private 5G Wireless Networks for Real-Time Digital Agriculture

Advanced scientific applications require coupling distributed sensor networks with centralized high-performance computing facilities. Citrus Under Protective Screening (CUPS) exemplifies this need in digital agriculture, where citrus research facilities are instrumented with numerous sensors monitoring environmental conditions and detecting protective screening damage. CUPS demands access to computational fluid dynamics codes for modeling environmental conditions and guiding real-time interventions like water application or robotic repairs. These computing domains have contrasting properties: sensor networks provide low-performance, limited-capacity, unreliable data access, while high-performance facilities offer enormous computing power through high-latency batch processing. Private 5G networks present novel capabilities addressing this challenge by providing low latency, high throughput, and reliability necessary for near-real-time coupling of edge sensor networks with HPC simulations. This work presents xGFabric, an end-to-end system coupling sensor networks with HPC facilities through Private 5G networks. The prototype connects remote sensors via 5G network slicing to HPC systems, enabling real-time digital agriculture simulation.

Digital Agriculture↗

DOE OSTI2025

Development of a High-Intensity Heat Flux Gauge and Characterization Facility (Final Report)

Sandia National Laboratories (SNL) and Hukseflux Thermal Sensors (HTS) collaborated to advance the design and calibration of high-intensity heat flux gauges capable of measuring 2500 kW/m². An industry trade study was first conducted and highlighted the need for enhanced gauge designs and calibration methodology & services suited for high-intensities and broadband flux. We then developed, tested, and evaluated three prototype gauge designs along with four distinct coating types, each designed to extend the measurable flux range of existing HTS products to higher intensity levels. Following a down selection process, the project team refined the focus to a final product design, incorporating updated features to enhance its robustness during high flux exposure.

46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND ↗

DOE OSTI2023

Predicting chatter using machine learning and acoustic signals from low-cost microphones

Machining chatter is a phenomenon resulting from self-oscillation between a machining tool and workpiece. This self-oscillation results in variation on the machined product that reduces the ability to meet desired specifications. Chatter is a widely studied topic as it directly relates to the quality of machined products. Here, this study details the application of a Random Forest (RF) classifier with Recursive Feature Elimination (RFE) to machining audio collected by a single microphone during down-milling operations. This approach allows straightforward feature elimination that results in an easily understood set of analyzed dimensions. Stability is predicted solely based on the classification output of the RF classifier. Our approach proves highly predictive with consistent machining setup and a small sample set. We also review transferability between machining setups and present key findings. Our RF approach demonstrates the ability to analyze and classify chatter through a low-cost approach with limited training data required. The motivation for using a single microphone is to enable detection on machines without other sensors, such as accelerometers, present in the machining setup. The value of the in-process sensor and chatter classifier is highlighted because the machining setup included asymmetric dynamics that reduced the accuracy of the traditional analytical stability solution. We see a natural progression to deploying this audio-only methodology with real-time processing and classification using either a laptop or smartphone. This progression will allow visual indicators during the machining process that can alert machinists of progression into unstable machining processes.

42 ENGINEERING↗

DOE OSTI2020

Enhanced flow boiling heat transfer on chromium coated zircaloy-4 using cold spray technique for accident tolerant fuel (ATF) materials

Flow boiling heat transfer tests were conducted to evaluate the Critical Heat Flux (CHF) and Heat Transfer Coefficient (HTC) of conventional and accident tolerant fuel (ATF) cladding materials, i.e., bare Zircaloy-4 (Zr4), and Zircaloy-4 coated with Chromium using physical vapor deposition (PVD) (Zr4-Cr-PVD) and cold spray process (Zr4-Cr-CS), respectively. The tests were performed on a single heater rod with a uniform heat flux profile, at the atmospheric pressure, inlet temperature (24 °C) and mass flow rate (750 kg/m 2 s). HTC’s were increased by 5.2% and 3.3% on Zr4-Cr-CS compared to bare Zr4 and Zr4-Cr-PVD. Improved HTCwas attributed to the increased void fraction and high roughness (Ra = 532 nm) of the Zr4-Cr-CS. In detail, the micro-cavities, which act as bubble seeds, were entirely distributed on the Zr4-Cr-CS, and these micro-cavities generated smaller and faster bubbles, thus the void fraction increased by 10.9% compared to bare Zr4. HTC is improved by these smaller and faster bubbles which can remove heat from the heater rod surface more efficiently. To identify the exact location of CHF, the surface temperature profile was measured using advanced fiber-optic sensors which have high temporal/spatial resolution (distance between point to point: ~2.5 mm, frequency ~100 Hz). CHF occurred at 80–95% along the heated length and showed an 11.6% reduction on Zr4-Cr-CS compared to bare Zr4. We hypothesize the CHF reduction was caused by the lower wettability of the Zr4-Cr-CS which reduces the liquid supply to the surface, and vigorous bubble accumulation owing to high void fraction near the outlet, which results in early formation of the vapor film. During the post-CHF quenching phase, the rougher Zr4-Cr-CS showed larger cooling rates compared to the bare Zr4 and Zr4-Cr-PVD, preventing of oxidation by chromium layer. In conclusion, we demonstrate that the high roughness on the Zr4-Cr-CS increased the number of micro-cavities on the surface, resulting in 5.2% increase in HTC and improvement in the quenching heat transfer performance whereas CHF was 11.6% reduced compared to bare Zr4. Overall statistically there is little effect in the coating with regards to CHF and slight improvement in HTC.

42 ENGINEERING↗

DOE OSTI2025

Geometric control of magnetic domain morphology in electrodeposited Ni rich NiPt mesoscopic wires

In this paper, we study the electrodeposited Ni—Pt magnetic wires, exploring the mechanisms by which changes in geometry and composition influence the local magnetic structure. The samples were prepared by electrodeposition, their elemental composition was analyzed by energy dispersive X-ray spectroscopy, and phase analysis was carried out by an X-ray diffraction measurement. Magnetic force microscopy measurements, and magnetic force microscopy measurements in magnetic field were carried out to study the domain morphology of the prepared samples. Magnetization characteristics were measured via vibrating sample magnetometry mode of a superconducting quantum interference device. Here, the results were analyzed and compared with micromagnetic simulations. It is shown that a strong perpendicular magnetic anisotropy is responsible for the characteristic checkerboard-like surface magnetization patterns, which can be tuned by geometrical constriction and Ni content. Understanding these relationships provides insights into the design of magnetic materials with tailored properties for advanced applications, like magnetic sensors.

Binary alloy↗

DOE OSTI2025

Polarization-Controlled Structural Modulation in the Single Atomic Layer at the PbZr 0.2 Ti 0.8 O 3 /LaNiO 3 Interface

Conductivity modulation via ferroelectric polarization coupling with LaNiO 3 (LNO) is demonstrated at an epitaxial ferroelectric–LNO interface. Conductivity measurements, varying the thickness of the LNO channel, show that this phenomenon is confined to a few atomic layers at the interface. Combining in situ biasing and off-axis holography, we mapped out electrostatic potentials at the PbZr 0.2 Ti 0.8 O 3 (PZT)/LNO/SrTiO 3 (STO) heterostructure upon polarization switching. Using aberration-corrected STEM, the interfacial atomic structures were investigated for the two different PZT polarization states. Polarization in PZT induces a significant change in the in-plane O–Ni–O bond angles, with a 37° modulation in the topmost 1 or 2 LNO unit cells, driven by strain in the oxygen sublattice for the two opposite polarization directions in PZT. Both oxygen and cation sublattices exhibit strain responses exceeding 10% upon switching. This atomic-layer structural modulation highlights a mechanism for functional oxide heterostructure development, offering pathways for advancements in nonvolatile memory, sensors, and energy-efficient transistors.

75 CONDENSED MATTER PHYSICS, SUPERCONDUCTIVITY AND↗

DOE OSTI2024

Online Detection of Power Grid Anomalies via Federated Learning

Data from sensors is critical for advanced applica- tions that support efficient, reliable, and resilient electric grid operations. Historically, data from phasor measurement units (PMU) has been utilized to develop a wide variety of wide area control and protection applications suitable for power grid control centers. However, until now, most of these could not be deployed for automated operations due to a set of data corruption challenges and uncertainty in the incoming data pipeline. In this paper, we address the problem of detecting different variety of anomalies that are evident in different high- speed power grid measurements. The paper discusses a workflow for handling problems with data acquisition and highlights some of the key findings suitable for anomaly detection in a centralized and distributed environment. The effectiveness of the proposed method was demonstrated with results utilizing realistic PMU datasets

Shinkle, Matthew W.↗

DOE OSTI2023

State Machine Operation of Complex Systems

SQMS, affiliated with Fermilab, is one of five research centers funded by the DOE dedicated to the development and deployment of advanced quantum computers and sensors. The objective of our research was to devise an experimental design for quantum transduction, with the aim of facilitating the development of a distributed quantum network to connect quantum computers over large distances.

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗

DOE OSTI2023

Internship Presentation

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗

DOE OSTI2023

Indoor Occupancy Sensing via Networked Nodes (2012–2022): A Review

In the past decade, different sensing mechanisms and algorithms have been developed to detect or estimate indoor occupancy. One of the most recent advancements is using networked sensor nodes to create a more comprehensive occupancy detection system where multiple sensors can identify human presence within more expansive areas while delivering enhanced accuracy compared to a system that relies on stand-alone sensor nodes. The present work reviews the studies from 2012 to 2022 that use networked sensor nodes to detect indoor occupancy, focusing on PIR-based sensors. Methods are compared based on pivotal ADPs that play a significant role in selecting an occupancy detection system for applications such as Health and Safety or occupant comfort. These parameters include accuracy, information requirement, maximum sensor failure and minimum observation rate, and feasible detection area. We briefly describe the overview of occupancy detection criteria used by each study and introduce a metric called “sensor node deployment density” through our analysis. This metric captures the strength of network-level data filtering and fusion algorithms found in the literature. It is hinged on the fact that a robust occupancy estimation algorithm requires a minimal number of nodes to estimate occupancy. This review only focuses on the occupancy estimation models for networked sensor nodes. It thus provides a standardized insight into networked nodes’ occupancy sensing pipelines, which employ data fusion strategies, network-level machine learning algorithms, and occupancy estimation algorithms. This review thus helps determine the suitability of the reviewed methods to a standard set of application areas by analyzing their gaps.

Emad-Ud-Din, Muhammad (ORCID:0000000279515538)↗