Results for “sensor selection” — Page 16

DOE OSTI2023

A Co-Registered In-Situ and Ex-Situ Dataset from a Laser Powder Bed Fusion Additive Manufacturing Process (Peregrine v2023-10)

This release contains a co-registered in-situ and ex-situ Peregrine dataset from a single Concept Laser M2 Laser Powder Bed Fusion (L-PBF) stainless steel 316L build. These data were collected at the Manufacturing Demonstration Facility (MDF) located at Oak Ridge National Laboratory (ORNL). The dataset includes layer-wise visible-light in-situ imaging data, the laser scan paths and parameters, in-situ temporal sensor data, X-Ray Computed Tomography (X-CT) scans, pycnometry and tensile test results, etched micrographs from selected locations, and the target part geometries. Additionally, anomaly detections produced by a modified Dynamic Segmentation Convolutional Neural Network (DSCNN) are provided.

36 MATERIALS SCIENCE↗

DOE OSTI2021

Systems, methods and tools for subterranean electrochemical characterization and enthalpy measurement in geothermal reservoirs

The present disclosure is directed to systems, methods and tools that measure ionic concentrations and downhole enthalpy of a flowing geothermal fluid in real-time at high-temperature and pressure. The systems, methods and tools include measuring the concentration of selected naturally occurring ions found in the liquid phase of the geothermal fluid throughout the wellbore using novel electrochemical sensor technologies. The change in liquid-phase ion concentration will be used to calculate the proportion of liquid to steam and allow for accurate enthalpy measurements. The techniques and technologies described here can be applied to any application of electrochemical sensing in extreme environments.

Cieslewski, Grzegorz↗

DOE OSTI2020

Enhanced detection of volatile organic compounds (VOCs) by caffeine modified carbon nanotube junctions

Detection of vapor phase analytes and gas molecules is primarily essential for numerous applications including medical diagnostic, environmental monitoring, chemical threat detection, food quality control, and chemical warfare agents. Here, we developed Caffeine functionalized carbon nanotubes (CNTs) random network based vapor quantum resistive sensors (vQRSs) for volatile organic compounds (VOCs) detection. The localized transmission electron microscopy (TEM) image and energy dispersive X-ray spectroscopy (EDS) confirm migration of caffeine to CNT–CNT junction (CNTj). We have recorded more than one order of magnitude higher sensitivity and unique selectivity towards selective VOCs (e.g., acetone, toluene, ethanol, methanol, etc.) with short response time (few seconds) for caffeine modified CNT junction (Caf-CNTj) vQRSs. Molecular dynamics (MD) calculations suggest that the sensitivity of the sensor is diffusion limited, with an advective contribution for ethanol. Furthermore, the designed Caf-CNTj sensors can potentially be important candidate to be a part of electronic nose used for various application such as breath analysis and food quality monitoring.

36 MATERIALS SCIENCE↗

DOE OSTI2021

Real-time and Autonomous Water Quality Monitoring System Based on Remotely Operated Vehicle

Existing water quality monitoring systems near hydropower facilities are limited by the lack of mobility of the sensors’ carrier platform. Most systems use a buoy, a mounting fixture attached to a solid structure, or a human worker, which significantly limits the selection of the sampling sites and poses safety risks during data collection and equipment maintenance. To improve on this technology, we developed an autonomous water quality monitoring system that can operate in dangerous water environments near hydropower facilities for water sampling at multiple locations. The goal is to enable safe, timely, and comprehensive water-quality data collection; maximize power generation revenue with improved operational control; and reduce Federal Energy Regulatory Commission and state water quality monitoring costs for compliance. The system incorporates a remotely operated vehicle as the mobile monitoring platform, a dissolved oxygen sensor for monitoring water quality, a tether management system for automatically winding the tether, a solar mobile docking platform for suppling power to the ROV, and a web-based graphical user interface for data post-processing and visualization. In addition, preliminary field research are presented to demonstrate the system capabilities.

Salalila, Aljon L.↗

DOE OSTI2020

Advances in PFAS Monitoring and Remediation Using a Functionalized Material Approach - 20080

The growing global concerns about the effects to public health from human exposure to per- and polyfluoroalkyl substances (PFAS) motivates the development of strategies for reliable monitoring of PFAS in environmental streams, as well as for their rapid, effective removal if detected. For the continuous PFAS monitoring, an inexpensive, field-deployable, in situ sensor is urgently needed; yet the prevalent in situ techniques often struggle to strike a balance between the practical sensitivity and selectivity demands of the real world. Similarly, for effective PFAS removal, strategies for their fast, selective, and quantitative capture are desired, yet the present commercially available sorbents are unable to meet the requirements of rapid, quantitative capture of all PFAS components, and are notably inefficient in removing the more toxic smaller chains. To address these twin challenges, Pacific Northwest National Laboratory is developing strategies for improved detection and remediation of PFAS. For the rapid, selective, quantitative removal of PFAS from environmental streams, the strategy relies on designing capture probes with exclusively tailored electronic and spatial affinities for the PFAS that are able to selectively capture them from environmental streams. For the in situ detection and quantification of PFAS in complex, multicomponent matrices such as groundwater, the approach relies on the targeted capture of specific PFAS by these PFAS-specific capture probes immobilized on a platform. The platform acts as an electrode to directly measure PFAS concentration through a proportional change in electrical response upon their capture. A combination of optimization of platform design and incorporation of additional, sensitive detection modalities have allowed us to achieve detection limits as low as 0.5 ng/L for detection of PFAS compounds (compared to the 70 ng/L Health Advisory Limit of the U.S. Environmental Protection Agency). (authors)

47 OTHER INSTRUMENTATION↗

DOE OSTI2025

Integrated soft snake robot for gamma spectroscopy of radiologically contaminated ducting

Anticipated growth in the number and complexity of nuclear facilities entering the decommissioning phase of their lifespan requires innovative methods of mitigating radiological and safety hazards for workers. Advancements in robotics provide opportunities for remote characterization of radiologically contaminated ducts and pipes, reducing worker exposure. However, the nuclear decommissioning industry has shown a hesitancy in adopting new technology due to concerns regarding capital costs and a lack of demonstration data in representative environments. This work aims to leverage novel soft robotics to develop and demonstrate a sensor-integrated characterization tool for contaminated ducting systems. Two gamma spectrometers – one scintillation-based and one solid-state – were selected and integrated into a pneumatically-actuated snake-like soft robot using stretchable electronic cabling. The robot was then demonstrated at the Idaho National Laboratory’s Critical Infrastructure Test Range Complex. The robot maneuvered through a pseudo-constrained corridor containing several sealed sources and the integrated spectrometers collected spectra at various points along the path of travel. Both spectrometers successfully collected spectra with identifiable, isotope-specific peaks. To evaluate the effect of contamination buildup on the collected spectra, the robot with the integrated scintillation detector was tested using a liquid radioactive source. The resulting spectra showed little detection interference from the liquid contamination; however, a short-lived isotope was used which may not be representative of longer-lived contamination.

11 - NUCLEAR FUEL CYCLE AND FUEL MATERIALS↗

DOE OSTI2020

Voltammetry measurements in lithium chloride-lithium oxide (LiCl–Li 2 O) salt: An evaluation of working electrode materials

Instrumentation to provide process monitoring (PM) and safeguards for the oxide reduction (OR) step in the electrochemical processing of used oxide nuclear fuel is necessary to ensure equipment is operated as declared. Cyclic voltammetry (CV) has been proposed for real-time monitoring of the operation of an OR process for safeguards purposes. In this study, different materials including 316 stainless steel (SS), tantalum (Ta), molybdenum (Mo), tungsten (W), platinum (Pt), and iridium (Ir) were evaluated as potential working electrode (WE) materials based on their chemical inertness, corrosion resistance, and sensitivity in detecting Li 2 O and other chloride salts in the OR electrolyte. Of the electrodes evaluated, 316 SS, Ir, and Pt all performed reasonably well in the LiCl-Li 2 O electrolyte. Here, stainless steel was operated in the cathodic potential and had reasonable corrosion resistance and is relatively inexpensive. Iridium could be operated in both the cathodic and anodic potentials and was the most corrosion resistant of those evaluated. Platinum is limited to the anodic potential range, had reasonable corrosion resistance, and was the most sensitive to Li 2 O concentrations in the salt. In the development of a stand-alone safeguards instrumentation, our recommendation is a CV sensor fitted with both Ir and Pt WEs. The results in this study may also be helpful for anode material selection in other electrometallurgy industries, such as molten salt electrolysis.

98 NUCLEAR DISARMAMENT, SAFEGUARDS, AND PHYSICAL P↗

DOE OSTI2025

One-shot gas detection with transformer paired neural networks in Mako collected longwave infrared hyperspectral imagery

To date, careful data treatment workflows and statistical detectors are used to perform hyperspectral image (HSI) detection of any gas contained in a spectral library, which is often expanded with physics models to incorporate different spectral characteristics. In general, surrounding evidence or known gas-release parameters are used to provide confidence in or confirm detection capability, respectively. This makes quantifying detection performance difficult as it is nearly impossible to develop an absolute ground truth for gas target pixel presence in collected HSI. Consequently, developing and comparing new detection methods, especially machine learning (ML)-based methods, is susceptible to subjectivity in derived detection map quality. Here, in this work, we demonstrate the first use of transformer-based paired neural networks (PNNs) for one-shot gas target detection for multiple gases while providing quantitative classification and detection metrics for their use on labeled data. Terabytes of training data are generated from a database of long-wave infrared HSI obtained from historical Mako sensor campaigns over Los Angeles. By incorporating labels, singular signature representations, and a model development pipeline, we can tune and select PNNs to detect multiple gas targets that are not seen in training on a quantitative basis. We additionally assess our test set detections using interpretability techniques widely employed with ML-based predictors, but less common with detection methods relying on learned latent spaces.

Hyperspectral imaging↗

DOE OSTI2024

An Update on Microreactor Automated Control System (MACS)

Automation of control systems is expected to be important in the economic and safe operation of microreactors. There is a need to develop and demonstrate automated control for microreactors, along with the development of testbeds for this purpose. This report provides updates on the status of a microreactor automated control system (MACS) testbed developed to test control system automation. While a future goal is to demonstrate this system using a prototypic microreactor such as MARVEL, the present focus is on developing and testing within a non-nuclear testbed. The testbed, developed in collaboration with Idaho National Laboratory, includes hardware-in-the-loop simulation and uses a Modelica-based model of a prototypic microreactor for use in testing control automation. Research to date at Oak Ridge National Laboratory has focused on the development of prototypic software for automating plant-level control under selected scenarios. Empirical testing on the integrated MACS testbed was performed to quantify key characteristics of the integrated testbed and to demonstrate the use of the software for automating the calculation and use of actuation setpoints for selected load-following scenarios. Ongoing research is focused on integrating additional control algorithms that utilize data from newly included sensors within the MACS hardware testbed, as well as demonstrating and assessing the performance of the different automated control algorithms on multiple additional operational scenarios.

22 GENERAL STUDIES OF NUCLEAR REACTORS↗

DOE OSTI2023

Boosting Electrochemical Catalysis and Nonenzymatic Sensing Toward Glucose by Single‐Atom Pt Supported on Cu@CuO Core–Shell Nanowires

Abstract It is critical to develop high‐performance electrocatalyst for electrochemical nonenzymatic glucose sensing. In this work, a single‐atom Pt supported on Cu@CuO core–shell nanowires (Pt 1 /Cu@CuO NWs) for electrochemical nonenzymatic glucose sensor is designed. Pt 1 /Cu@CuO NWs exhibit excellent electrocatalytic oxidation toward glucose with 70 mV lower onset potential (0.131 V) and 2.4 times higher response current than Cu NWs. Sensors fabricated using Pt 1 /Cu@CuO NWs also show high sensitivity (852.163 µA mM −1 cm −2 ), low detection limit (3.6 µM), wide linear range (0.01–5.18 µM), excellent selectivity, and great long‐term stability. The outstanding sensing performance of Pt 1 /Cu@CuO NWs, investigated by experiments and density functional theory (DFT) calculations, is attributed to the synergistic effect between Pt single atoms and Cu@CuO core–shell nanowires that generates strong binding energy of glucose on the nanowires. The work provides a new pathway for exploring highly active SACs for electrochemical nonenzymatic glucose sensor.

Chemistry↗

DOE OSTI2026

Robotics for HVAC applications: A critical review and future perspectives

Recent advances in artificial intelligence (AI), enhanced computational capabilities, and innovations in sensors and hardware have driven the increasing development and application of robots in heating, ventilation, and air conditioning (HVAC) systems. We selected and reviewed 101 studies published between 2005 and 2025, sourced from IEEE Xplore, Scopus, Web of Science, and the ACM Digital Library. To analyze these works, we developed a five-dimensional analytical framework (morphology, sensing, navigation, task execution, and system integration), inspired by the Springer Handbook of Robotics and tailored specifically for robotic applications in HVAC. Based on the reviewed studies, six distinct tasks spanning the entire HVAC lifecycle have been identified. Among the six tasks, inspection and maintenance dominate (59 %), followed by indoor monitoring and auditing (21 %), whereas leakage detection, comfort support, and installation/retrofit remain less explored. To address the identified gaps, this review proposes future research directions including investigating robot-aware HVAC design principles, developing multimodal HVAC sensing and data fusion techniques, enhancing robot training and hardware capabilities, and expanding robotic applications beyond Maintenance and Operations (M&O). The findings from this review inform future robotics research for HVAC applications and ultimately enhance system affordability, energy efficiency, resilience or reliability, and occupant environmental comfort. Moreover, it seeks to inspire researchers to explore the intersections of robotics, computer science, building science, and HVAC engineering fostering advancements in this multidisciplinary field.

AI↗

DOE OSTI2024

Code Validation of SAM Using Forced and Natural Circulation Data from NACIE-UP Benchmark

Heavy liquid metals (HLMs) are promising candidates as coolants of Generation IV fast reactors due to their thermophysical properties. In the last decade, experimental work has been proposed as part of research and development efforts to develop such systems. In this context, researchers from the Brasimone Research Center have conducted many experiments using the Natural Circulation Experiment Upgrade (NACIE-UP) facility to study the thermofluid dynamic behavior of HLMs in rod bundle configurations with or without wire wrappers. This facility consists of a rectangular loop operated with lead-bismuth eutectic. Sensors across the loop monitor relevant parameters, i.e. temperatures, heat transfer, and flow conditions. Here, in the present work, we carefully select published data from NACIE-UP to validate the System Analysis Module (SAM), a modern system analysis code developed at Argonne National Laboratory. We developed one SAM model using specifications of the facility geometry and materials existing in relevant papers and reports. On top of that, these references provided the boundary conditions for simulating natural circulation and forced convection experiments in either steady or transient conditions. The SAM model simulates five test cases with diverse operating conditions. Ultimately, the code is proven to predict temperatures and mass flow rates that closely match the experiments. The discrepancies between numerical predictions and diverse transients are limited to a few degrees Celsius, showcasing that SAM is well suited for analyzing nuclear systems relying on HLM coolants.

advanced reactors↗

DOE OSTI2020

Review—Thermal Safety Management in Li-Ion Batteries: Current Issues and Perspectives

Approaches for thermal management of lithium-ion (Li-ion) batteries do not always keep pace with advances in energy storage and power delivering capabilities. Root-cause analysis and empirical evidence indicate that thermal runaway (TR) in cells and cell-to-cell thermal propagation are due to adverse changes in physical and chemical characteristics internal to the cell. However, industry widely uses battery management systems (BMS) originally designed for aqueous-based batteries to manage Li-ion batteries. Even the "best" BMS that monitor both voltage and outside-surface temperature of each cell are not capable of preventing TR or TR propagation, because voltage and surface-mounted temperature sensors do not track fast-emerging adverse events inside a cell. Most BMS typically include a few thermistors mounted on select cells to monitor their surface temperature. Technology to track intra-cell changes that are TR precursors is becoming available. Simultaneously, the complex pathways resulting in cell-to-cell TR propagation are being successfully modelled and mapped. Innovative solutions to prevent TR and thermal propagation are being advanced. These include modern BMS for rapid monitoring the internal health of each individual cell and physical as well as chemical methods to reduce the deleterious effects of rapid cell-to-cell heat and material transport in case of TR.

25 ENERGY STORAGE↗

DOE OSTI2020

Source identification by non-negative matrix factorization combined with semi-supervised clustering

Machine-learning methods and apparatus are provided to solve blind source separation problems with an unknown number of sources and having a signal propagation model with features such as wave-like propagation, medium-dependent velocity, attenuation, diffusion, and/or advection, between sources and sensors. In exemplary embodiments, multiple trials of non-negative matrix factorization are performed for a fixed number of sources, with selection criteria applied to determine successful trials. A semi-supervised clustering procedure is applied to trial results, and the clustering results are evaluated for robustness using measures for reconstruction quality and cluster separation. The number of sources is determined by comparing these measures for different trial numbers of sources. Source locations and parameters of the signal propagation model can also be determined. Disclosed methods are applicable to a wide range of spatial problems including chemical dispersal, pressure transients, and electromagnetic signals, and also to non-spatial problems such as cancer mutation.

97 MATHEMATICS AND COMPUTING↗

DOE OSTI2021

Ionic liquid carbon nanotube composites for wireless chemical sensing

A wireless sensor platform design and a single walled carbon nanotube/ionic liquid-based chemidosimeter system can incorporated into a highly sensitive and selective chemical hazard badge that can dosimetrically detect an analyte down to a sub parts-per-million concentration.

Swager, Timothy M.↗

DOE OSTI2020

Advances in PFAS Monitoring and Remediation Using a Functionalized Material Approach

The growing global concerns about the effects to public health from human exposure to per- and polyfluoroalkyl substances (PFAS) motivates the development of strategies for reliable monitoring of PFAS in environmental streams, as well as for their rapid, effective removal if detected. For the continuous PFAS monitoring, an inexpensive, field-deployable, in situ sensor is urgently needed; yet the prevalent in situ techniques often struggle to strike a balance between the practical sensitivity and selectivity demands of the real world. Similarly, for effective PFAS removal, strategies for their fast, selective, and quantitative capture are desired, yet the present commercially available sorbents are unable to meet the requirements of rapid, quantitative capture of all PFAS components, and are notably inefficient in removing the more toxic smaller chains. To address these twin challenges, Pacific Northwest National Laboratory is developing strategies for improved detection and remediation of PFAS. For the rapid, selective, quantitative removal of PFAS from environmental streams, the strategy relies on designing capture probes with exclusively tailored electronic and spatial affinities for the PFAS that are able to selectively capture them from environmental streams. For the in situ detection and quantification of PFAS in complex, multicomponent matrices such as groundwater, the approach relies on the targeted capture of specific PFAS by these PFAS-specific capture probes immobilized on a platform. The platform acts as an electrode to directly measure PFAS concentration through a proportional change in electrical response upon their capture. A combination of optimization of platform design and incorporation of additional, sensitive detection modalities have allowed us to achieve detection limits as low as 0.5 ng/L for detection of PFAS compounds (compared to the 70 ng/L Health Advisory Limit of the U.S. Environmental Protection Agency).

Per- and poly-fluorinated alkyl substances (PFAS),↗

DOE OSTI2023

Source identification by non-negative matrix factorization combined with semi-supervised clustering

Machine-learning methods and apparatus are provided to solve blind source separation problems with an unknown number of sources and having a signal propagation model with features such as wave-like propagation, medium-dependent velocity, attenuation, diffusion, and/or advection, between sources and sensors. In exemplary embodiments, multiple trials of non-negative matrix factorization are performed for a fixed number of sources, with selection criteria applied to determine successful trials. A semi-supervised clustering procedure is applied to trial results, and the clustering results are evaluated for robustness using measures for reconstruction quality and cluster separation. The number of sources is determined by comparing these measures for different trial numbers of sources. Source locations and parameters of the signal propagation model can also be determined. Disclosed methods are applicable to a wide range of spatial problems including chemical dispersal, pressure transients, and electromagnetic signals, and also to non-spatial problems such as cancer mutation.

Alexandrov, Boian S.↗

DOE OSTI2024

Tailoring the d-band center on Ru 1 Cu single-atom alloy nanotubes for boosting electrochemical non-enzymatic glucose sensing

The development of cost-effective and highly efficient electrocatalysts is critical to help electrochemical non-enzymatic sensors achieve high performance. Here, in this work, a new class of catalyst, Ru single atoms confined on Cu nanotubes as a single-atom alloy (Ru 1 Cu NTs), with a unique electronic structure and property, was developed to construct a novel electrochemical non-enzymatic glucose sensor for the first time. The Ru 1 Cu NTs with a diameter of about 24.0 nm showed a much lower oxidation potential (0.38 V) and 9.0-fold higher response (66.5 μA) current than Cu nanowires (Cu NWs, oxidation potential 0.47 V and current 7.4 μA) for glucose electrocatalysis. Moreover, as an electrochemical non-enzymatic glucose sensor, Ru1Cu NTs not only exhibited twofold higher sensitivity (54.9 μA mM −1 cm −2 ) and wider linear range (0.5–8 mM) than Cu NWs, but also showed a low detection limit (5.0 μM), excellent selectivity, and great stability. According to theoretical calculation results, the outstanding catalytic and sensing performance of Ru 1 Cu NTs could be ascribed to the upshift of the d-band center that helped promote glucose adsorption. This work presents a new avenue for developing highly active catalysts for electrochemical non-enzymatic sensors.

electrochemistry↗