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DOE OSTI2025

Flow Enhanced Electrochemical Sensor Performance in Complex Salt Systems

To advance MSR MC&A practices, Argonne National Laboratory developed and optimized robust flow enhanced electrochemical sensors (FEES) and multielectrode array voltammetry sensors (MAVS) which provide accurate near-real time measurements of actinides in molten salts. Flow-enhanced electrochemical sensors are installed directly into MSR flow conduits for inline salt chemistry measurement, while MAVS are deployed in quiescent salt conditions enabling online species concentration determination in stationary salt vessels. This report summarizes efforts to improve electrochemical sensor technological readiness through (1) sensor testing in complex, high concentration molten salt systems to demonstrate low uncertainty actinide measurements in MSR-representative solutions and (2) demonstrations of sensor performance in challenging real-world conditions in collaboration with partner MSR institutions.

22 GENERAL STUDIES OF NUCLEAR REACTORS↗

DOE OSTI2024

Leveraging Optimal Sparse Sensor Placement to Aggregate a Network of Digital Twins for Nuclear Subsystems

Nuclear power plants (NPPs) require continuous monitoring of various systems, structures, and components to ensure safe and efficient operations. The critical safety testing of new fuel compositions and the analysis of the effects of power transients on core temperatures can be achieved through modeling and simulations. They capture the dynamics of the physical phenomenon associated with failure modes and facilitate the creation of digital twins (DTs). Accurate reconstruction of fields of interest (e.g., temperature, pressure, velocity) from sensor measurements is crucial to establish a two-way communication between physical experiments and models. Sensor placement is highly constrained in most nuclear subsystems due to challenging operating conditions and inherent spatial limitations. This study develops optimized data-driven sensor placements for full-field reconstruction within reactor and steam generator subsystems of NPPs. Optimized constrained sensors reconstruct field of interest within a tri-structural isotropic (TRISO) fuel irradiation experiment, a lumped parameter model of a nuclear fuel test rod and a steam generator. The optimization procedure leverages reduced-order models of flow physics to provide a highly accurate full-field reconstruction of responses of interest, noise-induced uncertainty quantification and physically feasible sensor locations. Accurate sensor-based reconstructions establish a foundation for the digital twinning of subsystems, culminating in a comprehensive DT aggregate of an NPP.

42 ENGINEERING↗

DOE OSTI2025

Autonomous sensor suite for evaluating fish-turbine interactions and environmental impacts in marine renewable energy and hydropower

Marine renewable energy (MRE) harnesses ocean-based resources such as waves, tides, currents, and thermal or salinity gradients for sustainable power generation. It has the potential to complement existing renewable resources, support remote communities, and contribute to decarbonization efforts. However, understanding the hydrodynamic forces created by MRE devices and their impacts on marine life is critical for responsible deployment. Here, to address these concerns, advanced sensor devices, including the Marine Sensor Fish (MSF), Sensor Fish Mini (SF Mini), and Flexible Sensor Fish (FSF), were developed to measure interactions between aquatic organisms and MRE systems. This paper details the design, manufacturing, calibration, and field deployment of these sensor suites, highlighting their ability to capture key physical stressors such as shear forces, pressure changes, and collision impacts. The MSF successfully evaluated turbine interactions at a tidal turbine in the Salish Sea, capturing data on turbulence, collision impact, and pressure gradients. The SF Mini validated hydrodynamic conditions in scaled hydraulic models, supporting computational fluid dynamics simulations. The FSF, with its flexible silicone body, measured species-specific impacts in turbulent environments. This research demonstrates the potential of Sensor Fish technology to advance sustainable marine energy systems by reducing biological impacts and informing environmentally sustainable designs.

Ecological impacts↗

DOE OSTI2025

Exceptional NH 3 Detection by Cu(cyhdc) MOF Sensor Due to H 2 O Coadsorption

Metal–organic framework (MOF)-based electrical impedance sensors are a growing class of sensors that show utility in the detection of environmentally toxic gases. Detection of trace NH 3 has been particularly difficult to design due to the low electrical response of NH 3 . However, this has been circumvented by judiciously selecting a MOF that has enhanced electrical response to NH 3 due to coadsorption with predominant atmospheric gases such as water. Herein, an MOF Cu(cyhdc) based sensor has been successfully demonstrated for the enhanced detection of environmentally toxic NH 3 gas. The sensor shows a change in impedance when it is exposed to 5 ppm of NH 3 . At 20 °C, >30% relative humidity (RH) is necessary to elicit a change, with the response increasing with RH and reaching 3170× at 92% RH, producing the largest published response to NH 3 for a MOF direct electrical sensor. In the absence of water, no change is observed toward 5 ppm of NH 3 over 20–50 °C. Here, this electrical response is largely driven by huge decreases in the imaginary component of the impedance, attributed to increased capacitance at the surface of the MOF crystallites upon NH 3 and H 2 O coadsorption. Complementary structural and microstructural characterization proves that the Cu(cyhdc) crystalline structure and morphology remain intact under trace NH 3 and/or H 2 O adsorption. Despite extremely low NH 3 , loadings seen in TGA, XPS, and FT-IR confirm NH 3 and H 2 O adsorption, and changes to the metal-carboxylate IR peak positions are observed upon NH 3 adsorption. Concentrated primarily at the outer surfaces of the MOF crystallites, this NH 3 and H 2 O coadsorption effectively increases the surface capacitance across the Cu(cyhdc) powder and enables direct electrical detection of trace NH 3 . Together, these results demonstrate how coadsorption of specific molecules (H 2 O) can be used to enable the electrical detection of trace toxic gases that would otherwise not have produced a MOF sensor response.

ammonia↗

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↗

NASA NTRS1994

Ampoule failure sensor development for semiconductor crystal growth experiments

Currently there are no devices to detect an ampoule failure in semiconductor crystal growth experiments. If an ampoule fails, it will go undetected until the containing cartridge is breached due to chemical degradation. The experiment will then be terminated resulting in a failed experiment and a loss of data. The objective of this research was to develop a reliable failure sensor that would detect a specific liquid or vapor material before the metallic cartridge is degraded and the processing furnace contaminated. The sensor is a chemical fuse made from a metal with which the semiconductor material reacts more rapidly than it does with the containing cartridge. Upon ampoule failure, the sensor is exposed to the vapor or liquid semiconductor and the chemical reaction causes a resistance change in the sensor material. The sensor shows a step change in resistance on the order of megohms when exposed to mercury zinc telluride (HgZnTe), mercury cadmium telluride (HgCdTe), or gallium arsenide (GaAs). This ampoule failure sensor is being tested for possible use on the second United States Microgravity Mission (USML-2) and is the subject of a NASA patent application.

CHEMICAL FUSE↗

NASA NTRS2014

High Temperature Dynamic Pressure Measurements Using Silicon Carbide Pressure Sensors

Un-cooled, MEMS-based silicon carbide (SiC) static pressure sensors were used for the first time to measure pressure perturbations at temperatures as high as 600 C during laboratory characterization, and subsequently evaluated in a combustor rig operated under various engine conditions to extract the frequencies that are associated with thermoacoustic instabilities. One SiC sensor was placed directly in the flow stream of the combustor rig while a benchmark commercial water-cooled piezoceramic dynamic pressure transducer was co-located axially but kept some distance away from the hot flow stream. In the combustor rig test, the SiC sensor detected thermoacoustic instabilities across a range of engine operating conditions, amplitude magnitude as low as 0.5 psi at 585 C, in good agreement with the benchmark piezoceramic sensor. The SiC sensor experienced low signal to noise ratio at higher temperature, primarily due to the fact that it was a static sensor with low sensitivity.

Pressure Sensors↗

NASA NTRS2021

State of the Industry: UAS Sensor Review

This report is a follow on from HF Designworks’ 2018 report on state of the present and state of the art sensor technologies as they related to Ground Based Detect and Avoid (GBDAA) systems for Unmanned Aircraft Systems (UAS). In this update, HF Designworks again looks to UAS test sites, manufacturers, vendors, and users to identify the latest sensor technologies covering not just GBDAA, but additional UAS sensor types; including airborne. For this latest report, the following sensor types were reviewed, as well as a few that don’t quite fit within the following categories: Electro-Optical / Infrared (EO/IR), LiDAR, RADAR, Radiofrequency (RF) scanner, Acoustic, and Other. From our interviews and research with various organizations and individuals, we found both interesting new technologies as well as improvements to many technologies first reviewed in 2018. Compared with our 2018 findings, many of the new technologies have helped produce sensors that are smaller, lighter, require less power, and have better range and resolution than those discussed in 2018. However, challenges still exist. There is no single currently available and FAA approved sensor suite that can adequately provide complete detect and avoid capabilities to meet the needs of UAS beyond visual line of sight (BVLOS) in the civilian airspace.

unmanned aircraft systems↗

NASA NTRSDate not supplied

Evaluation of Silicon Carbide Pressure Sensor in Turbofan Engine Core Exhaust Nozzle

We report the results of the evaluation of single crystal 4H-silicon carbide piezoresistive pressure sensors that were directly inserted at the compressor and core exhaust nozzle exits, in shear contact with the engine-internal flow streams of the NASA DGEN Aeropropulsion Research Turbofan, where the operating temperatures at maximum power were ~230 oC, and ~460 oC, respectively. In addition to demonstrating sensor survivability during this maiden test, the frequency responses of the silicon-carbide sensors at the core-nozzle exit were compared against industry-standard silicon-based piezoresistive pressure transducers that externally placed, using an infinite-tube-probe arrangement. The low-voltage output of these early-development SiC sensors limited the frequency range with an acceptable signal-to-noise-ratio for the current application to less than about 2,500 Hz. The low output of the SiC sensors were due to a conservative burst-pressure diaphragm design that can be optimized to increase the frequency range. The results from this initial campaign offered insights to aspects of the sensor that would require further improvement, with the goal of achieving full-bandwidth capture of engine unsteady pressure fluctuations by direct interrogation of the flow field in hostile-environment engine-core components.

Silicon Carbide↗

NASA NTRSDate not supplied

Evaluation of SiC Pressure Sensors in Turbofan Engine Core Exhaust Nozzle

We report the results of the evaluation of single crystal 4H-silicon carbide piezoresistive pressure sensors that were directly inserted at the compressor and core exhaust nozzle exits, in shear contact with the engine-internal flow streams of the NASA DGEN Aeropropulsion Research Turbofan, where the operating temperatures at maximum power were ~230°C, and ~460°C, respectively. In addition to demonstrating sensor survivability during this maiden test, the frequency responses of the silicon-carbide sensors at the core-nozzle exit were compared against industry-standard silicon-based piezoresistive pressure transducers that externally placed, using an infinite-tube-probe arrangement. The low-voltage output of these early-development SiC sensors limited the frequency range with an acceptable signal-to-noise-ratio for the current application to less than about 2,500 Hz. The low output of the SiC sensors were due to a conservative burst-pressure diaphragm design that can be optimized to increase the frequency range. The results from this initial campaign offered insights to aspects of the sensor that would require further improvement, with the goal of achieving full-bandwidth capture of engine unsteady pressure fluctuations by direct interrogation of the flow field in hostile-environment engine-core components.

Silicon Carbide↗

DOE OSTI2025

Freestanding VO 2 membranes on epidermal nanomesh for ultra-sensitive correlated breathable sensors

The interest in highly sensitive sensors is rapidly increasing for detecting very tiny signals for Internet of Things devices. Here, we achieve ultra-sensitive correlated breathable sensors based on freestanding VO 2 membranes. We fabricate the membranes by growing VO 2 films onto sacrificial Sr 3 Al 2 O 6 layer grown on SrTiO 3 , selectively dissolving the Sr 3 Al 2 O 6 in water, and then rendering freestanding VO 2 membrane on nanomesh. The nanomeshes are extremely flexible, sweat permeable, and readily skin-adhesive. The resistance of the VO 2 membranes is reversibly tuned by human’s tiny mechanical stimuli and breath stimuli. The stimuli modulate the Peierls dimerization of one-dimensional V-V chains in the VO 2 lattice which concomitantly controls the electron correlation and hence resistivity. Since our breathable sensors operate based on quantum-mechanical correlation effects, their sensitivity is 1-2 orders of magnitude higher than conventional tactile and respiratory sensors based on other materials. Thus, the freestanding membranes of correlated oxides on epidermal nanomeshes are multifunctional platforms for developing ultra-sensitive correlated breathable sensors.

36 MATERIALS SCIENCE↗

DOE OSTI2025

Fully‐Printed Ion Sensor Arrays for Measuring Agricultural Nitrogen and Potassium Concentrations Using Nernstian and AI Models

Abstract The chemical composition of growing media is a key factor for plant growth, impacting agricultural yield and sustainability. However, there is a lack of affordable chemical sensors for ubiquitous nutrient ion monitoring in agricultural applications. This work investigates using fully printed ion‐sensor arrays to measure the concentrations of nitrate, ammonium, and potassium in mixed‐electrolyte media. Ion sensor arrays composed of nitrate, ammonium, and potassium ion‐selective electrodes and a printed silver‐silver chloride (Ag/AgCl) reference electrode are fabricated and characterized in aqueous solutions in a range of concentrations that encompass what is typical for agricultural growing media (0.01 m m –1 m ). The sensors are also tested in mixed‐electrolyte solutions of NaNO 3 , NH 4 Cl, and KCl of varying concentrations, and the recorded potentials are input into Nernstian and artificial neural network models to compare the prediction accuracy of the models against ground truth. The artificial neural network models demonstrated higher accuracy over the Nernstian model, and the model using only ion‐sensor inputs is 7.5% more accurate than the Nernstian model under the same conditions. By enabling more precise and efficient fertilizer application, these sensor arrays coupled to computational models can help increase crop yields, optimize resource use, and reduce environmental impact.

Goodrich, Payton [University of California Berkele↗

DOE OSTI2026

Robustness of the smartpixels classifier for different simulated sensor geometries and non-ideal detector conditions

Pixel tracking detectors at upcoming collider experiments will see unprecedented charged-particle densities. Real-time data reduction on the detector will enable higher granularity and faster readout, possibly enabling the use of the pixel detector in high-rate online event selection, such as the ATLAS or CMS first-level trigger systems. This data reduction can be accomplished with a neural network (NN) in the readout chip bonded with the sensor that recognizes and rejects tracks with low transverse momentum (p T ) based on the geometrical shape of the charge deposition (“cluster”). To design viable detectors for deployment, the dependence of the NN as a function of the sensor geometry, external magnetic field, irradiation, and noise must be understood. In this paper, we present first studies of the efficiency and data reduction for planar pixel sensors exploring these parameters. For the CMS HL-LHC sensor geometry, we obtain a signal efficiency of (91.9 ± 0.7)% and a data reduction of (29.7 ± 1.0)%. A smaller sensor pitch in the bending direction improves the p T discrimination, but a larger pitch can be partially compensated with detector thickness. Any accumulated radiation damage also changes the cluster shape, reducing the signal efficiency compared to the baseline by approximately 30–60% in absolute terms, but nearly all of the performance can be recovered through retraining of the network and updating the weights. Finally, the impact of noise was investigated, and retraining the network on noise-injected datasets was found to maintain performance within 6% of the baseline network trained and evaluated on noiseless data. •ASIC-compatible track-momentum classifier is robust in realistic detector conditions.•About 90% signal efficiency and 30% data reduction per layer for CMS HL-LHC geometry.•Single-layer signal efficiency increases for smaller pixel pitch or thicker sensors.•Performance with noise or after radiation damage mostly recovered by retraining.

Shekar, Danush [Illinois U., Chicago] (ORCID:00000↗

DOE OSTI2025

Silicon wafer fracture stress for tracking sensors in particle physics experiments

For the construction of the ATLAS Inner Tracker strip detector, silicon strip sensor modules are glued directly onto carbon fibre support structures using a soft silicone gel. During tests at temperatures below -35°C, several of the sensors were found to crack due to a mismatch in coefficients of thermal expansion between polyimide circuit boards with copper metal layers (glued onto the sensor) and the silicon sensor itself. While module assembly procedures were developed to minimise variations between modules, cold tests showed a wide range of temperatures at which supposedly comparable modules failed. The observed variance (fracture temperatures between -35°C and -70°C) for supposedly comparable modules suggests an undetected variation between modules suspected to be intrinsic to the silicon wafer itself. Therefore, a test programme was developed to investigate the fracture stress of representative sensor wafer cutoffs. This paper presents results for the fracture stress of silicon sensors used in detector modules.

Detector design and construction technologies and ↗

DOE OSTI2024

Journey over Destination: Dynamic Sensor Placement Enhances Generalization

Reconstructing complex, high-dimensional global fields from limited data points is a challenge across various scientific and industrial domains. This is particularly important for recovering spatio-temporal fields using sensor data from, for example, laboratory-based scientific experiments, weather forecasting, or drone surveys. Given the prohibitive costs of specialized sensors and the inaccessibility ofcertain regions of the domain, achieving full field coverage is typically not feasible. Therefore, the development of machine learning algorithms trained to reconstruct fields given a limited dataset is of critical importance. In this study, we introduce a generalapproach that employs moving sensors to enhance data exploitation during the training of an attention based neural network, thereby improving field reconstruction. The training of sensor locations is accomplished using an end-to-end workflow, ensuringdifferentiability in the interpolation of field values associated to the sensors, and is simple to implement using differentiable programming. Additionally, we have incorporated a correction mechanism to prevent sensors from entering invalid regions within the domain. We evaluated our method using two distinct datasets; the results show that our approach enhances learning, as evidenced by improved test scores.

54 ENVIRONMENTAL SCIENCES↗

DOE OSTI2026

Asi Nuclear Energy Sensors Data Portal Chatbot And Data Structuring Tool

The Idaho National Laboratory (INL) is advancing the development of an AI-powered chatbot and data structuring tool specifically designed to accelerate data mining processes for sensor-related information and seamlessly integrate the results into the ASI Sensors Data Portal (https://nes.energy.gov/). By doing so, the software aims to enhance the accessibility, usability, and organization of sensor data for nuclear energy applications. The software initial phase focuses on retrieving comprehensive datasets, prioritizing the past five years of publicly available information from the Office of Scientific and Technical Information (OSTI). These datasets will be meticulously processed to ensure compatibility, employing cleaning and preprocessing steps to eliminate irrelevant, incomplete, or corrupted information, thus establishing a robust foundation for subsequent AI use. The data will serve as the backbone for training an AI model and chatbot, which will act as an interactive tool enabling users to ask complex, context-specific questions and receive accurate, validated answers derived from constrained literature. In parallel, the project incorporates a data structuring process supported by AI to organize sensor information from multiple sources into a standardized format. This structured data will include detailed sensor specifications, such as measurement range, applications, accuracy, and operating conditions, generated and documented with AI. These specifications will be systematically integrated into the sensor portal. To maintain the highest levels of accuracy and relevance, all AI-generated outputs will be reviewed and validated by subject matter experts (SMEs), with additional fields or parameters added as needed. Future stages of the project aim to expand the dataset beyond OSTI to include other sources and potentially incorporate unclassified controlled information (UCI) with restricted access protocols to address security and confidentiality requirements.

Mapes, NormanJ. [Idaho National Laboratory (INL), ↗

DOE OSTI2026

Soil and groundwater environmental sensor data, Wax Lake Delta, Louisiana, March 2023 - March 2024

This study evaluates how environmental parameters that integrate biogeochemical processes vary with water table fluctuations in the freshwater Wax Lake Delta (WLD) in Louisiana, U.S.A. This data package contains seven *.csv files and one Excel file that compiles all the data from the individual .csv files. This dataset reports high frequency (15-min) observations of water level, soil redox potential, specific conductance, and pH made for one year along elevation transects located on the older, proximal (OT) and younger, distal (YT) ends of a deltaic island. Water depth relative to the ground surface (cm; HOBO U20L-04; error ± 0.4 cm), water pH and temperature (HOBO MX2501), and specific conductance and temperature (HOBO U24-001) sensors were installed in March 2023. Water depth was corrected for barometric pressure recorded by a separate logger secured to a platform above the highest water level. Soil redox probes (SWAP ORP-40-4-B) were also installed in March 2023. Each probe had four Pt sensors (2 mm width) placed at 10 cm, 20 cm, 30 cm, and 40 cm below the ground surface. Redox data were referenced to an external Ag0/AgCl (3M KCl) reference probe placed in saturated ground and recorded on CR1000X dataloggers (Campbell Scientific) powered by solar panels. A second reference probe was positioned near the primary reference probe for backup and data correction. The tops of the soil redox probes and soil moisture probes were flush with the soil surface so that sensors are reported at their indicated depths below ground surface. Here, we report data collected between 15 March 2023 to 15 March 2024 for all sensors, with some differences due to exact dates of sensor placement or data gaps associated with sensor malfunction. For example, water depth at OT4 was not recorded between March to November 2023. Data flags indicate whether a value is valid (1) or was excluded from data analysis in the associated manuscript (-1).

EARTH SCIENCE > LAND SURFACE > SOILS↗

DOE OSTI2024

Applications of fuzzy logic and best-worst method for tritium sensor selection

Accurate assessment of tritium as a fuel source is critical in fusion reactions, necessitating effective sensor evaluation methods. This study investigates a multi-criteria decision-making framework for selecting tritium sensors, integrating fuzzy logic to enhance decision quality. Initial attempts at applying fuzzy logic were found to be too elementary and failed to capture the complexity of multi-criteria selection; this prompted a refined approach that incorporated expert insights and advanced ranking techniques for sensor evaluation. The research used a two-stage methodology. In the first stage, important criteria and sub-criteria for sensor performance were identified and defined. These criteria were then weighted and scored using a fuzzy best-worst method, drawing upon expert opinions to ensure relevance and validity. The second stage involved interpreting information about varying sensors to rank them based on their overall criteria scores, encouraging the selection of the most suitable options. The result of the study is a proposed method for effective sensor selection in fusion reactors, which in turn will significantly improve the reliability of tritium monitoring in fusion applications.

11 NUCLEAR FUEL CYCLE AND FUEL MATERIALS↗