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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 55 records · Page 3
All sourcesNASA NTRSDOE OSTI
DOE OSTI2018

A Wearable Microfluidic Sensing Patch for Dynamic Sweat Secretion Analysis

Wearable sweat sensing is a rapidly rising research area driven by its promising potential in health, fitness, and diagnostic applications. Despite the growth in the field, major challenges in relation to sweat metrics remain to be addressed. These challenges include sweat rate monitoring for its complex relation with sweat compositions and sweat sampling for sweat dynamics studies. Here, we present a flexible microfluidic sweat sensing patch that enhances real-time electrochemical sensing and sweat rate analysis via sweat sampling. The device contains a spiral-patterned microfluidic component that is embedded with ion-selective sensors and an electrical impedance-based sweat rate sensor on a flexible plastic substrate. The patch is enabled to autonomously perform sweat analysis by interfacing the sensing component with a printed circuit board that is capable of on-site signal conditioning, analysis, and transmission. Progressive sweat flow in the microfluidic device, governed by the pressure induced by the secreted sweat, enhances sweat sampling and electrochemical detection via a defined sweat collection chamber and a directed sweat route. The characteristic of the sweat rate sensor is validated through a theoretical simulation, and the precision and accuracy of the flow rate is verified with a commercial syringe pump and a Macroduct sweat collector. On-body simultaneous monitoring of ion (H + , Na + , K + , Cl – ) concentration and sweat rate is also demonstrated for sensor functionality. This sweat sensing patch provides an integrated platform for a comprehensive sweat secretion analysis and facilitates physiological and clinical investigations by closely monitoring interrelated sweat parameters.

47 OTHER INSTRUMENTATION↗
DOE OSTI2023

Abstract for CRADA between National Energy Technology Laboratory and Colonial Pipeline Company

The National Energy Technology Laboratory (NETL) and Colonial Pipeline Company (Participant) will collaborate in the field demonstration of optical fiber sensor systems developed at NETL on Participant’s fuel pipeline. The optical fiber sensor technologies are capable of distributed temperature and strain sensing, distributed acoustic sensing, and ultrasensitive acoustic sensing. Real-time monitoring of these parameters enables pipeline integrity monitoring, security monitoring, flow rate monitoring, etc. Successful demonstration on a real fuel pipeline at Participant’s facilities will validate the sensor technologies and installation methods at a real scale. This effort aligns with NETL’s mission in reliable and sustainable energy and reducing environmental effects due to pipeline failures.

42 ENGINEERING↗
DOE OSTI2025

Engineering a new tripartite split-ccGFP system from Corynactis californica for detecting protein–protein interactions

Protein-protein interactions (PPIs) are critical to a range of biological processes and, consequently, aberrant interactions are implicated in many disorders. The study of the complex networks of PPIs promises to elucidate undiscovered roles in cellular processes and the mechanisms of disease. To accomplish this, tools to effectively sense PPIs are necessary. Effective PPI sensors must rapidly detect interactions in real-time with high sensitivity without perturbing the proteins of interest (POIs) under study. Split fluorescent proteins have previously been used to successfully monitor PPIs, in part due to the small size of the tags. Here, we developed an optimized tripartite split GFP system based on Corynactis californica GFP (ccGFP) to detect PPIs in vitro. In this sensor system, ccGFP fragments ccGFP10 and ccGFP11 are tagged to two POIs. PPIs can then be detected via fluorescence by complementation to the third fragment, ccGFP1-9, which reconstitutes functional ccGFP. The optimized ccGFP system shows improved detection kinetics and pH and temperature stability compared to a previous system. We then validated the sensor by monitoring PPIs in two model systems: attractive/repulsive coiled-coils and rapamycin-inducible FRB/FKBP heterodimerization. Finally, we developed an anti-tripartite ccGFP single-chain variable fragment (scFv), which could enable versatile detection of identified protein-protein complexes.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2022

Bio‐Imaging quorum sensing signal molecules in a soil‐mimic gel

Rhizosphere, the narrow region surrounding plant roots directly influenced by root exudates and the root associated microbiome, plays an important role in plant productivity and the rhizosphere is well known for stimulating microbial metabolic activities. How microbial communities interact to form stable, metabolically interconnected functional communities is an area of intense interest. The question remains on how microbially produced secreted molecules that function as intercellular communication signals shape the structure and function of microbial communities. Our goal is to detect and quantify signal molecules produced by microbes or plants in the root‐soil environment, spatially and temporarily. As a proof‐of‐concept, we are imaging diffusible extracellular microbial metabolites involved in a bacteria‐bacteria communication process called quorum‐sensing. Quorum‐sensing relies on the accumulation of high concentrations of signal molecules in the environment to control bacterial gene expression, influencing rhizosphere colonization and plant health. Local concentration of quorum sensing molecules are determined using aptamer based sensors. Aptamers that can specifically bind the desired signal molecules are selected through SELEX and immobilized on the surface of nano‐porous membranes. Binding of the signal molecules and aptamer covered surface results in changes in surface charge distribution and steric hinderance and thus modifying the transmembrane ionic transport. Changes in transmembrane impedance can be measured through electrochemical impedance spectroscopy methods to monitor local concentrations of signal molecules. Sensor responses were determined for different concentrations of signal molecules and results showed detection of C4‐HSL in a soil‐mimic solution with K D of 10 nM. We demonstrate that the quorum‐sensing signal molecule C4‐homoserine lactone and (C4‐HSL) can rapidly diffuse in a soil‐mimic gel, providing evidence for our use of a soil‐mimic for further aptasensor development and validation. The sensors were then inserted into soil mimic gel for monitoring C4‐HSL diffusion and the resulted impedance changes were used to determine the C4‐HSL concentration at different positions in the gel. Measurements of local C4‐HSL concentrations variation and numerical solution of diffusion equation were used to create 4D images of C4‐HSL molecule diffusion in the soil‐mimic gel.

Jiang, Nianyu↗
DOE OSTI2022

Monitoring of Temperature Measurements for Different Flow Regimes in Water and Galinstan with Long Short-Term Memory Networks and Transfer Learning of Sensors

Temperature sensing is one of the most common measurements of a nuclear reactor monitoring system. The coolant fluid flow in a reactor core depends on the reactor power state. We investigated the monitoring and estimation of the thermocouple time series using machine learning for a range of flow regimes. Measurement data were obtained, in two separate experiments, in a flow loop filled with water and with liquid metal Galinstan. We developed long short-term memory (LSTM) recurrent neural networks (RNNs) for sensor predictions by training on the sensor’s own prior history, and transfer learning LSTM (TL-LSTM) by training on a correlated sensor’s prior history. Sensor cross-correlations were identified by calculating the Pearson correlation coefficient of the time series. The accuracy of LSTM and TL-LSTM predictions of temperature was studied as a function of Reynolds number (Re). The root-mean-square error (RMSE) for the test segment of time series of each sensor was shown to linearly increase with Re for both water and Galinstan fluids. Using linear correlations, we estimated the range of values of Re for which RMSE is smaller than the thermocouple measurement uncertainty. For both water and Galinstan fluids, we showed that both LSTM and TL-LSTM provide reliable estimations of temperature for typical flow regimes in a nuclear reactor. The LSTM runtime was shown to be substantially smaller than the data acquisition rate, which allows for performing estimation and validation of sensor measurements in real time.

Pantopoulou, Stella↗
DOE OSTI2016

“Virtual IED sensor” at an rf-biased electrode in low-pressure plasma

Energy distribution and the flux of the ions coming on a surface are considered as the key-parameters in anisotropic plasma etching. Since direct ion energy distribution (IED) measurements at the treated surface during plasma processing are often hardly possible, there is an opportunity for virtual ones. This work is devoted to the possibility of such indirect IED and ion flux measurements at an rf-biased electrode in low-pressure rf plasma by using a “virtual IED sensor” which represents “in-situ” IED calculations on the absolute scale in accordance with a plasma sheath model containing a set of measurable external parameters. The “virtual IED sensor” should also involve some external calibration procedure. Applicability and accuracy of the “virtual IED sensor” are validated for a dual-frequency reactive ion etching (RIE) inductively coupled plasma (ICP) reactor with a capacitively coupled rf-biased electrode. The validation is carried out for heavy (Ar) and light (H{sub 2}) gases under different discharge conditions (different ICP powers, rf-bias frequencies, and voltages). An EQP mass-spectrometer and an rf-compensated Langmuir probe (LP) are used to characterize plasma, while an rf-compensated retarded field energy analyzer (RFEA) is applied to measure IED and ion flux at the rf-biased electrode. Besides, the pulsed selfbias method is used as an external calibration procedure for ion flux estimating at the rf-biased electrode. It is shown that pulsed selfbias method allows calibrating the IED absolute scale quite accurately. It is also shown that the “virtual IED sensor” based on the simplest collisionless sheath model allows reproducing well enough the experimental IEDs at the pressures when the sheath thickness s is less than the ion mean free path λ{sub i} (s < λ{sub i}). At higher pressure (when s > λ{sub i}), the difference between calculated and experimental IEDs due to ion collisions in the sheath is observed in the low energy range. The effect of electron impact ionization in the sheath on the origin and intensity of low-energy peaks in IED is discussed compared to ion charge-exchange collisions. Obviously, the extrapolation of the “virtual IED sensor” approach to higher pressures requires developing some other sheath models, taking into account both ion and electron collisions and probably including even a model of the whole plasma volume instead of plasma sheath one.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗
DOE OSTI2016

Radiation hardness and precision timing study of silicon detectors for the CMS High Granularity Calorimeter (HGC)

The high luminosity upgraded LHC or Phase-II is expected to increase the instantaneous luminosity by a factor of 10 beyond the LHC's design value, expecting to deliver 250 fb –1 per year for a further 10 years of operation. Under these conditions the performance degradation due to integrated radiation dose will need to be addressed. The CMS collaboration is planning to upgrade the forward calorimeters. The replacement is called the High Granularity Calorimeter (HGC) and it will be realized as a sampling calorimeter with layers of silicon detectors interleaved. The sensors will be realized as pad detectors with sizes of less that ~1.0 cm 2 and an active thickness between 100 and 300 μm depending on the position, respectively, the expected radiation levels. For an integrated luminosity of 3000 fb –1 , the electromagnetic calorimetry will sustain integrated doses of 1.5 MGy (150 Mrads) and neutron fluences up to 10 16 neq/cm 2 . A radiation tolerance study after neutron irradiation of 300, 200, and 100 μm n-on-p and p-on-n silicon pads irradiated to fluences up to 1.6×10 16 neq/cm 2 is presented. In this work, the properties of these diodes studied before and after irradiation were leakage current, capacitance, charge collection efficiency, annealing effects and timing capability. The results of these measurements validate these sensors as candidates for the HGC system.

46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND ↗
DOE OSTI2017

Demonstration and Validation of a Portable Raman Sensor for In-Situ Detection and Monitoring of Perchlorate (ClO 4 - )

Costs for environmental analysis and monitoring are increasing at a rapid rate and represent a significant percentage of the total and future remedial expenses at many U.S. Department of Defense (DoD) contaminated sites. It has been reported that about 30 to 40% of the remediation budget is usually spent on long-term monitoring (LTM), of which a large percentage represents laboratory analytical costs. Energetics such as perchlorate (ClO 4 - ) are among the most frequently detected contaminants in groundwater and surface water at or near military installations due to their persistence and mobility. Currently, the standard protocol entails collecting samples in the field, packaging them, and shipping them overnight to a designated laboratory for analysis. This process requires significant sample preparation and handling, and analytical results may not be available for several days to weeks. In this project, we developed and demonstrated a portable Raman sensor based on surface enhanced Raman scattering (SERS) technology to detect ClO 4 - in contaminated water. We summarize major accomplishments as follows: • A SERS sensor based on elevated gold (Au) nano-ellipse dimer architectures was designed and developed for ClO 4 - with a detection limit of ~10 -6 M (or 100 μg/L); The performance of these sensors was evaluated and optimized through variation of their geometric characteristics (i.e., dimer aspect ratio, dimer separation, etc.). • Large-scale commercial production of SERS substrate sensors via nanoimprinting by Nanova Inc. and Nanoimprint lithography (NIL) technology was successfully demonstrated. This is a substantial step forward toward the commercialization of the SERS sensors and may potentially lead to significantly reduced fabrication costs of SERS substrates. • Commercially produced SERS sensors were demonstrated to detect ClO 4 - at levels above 10 -6 M using a portable Raman analyzer. The performance of the commercial SERS sensors for ClO 4 - detection in the presence and absence of interferences was determined for a series of standard solutions. Sulfate (SO 4 2- ) was found to exhibit the greatest interference for the anions tested, which included Cl-, NO 3 - , and SO 4 2- . • Field demonstration of the portable Raman sensor with commercially produced SERS substrates was completed at two Department of Defense (DoD) sites; twice at the Indian Head Naval Surface Warfare Center, Indian Head, MD, and once at Redstone Arsenal, Huntsville, AL. Multiple wells were sampled at both DoD sites, where a standard addition method was employed using the sensor to determine the ClO 4 - 4 - and possibly other energetics that are both important for environmental monitoring and of interest for national security. However, we point out that SERS technology is also prone to interferences due to its sensitivity and responses to other ionic species, such as NO 3 - , SO 4 2- , and dissolved organics or co-contaminants present in the groundwater, which could potentially mask the SERS signal of the target analyte (i.e., ClO 4 - ). As such, SERS analysis was subject to significant variations (e.g., ±20% or more), and its detection limit for ClO 4 - -8 M) and was substantially higher than what we anticipated from laboratory studies. However, despite these complications, the portable Raman sensor developed in this project could be used as a rapid screening tool for ClO 4 - at concentrations above 10 -6 M. Future studies are warranted to further develop the technology and to optimize its performance, and eventually to bring the technology to the market. With additional development and demonstration, the sensor has the potential to reduce analytical costs by eliminating shipping and typical costs associated with laboratory analysis. A cost savings of 30–45% may be realized during a typical sampling event and, more importantly, the technology could allow rapid turn-around of information to decision makers for site characterization and remediation.

47 OTHER INSTRUMENTATION↗
DOE OSTI2021

Cooperative Research and Development Agreement between National Energy Technology Laboratory and Ampere Scientific [Abstract]

Electro-Slag-Remelting (ESR) is an industry standard process used to manufacture alloys for mission critical components associated with energy, aerospace, defense, and other applications. For example, ESR is specifically used in the production and refinement of Nickel (Ni)-alloys such as IN 740H used for critical turbine applications. The aim of this CRADA is to develop and validate a sensor technology to monitor the melting of ingots during the ESR. The sensor will allow for feedback on the melting operation, allowing the operator to make adjustment on the fly and produce better ingots. Further, through better control, operators can more efficiently produce ingots by ESR.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗
DOE OSTI2025

Pilot-Scale Validation of Distributed Optical Fiber Sensors for Underground Pipeline Monitoring

Monitoring parameters such as hoop strain, pressure, and acoustic vibrations is key to detecting potential leaks, intrusions, or structural issues. Distributed optical fiber sensor (DOFS) systems provide a compelling solution for continuous, real-time monitoring over long distances. This paper details the development and pilot-scale implementation of DOFS systems for underground pipeline monitoring, evolving from a proof-of-concept stage. Multiple custom-designed DOFS interrogator units—such as optical frequency-domain reflectometry (OFDR), Brillouin optical time-domain analysis (BOTDA), and multimodal interferometer-based fiber acoustic sensor systems were tested to measure the key parameters, such as hoop strain, pipe pressure, surrounding soil temperature, and acoustic vibrations. The underground product pipeline’s outer diameter is 30 inches, the wall thickness is 1.28 inches, and 3 feet deep from the surface. The fiber deployment strategies and sensing data acquisition methods for these systems are discussed. The results demonstrate the effectiveness of DOFS in detecting hoop strain, temperature changes, and acoustic vibrations, showcasing their potential for real-time monitoring and enhancing pipeline safety. These findings from pilot-scale testing offer valuable insights into advancing pipeline monitoring technologies and improving the reliability of underground pipeline systems.

fiber optic sensors↗
DOE OSTI2025

Pilot-Scale Validation of Distributed Optical Fiber Sensors for Underground Pipeline Monitoring

Distributed fiber optic sensing is a cutting-edge technology that has found extensive applications in the monitoring of Ensuring the safety, integrity, and operational efficiency of underground product pipelines is vital for maintaining the nation’s critical infrastructure. Monitoring parameters such as hoop strain, pressure, and acoustic vibrations is key to detecting potential leaks, intrusions, or structural issues. Distributed optical fiber sensor (DOFS) systems provide a compelling solution for continuous, real-time monitoring over long distances. This paper details the development and pilot-scale implementation of DOFS systems for underground pipeline monitoring, evolving from a proof-of-concept stage. Multiple custom-designed DOFS interrogator units—such as optical frequency-domain reflectometry (OFDR), Brillouin optical time-domain analysis (BOTDA), and multimodal interferometer-based fiber acoustic sensors—were employed to measure key parameters like hoop strain, pressure, and acoustic vibrations. The underground product pipeline's outer diameter is 30 inches, the wall thickness is 1.28 inches, and the 3-foot depth. The fiber deployment strategies, and sensing data acquisition methods for these systems are discussed. The results demonstrate the effectiveness of DOFS in detecting hoop strain, temperature changes, and acoustic vibrations, showcasing their potential for real-time monitoring and enhancing pipeline safety.

distributed fiber sensing↗
DOE OSTI2017

EDGE COMPUTING AND CONTEXTUAL INFORMATION FOR THE INTERNET OF THINGS SENSORS

Interpreting sensor data require knowledge about sensor placement and the surrounding environment. For a single sensor measurement, it is easy to document the context by visual observation, however for millions of sensors reporting data back to a server, the contextual information needs to be automatically extracted from either data analysis or leveraging complimentary data sources. Data layers that overlap spatially or temporally with sensor locations, can be used to extract the context and to validate the measurement. To minimize the amount of data transmitted through the internet, while preserving signal information content, two methods are explored; computation at the edge and compressed sensing. We validate the above methods on wind and chemical sensor data (1) eliminate redundant measurement from wind sensors and (2) extract peak value of a chemical sensor measuring a methane plume. We present a general cloud based framework to validate sensor data based on statistical and physical modeling and contextual data extracted from geospatial data.

03 NATURAL GAS↗
DOE OSTI2017

EDGE COMPUTING AND CONTEXTUAL INFORMATION FOR THE INTERNET OF THINGS SENSORS

Interpreting sensor data require knowledge about sensor placement and the surrounding environment. For a single sensor measurement, it is easy to document the context by visual observation, however for millions of sensors reporting data back to a server, the contextual information needs to be automatically extracted from either data analysis or leveraging complimentary data sources. Data layers that overlap spatially or temporally with sensor locations, can be used to extract the context and to validate the measurement. To minimize the amount of data transmitted through the internet, while preserving signal information content, two methods are explored; computation at the edge and compressed sensing. We validate the above methods on wind and chemical sensor data (1) eliminate redundant measurement from wind sensors and (2) extract peak value of a chemical sensor measuring a methane plume. We present a general cloud based framework to validate sensor data based on statistical and physical modeling and contextual data extracted from geospatial data.

Klein, Levente↗
DOE OSTI2021

Field Validation of a Smart Energy Recovery Ventilation System Using Low-Cost Indoor Air Quality Sensors

This project is a field validation, using low-cost indoor air quality (IAQ) sensors, of a smart ventilation system that can help low-load homes in humid environments maintain acceptable indoor humidity conditions while providing adequate ventilation according to ASHRAE 62.2. The objectives of this research were to (1) address builders’ concerns with mechanical ventilation in humid environments and (2) answer the question of whether smart control logic helps with occupant comfort and the creation of a more acceptable indoor environment. To address the objectives of the study, the Southface team collected field data for one year in four Charleston, South Carolina, new construction homes in order to determine the differences in occupant comfort; comfort metrics; IAQ; and heating, ventilating, and air-conditioning (HVAC) energy consumption when toggling biweekly between an energy recovery ventilator (ERV) operating continuously and an ERV operating with smart, time-varying humidity control logic. The smart ventilation algorithm under consideration in this field test did create a less humid indoor environment on an annual basis as quantitatively measured through temperature and relative humidity (T/RH) readings, expressed most discernably as “percentage of time above 60% RH” and “percentage of time above 55°F dewpoint.” However, the difference it made was inconsistent during the spring, summer, and fall months, and it was only directionally consistent during the winter months. We suspect that this is primarily due to the long runtimes and concomitant dehumidification activity of the air-conditioning (A/C) units in response to the high sensible loads in Charleston. The effect of the smart ventilation algorithm was not discernable to the occupants in this study, as recorded through seasonal surveys.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗
DOE OSTI2018

Performance of low-cost monitors to assess household air pollution

Highlights: • We assessed PM{sub 2.5} and CO low-cost monitors in semi-controlled and field studies. • Devices experienced high failure rate and susceptibility to environmental changes. • Low-cost monitors tested may not be ready to replace more established methods. Exposure to household air pollution is a leading cause of morbidity and mortality globally. However, due to the lack of validated low-cost monitors with long-lasting batteries in indoor environments, most epidemiologic studies use self-reported data or short-term household air pollution assessments as proxies of long-term exposure. We evaluated the performance of three low-cost monitors measuring fine particulate matter (PM{sub 2.5}) and carbon monoxide (CO) in a wood-combustion experiment conducted in one household of Spain for 5 days (including the co-location of 2 units of HAPEX and 3 units of TZOA-R for PM{sub 2.5} and 3 units of EL-USB-CO for CO; a total of 40 unit-days). We used Spearman correlation (ρ) and Concordance Correlation Coefficient (CCC) to assess accuracy of low-cost monitors versus equivalent research-grade devices. We also conducted a field study in India for 1 week (including HAPEX in 3 households and EL-USB-CO in 4 households; a total of 49 unit-days). Correlation and agreement at 5-min were moderate-high for one unit of HAPEX (ρ = 0.73 / CCC = 0.59), for one unit of TZOA-R (ρ = 0.89 / CCC = 0.62) and for three units of EL-USB-CO (ρ = 0.82–0.89 / CCC = 0.66–0.91) in Spain, although the failure or malfunction rate among low-cost units was high in both settings (60% of unit-days in Spain and 43% in India). Low-cost monitors tested here are not yet ready to replace more established exposure assessment methods in long-term household air pollution epidemiologic studies. More field validation is needed to assess evolving sensors and monitors with application to health studies.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2017

Robust Online Monitoring for Calibration Assessment of Transmitters and Instrumentation

Robust online monitoring (OLM) technologies are expected to enable the extension or elimination of periodic sensor calibration intervals in operating and new reactors. These advances in OLM technologies will improve the safety and reliability of current and planned nuclear power systems through improved accuracy and increased reliability of sensors used to monitor key parameters. In this article, we discuss an overview of research being performed within the Nuclear Energy Enabling Technologies (NEET)/Advanced Sensors and Instrumentation (ASI) program, for the development of OLM algorithms to use sensor outputs and, in combination with other available information, 1) determine whether one or more sensors are out of calibration or failing and 2) replace a failing sensor with reliable, accurate sensor outputs. Algorithm development is focused on the following OLM functions: • Signal validation • Virtual sensing • Sensor response-time assessment These algorithms incorporate, at their base, a Gaussian Process-based uncertainty quantification (UQ) method. Various plant models (using kernel regression, GP, or hierarchical models) may be used to predict sensor responses under various plant conditions. These predicted responses can then be applied in fault detection (sensor output and response time) and in computing the correct value (virtual sensing) of a failing physical sensor. The methods being evaluated in this work can compute confidence levels along with the predicted sensor responses, and as a result, may have the potential for compensating for sensor drift in real-time (online recalibration). Evaluation was conducted using data from multiple sources (laboratory flow loops and plant data). Ongoing research in this project is focused on further evaluation of the algorithms, optimization for accuracy and computational efficiency, and integration into a suite of tools for robust OLM that are applicable to monitoring sensor calibration state in nuclear power plants.

fault detection↗
DOE OSTI2025

Casing Annulus Monitoring of CO 2 Injection Using Wireless Autonomous Distributed Sensor Networks

Effective and secure carbon subsurface storage, involving the deep underground injection of CO 2 into geological formations where it is permanently trapped, is paramount to mitigating CO 2 emissions (Figure I). Ensuring the integrity of these storage sites and detecting potential leakage through the casing annulus necessitates robust monitoring. This work provides the first integrated demonstration of a wireless casing-annulus monitoring architecture that can operate in highly attenuating cement-brine environments relevant to CO 2 storage. This project focused on developing and validating a novel sensor system for integration with autonomous monitoring near the cement reservoir interface. The goal was a fully integrated Technology Readiness Level (TRL) 4/5 field validation of a distributed wireless intelligent sensor system providing real-time, direct subsurface formation measurements to enhance fluid movement monitoring in the cemented casing annulus. Achieving this objective required the development and integration of 1) wireless autonomous microsensor technology by California Institute of Technology (Caltech); 2) sensor packaging and emplacement technology by Research Triangle Institute (RTI); and 3) smart well completions using wireless active casing collars and NOV pipe by the Sandia National Lab (SNL). The collaboration with the Caltech team in this project aimed to develop millimeter-scale radio frequency identification (RFID) sensors capable of detecting CO 2 , pH, and/or methane levels. These sensors are engineered to be impervious to fluids, allowing them to be mixed with cement and installed within the casing annulus. They operate using RFID protocols at frequencies of 902–928 MHz for both power and communication. A Sandia National Laboratories’ team engaged their expertise in the development of a Smart Collar system designed for the wireless data collection from these RFID sensors embedded in the cement annulus and transmission of this information to the ground surface via IntelliPipe/IntelliServ NOV drill pipe. This is accomplished through inductive coupling at the collar, which facilitates data transfer through each segment of the pipe. Because the system cannot transmit a direct current signal to power the Smart Collar, both power and communication were implemented using alternating current and electromagnetic signals at varying frequencies. Furthermore, the developed microsensor technology had to be demonstrated and validated in comparison with reference transducer measurements in a field test site at The University of Texas at Austin (UT-Austin). Although the full sensor suite did not reach field-deployment readiness, the system-level integration achieved in this project establishes a validated pathway for future incorporation of advanced microsensors.

47 OTHER INSTRUMENTATION↗