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DOE OSTI2020

A holistic cyber-physical security protocol for authenticating the provenance and integrity of structural health monitoring imagery data

Modern infrastructure systems, such as bridges, dams, power generation stations, and buildings increasingly have an intrinsic cyber-physical nature to them. Infrastructure now commonly, includes actuators, network connections, sensors, control systems, and computational resources. It is of increasing concern that modern infrastructure is vulnerable to cyber-attacks that can damage both the cyber and physical nature of the infrastructure. To date, the physical and cyber health of infrastructure has been considered separately. However, the increasing concerns associated with the cyber-physical security of infrastructure coupled with the emergence of 5G networks made using components that are not universally considered trustworthy, and the emergence of techniques for creating deepfakes and adversarial examples suggests the time has come to begin considering cyber health and structural health with a more holistic approach. In this work, a protocol is developed for ensuring the imagery data captured by a structural health monitoring system can be unambiguously attributed to legitimate sensors associated with the structural health monitoring system. A computer vision approach based on the idea of mutual information is then presented to detect damage in an image. This work presents the protocol for authenticating the provenance of imager data and demonstrates that this protocol does not have overly adverse effects when used with the mutual information-based technique for detecting damage in the resulting imagery data.

Jung, HweeKwon↗

DOE OSTI2025

Enhanced Design of Radiation Tolerant High-Temperature Structural Health Monitoring Sensors

Acoustic emission sensors are vital in the nuclear industry for real-time structural health monitoring and early detection of material degradation. By capturing high-frequency stress waves emitted from defects like cracks, corrosion, or fatigue, acoustic emission sensors enable non-invasive monitoring of critical components such as reactor vessels, piping, and containment structures. This technology supports predictive maintenance, enhances safety, and ensures regulatory compliance by providing early warnings of potential failures. It is also instrumental in research, particularly in material testing reactors, where it is used to monitor the behavior of fuels and materials under irradiation, by allowing the detection of cracking or other acoustic signals in real time. This enables the evaluation of performance and accident behavior of advanced fuel concepts.

46 - INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AN↗

DOE OSTI2023

Development, Testing, and Validation of Fabry-Pérot Cavity Acoustic Sensors for Microreactor Applications

Nuclear microreactors are a unique reactor design which may allow for the nuclear energy industry to tap into unconventional markets, and their modularity presents some advantages over larger, conventional reactor designs. For nuclear microreactors to be economically viable, on-line structural health monitoring may be necessary to reduce operation and maintenance costs with lower power production. Optical fiber sensors, and specifically optical Fabry-Pérot cavities (FPCs), may present the opportunity to monitor vibrational frequencies of microreactor components during operation, enabling identification of the location and severity of potential damage. In this work, FPCs were designed and fabricated with materials which may be able to survive in the extremely harsh environment of nuclear microreactors. The FPCs were bonded to metal test specimens to monitor their vibrational frequencies, along with a reference piezoelectric accelerometer. Euler-Bernoulli theory was used to calculate the fundamental modes and mode-shapes to compare with experimentally measured frequency spectra. The peak frequencies detected with the FPC and piezoelectric accelerometer, and theoretical modes, all agreed well for the first four fundamental modes (within 5%). Furthermore, when the FPC and accelerometer were co-located on the test specimen, their peak frequencies all agreed within 1%.

Birri, Tony↗

DOE OSTI2021

Feasibility of Lead Fast Reactor Heat Exchanger Tube Online Monitoring

Within virtually all reactor systems with heat exchangers, the thinnest barriers between primary and secondary fluids are the heat exchanger tubes. Consistent with light water reactor experience, the advanced reactor heat exchangers are the most susceptible regions for corrosion and leakage (NRC, 2019). Moreover, traditional inspections and repairs using robotics will be expensive or impractical. On-line structural health corrosion monitoring (OLSHM) is therefore desirable to detect tube degradation prior to leaks that may allow mixing of heat exchanger fluids. Rhetorically many molten salt reactor (MSR) designers plan for on-line monitoring of critical reactor components including heat exchangers but there are no proven OLSHM sensors or systems for MSR heat exchangers. Guided wave ultrasound from the tube-end or ID using conventional low-temperature piezoelectric sensors has been shown to be sensitive to pits and cracks less than 50% through-wall in long pipes and tubes (Narayanan M.M., 2019) . Mode and frequency selection are important for practical sensor design decisions and ultimate performance. The L-0 wave-mode is easily excited by L-mode transducers but they have surface normal wave motion that will be sensitive to and more attenuative because of fluid interaction with the tube surface (Rose J.L., 1994). Shear Horizontal (SH) wave modes have material motion parallel to the tube surface therefore are virtually unaffected by fluid interaction. For SH-0 to be nondispersive in steel, the frequency-thickness product (MHz-mm) must be less than approximately 1.5. SH mode transducers must be coupled by a material that can support shear stress; a thin layer of honey works well for temporary sensors and an adhesive or a brazed metallurgical bond for permanent sensors. If sensors are incorporated into the fabrication process, the same kinds of SH-0 waves can be generated from the stagnant flow area of the tube OD without compromising the heat-exchange tube function. Piezoelectric materials are also available that can withstand harsh temperatures of molten salt heat exchangers (Stevenson T., 2015) (Machura et al., 2008). Though they may not have as high-performance characteristics, their performance only needs to be good enough to detect flaws of interest. This project attempts to demonstrate feasibility to develop a sensor that can be sensitive to flaws of interest and perform long-term monitoring of a molten salt reactor heat exchanger. Benefits to the advanced reactor system include: • Minimizing the expense of a reactor shut down for periodic inspection—only shutdown for cause. • Reduce risk of unexpected tube rupture by detecting flaws before they grow to through-wall leaks. • Cost reduction by minimizing need to design for manual/robotic inspectability. Transducer design, characterization of the beam profile, wedges vs. direct application of piezoelectric sensors, and ultrasonic pulse-echo responses from a representative heat exchange tube with fabricated flaw types are discussed.

On-Line Tube Monitor, Guided Wave Ultrasound, SH-0↗

DOE OSTI2020

Progress Toward Molten Salt Reactor Heat Exchanger On-Line Monitoring

Within virtually all reactor systems with heat exchangers, the thinnest barriers between primary and secondary fluids are the heat exchanger tubes. Consistent with light water reactor experience, the advanced reactor heat exchangers are the most susceptible regions for corrosion and leakage (NRC, 2019). Moreover, traditional inspections and repairs using robotics will be expensive or impractical. On-line structural health corrosion monitoring (OLSHM) is therefore desirable to detect tube degradation prior to leaks that may allow mixing of heat exchanger fluids. Rhetorically many molten salt reactor (MSR) designers plan for on-line monitoring of critical reactor components including heat exchangers but there are no proven OLSHM sensors or systems for MSR heat exchangers. Guided wave ultrasound from the tube-end or ID using conventional low-temperature piezoelectric sensors has been shown to be sensitive to pits and cracks less than 50% through-wall in long pipes and tubes (Narayanan M.M., 2019) . Mode and frequency selection are important for practical sensor design decisions and ultimate performance. The L-0 wave-mode is easily excited by L-mode transducers but they have surface normal wave motion that will be sensitive to and more attenuative because of fluid interaction with the tube surface (Rose J.L., 1994). Shear Horizontal (SH) wave modes have material motion parallel to the tube surface therefore are virtually unaffected by fluid interaction. For SH-0 to be nondispersive in steel, the frequency-thickness product (MHz-mm) must be less than approximately 1.5. SH mode transducers must be coupled by a material that can support shear stress; a thin layer of honey works well for temporary sensors and an adhesive or a brazed metallurgical bond for permanent sensors. If sensors are incorporated into the fabrication process, the same kinds of SH-0 waves can be generated from the stagnant flow area of the tube OD without compromising the heat-exchange tube function. Piezoelectric materials are also available that can withstand harsh temperatures of molten salt heat exchangers (Stevenson T., 2015) (Machura et al., 2008). Though they may not have as high-performance characteristics, their performance only needs to be good enough to detect flaws of interest. This project attempts to demonstrate feasibility to develop a sensor that can be sensitive to flaws of interest and perform long-term monitoring of a molten salt reactor heat exchanger. Benefits to the advanced reactor system include: • Minimizing the expense of a reactor shut down for periodic inspection—only shutdown for cause. • Reduce risk of unexpected tube rupture by detecting flaws before they grow to through-wall leaks. • Cost reduction by minimizing need to design for manual/robotic inspectability. Transducer design, characterization of the beam profile, and ultrasonic pulse-echo responses from a representative heat exchange tube with fabricated flaw types are discussed.

On-Line Tube Monitor, Guided Wave Ultrasound, SH-0↗

DOE OSTI2024

Mini-cell: Compact all optical gas monitoring sensor for persistent surveillance of complex system

Persistent surveillance of complex systems by embedded sensing is the new paradigm of aging awareness and monitoring. In any complex system with a long-term lifespan plan, the persistent surveillance of critical parameters warrants the reliability and safety/security of the system by the continuous monitoring of the state of health and aging conditions. Therefore, embedded sensors of physical, chemical, and structural features/traits are critical for uncovering occurrences of undesired/unexpected events. Gas sensing can provide early detection of a broad range of problems such as decomposing components, corrosion, failures, and leaks. For the application of interest, the sensors must be: 1/highly selective and sensitive for a broad range of gas molecules, 2/compact, 3/minimally invasive, 4/rugged for vibration and thermal excursions, and 5/ environmentally inert.

47 OTHER INSTRUMENTATION↗

DOE OSTI2024

An Operations and Maintenance Roadmap for U.S. Offshore Wind: Enabling a Cost-Effective and Sustainable U.S. Offshore Wind Energy Industry Through Innovative Operations and Maintenance

The United States is currently targeting 30GW of offshore wind to be installed by 2030, and 150GW by 2050. Even considering future turbine sizes, this represents thousands of new turbines installed in a diverse set of environments, each with their unique design, installation, and maintenance challenges. While much can be learned from European and Asian experience with offshore wind over the past two decades, it is important to understand the unique circumstances of the U.S. This document explores operations and maintenance of offshore wind energy, specific to the U.S. and attempts to lay out a roadmap for needed activities to ensure reliability of future installations. The roadmap was informed through dozens of interviews with a wide cross-section of the industry, including representatives from OEMs, owner/operators, service companies, certification agencies, service providers, and researchers. The roadmap first describes the problem by component - blades, drivetrain and nacelle, structures and foundations, and electrical systems - through a look at current practices and opportunities for improvement in the areas of Failure Mode Analysis and Mitigation; Monitoring, Sensing, and Inspection; and Maintenance Execution. Crosscutting areas of Digitalization, Robotics and Automation, Prognostics and Health Management and O&M Optimization, Experimentation and Demonstration, Standardization, and Design Optimization Considering Reliability and O&M are then discussed. Finally, the roadmap summarizes all of these topics with recommendations for short (1-3 years), medium (4-7 years), and long term (8-12 years) activities, with a description of needed public and private sector contributions.

17 WIND ENERGY↗

DOE OSTI2022

A Review: PFAS Adsorption, Sensing, and Remediation with Engineered Nanoporous Materials

Per- and polyfluoroalkyl substances (PFAS) are among the most abundant environmental contaminant species. They are widespread due to uncontrolled industrial and commercial use and have been linked to health risks such as cancer. With rising global concerns about the public health effects of PFAS, there is an incentive to develop strategies for reliable monitoring and effective PFAS removal, particularly in drinking water. Traditional PFAS sensing techniques are inefficient due to long measurement times, high labor input, high costs, and limitations to ex situ analysis. Current commercially available sorbents for PFAS removal generally lack the ability to capture PFAS components rapidly and quantitatively. Furthermore, existing sorbents are notably inefficient in removing the more toxic, smaller PFAS chemical chains. Large-scale applications of these methods tend to be costly and resource-intensive. Pacific Northwest National Laboratory has developed unique strategies for PFAS sensing and removal with capture probe technology that has an affinity for fluorocarbons, including PFAS. For both sensing and removal, the customizable capture probes can target specific PFAS compounds since they used a metal-organic framework (MOF)-based technology that can be fine-tuned and molecularly tailored. This tailoring of the materials enables high PFAS sensitivity, selectivity, and faster uptake. PNNL’s capture probe materials have been developed into a sensing technique based on the interactions of the capture probes with PFAS at the molecular level and further transduced that to a quantifiable electrochemical response. The materials have been integrated into a sensor platform developed by New Jersey Institute of Technology (NJIT). Tunable capture probes with a range of detection sensitivities allow for faster and more sensitive PFAS detection limits as low as what appears to be 0.5 ng/L (compared to the U.S. Environmental Protection Agency health advisory level of 70 ng/L). Customization of the capture probe results in improved sorption capacities (fast kinetics and high capacities) compared to commercial granular activated carbons. This research applied various experimental and modeling tools to improve understanding the molecular level interactions between the sorbents towards PFAS adsorption properties.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗

DOE OSTI2025

Non-destructive structural characterization of graphite components using mechanical resonance and deep learning

As compared to conventional nuclear reactors, microreactors have the potential to significantly reduce construction timelines and capital costs, decreasing the barriers for advanced nuclear reactor technologies. However, the lower power output of these microreactors (typically < 20 MWe) creates challenging economics if operation and maintenance costs cannot be sufficiently reduced. The compact size of these designs presents an opportunity for comprehensive in-situ structural health monitoring to provide real-time feedback in order to reduce operational costs associated with maintenance and downtime. Many microreactor concepts use graphite for both in-core neutron moderation and as a structural material, which has typically required some form of periodic and laborious inspection. This report provides a description and assessment of recent work with graphite to couple acoustic-based experimental measurements and characterization with machine learning models to mature structural health monitoring capabilities and generate benefits for the nuclear microreactor industry. With resilient embedded sensors in development in other programs funded by the US Department of Energy’s Office of Nuclear Energy and elsewhere, the work described herein builds upon previously funded efforts to mature non-destructive testing technology that relates measured vibrational signatures to structural changes, using a combination of new experimental measurements and machine learning processing. Building on past successful demonstrations of predictive workflows to identify structural changes in a hexagonal stainless steel test article with excellent acoustic propagation, we first performed baseline characterization on graphite samples with canonical geometries to ensure compatibility and confidence in the applied techniques for a material with distinctly different mechanical properties. In contrast to efforts in previous years, we worked exclusively with unidirectional vibration data that is more comparable to those expected from the existing embedded sensor technologies which are suitable for deployment in a reactor setting. Established acoustic and modern machine-learning-based characterization approaches were applied to the resulting datasets from these simple geometries. Both approaches were found to be highly capable of detecting even small geometric irregularities amongst nominally identical samples. As such, we then moved to testing these approaches for detection of artificial local stress perturbations introduced into a more complex geometry: a hexagonal block with drilled holes. A main outcome of this work is that a generalizable ML workflow can be used to detect and predict the characteristics of small artificial anomalies in a graphite component with a relevant geometry. While this work was performed using surficial vibration data, we expect the approach to be flexible and viable for other monitoring scenarios, such as those with different arrangements or types of sensor arrays. As compared to previously funded efforts, an existing ML workflow based on neural networks was enhanced through the addition of recently developed Fourier neural operators. As applied to previously collected and new vibration datasets, prediction accuracies of anomaly characterizations were greatly improved with minimal added computational cost. As trained on small durations of vibration data (tens of seconds) collected over a realistic number of locations, the model was able to reliably determine the presence of a subtle stress anomaly and begin to provide location estimates. Such an approach is likely to be viable for more relevant reactor damage scenarios for graphite components, such as progressive crack growth or creep.

36 MATERIALS SCIENCE↗

DOE OSTI2024

Hear It? – New Physics Calls For a Healthy Target!

Nowadays, when the attention of the physics community is drawn to muon collider prospects and neutrino projects, a target as the primary source of such exotic particles is a key component of a particle physics experiment. The extreme conditions placed on the target, though, decrease its functionality and threaten the future of high-power targetry. Subject to superb magnetic fields, extreme temperatures, and radiation damage, the target is constantly at risk of unexpected failures. The Target Health Monitor (THM) aims to enable the continuous analysis of the target state throughout the experiment. Based on the optical concept of Brewster s angle and the reflectivity variation with the compositional changes in the target material, the THM will record and evaluate the radiation-caused transmutations in the target to foresee its malfunction before it affects the experimental results. The first steps in validating our THM concept have been made this summer. Continuing reflectivity measurements of the prospective target materials, we anticipate proving the THM potential to monitor target health effectively.

Havryshchuk, Kateryna↗

DOE OSTI2026

Embedding Fiber Optic Sensors in Stainless Steel using Spark Plasma Sintering for Structural Health Monitoring in Harsh Environments

Embedded fiber optic sensors such as fiber Bragg gratings (FBGs) offer a unique route for distributed real-time in-situ imaging of various engineering parameters for numerous purposes. This study advanced the current sensor embedding approaches by exploring a spark plasma sintering (SPS)-assisted technology to embed FBGs in high-temperature structural materials and demonstrated the capability of temperature measurement. In this approach, single-mode FBGs were integrated into stainless steel (SS) 316L components using SPS, followed by the evaluation of the bonding quality between the FBGs and matrix, the optical attenuation of the fibers induced by embedding, and the sensing characters of the FBGs under temperature stimuli. The results demonstrated that superior bonding was achieved between the FBGs and highly-densified SS316L. Examination of the behavior of Bragg gratings validated signal fidelity after embedding. Real-time thermal imaging under temperature cycling using the FBGs demonstrated the effectiveness of the technique for smart materials manufacturing.

36 - MATERIALS SCIENCE↗

DOE OSTI2023

Demonstration of acoustic monitoring for structural health of microreactors: Through use of neural networks and resonant ultrasound spectroscopy

Nuclear microreactors prioritize modularity and portability and are intended to be a cost-effective technology for non-conventional nuclear markets. As such, the development of microreactors into a safe and feasible solution for energy security applications will necessitate the development of non-destructive technologies to monitor the integrity of inaccessible reactors components during operation. This demonstration applies linear and nonlinear acoustic techniques, in combination with machine learning, to detect and classify mechanical changes (stress and damage) in a test article which are broadly representative of potential operating challenges within a functioning microreactor. All necessary data has been collected for this demonstration, with minor experimental issues identified that can be addressed in follow-on work. Motivated by the expected conditions within a functioning microreactor, we have demonstrated our monitoring techniques on a core-block-like test article using an unstructured excitation source that approximates the noisy acoustic environment expected during reactor operation. At all stress states mechanically applied to the test article, a machine learning model using an artificial neural network was able to classify with 100% accuracy whether a 3D laser vibrometry point measurement was made on an intact or artificially defective test article. Further, model predictions about whether the defect interface was rough or smooth were 95% accurate, indicating the ability of acoustic techniques to recover defect characteristics. Resonant ultrasound spectroscopy (RUS) was also applied to the dataset to provide further quantitative insights about material properties. RUS analysis was ultimately hampered by several minor experimental and data issues, limiting results to certain cases for this demonstration. Last, analysis using nonlinear RUS exhibited sensitivity to changing levels of applied stresses for each intact and defective state. As presented in this demonstration, acoustic monitoring exhibits sensitivity to stress changes, which are of concern due to high thermal gradients expected during startup and operation. Further, our techniques distinguish between measurements made on intact and damaged test articles.

21 SPECIFIC NUCLEAR REACTORS AND ASSOCIATED PLANTS↗

DOE OSTI2025

Development of High-Temperature Bonding Techniques to Enable High-Temperature Static or Dynamic Strain Measurements

Current light-water nuclear reactors rely on a variety of different sensors and sensor applications to meet their structural health monitoring needs throughout the entirety of the reactor primary, secondary, and containment systems. Optical fiber–based sensor technologies could provide solutions to reduce the sensor system footprint while enhancing the measurement fidelity and spatial resolution by leveraging distributed monitoring techniques. Moreover, advanced reactors may require optical fiber–based sensors for structural health monitoring because their operating temperatures will exceed the limits of conventional transducers used to acquire dynamic strain or acoustic data in nuclear power plants. Therefore, this report describes experiments targeting the development of high-temperature bonding techniques that would allow for potentially long lengths of fibers to be bonded to metallic reactor components in advanced reactor systems. The high temperatures experienced within target application, next-generation nuclear reactors, necessitate a high-temperature resistant bond to limit the amount of tension on the fiber at the target application temperature. The primary bonding method investigated in this work is brazing; hot-rolling has also been investigated to a lesser extent. Both techniques are well-suited to bonding optical fibers to large reactor components such as primary coolant piping, pressure vessels, or heat exchangers. Optical frequency domain reflectometry was used to monitor the strain in metal-coated optical fibers before, during, and after the high-temperature bonding process. On select optical fibers that were successfully bonded, additional thermal cycling was performed to assess the extent to which the fiber remained bonded based on the expected thermal expansion of the test specimen material. The results of the various experiments yielded the following general conclusions: (1) brazing is a viable technique for bonding and allows significant compressive strain to be applied to the fiber at room temperature; (2) hot-rolling is a viable technique as well, which has been more optimized than the brazing technique for bonding, but less residual compressive strain has been observed with this technique; and (3) both techniques will need further development and optimization to demonstrate bonding of a long length of fiber that can provably operate at relevant temperatures for an advanced nuclear reactor application.

22 GENERAL STUDIES OF NUCLEAR REACTORS↗

DOE OSTI2023

On the Language of Reliability: A System Engineer Perspective

In its classical definition, risk is defined by three elements: what can go wrong, what are its consequences, and how likely is it to occur? While this definition makes sense in a regulatory-based framework where for the current fleet of operating light water reactors (LWRs), the risks associated with nuclear power plants typically are characterized in terms of core damage and large early release frequency (LERF), this approach does not provide a useful snapshot of the health of the plant from a broader perspective. This is due to the very narrow context in which the term “risk” typically is defined as nuclear safety aspects that have the potential to impact public health. In this paper, we take the viewpoint of nuclear safety that is reflective of the current fleet of operating LWRs for which core damage frequency and LERF are appropriate metrics. For other advanced reactor designs, other more applicable technology neutral metrics of reactor safety metrics would be specified. A possible alternate path would start by redefining the word risk with a broader meaning that better reflects the needs of a system health and asset management decision-making process. Rather than asking how likely an event could occur (in probabilistic terms), we can ask how far this event is from occurring. Our approach starts by defining and quantifying component and system health in terms of a “distance” between its actual and limiting conditions, i.e., determination of the margin that exists between the current state/condition and the state where the component/system is no longer capable of achieving its intended function. A margin is a measure that is more reflective of the current state or performance of a component, and therefore more closely tied to decisions that are made on an ongoing basis. Further, we will show how, given the data available from plant equipment reliability and monitoring (e.g., pump vibration data) and prognostic (e.g., component remaining useful life estimation) data, a margin can be described and determined for all types of maintenance approaches (e.g., corrective or predictive maintenance). We show how classical reliability models (e.g., fault trees) can be used to quantify the system margin provided component margin values. In the approach described in this paper, the propagation of margin values through classical reliability models are not performed using classical probabilistic calculations applied to sets (as performed in a typical plant probabilistic risk assessment). Instead, we show how it is possible to propagate margin values through Boolean logic gates (i.e., AND and OR operators) through distance-based operations.

97 MATHEMATICS AND COMPUTING↗

DOE OSTI2024

MODAQ (Modular Offshore Data Acquisition System) [SWR-24-58]

The National Renewable Energy Laboratory (NREL) has developed a data acquisition system to support laboratory (component) and in-water validation and testing of Marine Renewable Energy (MRE) technologies. The Modular Ocean Data AcQuisition System (MODAQ) is a robust system built on industrial grade hardware that can acquire high-quality and summary data from many sensor and instrument types. MODAQ includes modules to perform real-time QC screening, system health monitoring, and send alerts. MODAQ is also part of a larger data management, processing, visualization, and archiving system.

Raye, Robert↗

DOE OSTI2024

The value of adding black carbon to community monitoring of particulate matter

Low-cost particulate matter (PM) sensors are increasingly used by researchers, public health agencies, and the public to measure spatial and temporal variations in air pollution, which can inform strategies for community air pollution reduction. While low-cost PM sensors provide a valuable measure of harmful fine particulate matter (PM 2.5 ), a significant portion of ambient PM 2.5 is typically the secondary product of air pollution emitted by varied sources outside of community boundaries. In contrast, concentrations of black carbon (BC), a component of PM 2.5 , are directly emitted by a few specific sources, such as diesel engines within communities. Motivated by community organizations seeking to understand persistent sources of local pollution, this study deployed a suite of custom-built BC sensors alongside a network of low-cost PM sensors for four weeks in two seasons at 50 stationary locations in the adjacent cities of Richmond, North Richmond, and San Pablo, California, east of the San Francisco Bay. Concentrations of BC varied more than PM 2.5 both temporally and spatially. Monthly network-average BC was 3×higher in winter than late spring, while PM 2.5 was only 10% lower. In both seasons, average PM 2.5 concentrations at two-thirds of sites were within ±10% of the network average, whereas two-thirds of sites had BC levels outside of ±10% of the network-average concentration. The most and least polluted locations were more persistent across seasons for BC than PM 2.5 , and the temporal dynamics of BC at these sites were similar, signifying that they are impacted by the same emission sources. Together, these spatiotemporal trends show that BC is a better indicator of the proximity and activity of local pollution sources than PM 2.5 . Thus, including BC in addition to PM 2.5 in community monitoring networks can provide additional insights about local sources of air pollution.

54 ENVIRONMENTAL SCIENCES↗

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 OSTI2022

Real-time bioelectronic sensing of environmental contaminants

Real-time chemical sensing is crucial for applications in environmental and health monitoring. Biosensors can detect a variety of molecules through genetic circuits that use these chemicals to trigger the synthesis of a coloured protein, thereby producing an optical signal. However, the process of protein expression limits the speed of this sensing to approximately half an hour, and optical signals are often difficult to detect in situ. Here we combine synthetic biology and materials engineering to develop biosensors that produce electrical readouts and have detection times of minutes. Here we programmed Escherichia coli to produce an electrical current in response to specific chemicals using a modular, eight-component, synthetic electron transport chain. As designed, this strain produced current following exposure to thiosulfate, an anion that causes microbial blooms, within 2 min. This amperometric sensor was then modified to detect an endocrine disruptor. The incorporation of a protein switch into the synthetic pathway and encapsulation of the bacteria with conductive nanomaterials enabled the detection of the endocrine disruptor in urban waterway samples within 3 min. Our results provide design rules to sense various chemicals with mass-transport-limited detection times and a new platform for miniature, low-power bioelectronic sensors that safeguard ecological and human health.

59 BASIC BIOLOGICAL SCIENCES↗