Continuous Monitoring Technology Research Articles

Use of Transdermal Alcohol Sensors in Conjunction With Contingency Management to Reduce Alcohol Consumption in People With Alcohol Dependence Attending Alcohol Treatment Services: Protocol for a Pilot Feasibility Randomized Controlled Trial.

Wearable technology for objective, continuous, and reliable alcohol monitoring has been developed. These are known as transdermal alcohol sensors (TASs). They can be worn on the wrist or ankle with the sensor pressed against the skin and can measure sweat vapors being emitted from the skin, to record transdermal alcohol concentration (TAC). Previous studies have investigated the accuracy and acceptability of the available TAS brands, but there has been little research into their use in people with alcohol use disorders (AUD). This feasibility randomized controlled trial aims to explore the feasibility, strengths, and limitations of using a TAS to monitor alcohol consumption in individuals in treatment for AUD with or without contingency management (CM) to promote abstinence or low-level alcohol consumption. The target sample size is 30 (15 randomized to each group). Participants will be recruited through poster adverts at alcohol services. Both groups (control and CM) will wear the TAS (BACtrack Skyn) for 2 weeks in the context of their usual treatment, meeting with the researcher every other weekday. In the last meeting, the participants will complete a postwear survey on their experience of wearing the TAS. The CM group will also receive small financial incentives for low or no alcohol consumption, as measured by the TAS. On days where the TAC peak is below a set threshold (<115.660 g/L), CM group participants will be rewarded with a £5 (US $6.38) voucher. There are financial bonuses if this target is achieved on consecutive days. The researcher will monitor TAC for each day of the study at each research visit and allocate financial incentives to participants according to a set reinforcement schedule. The first participant was enrolled in June 2023, and the last in December 2023. Data analysis is underway and is estimated to be completed by June 2024. A total of 32 participants were enrolled. Most TAS brands have had limited application in clinical settings, and most studies have included healthy adults rather than people with AUD. TAS has the potential to enhance treatment outcomes in clinical alcohol treatment. The accuracy, acceptability, and feasibility of TAS for people with AUD in clinical settings need to be investigated. This is the first study to use TAS in specialized alcohol services with diagnosed AUD individuals currently receiving treatment from a south London alcohol service. ISRCTN Registry ISRCTN46845361; https://www.isrctn.com/ISRCTN46845361. DERR1-10.2196/57653.

Characterising mobility and pressure exposure in community dwelling residents with pressure ulcers using monitoring technology and intelligent algorithm

AimIndividuals in the community with reduced mobility are at risk of exposure to prolonged lying and sitting postures, which may cause pressure ulcers. The present study combines continuous pressure monitoring technology and intelligent algorithms to evaluate posture, mobility, and pressure profiles in a cohort of community dwelling patients, who had acquired pressure ulcers. Materials and methodsThis study represents a secondary analysis of the data from the Quality Improvement project ‘Pressure Reduction through COntinuous Monitoring In the community SEtting (PROMISE)’. 22 patients with pressure ulcers were purposely selected from 105 recruited community residents. Data were collected using a commercial continuous pressure monitoring system over a period of 1–4 days, and analysed with an intelligent algorithm using machine learning to determine posture and mobility events. Duration and magnitude of pressure signatures of each static posture and exposure thresholds were identified based on a sigmoid relationship between pressure and time. ResultsPatients revealed a wide range of ages (30–95 years), BMI (17.5–47 kg/m2) and a series of co-morbidities, which may have influenced the susceptibility to skin damage. Posture, mobility, and pressure data revealed a high degree of inter-subject variability. Largest duration of static postures ranged between 1.7 and 19.8 h, with 17/22 patients spending at least 60 % of their monitoring period in static postures which lasted >2 h. Data revealed that many patients spent prolonged periods with potentially harmful interface pressure conditions, including pressure gradients >60 mmHg/cm. ConclusionThis study combined posture, mobility, and pressure data from a commercial pressure monitoring technology through an intelligent algorithm. The community residents who had acquired a pressure ulcer at the time of monitoring exhibited trends which exposed their skin and subdermal tissues to prolonged high pressures during static postures. These indicators need further validation through prospective clinical trials.

Monitoring ocular disease via optical nanostructures potentially applicable to corneal contact lens products

Ocular diseases can cause vision problems or even blindness if they are not detected early. Some ocular diseases generate irregular physical changes in the eye; therefore, reliable diagnostic technology for continuous monitoring of the eye is an unmet clinical need. In this study, a pulsed laser (Nd:YAG) was used to create optical nanostructures on a hydrogel-based commercial contact lens. Simulations were used to determine the spacing of the nanostructures, which were then produced and tested on the lens in ambient humidity and fully hydrated environments. The nanostructures produced a 4° diffraction angle difference in response to the environmental changes. Vision obstruction was considered while designing the nanostructure features on the lens. The curved nanostructures exhibited a series of visible rainbow colors with an average range of 8° under normal room light. A spherical surface was also used to simulate the human eye, and application of a force (curvature change) caused the nanostructure spacing to change, influencing the visible color of the contact lenses. A smartphone camera application was used to measure the progress of ocular diseases by analyzing the RGB color values of the visible color. The nanostructures were also responsive to K+ ion variations in artificial tear fluids, with a 12 mmol L−1 sensitivity, which may allow the detection of ocular ionic strength changes.

Artificial Intelligence in Sports Medicine: Reshaping Electrocardiogram Analysis for Athlete Safety-A Narrative Review.

Artificial Intelligence (AI) is redefining electrocardiogram (ECG) analysis in pre-participation examination (PPE) of athletes, enhancing the detection and monitoring of cardiovascular health. Cardiovascular concerns, including sudden cardiac death, pose significant risks during sports activities. Traditional ECG, essential yet limited, often fails to distinguish between benign cardiac adaptations and serious conditions. This narrative review investigates the application of machine learning (ML) and deep learning (DL) in ECG interpretation, aiming to improve the detection of arrhythmias, channelopathies, and hypertrophic cardiomyopathies. A literature review over the past decade, sourcing from PubMed and Google Scholar, highlights the growing adoption of AI in sports medicine for its precision and predictive capabilities. AI algorithms excel at identifying complex cardiac patterns, potentially overlooked by traditional methods, and are increasingly integrated into wearable technologies for continuous monitoring. Overall, by offering a comprehensive overview of current innovations and outlining future advancements, this review supports sports medicine professionals in merging traditional screening methods with state-of-the-art AI technologies. This approach aims to enhance diagnostic accuracy and efficiency in athlete care, promoting early detection and more effective monitoring through AI-enhanced ECG analysis within athlete PPEs.

Effectiveness of a hybrid approach in integrating GLP-1 agonists and lifestyle guidance for obesity and pre-diabetes management: RWE retrospective study

AimEmerging anti-obesity pharmacotherapy provides an option to correct maladaptive physiological and hormonal changes associated with obesity. One of the widely used medications in this context is glucagon-like peptide 1 (GLP-1) agonists. However, the misuse of these medications without any guidance and monitoring of lifestyle modifications can lead to unfavorable outcomes. The study aims to evaluate the effectiveness of a hybrid care model, incorporating GLP-1 and GLP-1/GIP agonist therapies, in managing obese patients with/without pre-diabetes. This study showcases the midway results of a 6-month program, which includes a multidisciplinary care team and digital technology for continuous engagement and monitoring of patients, both in-clinic and remotely. MethodsIn a retrospective observational study, 115 participants were treated with GLP-1s (semaglutide, tirzepatide, and liraglutide). Physicians, dietitians, and coaches worked together to support behavioral changes using a dedicated app provided to patients. At the care team end, an integrated portal enabled continuous data flow allowing for the care team to provide personalized care via chat at regular intervals. Data collected included food logs, continuous glucose monitoring (CGM), and digital biomarkers such as sleep and activity. ResultsAt the midpoint of the program, participants exhibited statistically significant improvements in various metabolic parameters. Mean weight reduction was 8 %, with significant reductions in BMI, fat mass, and cholesterol levels. 24 (20.9 %) of patients lost ≥5 % of body weight, 55 (47.8 %) patients lost ≥10 % weight, and 36 (31.3 %) patients lost ≥15 % weight. Sub-analysis of pre-diabetic patients (n=36) demonstrated substantial improvements, including control of pre-diabetes in 80.6 % of cases and reduced HbA1c levels back to normoglycemia (5.39 ± 0.27). ConclusionThe Zone.Health's program, which combines pharmacotherapy with continuous engagement and monitoring to enable sustainable lifestyle modifications, demonstrated significant improvements in weight, body composition, and metabolic markers. Pre-diabetes was also effectively addressed. It is necessary to conduct further research to assess the long-term sustainability and optimal adoption of such care models into clinical practice.

Application of Closed-loop Theory in Deep Learning Training Guided by High-strength Intelligent Machinery

Artificial intelligence (AI) algorithms and continuous monitoring technologies have the potential to transform the way chronic illnesses are managed. We will also talk about the problems and potential that AI technology presents for CGM in individualised and preventive medicine. Furthermore, we assessed the AHCL system's usefulness in patients with impaired awareness of hypoglycemia (IAH) and those who correctly recognised hypoglycemia symptoms. The participants' ages varied from 37 to 15, and they had received diabetes medication for an average of 20 to 10 years. IAH was seen in 12 individuals (27%) with a Clarke's score of less than 3. Patients with IAH were older than those who did not have IAH. The baseline CGM readings and A1c were the same, but the estimated glomerular filtration rate (eGFR) was lower. Despite prior insulin treatment, the AHCL system resulted in an overall drop in A1c (from 6.9 0.5% to 6.7 0.6%, P 0.001). Only three patients (7%) received Clarke's three scores after six months on the AHCL system, resulting in a 20% absolute risk decrease for IAH (95% confidence interval: 7-32).

Harnessing the Synergy of SGLT2 Inhibitors and Continuous Ketone Monitoring (CKM) in Managing Heart Failure among Patients with Type 1 Diabetes.

Heart failure (HF) management in type 1 diabetes (T1D) is particularly challenging due to its increased prevalence and the associated risks of hospitalization and mortality, driven by diabetic cardiomyopathy. Sodium-glucose cotransporter-2 inhibitors (SGLT2-is) offer a promising avenue for treating HF, specifically the preserved ejection fraction variant most common in T1D, but their utility is hampered by the risk of euglycemic diabetic ketoacidosis (DKA). This review investigates the potential of SGLT2-is in T1D HF management alongside emergent Continuous Ketone Monitoring (CKM) technology as a means to mitigate DKA risk through a comprehensive analysis of clinical trials, observational studies, and reviews. The evidence suggests that SGLT2-is significantly reduce HF hospitalization and enhance cardiovascular outcomes. However, their application in T1D patients remains limited due to DKA concerns. CKM technology emerges as a crucial tool in this context, offering real-time monitoring of ketone levels, which enables the safe incorporation of SGLT2-is into treatment regimes by allowing for early detection and intervention in the development of ketosis. The synergy between SGLT2-is and CKM has the potential to revolutionize HF treatment in T1D, promising improved patient safety, quality of life, and reduced HF-related morbidity and mortality. Future research should aim to employ clinical trials directly assessing this integrated approach, potentially guiding new management protocols for HF in T1D.

Direct and Continuous Monitoring of Multicomponent Antibiotic Gentamicin in Blood at Single-Molecule Resolution.

Point-of-care monitoring of small molecules in biofluids is crucial for clinical diagnosis and treatment. However, the inherent low degree of recognition of small molecules and the complex composition of biofluids present significant obstacles for current detection technologies. Although nanopore sensing excels in the analysis of small molecules, the direct detection of small molecules in complex biofluids remains a challenge. In this study, we present a method for sensing the small molecule drug gentamicin in whole blood based on the mechanosensitive channel of small conductance in Pseudomonas aeruginosa (PaMscS) nanopore. PaMscS can directly detect gentamicin and distinguish its main components with only a monomethyl difference. The 'molecular sieve' structure of PaMscS enables the direct measurement of gentamicin in human whole blood within 10 min. Furthermore, a continuous monitoring device constructed based on PaMscS achieved continuous monitoring of gentamicin in live rats for approximately 2.5 h without blood consumption, while the drug components can be analyzed in situ. This approach enables rapid and convenient drug monitoring with single-molecule level resolution, which can significantly lower the threshold for drug concentration monitoring and promote more efficient drug use. Moreover, this work also lays the foundation for the future development of continuous monitoring technology with single-molecule level resolution in the living body.

Rapid Automatic Cacao Pod Borer Detection Using Edge Computing on Low-End Mobile Devices

The cacao pod borer (CPB) (Conopomorpha cramerella) is an invasive insect that causes significant economic loss for cacao farmers. One of the most efficient ways to reduce CPB damage is to continuously monitor its presence. Currently, most automated technologies for continuous insect pest monitoring rely on an internet connection and a power source. However, most cacao plantations are remotely located and have limited access to internet and power sources; therefore, a simpler and readily available tool is necessary to enable continuous monitoring. This research proposes a mobile application developed for rapid and on-site counting of CPBs on sticky paper traps. A CPB counting algorithm was developed and optimized to enable on-device computations despite memory constraints and limited capacity of low-end mobile phones. The proposed algorithm has an F1-score of 0.88, with no significant difference from expert counts (R2 = 0.97, p-value = 0.55, α = 0.05). The mobile application can be used to provide the required information for pest control methods on-demand and is also accessible for low-income farms. This is one of the first few works on enabling on-device processing for insect pest monitoring.

Performance comparison of 6 in-hospital patient monitoring systems in the detection and alarm of ventricular cardiac arrhythmias

BackgroundPatient monitoring devices are critical in alerting potential cardiac arrhythmias during hospitalization; however, there are concerns of alarm fatigue due to high false alarm rates. ObjectiveTo evaluate sensitivity and false alarm rate of hospital-based continuous electrocardiographic (ECG) monitoring technologies. MethodsSix commonly-used multiparameter bedside monitoring systems available in the United States were evaluated: B125M (GE HealthCare), ePM10 and iPM12 (Mindray), Efficia and IntelliVue (Philips), and Life Scope (Nihon Kohden). Sensitivity was tested using ECG recordings containing 57 true ventricular tachycardia (VT) events. False positive rate testing used 205 patient-hours of ECG recordings containing no cardiac arrhythmias. Signals from ECG recordings were fed to devices simultaneously; high-severity arrhythmia alarms were tracked. Sensitivity to true VT events and false positive rates were determined. Differences were assessed using Fisher’s exact tests (sensitivity) and Z-tests (false positive rates). ResultsB125M raised 56 total alarms for 57 annotated VT events and had the highest sensitivity (98%, P<0.05), followed by iPM12 (84%), Life Scope (81%), Efficia (79%), ePM10 (77%), and IntelliVue (75%). B125M raised 20 false alarms, significantly lower (P<0.0001) than iPM12 (284), Life Scope (292), IntelliVue (304), ePM10 (324), and Efficia (493). The most common false alarm was VT, followed by non-sustained VT. ConclusionWe found significant performance differences among multiparameter bedside ECG monitoring systems using previously-collected recordings. B125M had the highest sensitivity in detecting true VT events and lowest false alarm rate. These results can assist in minimizing alarm fatigue and optimizing patient safety by careful selection of in-hospital continuous monitoring technology.

Field Testing of a Mixed Potential IoT Sensor Platform for Methane Quantification

Emissions of CH4 from natural gas infrastructure must urgently be addressed to mitigate its effect on global climate. With hundreds of thousands of miles of pipeline in the US used to transport natural gas, current methods of surveying for leaks are inadequate. Mixed potential sensors are a low cost, field deployable technology for remote and continuous monitoring of natural gas infrastructure. We demonstrate for the first time a field trial of a mixed potential sensor device coupled with machine learning and internet-of-things platform at Colorado State University’s Methane Emissions Technology Evaluation Center (METEC). Emissions were detected from a simulated buried underground pipeline source. Sensor data was acquired and transmitted from the field test site to a remote cloud server. Quantification of concentration as a function of vertical distance is consistent with previously reported transport modelling efforts and experimental surveys of methane emissions by more sophisticated CH4 analyzers.

Feasibility of Leveraging Consumer Wearable Devices with Data Platform Integration for Patient Vital Monitoring in Low-Resource Settings.

Manual monitoring of vital signs, which often fails to capture the onset of deterioration, is the main monitoring modality in most Ghanaian hospitals due to the high cost and inadequate supply of patient bedside monitors. Consumer wearable devices (CWDs) are emerging, relatively low-cost technologies for continuous monitoring of physiological status; however, their validity has not been established in low-resource clinical settings. We aimed to (1) investigate the validity of the heart rate (HR) and oxygen saturation (SpO2) data from two widely used CWDs, the Fitbit Versa 2 and Xiaomi Mi Smart Band 6, against gold standard bedside monitors in one Ghanaian hospital and (2) develop a web application to capture and display CWD data in a clinician-friendly way. A healthy volunteer simultaneously wore both CWDs and blood pressure cuffs to measure HR and SpO2. To test for concordance, we conducted the Bland-Altman and mean absolute percentage error analyses. We also developed a web application that retrieves and displays CWD data in near real time as text and graphical trends. Compared to gold standards (patient monitor and manual), the Fitbit Versa 2 had 96.87% and 96.67% measurement accuracies for HR, and the Xiaomi Mi Smart Band 6 had 94.24% and 93.21% measurement accuracies for HR. The Xiaomi Mi Smart Band 6 had 98.79% measurement accuracy for SpO2. The strong concordance between CWD and gold standards supports the potential implementation of these devices as a novel method of vital sign monitoring to replace manual monitoring, thus saving costs and improving patient outcomes. Further studies are needed for confirmation.

Overview of Health-Monitoring Technology for Long-Distance Transportation Pipeline and Progress in DAS Technology Application.

There are various health issues associated with the different stages of long-distance pipeline transportation. These issues pose potential risks to environmental pollution, resource waste, and the safety of human life and property. It is essential to have real-time knowledge of the overall health status of pipelines throughout their entire lifecycle. This article investigates various health-monitoring technologies for long-distance pipelines, providing references for addressing potential safety issues that may arise during long-term transportation. This review summarizes the factors and characteristics that affect pipeline health from the perspective of pipeline structure health. It introduces the principles of major pipeline health-monitoring technologies and their respective advantages and disadvantages. The review also focuses on the application of Distributed Acoustic Sensing (DAS) technology, specifically time and space continuous monitoring technology, in the field of pipeline structure health monitoring. This paper discusses the process of commercialization development of DAS technology, the main research progress in the experimental field, and the open research issues. DAS technology has broad application prospects in the field of long-distance transportation pipeline health monitoring.

On the Performance of Metamaterial based Printed Circuit Antenna for Blood Glucose Level Sensing Applications: A Case Study

Due to the urgent need to develop technologies for continuous glucose monitoring in diabetes individuals, poten tial research has been applied by invoking the microwave tech niques. Therefore, this work presents a novel technique based on a single port microwave circuit, antenna structure, based on Metamaterial (MTM) transmission line defected patch for sensing the blood glucose level in noninvasive process. For that, the proposed antenna is invoked to measure the blood glu cose through the field leakages penetrated to the human blood through the skin. The proposed sensor is constructed from a closed loop connected to an interdigital capacitor to magnify the electric field fringing at the patch center. The proposed an tenna sensor is found to operate excellently at the first mode, 0.6GHz, with S11 impedance matching less than -10dB. The proposed sensor performance is tested experimentally with 15 cases, different patients, through measuring the change in the S11 spectra after direct touching to the sensor a finger print of a patient. The proposed sensor is found to be effectively very efficient detector for blood glucose variation with a low manu facturing cost when printed on an FR-4 substrate. The experi mental measurements are analyzed mathematically to obtain the calibration equation of the sensor from the curve fitting. Finally, the theoretical and the experimental results are found to be agreed very well with a percentage of error less than 10%.

Techno-functional characteristics, and potential applications of edible coatings: A comprehensive review

This review emphasizes the growing technical and functional properties of edible coatings, as well as their numerous prospective applications. Edible coatings were developed using proteins, carbohydrates, lipids, and natural polymers. These ingredients could be altered and blended to create coatings with distinct properties. Food products were coated with edible coatings in a number of ways, including dipping, spraying, brushing, rolling, or twirling the food in the coating material. The application strategy applied is dependent on the distinctive food product and the desired outcome. These coatings are at the cutting-edge of innovation and sustainability, employing organic and biodegradable components to efficiently address major environmental challenges. This study additionally investigates the many technical and practical advantages that these coatings have, such as better barrier qualities, precise release mechanisms, and integration with cutting-edge sensor technologies for continuous quality monitoring. It also emphasizes the wide variety of applications, which vary from increasing product shelf life and decreasing food waste to addressing various nutritional demands and improving food aesthetic appeal. The advancements created in the edible coatings industry highlight their crucial role in redefining accepted paradigms of food packaging, preservation, and consumption by fusing scientific advancement with culinary inventiveness are also been discussed. Innovative materials, bioactive compounds for health benefits, and packaging and coatings with less environmental impact are being researched to produce edible coatings with better functionality and sustainability.

EVIDENCE FOR BENEFICIAL USE OF THE COMPENSATORY RESERVE MEASUREMENT IN GUIDING INTRAOPERATIVE RESUSCITATION: A PROSPECTIVE COHORT STUDY OF ORTHOTOPIC LIVER TRANSPLANT RECIPIENTS.

Introduction: The compensatory reserve measurement (CRM) is a continuous noninvasive monitoring technology that provides an assessment of the integrated capacity of all physiological mechanisms associated with responses to a hypovolemic stressor such as hemorrhagic shock. No prior studies have analyzed its use for intraoperative resuscitation guidance. Methods: A prospective observational study was conducted of 23 patients undergoing orthotopic liver transplant. Chart review was performed to identify timing of various intraoperative events. Data were compared based on predefined thresholds for existence of hemorrhagic shock: CRM lower than 40%, systolic blood pressure (SBP) lower than 90 mm Hg (SBP90), and heart rate (HR) higher than 100 beats per minute (HR100). Regression analysis was performed for predicting resuscitation events, and nonlinear eXtreme Gradient Boosting (XGBoost) models were used to compare CRM with standard vital sign measures. Results: Events where CRM dropped lower than 40% were 2.25 times more likely to lead to an intervention, whereas HR100 and SBP90 were not associated with intraoperative interventions. XGBoost prediction models showed superior discriminatory capacity of CRM alone compared with the model with SBP and HR and no difference when all three were combined (CRM-HR-SBP). All XGBoost models outperformed equivalent linear regression models. Conclusion: These results demonstrate that CRM can provide an adjunctive clinical tool that can augment early and accurate of hemodynamic compromise and promote goal-directed resuscitation in the perioperative setting.

Leveraging Classical Statistical Methods for Sustainable Maintenance in Automotive Assembly Equipment

Predictive maintenance management plays a crucial role in ensuring the reliable operation of equipment in industry. While continuous monitoring technology is available today, equipment without sensors limits continuous equipment state data recording. Predictive maintenance has been effectively carried out using artificial intelligence algorithms for datasets with sufficient data. However, replicating these results with limited data is challenging. This work proposes the use of time series models to implement predictive maintenance in the equipment of an automotive assembly company with few records available. For this purpose, three models are explored—Holt–Winters Exponential Smoothing (HWES), Autoregressive Integrated Moving Average (ARIMA), and Seasonal Autoregressive Integrated Moving Average (SARIMA)—to determine the most accurate forecasting of future equipment downtime and advocate the use of SAP PM for effective maintenance process management. The data were obtained from five equipment families from January 2020 to December 2022, representing 36 registers for each piece of equipment. After data fitting and forecasting, the results indicate that the SARIMA model best fits seasonal characteristics, and the forecasting offers valuable information to help in decision-making to avoid equipment downtime, despite having the highest error. The results were less favorable when handling datasets with random components, requiring model recalibration for short-term forecasting.

Low-cost fixed sensor deployments for leak detection in North American upstream oil and gas: Operational analysis and discussion of a prototypical program

Low-cost fixed sensors are an emerging option to aid in the management and reduction of methane emissions at upstream oil and gas sites. They have been touted as a cost-effective continuous monitoring technology to detect, localize, and quantify fugitive emissions. However, to support emissions management, the efficacy of low-cost fixed sensors must be assessed in the context of the sites, technologies, methods, work practices, action thresholds, and outcomes that constitute a broader program to manage and reduce emissions. Here, we build on technology-focused research and testing by defining a prototypical low-cost fixed sensor program framework and considering the deployment from an operational perspective. We outline potentially large operational cost penalties and risks to industry relative to incumbent programs. Most costs are caused by (i) follow-up callouts, (ii) nontarget emissions, and (iii) maintenance requirements. These represent core areas for improvement. Results highlight a need for careful consideration in regulations, ensuring that alerts protocols are carefully codified and system performance is maintained.

Detection of Per‐ and Polyfluoroalkyl Substances (PFAS) by Interrupted Energy Transfer

AbstractThe ubiquitous presence of per‐ and polyfluoroalkyl substances (PFAS) in aqueous environments has aroused societal concern. Nonetheless, effective sensing technologies for continuous monitoring of PFAS within water distribution infrastructures currently do not exist. Herein, we describe a ratiometric sensing approach to selectively detect aqueous perfluorooctanoic acid (PFOA) and perfluorooctane sulfonate (PFOS) at concentrations of μg ⋅ L−1. Our method relies on the excitonic transport in a highly fluorinated poly(p‐phenylene ethynylene) to amplify a ratiometric emission signal modulated by an embedded fluorinated squaraine dye. The electronic coupling between the polymer and dye occurs through overlap of π‐orbitals and is designed such that energy transfer is dominated by an electron‐exchange (Dexter) mechanism. Exposure to aqueous solutions of PFAS perturbs the orbital interactions between the squaraine dye and the polymer backbone, thereby diminishing the efficiency of the energy transfer and producing a “polymer‐ON/dye‐OFF” response. These polymer/dye combinations were evaluated in spin‐coated films and polymer nanoparticles and were able to selectively detect PFAS at concentrations of ca. 150 ppb and ca. 50 ppb, respectively. Both polymer films and nanoparticles are not affected by the type of water, and similar responses to PFAS were found in milliQ and well water.

Detection of Per- and Polyfluoroalkyl Substances (PFAS) by Interrupted Energy Transfer.

The ubiquitous presence of per- and polyfluoroalkyl substances (PFAS) in aqueous environments has aroused societal concern. Nonetheless, effective sensing technologies for continuous monitoring of PFAS within water distribution infrastructures currently do not exist. Herein, we describe a ratiometric sensing approach to selectively detect aqueous perfluorooctanoic acid (PFOA) and perfluorooctane sulfonate (PFOS) at concentrations of μg ⋅ L-1 . Our method relies on the excitonic transport in a highly fluorinated poly(p-phenylene ethynylene) to amplify a ratiometric emission signal modulated by an embedded fluorinated squaraine dye. The electronic coupling between the polymer and dye occurs through overlap of π-orbitals and is designed such that energy transfer is dominated by an electron-exchange (Dexter) mechanism. Exposure to aqueous solutions of PFAS perturbs the orbital interactions between the squaraine dye and the polymer backbone, thereby diminishing the efficiency of the energy transfer and producing a "polymer-ON/dye-OFF" response. These polymer/dye combinations were evaluated in spin-coated films and polymer nanoparticles and were able to selectively detect PFAS at concentrations of ca. 150 ppb and ca. 50 ppb, respectively. Both polymer films and nanoparticles are not affected by the type of water, and similar responses to PFAS were found in milliQ and well water.