Search Results (6)

Sweat is a valuable biofluid for non-invasive health monitoring, as it contains electrolytes, metabolites, and organic compounds that can correlate with blood levels, making it highly attractive for wearable sensing. Building on advances in low-cost, portable electrochemical sensors, sweat analysis enables tracking of hydration status, metabolic stress, and energy availability via key markers such as sodium, potassium, lactate, and glucose. In the sports context, such wearable platforms can support performance optimization and recovery by assessing fluid loss and electrolyte balance in real time. Here, a multiplexed wearable sweat patch is developed to simultaneously monitor temperature, pH, ammonium, sodium, and sweat rate. The integrated platform demonstrates sensitivities of 10.1 mV/ln[NH₄⁺], 9.1 mV/ln[K⁺], 1.11 mV/ln[Na⁺], 14 mV/pH, 0.19% °C⁻¹, and approximately −1.0 mA (mL/min)⁻¹ for sweat rate, with stable signals and linear calibration responses over relevant physiological ranges. The sensor is implemented on a lightweight, biocompatible laser-scribed graphene on a PDMS substrate suitable for prolonged skin contact and mechanical deformation. In addition, a custom PDMS adhesive patch with optimized suction-cup microstructures is engineered to improve skin adhesion under both dry and wet conditions. Finally, the design of the platform was inspired by an adaptive cycling marathon across Saudi Arabia, where an earlier prototype of a wearable patch was deployed for real-time monitoring during a 30-day campaign. Full article

Maintaining optimal hydration is critical for physiological function, particularly during intense physical activities, in which dehydration or overhydration can impair performance and recovery. Traditional methods for monitoring hydration status, such as body weight changes, bioelectrical impedance, and urine specific gravity, are limited by inconvenience and lack of real-time capability. This study introduces a novel graphene-based dual-sensing electrochemical sensor for the rapid and non-invasive quantification of sodium and potassium concentrations in human sweat, key biomarkers of hydration status. Leveraging graphene’s exceptional conductivity and functionalization potential, the sensor employs open-circuit potentiometry (OCP) to achieve high sensitivity and selectivity in detecting sodium and potassium. The sensor performance was validated against that of a commercial analyzer and ICP-OES, demonstrating a near-Nernstian response (61.93 mV/decade for sodium and 61.21 mV/decade for potassium detection) and a linear detection range spanning from 0.1 mM to 100 mM for both sodium and potassium monitoring in sweat. Sweat samples from an athlete during endurance exercise confirmed the sensor’s reliability, with results closely matching those of ICP-OES and outperforming the commercial analyzer in regards to accuracy and sample efficiency. This work represents a cross-validated study of a sweat-based sensor with a second analytical technique, highlighting its potential as a real-time hydration monitoring tool for use in sports and beyond. Full article

The continuous and real-time monitoring of physiological indicators is essential for early disease detection, prevention, and clinical diagnosis. In response to the growing demand for precise physiological parameter assessment, this study presents a flexible, organic electrochemical transistor (OECT)-based multi-ion sensing system designed to monitor key electrolyte concentrations—sodium (Na⁺), potassium (K⁺), and calcium (Ca²⁺)—in human biofluids. The system features a highly adaptable sensor array with a detection range tailored to physiological conditions, ensuring high selectivity and stability in complex biological environments. Our sensor demonstrated a sensitivity exceeding 1 mA/decade. To enhance measurement accuracy and mitigate cross-interference among ions, we integrate advanced machine learning algorithms, which optimize signal processing and significantly improve the system’s reliability. Additionally, we have developed a fully integrated hardware–software platform comprising customized signal acquisition circuitry and dedicated data analysis software, specifically tailored for OECT-based sensing applications. This comprehensive framework not only refines real-time ion detection but also paves the way for the broader clinical translation of OECT technology. The proposed system holds great promise for real-time physiological monitoring and point-of-care diagnostics, offering a potential paradigm shift in non-invasive, on-demand health assessment. Full article

Background: End-stage renal disease patients undergoing hemodialysis (ESRD-HD) therapy are highly susceptible to malignant ventricular arrhythmias caused by undetected potassium concentration ([$K^{+}$]) variations ($\mathsf{\Delta }\left[K^{+}\right]$) out of normal ranges. Therefore, a reliable method for continuous, noninvasive monitoring of [$K^{+}$] is crucial. The morphology of the T-wave in the electrocardiogram (ECG) reflects $\mathsf{\Delta }\left[K^{+}\right]$ and two time-warping-based T-wave morphological parameters, $d_w$ and its heart-rate corrected version $d_{w,c}$, have been shown to reliably track $\mathsf{\Delta }\left[K^{+}\right]$ from the ECG. The aim of this study is to derive polynomial models relating $d_w$ and $d_{w,c}$ with $\mathsf{\Delta }\left[K^{+}\right]$, and to test their ability to reliably sense and quantify $\mathsf{\Delta }\left[K^{+}\right]$ values. Methods: 48-hour Holter ECGs and [$K^{+}$] values from six blood samples were collected from 29 ESRD-HD patients. For every patient, $d_w$ and $d_{w,c}$ were computed, and linear, quadratic, and cubic fitting models were derived from them. Then, Spearman’s ($\rho$) and Pearson’s (r) correlation coefficients, and the estimation error ($e_d$) between $\mathsf{\Delta }\left[K^{+}\right]$ and the corresponding model-estimated values ($\widehat{\mathsf{\Delta }}\left[K^{+}\right]$) were calculated. Results and Discussions: Nonlinear models were the most suitable for $\mathsf{\Delta }\left[K^{+}\right]$ estimation, rendering higher Pearson’s correlation (median 0.77 $\le r\le$ 0.92) and smaller estimation error (median 0.20 $\le e_d\le$ 0.43) than the linear model (median 0.76 $\le r\le$ 0.86 and 0.30 $\le e_d\le$ 0.40), even if similar Spearman’s $\rho$ were found across models (median 0.77 $\le \rho \le$ 0.83). Conclusion: Results support the use of nonlinear T-wave-based models as $\mathsf{\Delta }\left[K^{+}\right]$ sensors in ESRD-HD patients. Full article

Symptoms of root stress are hard to detect using non-invasive tools. This study reveals proof of concept for vegetation indices’ ability, usually used to sense canopy status, to detect root stress, and performance status. Pepper plants were grown under controlled greenhouse conditions under different potassium and salinity treatments. The plants’ spectral reflectance was measured on the last day of the experiment when more than half of the plants were already naturally infected by root disease. Vegetation indices were calculated for testing the capability to distinguish between healthy and root-damaged plants using spectral measurements. While no visible symptoms were observed in the leaves, the vegetation indices and red-edge position showed clear differences between the healthy and the root-infected plants. These results were achieved after a growth period of 32 days, indicating the ability to monitor root damage at an early growing stage using leaf spectral reflectance. Full article

A newly synthesized nanomaterial known as K_xW₇O₂₂ (K_xWO) exhibits a stable room-temperature ferroelectric property. This unique ferroelectric property has revealed that K_xWO is a promising material for application in a breath sensor, which can be used for patients to monitor their daily health condition and diagnose disease at every early stage with low cost, convenience, and non-invasion. In this study, we successfully synthesized nano-structured K_xWO through a low cost but high yield hydrothermal method. The sensing response of K_xWO to acetone is examined based on a chemiresistive effect. For the first time, we systematically studied how material structures and the component, potassium (K), can affect K_xWO-based sensing performance. The results indicate that the low temperature ferroelectric property of K_xWO causes an excellent response to acetone, which is the biomarker for diabetes. The lowest detection limit can be down to 0.1 ppm and the K_xWO-based sensor can operate at room temperature. In addition, the K_x component K_xWO and its crystal structure also play an important role in improving its sensing performance. Our results provide advanced research in (1) exploring the study of K_xWO material properties by tailoring the concentration of the potassium in K_xWO and introducing the surfactant Pluronic L-121 in the growing process, and (2) optimizing K_xWO sensing performance by controlling its material properties. Full article