A wearable potentiometric device is reported based on an innovative butterfly-like paper-based microfluidic system, allowing for continuous monitoring of pH and Na+ levels in sweat during physical activity. Specifically, the use of the butterfly-like configuration avoids evaporation phenomena and memory effects, enabling precise and timely biomarker determination in sweat. Two ad hoc modified screen-printed electrodes were embedded in the butterfly-like paper-based microfluidics, and the sensing device was further integrated with a portable and miniaturized potentiostat, leveraging Bluetooth technology for efficient data transmission. First, the paper-based microfluidic configuration was tested for optimal fluidic management to obtain optimized performance of the device. Subsequently, the two electrodes were individually tested to detect the two biomarkers, namely pH and Na+. The results demonstrated highly promising near-Nernstian (0.056 ± 0.002 V/dec) and super-Nernstian (- 0.080 ± 0.003 V/pH) responses, for Na+ and pH detection, respectively. Additionally, several important parameters such as storage stability, interferents, and memory effect by hysteresis study were also investigated. Finally, the butterfly-like paper-based microfluidic wearable device was tested for Na+ and pH monitoring during the physical activity of three volunteers engaged in different exercises, obtaining a good correlation between Na+ increase and dehydration phenomena. Furthermore, one volunteer was tested through a cardiopulmonary test, demonstrating a correlation between sodium Na+ increase and the energetic effort by the volunteer. Our wearable device highlights the high potential to enable early evaluation of dehydration and open up new opportunities in sports activity monitoring.

Blood lactate concentration is an established circulating biomarker for measuring muscle acidity and can be evaluated for monitoring endurance, training routines, or athletic performance. Sweat is an alternative biofluid that may serve similar purposes and offers the advantage of noninvasive collection and continuous monitoring. The relationship between blood lactate and dynamic sweat biochemistry for wearable engineering applications in physiological fitness remains poorly defined. Here, we developed a microfluidic wearable band with an integrated colorimetric timer and biochemical assays that temporally captures sweat and measures pH and lactate concentration. A colorimetric silver nanoplasmonic assay was used to measure the concentration of lactate, and dye-conjugated SiO2 nanoparticle-agarose composite materials supported dynamic pH analysis. We evaluated these sweat biomarkers in relation to blood lactate in human participant studies during cycling exercise of varying intensity. Iontophoresis-generated sweat pH from regions of actively working muscles decreased with increasing heart rate during exercise and was negatively correlated with blood lactate concentration. In contrast, sweat pH from nonworking muscles did not correlate with blood lactate concentration. Changes in sweat pH and blood lactate were observed in participants who did not regularly exercise but not in individuals who regularly exercised, suggesting a relationship to physical fitness and supporting further development for noninvasive, biochemical fitness evaluations.

This study established a novel infield sensing approach based on detection of the volatile compound markers in skin secretions. This was based on analysis of volatile compounds in axillary sweat samples collected from RT-PCR-proven Coronavirus disease 2019 (COVID-19) positive and negative populations using gas chromatography-mass spectrometry (GC-MS). The analysis proposed the possible markers of the monoaromatic compounds and ethyl hexyl acrylate. A portable photo ionization detector (PID) incorporated with the selective material towards the marker compounds was then developed with the pressurized injection approach. This provided the accuracy of 100% in the research phase (n = 125). The developed approach was then applied for screening of 2207 COVID-19 related cases covering the periods of the Alpha, Beta, Delta and Omicron variants of SARS-CoV-2 infection in Bangkok, Thailand. This offered the sensitivity, specificity and accuracy ranges of 92-99, 93-98 and 95-97%, respectively.

The constrained resources on wearable devices pose a challenge in meeting the demands for comprehensive sensing information, and current wearable non-enzymatic sensors face difficulties in achieving specific detection in biofluids. To address this issue, we have developed a highly selective non-enzymatic sweat sensor that seamlessly integrates with machine learning, ensuring reliable sensing and physiological monitoring of sweat biomarkers during exercise. The sensor consists of two electrodes supported by a microsystem that incorporates signal processing and wireless communication. The device generates four explainable features that can be used to accurately predict tyrosine and tryptophan concentrations, as well as sweat pH. The reliability of this device has been validated through rigorous statistical analysis, and its performance has been tested in subjects with and without supplemental amino acid intake during cycling trials. Notably, a robust linear relationship has been identified between tryptophan and tyrosine concentrations in the collected samples, irrespective of the pH dimension. This innovative sensing platform is highly portable and has significant potential to advance the biomedical applications of non-enzymatic sensors. It can markedly improve accuracy while decreasing costs.

We introduce the FlexoSERS sensor, which is notable for its high stretchability, sensitivity, and patternability. Featuring a hierarchically oriented jellyfish-like architecture constructed from stretchable gold nanowires, this sensor provides an ultrasensitive SERS signal even under 50% strain, with an enhancement factor (EF) of 3.3 × 1010. Impressively, this heightened performance remains consistently robust across 2,500 stretch-release cycles. The integration of nanowires with 3D-printed hydrogel enables a customizable FlexoSERS sensor, facilitating localized sweat collection and detection. The FlexoSERS sensor successfully detects and quantifies uric acid (UA) in both artificial and human sweat and functions as a pH sensor with repeatability and sensitivity across a pH range of 4.2-7.8, enabling real-time sweat monitoring during exercise. In summary, the rational architectural design, scalable fabrication process, and hydrogel integration collectively position this nanowire-based FlexoSERS sensor as a highly promising platform for customizable wearable sweat diagnostics.

Electrodermal activity (EDA) is a widely used psychophysiological measurement in laboratory-based studies. In recent times, these measurements have seen a transfer from the laboratory to wearable devices due to the simplicity of EDA measurement as well as modern electronics. However, proper conditions for EDA measurement are recommended once wearable devices are used, and the ambient conditions may influence such measurements. It is not completely known how different types of ambient noise impact EDA measurement and how this translates to wearable EDA measurement. Therefore, this study explored the effects of various noise disturbances on the generation of EDA responses using a system for the simultaneous recording of all measures of EDA, i.e., skin conductance responses (SCRs), skin susceptance responses (SSRs), and skin potential responses (SPRs), at the same skin site. The SCRs, SSRs, and SPRs due to five types of noise stimuli at different sound pressure levels (70, 75, 80, 85, and 90 dB) were measured from 40 participants. The obtained results showed that EDA responses were generated at all levels and that the EDA response magnitudes were significantly (p < 0.001) influenced by the increasing noise levels. Different types of environmental noise may elicit EDA responses and influence wearable recordings outside the laboratory, where such noises are more likely than in standardized laboratory tests. Depending on the application, it is recommended to prevent these types of unwanted variation, presenting a challenge for the quality of wearable EDA measurement in real-world conditions. Future developments to shorten the quality gap between standardized laboratory-based and wearable EDA measurements may include adding microphone sensors and algorithms to detect, classify, and process the noise-related EDA.

Ketones, such as beta-hydroxybutyrate (BHB), are important metabolites that can be used to monitor for conditions such as diabetic ketoacidosis (DKA) and ketosis. Compared to conventional approaches that rely on samples of urine or blood evaluated using laboratory techniques, processes for monitoring of ketones in sweat using on-body sensors offer significant advantages. Here, we report a class of soft, skin-interfaced microfluidic devices that can quantify the concentrations of BHB in sweat based on simple and low-cost colorimetric schemes. These devices combine microfluidic structures and enzymatic colorimetric BHB assays for selective and accurate analysis. Human trials demonstrate the broad applicability of this technology in practical scenarios, and they also establish quantitative correlations between the concentration of BHB in sweat and in blood. The results represent a convenient means for managing DKA and aspects of personal nutrition/wellness.

Sweat analysis has advanced from diagnosing cystic fibrosis and testing for illicit drugs to noninvasive monitoring of health biomarkers. This article introduces the rapid development of wearable and flexible sweat sensors, highlighting key milestones and various sensing strategies for real-time monitoring of analytes. We discuss challenges such as developing high-performance nanomaterial-based biosensors, ensuring continuous sweat production and sampling, achieving high sweat/blood correlation, and biocompatibility. The potential of machine learning to enhance these sensors for personalized healthcare is presented, enabling real-time tracking and prediction of physiological changes and disease onset. Leveraging advancements in flexible electronics, nanomaterials, biosensing, and data analytics, wearable sweat biosensors promise to revolutionize disease management, prevention, and prediction, promoting healthier lifestyles and transforming medical practices globally.

Mental stress, a human\'s common emotion that is difficult to recognize and describe, can give rise to serious psychological disorders. Skin and sweat are easily accessible sources of biomarkers and bio-signals that contain information about mental stress. It is challenging for current wearable devices to monitor psychological stress in real-time with a non-invasive manner. Therefore, we have developed a smartwatch integrated with a sweat cortisol sensor and a heart rate variation (HRV) sensor. This smartwatch can simultaneously record the cortisol levels in sweat and HRV index in real time over a long period. The cortisol sensors based on organic electrochemical transistor (OECT) are fabricated by utilizing the Prussian-blue (PB) doped molecular imprinting polymer (MIP) modified gate electrode. The sensor signal current will decrease following the combination of sweat cortisol, due to the blocking of the PBMIP conductive path, demonstrating good sensitivity, selectivity, and stability. The HRV sensor is manufactured by a photoplethysmography method. We have integrated the two sensors into a wearable smartwatch that can match well with the mobile phone APP and the upper computer software. Through the use of this smartwatch, we have observed a negative correlation between cortisol levels in sweat and the HRV index in short-term stressful environments. Our research presents a great progress in real-time and non-invasive monitoring human\'s stress levels, which promotes not only the stress management, but also better psychological research.

Sweat biomarkers have the potential to offer valuable clinical insights into an individual\'s health and disease condition. Current sensors predominantly utilize enzymes and antibodies as biometric components to measure biomarkers present in sweat quantitatively. However, enzymes and antibodies are susceptible to interference by environmental factors, which may affect the performance of the sensor. Herein, we present a wearable microfluidic surface-enhanced Raman scattering (SERS) biosensor that enables the non-invasive and label-free detection of biomarkers in sweat. Concretely, we developed a bimetallic self-assembled anti-opal array structure with uniform hot spots, enhanced the Raman scattering effect, and integrated it into a silk fibroin-based sensing patch. Utilizing a silk fibroin substrate in the wearable SERS sensor imparts desirable properties such as softness, breathability, and biocompatibility, which enables the sensor to establish close contact with the skin without causing chemical or physical irritation. In addition, introducing microfluidic channels enables the controlled and high temporal resolution management of sweat, facilitating more efficient sweat collection. The proposed label-free SERS sensor can offer chemical \'fingerprint\' information, enabling the identification of sweat analytes. As an illustration of the feasibility, we have effectively monitored the creatinine and uric acid levels in sweat. This study presents a versatile and highly sensitive approach for the simultaneous detection of biomarkers in human sweat, showcasing significant potential for application in point-of-care monitoring.