Flexible wearable hydrogel patch integrated with a "sponge-like" cellulose SERS chip for sweat analysis of urea and lactate.

Measuring multiple ions in biofluids with high sensitivity and repeatability is crucial for human healthcare monitoring. Ion-sensitive field-effect transistors have gained widespread interest for their advantages of being label-free, low cost, fast response, and CMOS-compatible fabrication processes. However, they suffer from limited voltage sensitivity, known as the Nernst limit. Current strategies for breaking the Nernst limit face problems of low integration level or unstable responses. Herein, we propose an inverter-based near-sensor amplifier to address these restrictions and realize a multi-ion super-Nernstian sensing platform with an extended-gate configuration by coupling a molybdenum disulfide (MoS2) inverter array with a multi-ion sensor chip. The high gain and stability of the MoS2 inverter array are attributed to the excellent electrical properties of MoS2, the low contact barrier between graphene and MoS2, and the defect-free surface of h-BN. The platform demonstrates super-Nernstian detection of pH (469.1 mV/pH), Na+ (472.7 mV/dec), K+ (269.4 mV/dec), and Ca2+ (221.2 mV/dec) levels within the biofluid range and exhibits stable long-term responses. Its practicality is further confirmed by selectivity investigations and artificial sweat analysis. The proposed platform is anticipated to play an important role in future point-of-care diagnostics, while this in situ amplification configuration is also expected to stimulate further near-sensor computing applications.

Non-invasive skin patch with pressure-controlled microfluidic chip and hydrogel substrate for point-of-care sweat analysis

Machine-learning-optimized T3C2Tx/Bi2Se3 nanoflower-modified screen-printed electrodes for electrochemical detection of trace Pb2+ and Cd2+ in artificial sweat.

Nickel nanoparticle modified conductive hydrogel composite for wearable enzyme-free biosensor toward noninvasive diabetes monitoring.

Sweat is an alternative biological fluid to plasma, urine, hair, and saliva, and it is promising for various pharmaceutical research types. Excessive sweating is one of the symptoms of cystic fibrosis, a hereditary disease. In this study, an easy, simple, applicable, and economical HPLC method was proposed for sweat analysis of the lumacaftor/ivacaftor combination used in the treatment of the disease. The solvent for the method was selected using the Green Solvent Selection Tool (GSST). The mobile phase was gradient elution mode and contained a mixture of 0.1% formic acid in acetonitrile (v/v) and 0.1% formic acid in water (v/v). Analytes were detected at a wavelength of 220 nm. LOD values for LUMA and IVA are 3.16 and 0.92 μg/mL, respectively. The linearity range was 60–150 μg/mL for both analytes, and matrix‐matched calibration was performed. The greenness was evaluated with AGREE and ComplexGAPI, the whiteness with the red–green–blue 12 (RGB 12) algorithm, and the blueness with the Blue Applicability Degree Index (BAGI). The AGREE score of the method was calculated as 0.72, the BAGI score as 87.5, and the RGB 12 algorithm as 88.3. As a result, the method was presented to researchers as a sustainable, green, and efficient method.

Wearable sweat sensors have emerged as promising toolsfor noninvasivehealth monitoring, yet the low analyte concentrations in sweat comparedto blood pose significant challenges for the limit of detection. Inthis study, we developed a high-sensitivity electrochemical biosensorusing carbon nanotube (CNT)-induced enzyme polymerization to detecturic acid and glucose with ultralow detection limits. The CNTs werefunctionalized via EDC/NHS to achieve covalent enzyme immobilization,enhancing catalytic efficiency, electron transfer, and sensor stability.To enable multifunctional sensing, we integrated glucose and uricacid detection with a pH sensor into a single wearable platform. Aradially symmetric microfluidic module was designed through finiteelement analysis to optimize sweat flow and minimize refresh time,ensuring real-time biomarker tracking. The system also incorporatedpH-based signal correction to improve detection accuracy in complexsweat environments. Finally, the sensing performance was validatedthrough on-body sweat collection and analysis from six human volunteers,demonstrating its robustness, reliability, and potential for advancingnext-generation personalized healthcare applications. This work providesa framework for designing multifunctional wearable sweat sensors andhighlights the role of material and device innovations in overcomingkey challenges in this field.

A daily-durable wearable sweat biosensing device with robust reticular conductive biogel interface.

YOLOv5-aided paper-based microfluidic intelligent sensing platform for multiplex sweat biomarker analysis.

Sweat, which contains a rich array of biomarkers, serves as a vital biological fluid for non-invasive biosensing. Wearable sweat sensors have garnered significant interest owing to their portability and capacity for continuous monitoring. However, there are safety concerns regarding the direct contact of sweat sensors with the skin during the detection process. The chemical substances in the sensor patches may cause contamination of the epidermis when in contact with the skin, leading to skin allergic reactions. Sample collection and biosafety isolation are critical issues in wearable sweat detection. To address this, we develop a cactus-inspired biomimetic Janus membrane capable of unidirectionally transporting and concentrating sweat toward a designated detection zone. Through unidirectional transport from the hydrophobic layer to the hydrophilic layer of the Janus membrane, sweat droplets are enriched at the designated detection point of the conical hydrophilic pattern via Laplace pressure. The bionic osmosis-enrichment sensing patch effectively inhibits direct contact between indicators and skin, eliminating potential epidermal contamination. This achieved the effect of in situ perspiration collection under the premise of biosafety isolation. To rapidly and accurately analyze sweat biomarkers, we employ a deep learning (DL)-assisted fluorescence sensor for efficient and precise detection of biomarker concentrations. A dataset of 4500 fluorescence images are constructed and used to evaluate two DL and seven machine learning (ML) algorithms. The convolutional neural network (CNN) model could easily and accurately classify and quantitatively analyze the total concentration of the amino acid mixture, Ca2+ and Cl−, with 100% classification accuracy. The consistency between the detection results of actual sweat by the DL-assisted fluorescence method and fluorescence spectroscopy was 91.4–96.0%. This approach demonstrates high reliability in sweat collection and analysis, offering a practical tool for clinical health monitoring, early disease intervention, and diagnosis.

Microfluidic tesla valve sweat patch integrated smartwatch for optical continuous monitoring of glucose, oxygen, and heart rate.

An electrochemical sensor with both antifouling and self-cleaning strategies for uric acid detection in sweat.

Hydrogel-based sensors (HBS), as core devices at the intersection of flexible electronics and biomedical engineering, exhibit great application potential in areas such as human motion detection and health monitoring due to their excellent mechanical properties, conductivity, high sensitivity, and biocompatibility. This paper first explains the research progress of HBS, focusing on the intrinsic relationships among material design, structural optimization, performance regulation, and application scenarios. It then examines the sensing mechanisms, advantages, disadvantages, and application fields of four types of sensors: resistive, capacitive, piezoelectric, and triboelectric, with a focus on their materials and structures. By introducing polymer materials such as polyacrylamide (PAM), poly (2-acrylamide-2-methylpropanesulfonic acid) (PAMPS), and polyvinyl alcohol (PVA); polysaccharide materials like alginate, cellulose, and chitosan (CS); as well as conductive fillers such as MXene, carbon nanotubes, polypyrrole (PPy), and polyaniline (PANI), strategies for regulating electron and ion migration behaviors have been implemented. Through the construction of double-network (DN) or interpenetrating polymer network (IPN) structures, HBS with excellent mechanical properties, conductivity, anti-swelling performance, transparency, and biocompatibility have been developed. These sensors have been successfully applied in wearable and implantable scenarios, including human motion detection (e.g., pulse monitoring, joint motion tracking, facial expression recognition) and health monitoring (e.g., powering cardiac pacemakers, sweat analysis). Starting from the diverse sensing mechanisms of HBS, this paper provides a detailed summary of the construction principles, diversified material regulation, motion mode detection, and implantable sensing applications of HBS-based bioelectrical medical monitoring systems. Finally, the development prospects of HBS-based bioelectrical medical monitoring systems are discussed.

Bad living habits, such as the lack of exercise, unhealthy food consumption, and insufficient sleep, cause health problems such as reduced blood glucose regulation ability and early onset of diabetes. At the same time, the pursuit of sustainable development requires renewable energy generation, and water splitting driven by solar, wind, hydro, and geothermal power is one of the viable techniques. Therefore, a bifunctional nanoelectrode with dual functions of sweat analysis and the hydrogen evolution reaction (HER) is of great significance in the prevention of diabetes and the development of green energy. Herein, a CoFe2O4/CuS/RGO composite electrode is fabricated on nickel foam (NF with a surface area of 1 cm2) by simple digestion, chemical coprecipitation, and hydrothermal methods. The glucose redox and charge transfer processes have been systematically investigated. The results reveal that the composite not only has excellent glucose electrochemical activity but also can be used for hydrogen production. The detection boasts a glucose detection range of 1-5 mM, sensitivity of 1199 μA mM-1cm-2, signal-to-noise ratio of 3, and detection limit of 0.25 μM. As an electrode in a hydrogen evolution setup, it shows a current density of 10 mA cm-2 at an overpotential of 327 mV and a Tafel slope of 10.27 mV dec-1.

Achieving optimal skin comfort and good sensitivity is highly necessary for sweat detection in a wearable electrochemical sensor. Herein, this study develops a comfortable wearable platform consisting of Janus patterned cotton fabric (PCF) and 3D neighborhood electrochemical biosensor to monitor glucose and lactate in sweat. Inspired by skin's sweat glands, a biomimetic Janus PCF embedded with a polydimethylsiloxane pattern on the hydrophilic cotton fabric is fabricated by screen-printing to enable unidirectional water transport from the skin to the biosensor. Meanwhile, by mimicking enzyme pairs on cytomembrane, 3D neighborhood electrochemical biosensor constructed through electrochemical deposition and sensing material drop-casting achieves good sensitivities (Glu: 3.4 nA·µm-1; Lac: 0.21 µA·mm-1). Notably, nanostructured composite electrode with improved electron transfer rate and increased stability can anchor oxidase, and molecular docking simulations revealthat the oxidases and tannic acid in sensing composite are connected via hydrogen bonds successfully. Finally, the wearable biosensor platform can collect human sweat and detect biomarkers effectively. This study presents a promising strategy for evaluating human sweat in the fields of sports and biomedicine in the future.

Pencil lead-based wearable sensing platform for noninvasively monitoring sweat biomarkers.

Dual-crosslinked liquid metal-cellulose hydrogels with synergistic conduction networks for multimodal wearable biosensing.

Machine learning assisted wearable antifouling sensor for reliable sweat analysis under dynamic conditions

A portable sweat biosensor for multiple chronic kidney diseases biomarkers detection.

Purpose This study aims to provide a critical and comprehensive assessment of the latest advancements and remaining challenges in the development of electrochemical potassium ion (K+) sensors for sweat analysis, with particular emphasis on applications in sports performance monitoring and general health assessment. Design/methodology/approach The review systematically explores the physiological relevance of K+ in sweat, its utility in different application contexts (athletic vs clinical) and the underlying principles of potentiometric solid-contact ion-selective electrodes. It evaluates the evolution of solid-contact materials from conducting polymers to carbon nanomaterials and MXenes, alongside fabrication techniques and integration strategies for multiplexed sensing platforms. Furthermore, it identifies technical obstacles – such as signal drift, on-body calibration and lack of clinical validation – and discusses current research directions to address them. Findings Advanced SC materials such as MXenes and carbon nanomaterials significantly improve potential stability and sensing performance compared to traditional conducting polymers. Multiplexed wearable systems that combine K+ sensing with other biomarkers (e.g. Na+, pH and temperature) enable more reliable and contextualized physiological data. However, in spite of progress, challenges such as long-term operational stability, sensor calibration on the body and the absence of a validated correlation between sweat and blood K+ concentrations remain major barriers to clinical translation. Originality/value This review bridges materials science, electrochemical sensing, wearable systems engineering and personalized health care. It uniquely positions sweat K+ monitoring not only as a performance optimization tool for athletes but also as a potential early-warning indicator for physiological imbalances. This study provides an interdisciplinary roadmap towards realizing autonomous, smart and clinically meaningful sweat sensors.