An Adsorption Dominated Miniaturized Multifunctional Biosensor Based on In Situ Functionalized MXene and Laser Induced Graphene Based Electrodes for Health Monitoring

The advancement of multifunctional wearable, flexible, and stretchable sensors plays a crucial role in precisely monitoring human health biomarkers. However, high-performing sensors require highly sensitive, stable, and miniaturized electrodes. In this study, we introduced an in-situ Functionalized Ti-MXene and Laser-Induced Graphene (LIG) composite (FMLIG) electrode-based biosensor for monitoring body glucose using human sweats. The two-step direct laser printing contributed to the surface reduction of MXene onto LIG electrodes, which shifted the electrochemical reaction from diffusion-controlled to adsorption-controlled. The shift has been confirmed through both simulative and experimental approaches. The FMLIG electrochemical sensor exhibits exceptional glucose sensitivity of 2751.3µA/mM.cm² with a miniaturized electrode area of 0.0079 cm² and a low limit of detection of 0.3µM for sweat detection, along with excellent stability maintaining over 90.53% sensitivity for 21 days in ambient conditions. Furthermore, the sensor demonstrates high consistency in body glucose measurement across multiple human subjects. The miniaturized electrodes can further be implemented to fabricate highly sensitive multi-functional biosensor for space-constraint areas. The miniaturized size of the sensor enables seamless integration with VR-integrated health monitoring systems. Moreover, the FMLIG electrodes, when utilized as a humidity sensor, showcase impressive performance with a high sensitivity of 20197.01 pF/RH, a low response time of 27.57 s, and a rapid recovery time of 3.18 s. Additionally, the FMLIG composite electrodes exhibit remarkable capabilities in capturing skeletal muscle movements during grasping, with an observed increase in EMG amplitude by 167% compared to pristine LIG electrodes, even in the presence of sensible sweat, while maintaining an excellent signal-to-noise ratio. The sensors were further integrated with a VR mask to monitor physical and mental health. These devices show promising possibilities in health monitoring during VR mask usage