MXene-Based Wearable Sweat Biosensors for Personalized Health Monitoring: Materials Innovation and Data-Driven Approaches

MXenes, a diverse family of two-dimensional transition metal carbides and nitrides, have emerged as highly promising materials for wearable biosensors due to their exceptional conductivity, tunable surface chemistry, and mechanical flexibility. Among their most compelling applications is sweat-based health monitoring, which enables non-invasive, real-time access to dynamic physiological information. Unlike previous reviews that broadly survey MXene-enabled wearables, this work provides a unified perspective that integrates human-specific sweat variability, sweat-relevant structure–property relationships of MXene compositions, and data-driven methodologies for adaptive, personalized sensing. Translating MXene-based sensors into robust, personalized platforms requires innovations that extend beyond material design into adaptive signal processing, machine learning calibration, and individualized digital modeling. This review critically examines the structure–property relationships of various MXene compositions and outlines material engineering strategies for enhancing sensitivity, selectivity, and operational stability in sweat environments. Furthermore, it highlights how computational frameworks can calibrate, adapt, and simulate sensor behavior under individualized biochemical conditions. In silico sweat simulation and architecture optimization are also discussed as transformative tools for accelerating biosensor development. By bridging advanced materials with data-driven methodologies, this review establishes a blueprint for the next generation of MXene-based wearable systems capable of intelligent, personalized health monitoring.