10.57647/jnsc.2026.1601.05

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

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  • Ceren Karaman1,
  • Onur Karaman2,
  • Jun Jiang3,
  • Taotao Zhao3,
  1. Akdeniz University, Department of Electricity and Energy, Antalya, Turkey
  2. Akdeniz University, Department of Medical Services and Techniques, Antalya, Turkey
  3. Zhejiang Province Engineering Research Center for Endoscope Instruments and Technology Development, Ouzhou People's Hospital, The Quzhou Affiliated Hospital of Wenzhou Medical University, Quzhou, China

Received: 21-10-2025

Revised: 25-11-2025

Accepted: 20-12-2025

Published in Issue 28-02-2026

Copyright (c) 2026 Ceren Karaman, Onur Karaman, Jun Jiang, Taotao Zhao (Author)

Creative Commons License

This work is licensed under a Creative Commons Attribution 4.0 International License.

How to Cite

Karaman, C., Karaman, O., Jiang, J., & Zhao, T. (2026). MXene-Based Wearable Sweat Biosensors for Personalized Health Monitoring: Materials Innovation and Data-Driven Approaches. Journal of Nanostructure in Chemistry, 16(1 (February 2026). https://doi.org/10.57647/jnsc.2026.1601.05
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Abstract

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.

Keywords

  • MXenes,
  • Wearable Biosensors,
  • Sweat Analysis,
  • Personalized Bioelectronics,
  • Digital Twin,
  • Machine Learning,
  • Electrochemical Sensors,
  • In-silico Optimization
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