journal article·2025-04-30Open access

Non-Invasive Hydration Monitoring with a Graphene Dual Sweat Sensor

Joseph J. Q. Ng, Sergey Tkachev, Glendon C. F. Sim, Luiza Felippi de Lima, Gavin K. W. Koon, Alexandre P. Lima, Antonio H. Castro Neto

Applied Sciences· Volume 15· Issue 9

Abstract

Maintaining optimal hydration is critical for physiological function, particularly during intense physical activities, in which dehydration or overhydration can impair performance and recovery. Traditional methods for monitoring hydration status, such as body weight changes, bioelectrical impedance, and urine specific gravity, are limited by inconvenience and lack of real-time capability. This study introduces a novel graphene-based dual-sensing electrochemical sensor for the rapid and non-invasive quantification of sodium and potassium concentrations in human sweat, key biomarkers of hydration status. Leveraging graphene’s exceptional conductivity and functionalization potential, the sensor employs open-circuit potentiometry (OCP) to achieve high sensitivity and selectivity in detecting sodium and potassium. The sensor performance was validated against that of a commercial analyzer and ICP-OES, demonstrating a near-Nernstian response (61.93 mV/decade for sodium and 61.21 mV/decade for potassium detection) and a linear detection range spanning from 0.1 mM to 100 mM for both sodium and potassium monitoring in sweat. Sweat samples from an athlete during endurance exercise confirmed the sensor’s reliability, with results closely matching those of ICP-OES and outperforming the commercial analyzer in regards to accuracy and sample efficiency. This work represents a cross-validated study of a sweat-based sensor with a second analytical technique, highlighting its potential as a real-time hydration monitoring tool for use in sports and beyond.
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Research context

OpenAlex topics: Advanced Sensor and Energy Harvesting Materials · Non-Invasive Vital Sign Monitoring · Gas Sensing Nanomaterials and Sensors

SciVX discovery tags are terminology-based. OpenAlex topics are source-provided classifications.

Affiliations

Faculty of Dentistry, National University of Singapore, Singapore 119085, Singapore

Centre for Advanced 2D Materials, National University of Singapore, Singapore 117546, Singapore

Institute for Functional Intelligent Materials (I-FIM), National University of Singapore, Singapore 117544, Singapore

Department of Materials Science and Engineering, National University of Singapore, Singapore 117575, Singapore

Department of Physics, National University of Singapore, Singapore 119077, Singapore

Funding

National Research Foundation · Singapore Ministry of Education

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