MATLAB-Driven Electrochemical CV Analysis of Sweat Electrolyte

Abstract

Sweat-based biosensing is increasingly recognized as a promising approach for non-invasive health diagnostics, with particular relevance to monitoring hydration status and electrolyte imbalances. This study investigates the electrochemical characteristics of key sweat electrolytes—namely sodium (Na?), chloride (Cl?), and potassium (K?)—utilizing cyclic voltammetry (CV) and computational simulations performed with MATLAB. The primary aim is to characterize and validate a biosensor capable of detecting these ions through analysis of parameters such as peak current, Nernst response, logarithmic current, and diffusion coefficients across various concentrations (0.1 M, 1.0 M, and 5.0 M) and scan rates (1–100 mV/s). By employing a modified CV simulation model rooted in the Butler-Volmer equation, the research provides detailed electrochemical profiles and examines deviations from theoretical expectations. Results indicate that the electrochemical response intensifies with increasing analyte concentration and scan rate, while larger peak separations suggest the presence of quasi-reversible kinetics and capacitive effects at higher scan rates. Logarithmic analysis confirms diffusion-controlled kinetics, with slopes approaching the theoretical ideal of 0.5. Validation through Nernstian calibration further supports the electrochemical reversibility of the biosensor, and the analysis of diffusion coefficients elucidates limitations related to analyte saturation. The integration of simulation data with electrochemical analysis demonstrates the sensor's sensitivity and stability, underscoring its potential for continuous electrolyte monitoring in contexts such as dehydration, physical exercise, and clinical diagnostics. These findings establish a solid foundation for the future development of wearable biosensors aimed at real-time health monitoring applications.