Speaker
Description
Paper-based electrochemical biosensors are gaining an importance as disposable devices for ion detection in healthcare and environmental applications. Their performance depends on the combined effects of ion transport within the porous paper medium, receptor-ion interactions and electrochemical transduction at the electrode. While experimental studies provide valuable demonstrations, a clear quantitative understanding of how these processes interact is still developing. In this work, we present a modeling framework that connects ion transport, receptor binding and current generation in a unified manner. The ion transport through a paper strip was simulated using an advection-diffusion approach, yielding breakthrough curves and concentration distributions across the strip. These outlet concentrations were then applied to a receptor binding model described by association and dissociation rate constants ($k_{\text{on}}$, $k_{\text{off}}$). The comparison of target and interferent ions showed that the target achieved faster binding and earlier receptor saturation, confirming the role of binding kinetics in ensuring selectivity. The electrochemical readout was obtained by converting ion flux to current using Faraday’s law. The resulting chronoamperometric-like responses displayed an initial rise, a peak and a gradual decay, reflecting diffusion limitation and receptor occupancy. To extract design insights, parametric studies were performed by varying the effective diffusion coefficient ($D_{\text{eff}}$), electrode area ($A$) and binding kinetics. Larger electrodes scaled the current linearly whereas higher diffusion coefficients produced smoother transients and stronger binding accelerated receptor occupancy. A two-dimensional heatmap of peak current as a function of diffusion coefficient and electrode area highlighted the combined effect of transport and geometry, offering practical guidelines for sensor design. This study shows that the transport, binding and transduction must be considered together to capture the sensing behavior of paper-based electrochemical biosensors. The framework provides predictive insights that complement experimental studies on binding kinetics [1], nanomaterial assisted electrochemical interface [2] and pre-equilibrium biosensing [3]. The results demonstrate how computational modeling can guide the rational design of next-generation paper-based biosensors with improved selectivity and sensitivity.
