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<p>Paper-based electrochemical biosensors are increasingly used as disposable platforms for ion detection in healthcare, and environmental monitoring. Their performance arises from combined effects of ion transport within porous paper, receptor-ion interactions, and electrochemical conversion at the electrode interface; however, quantitative understanding of how these processes interact remains limited. This study presents a unified modelling framework linking ion transport, receptor binding, and electrochemical signal generation. Ion movement through a paper strip was modelled using an advection-diffusion approach to obtain concentration profiles and breakthrough behaviour, which served as inputs to a receptor binding model defined by association and dissociation rate constants (<InlineEquation ID="IEq1"> <EquationSource Format="TEX">\({k}_{\text {on}}\)</EquationSource> </InlineEquation>, <InlineEquation ID="IEq2"> <EquationSource Format="TEX">\({k}_{\text {off}}\)</EquationSource> </InlineEquation>). Comparison between target and interfering ions showed faster binding and earlier receptor saturation for the target species, confirming the role of binding kinetics in selectivity. The electrochemical response was estimated by converting ion flux into current using Faraday’s law, producing chronoamperometric-like signals with an initial rise, peak, and gradual decay due to diffusion limitations and receptor occupancy. Parametric analysis examined effective diffusion coefficient (<InlineEquation ID="IEq3"> <EquationSource Format="TEX">\({D}_{\text {eff}}\)</EquationSource> </InlineEquation>), electrode area (<i>A</i>), and binding kinetics. A two-dimensional heatmap of peak current as a function of <InlineEquation ID="IEq4"> <EquationSource Format="TEX">\({D}_{\text {eff}}\)</EquationSource> </InlineEquation> and <i>A</i> highlighted combined transport and geometric effects, offering design guidance. Overall, accurate prediction of sensor behaviour requires transport, binding, and transduction to be considered together and the framework provides predictive insight for designing paper-based electrochemical biosensors with improved selectivity and sensitivity.</p>

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An integrated modelling framework for ion transport and electrochemical responses in paper-based microfluidic sensors

  • 期刊论文

  • 发表日期：

    2026年4月25日

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  • Rimjim Shibam,
  • Partha Pratim Sahu

摘要

Paper-based electrochemical biosensors are increasingly used as disposable platforms for ion detection in healthcare, and environmental monitoring. Their performance arises from combined effects of ion transport within porous paper, receptor-ion interactions, and electrochemical conversion at the electrode interface; however, quantitative understanding of how these processes interact remains limited. This study presents a unified modelling framework linking ion transport, receptor binding, and electrochemical signal generation. Ion movement through a paper strip was modelled using an advection-diffusion approach to obtain concentration profiles and breakthrough behaviour, which served as inputs to a receptor binding model defined by association and dissociation rate constants ( \({k}_{\text {on}}\) , \({k}_{\text {off}}\) ). Comparison between target and interfering ions showed faster binding and earlier receptor saturation for the target species, confirming the role of binding kinetics in selectivity. The electrochemical response was estimated by converting ion flux into current using Faraday’s law, producing chronoamperometric-like signals with an initial rise, peak, and gradual decay due to diffusion limitations and receptor occupancy. Parametric analysis examined effective diffusion coefficient ( \({D}_{\text {eff}}\) ), electrode area (A), and binding kinetics. A two-dimensional heatmap of peak current as a function of \({D}_{\text {eff}}\) and A highlighted combined transport and geometric effects, offering design guidance. Overall, accurate prediction of sensor behaviour requires transport, binding, and transduction to be considered together and the framework provides predictive insight for designing paper-based electrochemical biosensors with improved selectivity and sensitivity.