Abstract
Electrochemical immunosensors (EIs) integrate biorecognition molecules (e.g., antibodies) with redox enzymes (e.g., horseradish peroxidase) to combine the advantages of immunoassays (high sensitivity and selectivity) with those of electrochemical biosensors (quantitative electrical signal). However, the complex network of mass-transfer, catalysis, and electrochemical reaction steps that produce the electrical signal makes the design and optimization of EI systems challenging. This paper presents an integrated experimental and modeling framework to address this challenge. The framework includes (1) a mechanistic mathematical model that describes the rate of key mass-transfer and reaction steps; (2) a statistical-design-of-experiments study to optimize operating conditions and validate the mechanistic model; and (3) a novel dimensional analysis to assess the degree to which individual mass-transfer and reaction steps limit the EI’s signal amplitude and sensitivity. The validated mechanistic model was able to predict the effect of four independent variables (working electrode overpotential, pH, and concentrations of catechol and hydrogen peroxide) on the EI’s signal magnitude. The model was then used to calculate dimensionless groups, including Damkohler numbers, novel current-control coefficients, and sensitivity-control coefficients that indicated the extent to which the individual mass-transfer or reaction steps limited the EI’s signal amplitude and sensitivity.
Highlights
Electrochemical biosensors are analytical devices that detect analytes by transforming a biochemical reaction into a quantitative, electrical signal
This paper addresses the need for such mechanistic models by presenting a novel, integrated experimental and mathematical framework to characterize Electrochemical immunosensors (EIs) performance, and applies the framework to optimize performance of a novel EI that can detect a target protein at the ng/mL level
We developed framework that leverages to quantitatively assess the degree to which individual steps control the magnitude of the dimensional analysis and the mechanistic model’s ability to predict the rates of the underlying steps to quantitatively assess the degree to which individual steps control the magnitude of the EI’s signal and its sensitivity
Summary
Electrochemical biosensors are analytical devices that detect analytes by transforming a biochemical reaction into a quantitative, electrical signal. Commercial implementation of EI systems has been hampered by the complexity of the multiple molecular mass-transfer, binding, and reaction steps that give rise to the electrical signal This complexity complicates efforts to design new EIs that achieve specified performance metrics, including the lower detection limit and sensitivity (defined as change in output per unit change in analyte concentration). Few mechanistic models of HRP-based EIs have been reported [19,20,21,22,23], and these models have not been sufficiently comprehensive to predict how the output would vary with key independent variables, including the working electrode’s applied voltage (E), the pH, and the concentrations of HRP’s substrates Such models are needed to help design EIs, identify factors that limit their performance properties, and guide research strategies to optimize EI systems. The paper concludes by discussing the utility of the integrated experimental and mathematical framework for future design, optimization, and validation of EI systems
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