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.
Computational modeling of drug separation from aqueous solutions using octanol organic solution in membranes
