Calibration, Compensation, Parameter Estimation, and Uncertainty Quantification for Nanoelectrode Array Biosensors

This paper presents the use of a statistical approach to estimate physical/electrochemical parameters of impedance spectroscopy experiments performed with a realistic nanoelectrodes array biosensor platform. The Bayesian estimation methodology is based on the combination of nanobiosensor simulations, performed with the ENBIOS tool, with Markov-Chain Monte Carlo (MCMC) analyses. A simple 1D electrode-electrolyte geometry is first considered as a validation test case, allowing the accurate estimation of Stern layer permittivity and salt concentration, as set by a reference analytical model. Then, full 3D analyses of the nanoelectrodes’ array system are performed in order to estimate a number of relevant parameters for measurements in electrolyte. Furthermore, moving to more challenging test cases, size/permittivity of microparticles suspended in electrolyte will also be discussed. This methodology allows for the determination of impedance spectroscopy data parameters, and quantification of parameter uncertainties in these multi-variable detection problems. It is thus a very promising approach in order to improve the precision of biosensor measurement predictions, which are intrinsically affected by many parameters.