Neural Network-based Real-Time Parameter Estimation in Electrochemical Sensors with Unknown Confounding Factors

Real-time parameter estimation from measurements in electrochemical sensors remains a challenge. Traditional methods used to characterize the response and estimate parameters of interest from electrochemical sensors are often slow and time-consuming, thus, not applicable for real-time applications. Here, we develop a workflow utilizing physics-based processing and deep learning to estimate parameters and confounding variables with uncertainties in real-time from large amplitude AC Voltammetry (LA-ACV) measurements on electrochemical sensors. The physics-based processing enables the extraction of physical information about the system from the measurement data, and deep learning enables rapid inverse-problem solutions. We experimentally demonstrate our approach in an electrochemical system (K3Fe(CN)6 in potassium phosphate buffer) to estimate the concentration of redox-active species (K3Fe(CN)6) in the presence of unknown viscosity of the medium (confounding variable), with 0.45 (± 0.07) mM median absolute error in concentration estimation. The proposed workflow leveraging physics-based processing and deep learning can be applied reproducibly to any electrochemical system for real-time parameter estimation.