Modeling of electrochemical properties of potential-induced defects in butane-thiol SAMs by using artificial neural network and impedance spectroscopy data

Abstract

Electrochemical impedance spectroscopy (EIS) is commonly used in studying solid–liquid interfacial properties. In this work, the electrochemical behavior of potential-induced defects in self-assembled CH3(CH2)3SH monolayer (SAM) electrodeposited on the surface of a mono-crystalline gold rod has been experimentally investigated and theoretically modeled. An equivalent electrical circuit with mixed kinetic and charge transfer is applied to analyze the impedance of the experimental data of the interface. The results are compared to those obtained with a second model calculation based on an artificial neural network (ANN) for predicting the electrochemical properties of the same interface. The proposed approach defines the equivalent circuit elements for the interface under study. The latter is based on adjusting the electrical parameters by using a detailed methodology proposed to build up the equivalent circuit. We illustrated the experimental and predicted numerical simulation of the corresponding model. Numerical simulation of the neural network model shows the impact of using equivalent circuits to describe the features of the materials under study. The results demonstrate a high performance in predicting and optimizing the equivalent circuit. The advantage of our adopted model appears by comparing our model results and experimental data analysis.