Neural networks approach for simulation of electrochemical impedance diagrams

Abstract

A new aporoach of data simulation using neural networks (NN) has been proposed for electrochemical impedance spectroscopy, applied for copper electrodeposition from sulphate acidic electrolytes. The trained NN, with data obtained in different experimental conditions (electrode potential and thiourea concentrations), have been used to generate impedance spectra for new electrode potential values, within the investigated potential range, as well as to estimate the maximum frequency value on Nyquist plot, by generating supplementary output data for new frequency values, inside the investigated domain of frequencies.