Emprego de técnicas computacionais estocásticas para simulação de diagramas de espectroscopia de impedância eletroquímica

Abstract

Electrochemical impedance spectroscopy is a widely used technique in electrochemical systems characterization. With applications in several areas, the technique is very useful in the study of corrosion because it is sensitive to transient changes that occur in the metal interface. The results from the technique can be expressed and interpreted in different ways, allowing different modeling and analysis methods, such as the use of kinetic models or equivalent circuits. In corrosion, the technique is usually applied only in a few specific potentials, such as the corrosion potential, the most important. With the motivation of improving the impedance modeling and analysis process, taking into consideration that the electrochemical phenomena are strongly linked to the potential, this work introduces the possibility to express the impedance data in a wide potential range, and use them to equivalent circuits fitting. Thus, different phenomena can be modeled adequately by equivalent circuits corresponding to different potentials. For this purpose, the related inverse problem is solved for each potential through a complex nonlinear optimization process. In addition to the transient data obtained by the spectroscopy, stationary data are also used in the optimization as a regularization factor, supporting a consistent solution to the physical phenomena involved, from the maximum experimental frequency to theoretical zero frequency. An analysis, modeling and simulation software was developed with the following features: 1) validation of experimental data, through the Kramers-Kronig relations; 2) simultaneous visualization of impedance results for a wide potential range; 3) fitting different equivalent circuits for different ranges using transient and stationary experimental data, in conjunction with deterministic or stochastic methods; 4) generation of confidence regions for the estimated parameters, making them statistically significant; 5) simulations using the fitted equivalent circuits in computer cluster; 6) parameter sensitivity analysis according to the applied potential, revealing important physical characteristics involved in the electrochemical processes. Finally, experimental fitting results and the corresponding simulations are shown and discussed. Results show that the use of a population-based stochastic optimization method not only increases the odds of finding the global optimum, but also enables the generation of confidence regions around the found values. Furthermore, only the circuit fitted with the new objective function has equivalence with both transient data and stationary data for the entire potential range involved.