Abstract:In this work, three models based on Artificial Neural Network (ANN) were developed to describe the behavior for the inhibition corrosion of bronze in 3.5% NaCl + 0.1 M Na 2 SO 4 , using the experimental data of Electrochemical Impedance Spectroscopy (EIS). The database was divided into training, validation, and test sets randomly. The parameters process used as the inputs of the ANN models were frequency, temperature, and inhibitor concentration. The outputs for each ANN model and the components in the EIS spectrum (Z re , Z im , and Z mod ) were predicted. The transfer functions used for the learning process were the hyperbolic tangent sigmoid in the hidden layer and linear in the output layer, while the Levenberg-Marquardt algorithm was applied to determine the optimum values of the weights and biases. The statistical analysis of the results revealed that ANN models for Z re , Z im , and Z mod can successfully predict the inhibition corrosion behavior of bronze in different conditions, where what was considered included variability in temperature, frequency, and inhibitor concentration. In addition, these three input parameters were keys to describe the behavior according to a sensitivity analysis.

An antifungal, Ketoconazole,[1][2][3][4]4S)-2-(2,4-dichlorophenil)-2-(imidazol-1-ilmetil)-1,3-dioxolan-4-yl]metoxi]phenil]piperazin-1-yl]ethanol, has been evaluated as corrosion inhibitor for bronze in 3.5% M NaCl + 0.1M Na 2 SO 4 solution with electrochemical techniques, including potentiodynamic polarization curves, linear polarization resistance and electrochemical impedance spectroscopy measurements at different concentrations and temperatures. Additionally, theoretical studies have been performed by using density functional theory (DFT) in order to know the relationship between the electronic properties of Ketoconazole and its inhibitive effect. Results show that Ketoconazole turned out to be a good mixed type of inhibitor, which is adsorbed in a physical way on to the metal surface by following a Langmuir type of adsorption isotherm. Inhibitor efficiency increases with time and inhibitor concentration, but it decreases with an increase in the temperature.

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