Influence of ruthenium doping on UV- and visible-light photoelectrocatalytic color removal from dye solutions using a TiO2 nanotube array photoanode.

The photocatalytic activity of TiO2 anodes was enhanced by synthesizing Ru-doped Ti|TiO2 nanotube arrays. Such photoanodes were fabricated via Ti anodization followed by Ru impregnation and annealing. The X-ray diffractograms revealed that anatase was the main TiO2 phase, while rutile was slightly present in all samples. Scanning electron microscopy evidenced a uniform morphology in all samples, with nanotube diameter ranging from 60 to 120 nm. The bias potential for the photoelectrochemical (PEC) treatment was selected from the electrochemical characterization of each electrode, made via linear sweep voltammetry. All the Ru-doped TiO2 nanotube array photoanodes showed a peak photocurrent (PP) and a saturation photocurrent (SP) upon their illumination with UV or visible light. In contrast, the undoped TiO2 nanotubes only showed the SP, which was higher than that reached with the Ru-doped photoanodes using UV light. An exception was the Ru(0.15 wt%)-doped TiO2, whose SP was comparable under visible light. Using that anode, the activity enhancement during the PEC treatment of a Terasil Blue dye solution at Ebias(PP) was much higher than that attained at Ebias(SP). The percentage of color removal at 120 min with the Ru(0.15 wt%)-doped TiO2 was 98% and 55% in PEC with UV and visible light, respectively, being much greater than 82% and 28% achieved in photocatalysis. The moderate visible-light photoactivity of the Ru-doped TiO2 nanotube arrays suggests their convenience to work under solar PEC conditions, aiming at using a large portion of the solar spectrum.

Modelling of Behavior for Inhibition Corrosion of Bronze Using Artificial Neural Network (ANN).

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 Na2SO4, 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 (Zre, Zim, and Zmod) 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 Zre, Zim, and Zmod 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.