Indigo Blue (IB) is a dye widely used by the textile sector for dyeing cellulose cotton fibers and jeans, being considered a recalcitrant substance, and therefore resistant to traditional treatments. Several methodologies are reported in the literature for the removal or degradation of dyes from the aqueous medium, among which photoelectrocatalysis stands out, which presents promising results in the degradation of dyes when a dimensionally stable anode (DSA) is used as a photoanode. In the present work, we sought to investigate the efficiency of a Ti/RuO2-TiO2 DSA modified with tin and tantalum for the degradation of indigo blue dye by photoelectrocatalysis. For this, electrodes were prepared by the thermal decomposition method and then a physical–chemical and electrochemical analysis of the material was carried out. The composition Ti/RuO2-TiO2-SnO2Ta2O5 (30:40:10:20) was compared to Ti/RuO2-TiO2 (30:70) in the photocatalysis, electrocatalysis, and photoelectrocatalysis tests. The photocatalysis was able to degrade only 63% of the IB at a concentration of 100 mg L−1 in 3 h, whereas the electrocatalysis and photoelectrocatalysis were able to degrade 100% of the IB at the same initial concentration in 65 and 60 min, respectively.

The data obtained through this work revealed that the vermicompost is a natural adsorbent able to removal two textile dyes from an aqueous medium. The values of maximum adsorption capacity for congo red (23.25 mg/g) and indigo blue (40.39 mg/g) obtained from the Mathematical Langmuir Model reveal it. The conditions of adsorbent mass, stirring time between adsorbent and dyes were optimized. Additionally, the values of Gibbs free energy demonstrate the predominance of physical interaction between both dyes and vermicompost. Through Langmuir constant values, it was possible to identify similar affinities between both dyes and vermicompost. The value of dimensionless constant indicates favorable adsorptions process. Finally, through physicochemical analysis from scanning electron microscopy and Fourier Transform Infra-Red Spectroscopy, the characteristics of vermicompost were verified revealing essential aspects to efficient adsorbent.

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This study aims to compare multivariate calibration methods developed from data obtained by square wave anodic stripping voltammetry using a hanging mercury drop electrode for simultaneous determination of metals in cachaça, the following metals were studied: copper, zinc and cadmium. Multivariate calibration, partial least squares (PLS) and artificial neural network (ANN) methods were used in previous studies using other electrodes for this determination. In this new study, besides ANN and PLS, a hybrid model that combines PLS and NN, namely PLS-Neural was used. Also, samples of industrial cachaças were incorporated into the study in addition to artisanal samples.  The quality of the methods was evaluated in terms of coefficient of determination (R2) and root mean square error of prediction (RMSEP).  F test was used for comparing methods at confidence level of 95%. Based on these studies, it was found that although all methods show good results, the method employing neural networks stands out in the determination of copper in samples of cachaça. All methods proved to be fast and relatively low-cost, and they can be used for such analyses.