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The degradation of the Reactive Blue 4 (RB4) dye by zero-valent copper nanoparticles (nZVC) was investigated. Degradation rates of approximately 90% were reached within 10 minutes of reaction. Total Organic Carbon (TOC) analysis shows that the dye molecules undergo mineralization, therefore indicating the degradation process is oxidative. Experimental tests, held in the presence of tertiary butyl alcohol, acting as a hydroxyl radicals (∙OH) captor, and copper (I) oxide, demonstrated that the reaction mechanism is governed by the concentration of Cu (I) instead of ∙OH. The second-order kinetics model was the most appropriate one to explain the experimental data. Higher values of the reaction rate constant were obtained in higher temperatures and higher nZVC doses and in lower RB4 initial concentrations. The initial pH in more acidic conditions (3 and 4) was kinetically more favorable to the degradation reaction; the activation energy was estimated to be 42 kJ mol−1 based on calculations using the experimental data. Finally, the recovered nanoparticles were utilized on a new reaction cycle, showing a small loss of their efficiency and catalytic activity.

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In this study, direct black dye removal was investigated using iron nanoparticles (Fe NPs), copper (Cu NPs), and Fe/Cu (Fe/Cu NPs). NPs were characterized by transmission electron microscopy (TEM) and X-ray diffraction (XRD). Using a dose of 0.25 g L of Fe, Cu, and Fe/Cu NPs, a degradation efficiency of 13, 26, and 43% respectively was obtained. For the 1.00 g L dose, the efficiency increased to 100, 43, and 100%, respectively. Studies in anoxic and oxic conditions presented degradation rates, respectively, of 100 and 30% for Fe NPs, 90 and 50% for Fe/Cu NPs, and 40% in both reactions for Cu NPs, indicating that the mechanism of dye degradation by NPs is predominantly reducing under the conditions studied. The addition of EDTA decreased the dye removal rate for Fe, Cu, and Fe/Cu NPs at 27, 10, and 35%, respectively. In addition to the degradation, the adsorption phenomena of the by-products formed during the reaction were confirmed by the Fourier transform infrared (FTIR) analysis and verified by the desorption tests. Fe and Fe/Cu NPs showed the highest efficiency in direct black dye reductive degradation and adsorption of by-products, removing 100% of the dye at a dose of 1 g L within 10 min of reaction. Graphical abstracts ᅟ.

The removal of the beta-lactam antibiotics (ceftriaxone and cefadroxil) through zero-valent copper nanoparticle (nZVC) was studied in this work. Excellent removal degrees (> 85%) were obtained for both analytes in only 20 min of reaction. Studies were performed in both oxic and anoxic conditions, and in the presence of t-butyl alcohol (TBA), an inhibitor of radicals. The results did not show significant changes. Therefore, the hydroxyl radicals are not the main species responsible for the removal. Total organic carbon cefadroxil analysis indicated a removal of 57% after 180 min of reaction. Studies involving Cu + indicated that probably these are the principal species responsible for the removal of antibiotics. Kinetic studies have shown that two-phase reaction occurred in the antibiotics removal process and both phases followed pseudo-first order kinetic model. The first mechanism is related to the antibiotics degradation by Cu + species and the second mechanism is related to the antibiotics adsorption by hydroxides/oxides of Cu 2+ species.

Some of the most common applications of machine learning (ML) algorithms dealing with small molecules usually fall within two distinct domains, namely, the prediction of molecular properties and the design of novel molecules with some desirable property. Here we unite these applications under a single molecular representation and ML algorithm by modifying the grammar variational autoencoder (GVAE) model with the incorporation of property information into its training procedure, thus creating a supervised GVAE (SGVAE). Results indicate that the biased latent space generated by this approach can successfully be used to predict the molecular properties of the input molecules, produce novel and unique molecules with some desired property and also estimate the properties of random sampled molecules. We illustrate these possibilities by sampling novel molecules from the latent space with specific values of the lowest unoccupied molecular orbital (LUMO) energy after training the model using the QM9 data set. Furthermore, the trained model is also used to predict the properties of a hold-out set and the resulting mean absolute error (MAE) shows values close to chemical accuracy for the dipole moment and atomization energies, even outperforming ML models designed to exclusive predict molecular properties using the SMILES as molecular representation. Therefore, these results show that the proposed approach is a viable way to provide generative ML models with molecular property information in a way that the generation of novel molecules is likely to achieve better results, with the benefit that these new molecules can also have their molecular properties accurately predicted.