RESUMO
The existence of artificial dyes in water is a significant environmental concern, as it can lead to poor water quality. Photodegradation is becoming an increasingly popular method for treating water contaminated with dyes. In this study, the photodegradation of Reactive Red 66 and Reactive Red 120 dyes, as well as textile wastewater, was investigated under UV and visible light irradiation. To enhance the photoresponse of the MFe2O4 (M = Co, Ni) nanoparticles, modifications were made by incorporating graphene oxide. The MFe2O4 nanoparticles and MFe2O4/GO nanocomposite photocatalysts were subjected to several characterization techniques, including FT-IR, Raman spectroscopy, XRD, DRS, zeta potential, VSM, TGA, DSC, BET, SEM, and EDAX analysis. Experiments were conducted to optimize several key parameters involved in the photodegradation process, including pH, photocatalyst dosage, initial dye concentration, and irradiation time. The removal efficiency of Reactive Red 66 and Reactive Red 120 dyes using CoFe2O4 nanoparticles was found to be 86.97 and 82.63%, respectively. Also, the removal percentage of these dyes using CoFe2O4/GO nanocomposite photocatalyst was 95.57 and 90.9% for Reactive Red 66 and Reactive Red 120, respectively. Experiments found that NiFe2O4 nanoparticles removed 90.92% of Reactive Red 66 dye and 84.7% of Reactive Red 120 dye. The NiFe2O4/GO nanocomposite photocatalyst showed even higher removal efficiencies, degrading 97.96% of Reactive Red 66 and 93.44% of Reactive Red 120. After three days of exposure to visible light irradiation, the removal percentage of Reactive Red 66 using MFe2O4 and MFe2O4/GO nanocomposite was investigated.
RESUMO
Quantitative structure-activity relationship (QSAR) analysis has been carried out with a series of 107 anti-HIV HEPT compounds with antiviral activity, which was performed by chemometrics methods. Bi-dimensional images were used to calculate some pixels and multivariate image analysis was applied to QSAR modelling of the anti-HIV potential of HEPT analogues by means of multivariate calibration, such as principal component regression (PCR) and partial least squares (PLS). In this paper, we investigated the effect of pixel selection by application of genetic algorithms (GAs) for the PLS model. GAs is very useful in the variable selection in modelling and calibration because of the strong effect of the relationship between presence/absence of variables in a calibration model and the prediction ability of the model itself. The subset of pixels, which resulted in the low prediction error, was selected by genetic algorithms. The resulted GA-PLS model had a high statistical quality (RMSEP = 0.0423 and R2 = 0.9412) in comparison with PCR (RMSEP = 0.4559, R2 = 0.7929) and PLS (RMSEP = 0.3275 and R2 = 0.0.8427) for predicting the activity of the compounds. Because of high correlation between values of predicted and experimental activities, MIA-QSAR proved to be a highly predictive approach.
