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In this study, spectrophotometry method was combined with artificial intelligence techniques for developing a simple, rapid, low-cost, and accurate approach for the simultaneous determination of Linagliptin (LIN) and Empagliflozin (EMPA) as antidiabetic drugs in mixtures and pharmaceutical formulation. Fuzzy inference system (FIS), adaptive Neuro-fuzzy inference system (ANFIS), and radial basis function neural network (RBF-NN) were proposed to predict the concentration of pharmaceutical components. The mean recovery and root mean square error (RMSE) of FIS were 98.44%, 103.50% and 0.0668, 0.1982 for LIN and EMPA, respectively. The mean recovery percentage of 100.07 and 99.83, as well as RMSE of 0.0033 and 0.0176 were obtained for LIN and EMPA, respectively. On the other hand, MSE related to the RBF-NN was found 1.46 × 10−26 and 1.72 × 10−25 for LIN and EMPA, respectively. Analysis of variance (ANOVA) test did not show a significant difference between high-performance liquid chromatography (HPLC) and proposed methods. The mean recovery (between 91.40% and 98.29%) and relative standard deviation (RSD) (lower than 0.5%) revealed that the suggested methods in a complex matrix such as urine had good predictive ability. Due to the obtained results, these methods can be used in quality control.

The effective removal of dyes from aqueous wastes is among the most important issues for many industrialized countries. Removal of methyl orange (MO) and malachite green (MG) from aqueous solutions were studied using single-walled carbon nanotubes (SWCNTs), carboxylate functionalized single-walled carbon nanotubes (SWCNT-COOH) and amide functionalized single-walled carbon nanotubes (SWCNT-NH2). The adsorption process was found to be controlled by temperature, ionic strength, initial concentration, adsorbent dosage and contact time. The microstructure of carbon nanotubes was characterized using SEM and FTIR. The adsorbents studied exhibits high efficiency for MO and MG adsorption and the equilibrium states could be achieved in 20, 20, 15 (min) for SWCNTs, SWCNT-COOH, SWCNT-NH2, respectively. Adsorption capacity of each adsorbent increased with increasing active groups on the surface of carbon nanotube, where SWCNT-NH2 was the most effectively adsorbent.