In this study dependency of simultaneous adsorption of Congo Red (CR), Phloxine B (BP) and Fast green FCF (FG) onto CuS/ZnS nanocomposites loaded on activated carbon (CuS/ZnS-NCs-AC) to pH, adsorbent mass, sonication time and initial dyes concentration were modeled and optimized, while CuS/ZnS-NCs-AC was identified by XRD, FESEM and EDS analysis. CR, PB and FG concentration determination were undertaken by first and second order derivative spectrophotometry in ternary mixture. According to central composite design (CCD) based on desirability function (DF), the best experimental conditions was set as pH 6.0, 0.02g CuS/ZnS-NCs-AC, 5min sonication time, 15mgL for PB and 10mgL for other dyes. Conduction of experiments to above conditions lead to highest dyes removal efficiency of 99.72, 98.8 and 98.17 for CR, PB and FG, respectively. The adsorption data efficiently fitted by Langmuir isotherm model, while the order of maximum adsorption capacity (Q) for PB (128.21mgg)>CR (88.57mgg)>FG (73.40mgg) is related to their different structure and charges. Kinetics of process was efficiently explained according to pseudo-second-order kinetic in cooperation of Weber and Morris based on intraparticle diffusion.

This study is devoted to an investigation of the effects of sonication time, adsorbent mass, pH and sunset yellow (SY) and disulfine blue (DB) concentration on the removal of DB and SY from water. Artificial neural network and response surface methodology approaches were used to optimize an analytical model to calculate the DB and SY removal performance of tin oxide nanoparticles loaded on activated carbon. The performance of both models was statistically evaluated in terms of the coefficient of determination (R2), root mean squared error (RMSE), mean absolute error (MAE) and absolute average deviation (AAD), and graphical plots were also used for comparison of the models. The obtained results show that the artificial neural network model outperforms the classical statistical model in terms of R2, RMSE, MAE and AAD for both dyes. Various isotherm models were studied for fitting the experimental equilibrium data, and the results confirm the applicability of the Langmuir isotherm for description of the adsorption equilibrium. Various kinetic models were applied to the experimental data and the results reveal that the pseudo‐second‐order model with better correlation is superior to the other kinetic models. The significant factors were optimized using the desirability function approach combined with central composite design. The obtained optimal point is located in the valid region and the experimental confirmation indicates good agreement between the predicted optimal points and the experimental data.

A sensitive procedure namely ultrasound-assisted (UA) coupled dispersive nano solid-phase microextraction spectrophotometry (DNSPME-UV-Vis) was designed for preconcentration and subsequent determination of gallic acid (GA) from water samples, while the detailed of composition and morphology and also purity and structure of this new sorbent was identified by techniques like field emission scanning electron microscopy (FE-SEM), X-ray diffraction (XRD) and Energy-dispersive X-ray spectroscopy (EDX) techniques. Among conventional parameters viz. pH, amount of sorbent, sonication time and volume of elution solvent based on Response Surface Methodology (RSM) and central composite design according to statistics based contour the best operational conditions was set at pH of 2.0; 1.5mg sorbent, 4.0min sonication and 150μL ethanol. Under these pre-qualified conditions the method has linear response over wide concentration range of 15-6000ngmL with a correlation coefficient of 0.9996. The good figure of merits like acceptable LOD (S/N=3) and LOQ (S/N=10) with numerical value of 2.923 and 9.744ngmL, respectively and relative recovery between 95.54 and 100.02% show the applicability and efficiency of this method for real samples analysis with RSDs below 6.0%. Finally the method with good performance were used for monitoring under study analyte in various real samples like tap, river and mineral waters.