Removal of brilliant green and malachite green from aqueous solution by a viable magnetic polymeric nanocomposite: Simultaneous spectrophotometric determination of 2 dyes by PLS using original and first derivative spectra

Here, we describe the successful synthesis of a new magnetic nanocomposite and investigate simultaneous removal of 2 cationic dyes, brilliant green (BG) and malachite green (MG), from aqueous solutions. The nanocomposite is characterized by scanning electron microscopy, energy-dispersive X-ray spectroscopy, thermal gravimetric analysis, and Fourier-transform infrared spectrophotometry. The spectra of BG and MG in their mixture show severe overlapping. Therefore, partial least square (PLS) regression is applied for simultaneous determination of target dyes. The original and first derivative spectra of the binary mixtures are used to perform the optimization of the calibration matrices by the PLS method. According to statistical values, PLS on first derivative spectral data has given better results with respect to the original data. The coefficients of determination (R-2) for the test data set were 0.998 and 0.9996; root mean square error of prediction values for BG and MG were 0.12 and 0.19, respectively. Furthermore, adsorption kinetics and effects of various variables such as solution pH, adsorbent amount, and initial dye concentration are investigated. Under optimized conditions (adsorbent mass, 0.08g; solution pH, 8; and contact time, 60min), equilibrium experimental data are represented by Langmuir, Freundlich, and Sips isotherms. Results briefly show that adsorption of dyes by nanocomposite obeys pseudo-second-order kinetic model and Sips isotherm. Here, we describe the successful synthesis of a new magnetic nanocomposite and investigate simultaneous removal of brilliant green and malachite green, from aqueous solutions. The spectra of these dyes in the mixture show severe overlapping. Therefore, the original and first derivative spectra of the binary mixtures are used to perform the optimization of the calibration matrices by the PLS method. According to statistical values, PLS on first derivative spectral data has given better results with respect to the original data.