β-cyclodextrin (CD) was grafted with multi-walled carbon nanotubes/chitosan (MWCNTs/Cs) to obtain MWCNTs/Cs/CD nanocomposite (NC) for methylene blue (MB) adsorption from aqueous media. TEM, XRD, TGA, Raman spectra, and BET & BJH analyses were utilized to characterize and confirm the successful synthesis of as-prepared NC. MB capture was investigated by considering the parameters of pH (1.9-9.0), temperature (∼16-63 °C), sonication time (∼5-15 min), MB concentration (∼1.2-48 mg/L), and NC dose (0.03-0.26 mg). The obtained responses were then modelled using CCD, generalized regression neural network (GRNN), and least squares support vector machine (LS-SVM), of which the latter found to provide most reliable and accurate results (RMSE = 0.0235, MAE = 0.020, AAD = 0.0047, and R2 = 0.999). Moreover, the genetic algorithm-based optimization results showed that under the respective values of 7.05, 45.5 °C, 10 min, 23 mg/L, 0.12 g, MWCNTs/Cs/CD NC would be able to remove 96.75% of MB with an adsorption capacity of 603 mg/g, through different mechanisms mainly electrostatic interactions. Following from Dubinin-Radushkevich (D-R) isotherm (qs = 460.66 ± 8.9 and R2 > 0.99) and intraparticle diffusion kinetic (R2 = 0.75-0.90) models indicated a chemical adsorption mechanism. Besides, thermodynamic parameters (ΔH◦ = -66.9 kJ/mol, ΔG◦ = between -3.77 kJ/mol and -8.52 kJ/mol, and ΔS◦ = 237.1818 J/mol K) confirmed an endothermic and spontaneous nature for the adsorption. These findings along with appropriate recyclability (five times), turn the as prepared NC to a promising material in removing MB from aqueous solutions.
Keywords: Adsorption; Cationic dyes; Generalized regression neural network; Least squares support vector machine.
Copyright © 2023 Elsevier Inc. All rights reserved.