Novel adsorbent g‐C₃N₄/ZnV₂O₄ for efficient removal of crystal violet dye: Removal process optimization, adsorption isotherms, and kinetic modeling

Abstract

Adsorption process is an effective and promising procedure for the removal of contaminations from wastewater. Nanocomposites have attracted extensive attention in the adsorption field owning to large surface/volumes ratio and large number of active sites. We aim at fabricating a novel binary g‐C3N4/ZnV2O4 nanocomposite, which can be used for removal of crystal violet dye from aqueous solution via the adsorption process. The as‐synthesized nanocomposite was well characterized by X‐ray diffraction (XRD), Fourier‐transform infrared spectroscopy (FTIR), field emission scanning microscope (FESEM), energy‐dispersive X‐ray (EDX), and mapping analyses. The effects of four key experimental parameters on the adsorption process, including adsorbent dosage, initial concentration of crystal violet dye, adsorption time, and solution pH, were investigated by using central composite design‐based response surface methodology. The high adsorption efficiency of 99.67% was obtained at the adsorbent dosage of 19.776 mg, initial crystal violet concentration of 20.048 ppm, contact time of 59.20 min, and pH of 8. The correlation coefficients values (R2 of 0.9969, predicted‐R2 of 0.9910, and adjusted‐R2 of 0.9948), model p value = 0.0001, and model F value = 489.78 confirm the adequacy and predictability of the proposed model. The results of adsorption kinetics demonstrate that the adsorption of crystal violet by g‐C3N4/ZnV2O4 was performed by chemical adsorption. Also, the Langmuir model was fitted effectively with the adsorption data. Furthermore, the mechanism of adsorption was explained via π–π stacking interaction, n–π stacking interaction, electrostatic attraction, coordination bonding, and hydrogen bonding.

Superior and effective adsorption of amoxicillin by using novel metal organic framework and its composite: Thermodynamic, kinetic, and optimization by Box–Behnken design

“…Upon closer inspection, the normal probability plot (Figures 7b and 8b) reveals a straight line indicating the typical pattern of errors. Additionally, the plots of residuals against run number and anticipated efficiency (Figures 7c,d and 8c,d) demonstrate the quadratic model's suitability and random data points distributed without any patterns 44 …”

“…Additionally, the plots of residuals against run number and anticipated efficiency (Figures 7c,d and 8c,d) demonstrate the quadratic model's suitability and random data points distributed without any patterns. 44…”