Process modeling and optimization of an iron oxide immobilized graphene oxide gadolinium nanocomposite for arsenic adsorption

Abstract

In this study, a novel nanosized iron-oxide-immobilized graphene oxide–gadolinium oxide (Fe-GO-Gd) adsorbent was synthesized and applied for adsorption removal of As(V). The purity, chemical composition, and crystalline size of Fe-GO-Gd was investigated using microscopic and spectroscopic studies. The results found that the mesoporous crystalline nanoparticles were 40 nm with active surface functional groups (–C=O, –C–C=C–, OH–C=O, Fe2+ and Fe3+, and Gd2O3). The adsorption process was optimized using response surface methodology with a central composite design framework. The synthesized nanocomposite showed significant arsenic adsorption removal. The study of the interaction effects showed that adsorption removal efficacy is directly proportional to the adsorbent dosage, pH, and residence time, whereas it is inversely proportional to the initial concentration. The curvature nature of the contour plots confirmed the interaction intensity among the independent process variables. The As(V) removal (%) enhanced from 16.3 to 98.0% as the dosage increased from 0.1 to 1.0 g/L. The quadratic model identified from analysis of variance, has resulted in model predictions that has R2 = 0.889, indicating the model efficiency. Based on the RSM model, the highest As(V) removal efficiency of 94.8% can be achieved at the optimal conditions of Initial concentrations, adsorbent dosage, pH and residence time of 25.0 mg/L, 0.70 g/L, 4.0 and 187 min respectively. The Fe-GO-Gd nanocomposite showed good stability without loss of adsorption capacity up to 3 cycles and hence can be a potential candidate for sustainable water purification.