Response surface methodology as a powerful tool to optimize the synthesis of polymer-based graphene oxide nanocomposites for simultaneous removal of cationic and anionic heavy metal contaminants

Abstract

Nanocomposites containing graphene oxide (GO), polyethyleneimine (PEI), and chitosan (CS) were synthesized for chromium(VI) and copper(II) removal from water. Response surface methodology (RSM) was used for the optimization of the synthesis of the CS-PEI-GO beads to achieve simultaneous maximum Cr(VI) and Cu(II) removals. The RSM experimental design involved investigating different concentrations of PEI (1.0-2.0%), GO (500-1500 ppm), and glutaraldehyde (GLA) (0.5-2.5%), simultaneously. Batch adsorption experiments were performed to obtain responses in terms of percent The optimum bead composition contained 2.0% PEI, 1500 ppm GO, and 2.08% GLA, and allowed Cr (VI) and Cu(II) removals of up to 91.10% and 78.18%, respectively. Finally, characterization of the structure and surface properties of the optimized CS-PEI-GO beads was carried out using X-ray diffraction (XRD), porosity and BET surface area analysis, scanning electron microscopy (SEM), Fourier transform infrared spectroscopy (FTIR), and X-ray photoelectron spectroscopy (XPS), which showed favorable adsorbent characteristics as given by a mesoporous structure with high surface area (358 m 2 g À1 ) and plenty of surface functional groups. Overall, the synthesized CS-PEI-GO beads were proven to be effective in removing both cationic and anionic heavy metal pollutants.

Redox mechanisms of conversion of Cr(VI) to Cr(III) by graphene oxide-polymer composite

“…Furthermore, in many cases, Cr (VI) has shown to be reduced to Cr (III); however, the chemical pathway behind this removal and reduction has not been fully elucidated. Primarily, it was theorized to be a combination of electrostatic attraction and chemical complexation based on previous reports with other carbonaceous materials [3][4][5][6] . For instance, Chen et al reported that Cr (VI), in the form of Cr 2 O 7 2− anions bound to positively charged surfaces of wood-based powdered activated carbon (WPAC), was reduced to Cr (III), suggesting that some graphitic carbons in WPAC allowed a redox reaction to happen 5 .…”

“…For instance, Chen et al reported that Cr (VI), in the form of Cr 2 O 7 2− anions bound to positively charged surfaces of wood-based powdered activated carbon (WPAC), was reduced to Cr (III), suggesting that some graphitic carbons in WPAC allowed a redox reaction to happen 5 . Similarly, several studies have indicated that the Cr (VI) ions in a solution can form ion complexes with graphene oxide functional groups or can be subjected to electrostatic attractions allowing easy removal from a solution by a CS-PEI-GO nanocomposite 3,4,6 . These studies showed the successful removal of Cr (VI) by carbon-based nanomaterials and polymer beads.…”

“…Based on this knowledge gap, in the present study, we synthesized graphene oxide (GO) embedded in chitosan (CS) and polyethyleneimine (PEI) with the aid of a crosslinking reagent, glutaraldehyde (GLA) 3 . This material was selected since it was shown to remove Cr (VI) and convert to Cr (III) and because these beads contain diverse functional groups, such as amine, hydroxyl, carboxyl, carbonyl and alkyl groups, which were previously suggested to be playing a significant role in this conversion of Cr (VI) to Cr(III).…”

Optimization and parametric study of AC electroosmotic micropumping by response surface method

“…The statistical analysis is performed in terms of analysis of variance (ANOVA) in order to find significant model terms. The values of the partial probability (p value) less than 0.05 and high F value imply that the model factors are significant, whereas the p values higher than 0.1 indicate the statistically insignificant terms [31]. From Online Resource Table S1, it can be seen that the quadratic model used here has the p value of < 0.0001 indicating that the model response model is significant and there is only < 0.01% chance that the F value of the model is because of the random error.…”

“…The relation of the design variables with the response value is derived by fitting the numerical analysis results to a second-order polynomial (Eq. 6) [31]. (6)…”