Removal of lead (II) from aqueous solutions using β-cyclodextrin functionalized magnetic graphene oxide nanocomposite, performance, and optimization with response surface methodology
Source abstract
Abstract
This study focused on a new functionalized magnetic graphene oxide (NH2/β-CD/NH2/β-CD MGO) nanoparticle with size in 31–47 nm and nearly spherical by the chemical precipitation method for the uptake of Pb (II) ions from aqueous media. The adsorption capacity was 296.16 mg g−1 under optimum conditions. The study indicated that the optimum conditions for lead (II) ions sequestration were pH = 6.5, nanoparticle dose = 17.75 mg L−1, and contact time = 75 min at room temperature. These results could be useful for the sequestration of lead (II) ions from wastewater. A central composite design (CCD) was employer for performing the experiments and the response surface methodology (RSM) was utilized for the analysis of these experiments. RSM was also used to model and improve the adsorption of lead (II) ions by the adsorbent. Based on the obtained results from different operating parameters, a maximum adsorption capacity of Pb (II) ions was 296.16 mg g−1 in pH = 6.5; at 75 min, a 17.75 mg L−1 adsorbent amount and a 55 mg L−1 initial concentration of Pb (II) ions. The RSM is practical for the optimization of MGO nanoadsorbent and improves the adsorption ability of MGO nanoadsorbent to remove Pb(II) ions from aqueous solutions. Pb(II) adsorption kinetics were conformed from the pseudo-second-order model (R2 > 0.9964) and the Freundlich isotherm model displayed the finest correlation with the equilibrium data (R2 > 0.9788). Basis as all of obtained data, the MGO-NH2/β-CD/NH2/β-CD with five times reusability was favorable adsorbent for purification of water pollution contains heavy metal ions. Graphical Abstract
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Research context
OpenAlex topics: Adsorption and biosorption for pollutant removal · Graphene and Nanomaterials Applications · Graphene research and applications
SciVX discovery tags are terminology-based. OpenAlex topics are source-provided classifications.
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