Adsorption Kinetics of Hg(II) on Eco-Friendly Prepared Oxidized Graphenes

Extra-functionalized oxidized graphenes are widely preferred for the removal of different pollutants, however, removal with pristine oxidized graphenes, i.e., graphene oxide (GO) and reduced graphene oxide (rGO) is vaguely explored. Herein, we report a comparative adsorption kinetics study of the removal of mercury(II) (Hg(II)) from water using eco-friendly prepared GO and rGO. This work consists of the synthesis protocol and the corresponding morphological and spectroscopical characterization of the obtained pristine adsorbents as well as the adsorption mechanism in terms of initial concentration, removal percentage, pseudo-first and pseudo-second-order models, intraparticle diffusion study, and pH analysis. In particular, scanning electron microscope (SEM) and transmission electron microscope (TEM) images evidence the presence of thin sheets with some defects on the GO structure, these defects substantially disappear in rGO, after reduction. Raman spectrum of rGO shows a less intense D* peak which is attributed to the diamond-like carbon phase. Most importantly, the equilibrium adsorption time in GO is 10 min with a removal percentage of similar to 28% while in rGO it is 20 min with a removal percentage of similar to 75%. The adsorption process of Hg(II) either in GO or rGO is more in line with the pseudo-second-order model, suggesting that the adsorption kinetics could be controlled by chemisorption. Our results evidence the interesting adsorbing properties of pristine oxidized graphenes and are expected to be useful for the proposal and study of non-extra functionalized graphene-based materials for water treatment.

Automated summary

This study compares the adsorption kinetics of environmentally friendly prepared oxidized graphene and reduced graphene oxide for the removal of mercury(II) from water. The results show that reduced graphene oxide has a higher removal percentage and shorter equilibrium adsorption time, and the adsorption process is more in line with the pseudo-second-order model.