Functionalized graphene oxide for Cr (VI) removal: A systematic review of functional groups, mechanisms, and environmental implications

Abstract

Hexavalent chromium Cr (VI) pollution poses significant health and environmental risks, prompting the exploration of graphene oxide (GO)-based adsorbents as a promising removal solution due to their unique properties. This systematic review (2014–2023) examines hexavalent chromium (Cr (VI)) removal using functionalized GO, focusing on the role of surface groups such as amine (-NH₂), carboxyl (-COOH), thiol (-SH), sulfonic (-SO₃H), and metal oxides in enhancing adsorption. Among 53 studies, amine-functionalized GO demonstrated superior efficiency owing to its protonation in acidic conditions, nitrogen's lone electron pair, increased positive surface charge, and expanded interlayer spacing. Carboxyl/hydroxyl groups facilitated ion exchange and Cr (VI)-to-Cr (III) reduction but showed weaker binding tendency. Sulfur-containing groups enabled physical adsorption via complexation but suffers lack of stability in acidic media. Despite limited direct Cr (VI) adsorption, metal oxides enhanced GO’s magnetic properties and stability. Reports indicated the pseudo-second-order model frequently described the adsorption processes, suggesting chemisorption is the rate-limiting step. Besides, depending on the functional groups and operating conditions, thermodynamic analysis revealed that Cr (VI) adsorption could occurs spontaneous, endothermic, or exothermic. Basically, a variety of the key operational parameters i.e. pH, initial concentration, contact time, and temperature, were evaluated for Cr (VI) adsorption on graphene-based adsorbents. The maximum removal efficiencies predominantly occurred in strong acidic condition i.e. pH 2–3, where protonation of functional groups develops strong electrostatic attraction to HCrO₄⁻. Tailoring carbon-based materials through rational functionalization and extending their working pH range toward neutrality warrants further attention in future studies. Within this context, emerging utilities such as machine learning and artificial intelligence offer a promising avenue to fulfill these goals.