Multivariate Statistical Insights into Copper Adsorption by Graphene Oxide-Based Adsorbents

Copper contamination in aquatic environments poses significant ecological and health risks, necessitating efficient and resilient treatment strategies. In this study, graphene oxide (GO) and magnetic graphene oxide (MGO) were synthesized and comprehensively evaluated for Cu(II) removal using an integrated multivariate approach combining kinetic and isotherm modelling, Response Surface Methodology (RSM), and advanced statistical analyses. Both adsorbents achieved high removal efficiencies (>90%) under optimized conditions, with Langmuir capacities of 59.2 mg g−1 for GO and 40.1 mg g−1 for MGO. Kinetic modelling confirmed reaction-controlled adsorption, while Freundlich isotherms highlighted heterogeneous surface binding. RSM identified pH as the dominant factor governing removal efficiency, with significant interactions among pH, Cu(II), and DOC reflecting competitive matrix effects. Principal Component Analysis (PCA) revealed that GO performance is strongly influenced by solution chemistry, whereas MGO exhibits reduced sensitivity due to modified physicochemical properties. FTIR analysis confirmed that adsorption proceeds primarily through electrostatic attraction and inner-sphere complexation, with Fe–O sites contributing to MGO’s enhanced affinity. Regeneration studies demonstrated superior reusability of MGO, which retained ~64% efficiency after five cycles compared to ~52% for GO. Collectively, these multivariate and mechanistic insights identify MGO as a more robust, versatile, and regenerable sorbent for Cu(II) removal under realistic water-matrix conditions.