Statistical physics modeling and optimization of norfloxacin adsorption onto graphene oxide

Abstract

The present research focused on graphene oxide (GO) synthesis and its application for norfloxacin (NOR) removal from aqueous solution. A better comprehension of the uptake process was verified by five different statistical models developed specifically for aqueous solutions, and the process was optimized by a central composite design (CCD) and desirability function. The elementary analysis confirmed the expanded graphite oxidation onto GO. The material is organized in an average of 8 stacked layers 0.9 nm apart, with a mean thickness of 7.2 nm. Functional groups were also identified on the material surface, which would promote a higher interaction between the GO and NOR. The uptake process was best described by Hill’s model involving two sites and energies (ε1 = 12.73 kJ/mol and ε2 = 18.85 kJ/mol), characterized by a multi-molecule adsorption process (n1 = 1.41 and n2 = 1.12). Regarding the CCD design, the most important factor was the initial GO concentration. Optimized operational conditions led to a theoretical removal efficiency of 100 %. The model was validated, and an experimental maximum removal was found to be 99.94 ± 0.05 %, with no significant difference between the theoretical one. These results corroborated to the development of a robust mathematical model capable to predict the removal efficiencies given the operational conditions, giving a complete disclosure on the mechanisms involved in the adsorption process.