Statistical physics modeling and optimization of norfloxacin adsorption onto graphene oxide

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 (epsilon(1) = 12.73 kJ/mol and epsilon(2) = 18.85 kJ/mol), characterized by a multi-molecule adsorption process (n(1) = 1.41 and n(2) = 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.