Herein, it was aimed to optimize, model, and forecast the biosorption of Congo Red onto biomass-derived biosorbent. Therefore, the waste-orange-peels were processed to fabricate biomass-derived carbon, which was activated by ZnCl2 and modified with cetyltrimethylammonium bromide. The physicochemical properties of the biosorbents were explored by scanning electron microscopy and N2 adsorption/desorption isotherms. The effects of pH, initial dye concentration, temperature, and contact duration on the biosorption capacity were investigated and optimized by batch experimental process, followed by the kinetics, equilibrium, and thermodynamics of biosorption were modeled. Furthermore, various artificial neural network (ANN) architectures were applied to experimental data to optimize the ANN model. The kinetic modeling of the biosorption offered that biosorption was in accordance both with the pseudo-second-order and saturation-type kinetic model, and the monolayer biosorption capacity was calculated as 666.67 mg g-1 at 25 °C according to Langmuir isotherm model. According to equilibrium modeling, the Freundlich isotherm model was better fitted to the experimental data than the Langmuir isotherm model. Moreover, the thermodynamic modeling revealed biosorption took place spontaneously as an exothermic process. The findings revealed that the best ANN architecture trained with trainlm as the backpropagation algorithm, with tansig-purelin transfer functions, and 14 neurons in the single hidden layer with the highest coefficient of determination (R2 = 0.9996) and the lowest mean-squared-error (MSE = 0.0002). The well-agreement between the experimental and ANN-forecasted data demonstrated that the optimized ANN model can predict the behavior of the anionic dye biosorption onto biomass-derived modified carbon materials under various operation conditions.
Graphene-based nanomaterials with remarkable properties, such as good biocompatibility, strong mechanical strength, and outstanding electrical conductivity, have dramatically shown excellent potential in various applications. Increasing surface area and porosity percentage, improvement of adsorption capacities, reduction of adsorption energy barrier, and also prevention of agglomeration of graphene layers are the main advantages of functionalized graphene nanocomposites. On the other hand, Cerium nanostructures with remarkable properties have received a great deal of attention in a wide range of fields; however, in some cases low conductivity limits their application in different applications. Therefore, the combination of cerium structures and graphene networks has been widely invesitaged to improve properties of the composite. In order to have a comprehensive information of these nanonetworks, this research reviews the recent developments in cerium functionalized graphene derivatives (graphene oxide (GO), reduced graphene oxide (RGO), and graphene quantum dot (GQD) and their industrial applications. The applications of functionalized graphene derivatives have also been successfully summarized. This systematic review study of graphene networks decorated with different structure of Cerium have potential to pave the way for scientific research not only in field of material science but also in fluorescent sensing, electrochemical sensing, supercapacitors, and catalyst as a new candidate.