Efficient removal of antidepressant Flupentixol using graphene oxide/cellulose nanogel composite: Particle swarm algorithm based artificial neural network modelling and optimization

Flupentixol (FPL) - one of the antidepressant drugs and an emerging micropollutant was taken as model pharmaceutical pollutant in this study. Graphene oxide (GO) nanoparticles were synthesized via chemical oxidation cum exfoliation, composited with cellulose (GOC) and utilized for FPL adsorption from aqueous medium. Batch adsorption of FPL onto GO or GOC was carried out in a Box-Behnken based design with a parameter set of pH (4.5, 6.5 and 8.5), adsorbent dosage (50, 100 and 150 mg/L), initial concentration (30, 50 and 70 mg/L), and solution temperature (15, 30, 45 degrees C). Particle swarm optimization (PSO) algorithm based artificial neural network (ANN) model was developed to optimize the adsorption process parameters. FPL adsorption onto GO and GOC was chemisorption followed by pore diffusion, exothermic, and spontaneous in nature. The molecular docking simulation of FPL and GO visualized the hydrogen bonding, hydrophobic interactions, pi-pi interactions, sulphur interaction, and lone pair interactions occurred during adsorptive removal of FPL using GO adsorbent. (C) 2020 Elsevier B.V. All rights reserved.