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 °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, π−π interactions, sulphur interaction, and lone pair interactions occurred during adsorptive removal of FPL using GO adsorbent.

在本研究中，氟哌噻吨（FPL）–一种抗抑郁药和一种新兴的微污染物被用作模型药物污染物。氧化石墨烯（GO）纳米粒子是通过化学氧化和剥离法合成的，与纤维素（GOC）复合，并用于从水性介质中吸附FPL。FPL在GO或GOC上的批量吸附是在基于Box-Behnken的设计中进行的，参数设置为pH（4.5、6.5和8.5），吸附剂剂量（50、100和150 mg / L），初始浓度（30， 50和70 mg / L）和溶液温度（15、30、45°C）。提出了基于粒子群优化算法的人工神经网络模型，以优化吸附过程参数。FPL在GO和GOC上的吸附是化学吸附，然后是孔扩散，放热和自然发生。