Bio-inspired, high, and fast adsorption of tetracycline from aqueous media using Fe3O4-g-CN@PEI-β-CD nanocomposite: Modeling by response surface methodology (RSM), boosted regression tree (BRT), and general regression neural network (GRNN)

Abstract Because antibiotic-containing wastewaters are able to contaminate all environmental matrices (e.g. water bodies, soil, etc.), a special attention should be paid on developing appropriate materials for their remediation. Herein, the novel nanocomposite (NC) of Fe3O4-g-CN@PEI-β-CD was synthesized and employed effectively for the adsorptive removal of tetracycline (TC), the second most produced and employed antibiotic around the world. The successful fabrication of the nanocomposite with a high specific surface area (57.12 m2/g) was confirmed using XRD, SEM, TEM, FTIR, TGA, EDX, and BET analyses. The Fe3O4-g-CN@PEI-β-CD NC exhibited fast adsorption rates towards TC and maximum adsorption capacity on the basis of the Langmuir model reached 833.33 mg g−1, much higher than that reported by different carbon- and/or nano-based materials. The adsorption process was modeled using the approaches of central composite design (CCD), boosted regression tree (BRT), and general regression neural network (GRNN) under various operational conditions of initial TC concentration, pH, adsorbent dose, tempreature, and time. The comparison of the models indicated good predictions of all, however, the BRT model was more accurate compared to the others, with R2 = 0.9992, RMSE = 0.0026, MAE = 0.0014, and AAD = 0.0028, proving that it is a powerful approach for modeling TC adsorption by Fe3O4-g-CN@PEI-β-CD nanocomposite. The results showed that the order of the variables’ effectiveness is as follow: pH > dose > TC concentration. The high adsorption capacity along with high efficiency (98 % in the optimized conditions by GA) ensures the potential of the as-prepared nanocomposite for in situ remediation of antibiotic-containing wastewaters.