Machine learning for the selection of carbon-based materials for tetracycline and sulfamethoxazole adsorption

Antiobiotics adsorption on carbon-based materials was modeled by machine learning. Random forest showed best prediction accuracy than GBT and ANN. Impact tendencies of SBET, pHsol, C0 on adsorption were similar for TC and SMX. Chemical compositions and pHpzc of CBMs showed different influences on TC and SMX. Antibiotics as emerging pollutants have attracted extensive attention due to their ecotoxicity and persistence in the environment.