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Modeling of multicomponent adsorption equilibria of phenol and ciprofloxacin on pristine, acid-modified and thermo-oxidatively-aged polyethylene terephthalate microplastics
Summary
PET microplastics — both fresh and weathered — strongly adsorb the pharmaceutical antibiotic ciprofloxacin and the industrial chemical phenol from water, with adsorption in mixtures exceeding single-contaminant levels and a neural network model outperforming classical isotherms in predicting outcomes. This demonstrates that microplastics act as carriers concentrating multiple pollutants simultaneously, potentially delivering higher toxic doses to aquatic organisms than the individual chemicals would cause alone.
Abstract Multicomponent systems are representative of the most common real situations as many industrial discharges contain a mixture of several pollutants. This study examines the concurrent adsorption of phenol (PHE) and ciprofloxacin (CIP) onto three types of polyethylene terephthalate microplastics (PET MPs): pristine, acid-modified, and thermal-oxidatively aged. Using extended Langmuir (EL), extended Freundlich (EF) isotherms, and a new artificial neural network (ANN) model, equilibrium adsorption capacities were predicted. The EL isotherm fit for pristine and aged PET MPs, while EF fit for modified PET MPs. Monolayer adsorption capacities ranged from 342.10–3715.73 mg/g for PHE and 2518.23–14498.79 mg/g for CIP, exceeding single-component adsorption. The ANN model used one hidden layer with 3 neurons for pristine and aged PET MPs, and 2 hidden layers with five neurons for modified PET MPs, with a hyperbolic tangent activation function. Models showed excellent performance metrics, including R 2 values of 0.989–0.999, RMSE of 0.001–0.413, and AAE of 0.009–0.327. Synergistic interactions were observed in the binary system, with PET MPs showing higher selectivity toward CIP. The study demonstrates the effectiveness of PET MPs for binary adsorption of PHE and CIP in aqueous solutions, highlighting their potential for multicomponent pollutant removal.