Remediation of pharmaceuticals from wastewater via computationally selected molecularly imprinted polymers

Abstract

Pharmaceuticals are vital components of our daily life; however, as micropollutants, they also pose a significant wastewater treatment challenge. As the complete avoidance of pharmaceuticals is not a desired or viable solution, a targeted wastewater treatment must be implemented. Molecularly imprinted polymers can remove these contaminants from wastewater; however, determining their optimal constituents is a costly and lengthy experimental process. Here, we present a computational protocol used to design imprinted polymers for the targeted removal of fluoxetine, leveraging rigorous molecular models and simulation methods to study the crucial complexation step during the pre-polymerisation mixtures. Our molecular dynamics results validated with available experimental measurements correlate calculated radial distribution functions and predicted hydrogen bonding with experimental imprinting factors for various functional monomers. The simulated results are also analysed via Kirkwood–Buff integrals for the study of the role of functional monomers in the whole pre-polymerisation mixture. Marked dependencies of the functional monomer's carbonyl, hydroxyl and esters interactions are described, and functional monomer selection criteria using hydrogen bonding time and KBI initial slope and limiting value are proposed. This analysis offers further insights into why itaconic acid, amongst methacrylic acid, 2 (hydroxyethyl)methacrylate, acrylamide and acrylonitrile, is the optimal monomer for imprinting fluoxetine when ethylene glycol dimethacrylate is used as crosslinker and dimethylsulfoxide as solvent. Our computational protocol compiles off-the-shelf open-source software with well-established simulation methodologies to offer a viable alternative to the resource and time-consuming experimental task of choosing the best functional monomer for a given target molecule.