Molecular Determinants of Contaminant Association with PET Nanoplastics Revealed by Atomistic Simulations

Chemical contaminants associated with nanoplastics can form environmentally pervasive, persistent complexes with potentially toxic effects. Yet, determining which contaminants are most likely to associate with nanoplastics and which molecular determinants govern this phenomenon remains challenging. Here, we used molecular dynamics (MD) to screen and compare the surface association of small-molecule contaminants with polyethylene terephthalate nanoplastic particles (PET-NPPs). Toward this aim, we developed an automated, open-source MD screening pipeline that requires only a SMILES string as input. By systematically analyzing 203 environmentally relevant contaminants in complex with a PET-NPP model, we observed widespread surface association governed by a specific set of physicochemical descriptors. Contaminant net charge defined the primary interaction regime. Among neutral contaminants, association increased strongly with molecular size at lower molecular weights. Among compounds of comparable size, the strongest binders were consistent with geometric complementarity with the PET surface, involving aromatic anchoring and, in some cases, conformational adaptability. Together, these results identify model-specific molecular determinants of contaminant association with PET-NPPs, providing a framework that enables rapid, internally consistent prioritization of contaminants for targeted experimental and mechanistic follow-up.