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Molecular Insights into Adsorption Mechanisms of Micro- and Nanoplastics on Effective Adsorbent Materials

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This review pulls together existing research on how different filter materials trap microplastics and nanoplastics from water, showing that success depends on matching the material to the specific type of plastic particle (its size, surface chemistry, and charge) rather than just picking the "best" material overall. This matters because it means there's no one-size-fits-all filter for removing these tiny pollutants from our water supply, and the study notes that many materials which work well in clean lab tests may perform worse in real-world water, so more realistic testing is needed before these solutions can be trusted at scale.

Existing reviews on micro- and nanoplastic (MNP) removal from water rarely link adsorbent structural features to the molecular interactions governing removal performance. This review addresses this gap by examining MNP adsorption from a mechanism-oriented perspective, mapping six canonical interaction pathways across five adsorbent classes. Adsorption emerges as a system-dependent process governed by the interplay between polymer properties and surface chemistry rather than by the material alone. Interactions such as π–π stacking and hydrophobic affinity dominate for non-functionalized polymers on carbon-rich surfaces, while electrostatic forces and hydrogen bonding become more relevant for oxidised particles. Pore structure becomes significant when particle size and porosity match, whereas chemisorption provides a stronger and faster pathway in systems containing reactive metal sites. Across material classes, differences relate more closely to scalability and sustainability than to intrinsic adsorption capacity. Bio-based materials offer a favourable balance between performance and practical implementation, while more advanced systems provide greater control but remain limited by synthesis complexity. Laboratory capacities often overestimate real performance, and removal efficiency in complex matrices is a more reliable metric. Future progress will depend on improved standardisation, integration with modelling, and validation under realistic conditions to support the transition from laboratory studies to practical applications.

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