We can't find the internet
Attempting to reconnect
Something went wrong!
Hang in there while we get back on track
Molecular Insights into Adsorption Mechanisms of Novel Adsorbents for Micro- and Nanoplastic Removal
Summary
This review pulls together research on materials that can filter tiny plastic particles out of water, showing that how well a material works depends on the specific chemistry of both the plastic and the filter—there's no single "best" solution. The key takeaway is that lab results often look better than what happens in real-world water, so as scientists work to remove microplastics from our drinking water and environment, testing needs to happen under realistic conditions before we can trust these cleanup methods to actually protect public health.
Existing reviews on 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-functionalised 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. Importantly, laboratory capacities often overestimate real performance, and removal efficiency in complex matrices is a more reliable metric. Future progress will depend on improved standardisation, better integration with modelling, and validation under realistic conditions to support the transition from laboratory studies to practical applications.