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Metal-phenolic network-assisted electrochemical selective tracking of polystyrene nanoplastic particles in drinking water
Summary
Researchers developed an electrochemical sensor for detecting polystyrene nanoplastics in drinking water by exploiting the selective affinity of tannic acid for PS surfaces, which — after copper coordination — generates quantifiable electrochemical signals, achieving a detection limit of 7.0 µg/L with polymer- and size-dependent selectivity validated in commercial bottled water.
Nanoplastics (NPLs) are emerging contaminants of growing concern due to their persistence, bioavailability, and potential risks to aquatic ecosystems and human health. Yet, their detection remains challenging even in bottled drinking water due to low cost, selectivity, and sensitivity. Herein, we report a mechanism-driven electrochemical sensing strategy for polystyrene nanoplastics (PS NPLs) based on the formation of a metal-phenolic network (MPN). Tannic acid (TA) exhibits strong and selective affinity toward PS NPLs through π-π stacking and hydrophobic interactions, resulting in preferential surface adsorption on PS compared with PMMA under the tested conditions. Subsequent coordination with Cu⁺ forms a compact TA-Cu network that uniformly coats the PS surface, serving as an electroactive interface for signal transduction. The resulting PS NPLs@TA-Cu assemblies exhibit well-defined electrochemical responses, enabling quantitative detection with a limit of detection of 7.0 μg/L and good reproducibility (RSD = 7.4%). Notably, the sensing performance shows pronounced polymer-dependent and size-dependent selectivity, with significantly weaker responses toward PMMA NPLs and smaller PS particles. The method was further validated in commercial bottled water samples, demonstrating reliable detection under realistic conditions. By integrating selective surface chemistry with electrochemical readout, this work provides a potentially field-deployable, rapid, and non-digestive labeling-and-readout strategy for monitoring PS NPLs, offering new insights into structure-dependent NPL detection and advancing electrochemical strategies for environmental risk assessment of NPLs.