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Mechanistic insights into electric field-driven specific ion effects on nanoplastics aggregation in heavy metal co-contaminated aquatic systems

Journal of Hazardous Materials 2025 2 citations ? Citation count from OpenAlex, updated daily. May differ slightly from the publisher's own count.
Peng Yan, Rui Li, Ying Tang, Xinmin Liu, Zhang Yao-wu, X. Y. Gao, Kai Wang, Rui Tian, Hang Li

Summary

Researchers investigated how heavy metal ions (Pb, Cu, Cd, Zn) drive nanoplastic aggregation in contaminated water, finding that strong electric fields at nanoplastic surfaces enhance metal-particle bonding through polarization effects, with lead causing aggregation at far lower concentrations than zinc, explaining why different metals pose different risks in co-contaminated aquatic systems.

Polymers

The concurrent pollution of nanoplastics and heavy metals poses emerging threats to relevant aquatic systems. To elucidate the interfacial mechanisms governing nanoplastics-heavy metal aggregation, specifically clarify the unreconized role of electric fields on these processes, systematic investigation of carboxyl-modified polystyrene nanoplastics (PNs) aggregation mediated by Pb²⁺, Cu²⁺, Cd²⁺ and Zn²⁺ were conducted. Quantitative analyses revealed distinct specific ion effects on PNs aggregation, as evidenced by critical coagulation concentrations (CCC: Pb (1.6 mM) < Cu (4.6 mM) < Cd (13.9 mM) < Zn (16.6 mM)) and effect magnitudes intensifying proportionally to electric field strength. Further theoretical calculations and correlation analyses demonstrated that the strong interfacial electric field (-1.23 ×10⁸ V/m) at PNs surfaces induces polarization-enhanced induction force and polarization-induced covalent bonding between heavy metal cations and O on the PNs surface. These electric field-dependent interactions contributed more than 41 % to PNs aggregation, accounting for the observed specific ion effects, corroborated by FTIR and XPS results. Our findings provide a paradigm shift in understanding nanoplastics fate, offering critical insights for predictive modeling and remediation strategies of multi-pollutant in relevant watersheds.

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