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Modeling polystyrene nanoplastics degradation in water via photo-Fenton treatment: A shrinking-particle approach
Summary
Researchers developed kinetic models for the degradation of polystyrene nanoplastics in water using photo-Fenton oxidation, a process combining UV light with iron-based catalysts. They investigated how particle size, agitation speed, and temperature affect the degradation rate using a shrinking-particle approach. The study demonstrates that photo-Fenton treatment is a promising method for breaking down nanoplastic pollution in water, though efficiency depends on optimizing multiple reaction parameters.
In this study, kinetic models for the photo-Fenton oxidation of polystyrene nanoplastics (NPs) in water were developed, considering particles with decreasing diameters. Various reaction parameters affecting the oxidation rate, such as particle size (140−909 nm), agitation speed (250−1000 rpm), and operating temperature (25 and 60 °C) were investigated. Oxidation progress was evaluated through turbidity measurements, TEM, and FTIR analysis, while leached intermediates were identified via Pyr-GC-MS and IC. Due to changes in NPs surface reactivity, the overall reaction rate was divided into two stages, following a free-radical mechanism. Using equations derived from the classic Shrinking Core Model, the oxidation of NPs was determined to proceed under chemical reaction control, with negligible mass transfer limitations. Additionally, the Prout-Tompkins model was found to accurately represent the degradation process. The proposed mechanisms and models provide valuable insights for describing and predicting the advanced oxidation of NPs under different operating conditions and treatment methods. • Polystyrene NPs are oxidized from the surface to the core until they disappear. • Shrinking Core Model accurately represents the conversion of PS NPs. • Reaction rate is under chemical control considering varying surface reactivities. • Mass-transfer limitations are not expected for nanoplastics as the reacting solid. • Prout-Tompkins model predicts PS NPs conversion using a global parameter.
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