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Integrating DFT Computations and QSAR Modeling to Predict Adsorption of Organic Pollutants onto Microplastics in Aqueous Environments
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This study examined microplastic fate dynamics in saltmarsh ecosystems, tracking particle transport, deposition, and retention in intertidal vegetation zones. The research reveals that saltmarshes function as significant microplastic sinks, trapping particles from tidal waters but also potentially releasing them back during storm events.
Understanding the adsorption of organic pollutants onto microplastics in aqueous environments is crucial for assessing their environmental behavior and ecological risks. Herein, we used density functional theory (DFT) computations to simulate the aqueous adsorption of 54 organic compounds onto three representative microplastics, namely polyethylene (PE), polyoxymethylene (POM), and polyvinyl alcohol (PVA). Afterwards, based on theoretical molecular structural descriptors, we developed six quantitative structure activity relationship (QSAR) models based on datasets of 43 and 54 organic compounds, respectively. The results demonstrated that the oxygen-containing POM and PVA microplastics exhibited weaker adsorption in the aqueous phase compared to that in the gas phase. Furthermore, it revealed that the electron-rich atoms, van der Waals volumes and molecular polarizability exert substantial effects on the adsorption process on microplastics in water. These robust QSAR models can enable the prediction of adsorption energies for various organic pollutants on microplastics, which can offer a rapid approach for generating adsorption data. Moreover, the insights into adsorption mechanisms can provide a theoretical basis for designing modified or alternative plastics with lower environmental risks.
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Quantitative structure-property relationship (QSPR) and q-RASPR models were developed using experimental adsorption data to predict how organic pollutants adsorb onto polyethylene, polypropylene, and polystyrene microplastics in different aqueous environments. The models provide computational tools to assess microplastic-contaminant interactions without extensive laboratory testing.
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Researchers combined DFT quantum chemical calculations with quantitative structure-property relationship models to predict how the functional groups of emerging organic contaminants govern adsorption to PET microplastics at distinct surface sites, finding that hydrogen-bonding capacity dominates at polar terminal sites while molecular size and multi-point interactions drive binding at non-polar sites.
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