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April 3, 2026Materials1 citationsOpen Access

Integrating DFT Computations and QSAR Modeling to Predict Adsorption of Organic Pollutants onto Microplastics in Aqueous Environments

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YWYa WangCLChao LiHYHonghong Yi

Key Points

  • The research aims to understand how organic pollutants adsorb onto microplastics in water and develop predictive models for this process.
  • Used density functional theory (DFT) computations to simulate adsorption of 54 organic compounds on three microplastics.
  • Developed six quantitative structure-activity relationship (QSAR) models using structural descriptors.
  • Analyzed adsorption in both aqueous and gas phases for comparison.
  • POM and PVA microplastics showed weaker adsorption in water than in gas phase.
  • Electron-rich atoms and molecular polarizability significantly affected adsorption on microplastics.
  • Robust QSAR models assist in predicting adsorption energies for various organic pollutants.

Abstract

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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Cite This Study

Wang et al. (2026) studied this question.

synapsesocial.com/papers/69cf5de95a333a821460bf96https://doi.org/10.3390/ma19071403
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