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In Silico Models for Predicting Adsorption of Organic Pollutants on Atmospheric Nanoplastics by Combining Grand Canonical Monte Carlo/Density Functional Theory and Quantitative Structure Activity Relationship Approach
Summary
This computational study used molecular simulations and machine learning to build predictive models for how 48 different organic pollutants — including flame retardants — adsorb onto 12 types of atmospheric nanoplastics. The resulting models revealed that van der Waals and electrostatic forces dominate these interactions, and a single multi-dimensional model can rapidly screen thousands of pollutant-nanoplastic combinations at once. Accurate adsorption predictions are crucial for assessing how nanoplastics carry toxic chemicals through the atmosphere and into ecosystems or human lungs.
Estimating the adsorption data and understanding the adsorption behavior and mechanism of organic pollutants on nanoplastics are crucial for assessing their ecological risks. Herein, in silico techniques, i.e., grand canonical Monte Carlo simulations, density functional theory computations, and quantitative structure activity relationship (QSAR) modeling, were integrated to examine the adsorption of 39 representative aliphatic and aromatic compounds and nine emerging pollutants (brominated flame retardants and phosphorus flame retardants) onto 12 different nanoplastics under atmospheric conditions. Three QSAR models were constructed to predict the adsorption equilibrium constant (logK) for polyethylene, polyoxymethylene, and polyvinyl alcohol nanoplastics individually, along with 12 QSAR models for separately estimating adsorption capacities (Cm) on different nanoplastics. Furthermore, a novel multi-dimensional prediction model was developed, enabling simultaneous, high-throughput prediction of adsorption capacities across multiple nanoplastics and pollutants with a single input. These results revealed that van der Waals and electrostatic interactions serve as the primary driving forces for the adsorption. The novel multi-dimensional prediction model facilitates rapid and comprehensive assessment of pollutant-nanoplastic interactions with one-click, and paves the way for improved risk evaluations and advancing predictive environmental research.
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