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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.

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Title: 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.
Authors: Wang, Ya1 (AUTHOR), Yi, Honghong1,2 (AUTHOR), Li, Chao2,3 (AUTHOR), Tang, Xiaolong1,4 (AUTHOR), Zhao, Peng1 (AUTHOR), Chen, Zhongfang2,4 (AUTHOR)
Source: Nanomaterials (2079-4991). Feb2026, Vol. 16 Issue 3, p178. 18p.
Subjects: Monte Carlo method, Density functional theory, Physisorption, Environmental risk assessment, QSAR models, Organic water pollutants, Plastic scrap, Computer simulation
Abstract: 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. [ABSTRACT FROM AUTHOR]
Copyright of Nanomaterials (2079-4991) is the property of MDPI and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)
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  Label: Title
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  Data: 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.
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  Data: <searchLink fieldCode="AR" term="%22Wang%2C+Ya%22">Wang, Ya</searchLink><relatesTo>1</relatesTo> (AUTHOR)<br /><searchLink fieldCode="AR" term="%22Yi%2C+Honghong%22">Yi, Honghong</searchLink><relatesTo>1,2</relatesTo> (AUTHOR)<br /><searchLink fieldCode="AR" term="%22Li%2C+Chao%22">Li, Chao</searchLink><relatesTo>2,3</relatesTo> (AUTHOR)<br /><searchLink fieldCode="AR" term="%22Tang%2C+Xiaolong%22">Tang, Xiaolong</searchLink><relatesTo>1,4</relatesTo> (AUTHOR)<br /><searchLink fieldCode="AR" term="%22Zhao%2C+Peng%22">Zhao, Peng</searchLink><relatesTo>1</relatesTo> (AUTHOR)<br /><searchLink fieldCode="AR" term="%22Chen%2C+Zhongfang%22">Chen, Zhongfang</searchLink><relatesTo>2,4</relatesTo> (AUTHOR)
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  Data: <searchLink fieldCode="JN" term="%22Nanomaterials+%282079-4991%29%22">Nanomaterials (2079-4991)</searchLink>. Feb2026, Vol. 16 Issue 3, p178. 18p.
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  Data: <searchLink fieldCode="DE" term="%22Monte+Carlo+method%22">Monte Carlo method</searchLink><br /><searchLink fieldCode="DE" term="%22Density+functional+theory%22">Density functional theory</searchLink><br /><searchLink fieldCode="DE" term="%22Physisorption%22">Physisorption</searchLink><br /><searchLink fieldCode="DE" term="%22Environmental+risk+assessment%22">Environmental risk assessment</searchLink><br /><searchLink fieldCode="DE" term="%22QSAR+models%22">QSAR models</searchLink><br /><searchLink fieldCode="DE" term="%22Organic+water+pollutants%22">Organic water pollutants</searchLink><br /><searchLink fieldCode="DE" term="%22Plastic+scrap%22">Plastic scrap</searchLink><br /><searchLink fieldCode="DE" term="%22Computer+simulation%22">Computer simulation</searchLink>
– Name: Abstract
  Label: Abstract
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  Data: 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. [ABSTRACT FROM AUTHOR]
– Name: AbstractSuppliedCopyright
  Label:
  Group: Ab
  Data: <i>Copyright of Nanomaterials (2079-4991) is the property of MDPI and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract.</i> (Copyright applies to all Abstracts.)
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        Value: 10.3390/nano16030178
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      – Code: eng
        Text: English
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        PageCount: 18
        StartPage: 178
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      – SubjectFull: Monte Carlo method
        Type: general
      – SubjectFull: Density functional theory
        Type: general
      – SubjectFull: Physisorption
        Type: general
      – SubjectFull: Environmental risk assessment
        Type: general
      – SubjectFull: QSAR models
        Type: general
      – SubjectFull: Organic water pollutants
        Type: general
      – SubjectFull: Plastic scrap
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      – SubjectFull: Computer simulation
        Type: general
    Titles:
      – TitleFull: 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.
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            NameFull: Wang, Ya
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            NameFull: Yi, Honghong
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            NameFull: Li, Chao
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            NameFull: Tang, Xiaolong
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            – D: 01
              M: 02
              Text: Feb2026
              Type: published
              Y: 2026
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