Predicting adsorption of organic pollutants onto graphene nanomaterials is not only useful for exploring their potential adsorbent applications, but also helpful for better understanding their fate and risks in aquatic environments. Herein molecular dynamics (MD) simulations and theoretical linear solvation energy relationships (TLSERs) were employed to construct prediction models for adsorption of neutral organic pollutants onto graphene and graphene oxides. The MD simulations for adsorption of 43 aromatic compounds onto graphene and diverse models of graphene oxides with various functional groups (hydroxyl, epoxy and carbonyl) demonstrate that graphene has a stronger affinity for the aromatic compounds than graphene oxides. The hydroxyl and carbonyl groups of graphene oxides were found to form hydrogen bonds with the aromatic adsorbates, while epoxy groups did not. TLSER models were developed for predicting the adsorption equilibrium coefficients ( K ) onto graphene and graphene oxide nanosheets. In the graphene prediction model, H-donating ability ( ε α ) and dispersion/hydrophobic interactions ( V ) have significant effects on log K values, while in the graphene oxide model, ε α is the most influential factor on log K values. The models provide in silico approaches for predicting adsorption affinities onto graphenic nanomaterials.
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Predicting the adsorption of organic pollutants on boron nitride nanosheets via in silico techniques: DFT computations and QSAR modeling
Investigating the adsorption of organic pollutants onto boron nitride nanosheets is crucial for designing novel boron nitride adsorbents so as to remove pollutants from the environment. In this study, we performed density functional theory (DFT) computations to investigate the adsorption of 28 aromatic compounds onto boron nitride nanosheets, and developed four quantitative structure–activity relationship (QSAR) models for predicting the logarithm of the adsorption equilibrium constant (log K ) values of organic pollutants adsorbed onto boron nitride nanosheets in both gaseous and aqueous environments. The DFT-predicted adsorption energies showed that boron nitride nanosheets exhibit stronger adsorption capability than graphene. Our QSAR analyses revealed that van der Waals interactions play dominant roles in gaseous adsorption, while van der Waals and hydrophobic interactions are the main driving forces in aqueous adsorption. This work demonstrates that in silico QSAR models can serve as efficient tools for high-throughput prediction of log K values for organic pollutants adsorbed onto boron nitride nanomaterials.
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- Award ID(s):
- 1849243
- NSF-PAR ID:
- 10221170
- Date Published:
- Journal Name:
- Environmental Science: Nano
- Volume:
- 8
- Issue:
- 3
- ISSN:
- 2051-8153
- Page Range / eLocation ID:
- 795 to 805
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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- Free Publicly Accessible Full Text
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Accepted Manuscript1.0
- Journal Article:
- https://doi.org/10.1039/D0EN01145B
