Linear solvation energy relationship to predict the adsorption of aromatic contaminants on graphene oxide. (October 2017)

• Record Type: Journal Article
• Authors: Shan, Sujie; Zhao, Ying; Tang, Huan; Cui, Fuyi
• Is Part Of: Chemosphere. Volume 185(2017)
• Page Start: 826
• Page End: 832
• Publication Date: 2017-10
• Subjects: LSER model -- Aromatic contaminants -- Adsorption -- Graphene oxide; Pollution -- Periodicals; Pollution -- Physiological effect -- Periodicals; Environmental sciences -- Periodicals; Atmospheric chemistry -- Periodicals; 551.511
• DOI: 10.1016/j.chemosphere.2017.07.062
• Languages: English
• ISSNs: 0045-6535

Abstract

Abstract: In this study, adsorption capability of aromatic contaminants on graphene oxide (GO) was predicted using linear solvation energy relationship (LSER) model for the first time. Adsorption data of 44 aromatic compounds collected from literature and our experimental results were used to establish LSER models with multiple linear regression. High value of R 2 (0.919), strong robustness (QLoo 2 = 0.862), and desirable predictability (Qext 2 = 0.834) demonstrated the model worked well for predicting the adsorption of small aromatic contaminants (descriptor V ＜3.099) on GO. The adsorption process was governed by the ability of cavity formation and dispersion forces captured by vV and hydrogen-bond interactions captured by bB . Effect of equilibrium concentrations and properties of GO on the model were explored; and the results indicated that upon an increase of equilibrium concentration, the values of regression coefficients ( a, b, v, e, and s ) changed at different levels. The oxygen content normalization of log K 0.001 decreased the value of b dramatically; however, no obvious changes of the model deduced by the surface area normalization of log K 0.001 were witnessed. Overall, our study showed that LSER model provided a potential approach for exploring the adsorption of organic compounds on GO. Graphical abstract: Highlights: LSER model was employed to predict the adsorption capability of aromatic contaminants on graphene oxide for the first time. The model we established performed well with high fitness and predictability. The cavity formation and dispersion forces, and hydrogen-bond interactions were the predominant mechanisms controlling the adsorption of aromatic contaminants by graphene oxide.