Externally predictive quantum-mechanical models for the adsorption of aromatic organic compounds by graphene-oxide nanomaterials

ABSTRACT

Graphene oxide is most often chosen as an alternative to graphene in the applications of carbon-based nanomaterials where adsorption is the primary process. However, its adsorption properties are poorly understood. The existing reports on the adsorption mechanism of graphene oxide rely on the linear free-energy/solvation-energy relationship (LFER/LSER) models. This computational work explores the role of quantum mechanical descriptors in the adsorption of aromatic organic compounds by graphene-oxide. For this, externally predictive quantitative models based on quantum-mechanical descriptors are developed and compared with the existing LSERs for the prediction of adsorption coefficients of organic compounds at three different adsorbate concentrations. The predictivity of the models is assessed using an external prediction set of compounds not used for developing the models. Notably, the mean polarizability, but originating from the quantum mechanical exchange interactions (between electrons of parallel spin), is found to be the most significant factor in driving the adsorption on graphene oxide. The present work also proposes quantum-mechanical-LSER models based on a combination of quantum-mechanical and LSER descriptors, which are in fact found to be equally predictive as the existing LSERs. The quantum-mechanical models proposed in this work are further utilized for the prediction of adsorption coefficients of aliphatic compounds.

KEYWORDS:

• Modelling
• adsorption
• nanomaterials
• LSER
• quantum-mechanical methods

Acknowledgements

One of the authors, Suman Lata is grateful to Council of Scientific and Industrial Research (CSIR) India for providing SRF-NET fellowship. The authors thank Department of Chemistry, Panjab University, Chandigarh, for providing computational software and resources. The authors are grateful to Prof. Paola Gramatica for providing the QSARINS software.

Disclosure statement

No potential conflict of interest was reported by the authors.

Supplementary Material

Supplemental data for this article can be accessed at: https://doi.org/10.1080/1062936X.2019.1666164.

Additional information

Funding

This work was supported by the Council of Scientific and Industrial Research (CSIR) India [CSIR-SRF(NET) fellowship].