Concentration dependent adsorption of aromatic organic compounds by SWCNTs: Quantum-mechanical descriptors for nano-toxicological studies of biomolecules and agrochemicals

Abstract

For evaluating the environmental risk associated using carbon nanotubes (CNTs), a successful prediction is desired for the adsorption of organic compounds (OCs) by CNTs at different adsorbate concentrations. This is most often achieved through poly-parameter linear solvation energy relationships (LSERs) based on solvatochromic descriptors. This study examines the real predictivity of the existing LSERs for predicting the adsorption of OCs by single-walled CNTs (SWCNTs) while comparing it with that of the models developed in the present work using quantum-mechanical descriptors. The real predictivity of the quantum-mechanical models and existing LSERs is compared using state-of-the-art statistical procedures employing an external prediction set of compounds not used in the model development. The quantum-mechanically computed mean polarizability, but originating from the interactions between electrons of parallel spin, is found to play an essential role in the adsorption of OCs by SWCNTs. Besides the solvatochromic descriptors (McGowan volume and molar excess refractivity), the instantaneous inter-electronic interactions, captured through electron-correlation based quantum-mechanical descriptors, are found to significantly affect the adsorption at varying adsorbate concentration. The models developed using a combination of quantum-mechanical and solvatochromic descriptors are found to be quite reliable. The models proposed were further employed to predict the adsorption of agrochemicals such as insecticides, pesticides, herbicides, as well as adsorption of endocrine disruptors and biomolecules such as nucleobases and steroid hormones. These are predicted to be strongly adsorbed by SWCNTs with Progesterone and Guanine exhibiting maximal interaction with the SWCNTs among biomolecules. The quantum-mechanical descriptors proposed in this work can be used for the risk assessment of SWCNTs in systems where adsorption is the primary process.