Quantum-mechanical LSERs for the concentration-dependent adsorption of aromatic organic compounds by activated carbon: Applications and comparison with carbon nanotubes

ABSTRACT

Carbon nanotubes (CNTs) have taken precedence over activated carbon in various applications where adsorption is the primary process. The adsorption of chemical compounds by CNTs and activated carbon is most often predicted through linear free energy/solvation energy relationships (LFERs/LSERs). This work proposes quantum-mechanical LSER models based on a combination of quantum-mechanical descriptors and solvatochromic descriptors of LSERs for predicting the adsorption of aromatic organic compounds by activated carbon at varying adsorbate concentrations. The models are validated using state-of-the-art procedures employing an external prediction set of compounds. This work reveals the hydrogen bond donating and accepting ability of compounds to be the most influencing – but a negative – factor in the adsorption process of activated carbon. The quantum-mechanical LSERs proposed in this work are analysed and found to be equally reliable as the existing LSERs. These were further used to predict the adsorption of nucleobases, steroid hormones, agrochemicals, endocrine disruptors and pharmaceutical drugs. Notably, agrochemicals and endocrine disruptors are predicted to be adsorbed more strongly by activated carbon when compared with their adsorption by CNTs. However, quantum-mechanical LSERs predict the adsorption strength of biomolecules on activated carbon to be similar to that on the CNTs, which can be used to assess the risk associated with using carbon materials.

KEYWORDS:

• Adsorption
• activated carbon
• quantum-mechanical descriptors
• risk assessment

Acknowledgement

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

Disclosure statement

No potential conflict of interest has been declared by the authors.

Supplementary material

Supplementary material contains Tables S1–S17 at five different adsorbate concentrations: 10⁻¹, 10⁻², 10⁻³, 10⁻⁴ and 10⁻⁵C_s, Table S18–S23 for biomolecules, agrochemicals and pharmaceutical drugs; Figures S1 and S2, respectively, for scatter plots and Williams plots, of best single-descriptor models at different concentrations.

Additional information

Funding

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