a Quantum Chemistry Group, Department of Chemistry and Centre of Advanced Studies in Chemistry, Panjab University , Chandigarh , India.
SAR QSAR Environ Res. 2019 Feb;30(2):109-130. doi: 10.1080/1062936X.2019.1566173. Epub 2019 Feb 7.
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.
碳纳米管（CNTs）在各种吸附是主要过程的应用中已经优先于活性炭。通过线性自由能/溶剂化能关系（LFER/LSER），通常可以预测 CNTs 和活性炭对化学化合物的吸附。这项工作提出了基于量子力学描述符和 LSER 溶剂化描述符组合的量子力学 LSER 模型，用于预测不同吸附物浓度下活性炭对芳香族有机化合物的吸附。该模型使用具有化合物外部预测集的最先进程序进行验证。这项工作揭示了化合物的氢键供体和受体能力是活性炭吸附过程中最具影响力的因素，但却是一个负面因素。本工作中提出的量子力学 LSER 经过分析，被发现与现有的 LSER 同样可靠。这些模型进一步用于预测核苷、甾体激素、农药、内分泌干扰物和药物的吸附。值得注意的是，与 CNTs 相比，农药和内分泌干扰物被预测为更强烈地被活性炭吸附。然而，量子力学 LSER 预测生物分子在活性炭上的吸附强度与在 CNTs 上的吸附强度相似，这可以用于评估使用碳材料相关的风险。
