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.

在吸附是主要过程的各种应用中，碳纳米管（CNTs）优先于活性炭。碳纳米管和活性炭对化合物的吸附通常是通过线性自由能/溶剂能关系（LFERs / LSERs）预测的。这项工作提出了一个基于LSERs的量子力学描述子和溶剂变色描述子组合的量子力学LSER模型，用于预测活性炭在不同吸附物浓度下对芳族有机化合物的吸附。使用最新的方法（使用化合物的外部预测集）对模型进行验证。这项工作揭示了化合物的氢键给予和接受能力是活性炭吸附过程中影响最大但不利的因素。分析了这项工作中提出的量子力学LSER，发现它们与现有LSER一样可靠。这些被进一步用于预测核碱基，类固醇激素，农用化学品，内分泌干扰物和药物的吸附。值得注意的是，与碳纳米管吸附相比，农用化学品和内分泌干扰物预计会更强地被活性炭吸附。但是，量子力学LSER预测生物分子在活性炭上的吸附强度与在CNT上的相似，这可用于评估与使用碳材料有关的风险。内分泌干​​扰物和药物。值得注意的是，与碳纳米管吸附相比，农用化学品和内分泌干扰物预计会更强地被活性炭吸附。但是，量子力学LSER预测生物分子在活性炭上的吸附强度与在CNT上的相似，这可以用来评估与使用碳材料有关的风险。内分泌干​​扰物和药物。值得注意的是，与碳纳米管吸附相比，农用化学品和内分泌干扰物预计会更强地被活性炭吸附。但是，量子力学LSER预测生物分子在活性炭上的吸附强度与在CNT上的相似，这可用于评估与使用碳材料有关的风险。