Investigation of the adsorption affinity of organic micropollutants on seaweed and its QSAR study

Abstract

Seaweed, one of the most abundant biomaterials, can be used as a biosorbent to remove organic micropollutants. In order to effectively use seaweed to remove a variety of micropollutants, it is vital to rapidly estimate the adsorption affinity according to the types of micropollutants. Thus, the isothermal adsorption affinities of 31 organic micropollutants in neutral or ionic form on seaweed were measured, and a predictive model using quantitative structure-adsorption relationship (QSAR) modeling was developed. As a result, it was found that the types of micropollutants had a significant effect on the adsorption of seaweed, as expected, and QSAR modeling with a predictability (R²) of 0.854 and a standard error (SE) of 0.27 log units using a training set could be developed. The model's predictability was internally and externally validated using leave-one-out cross validation and a test set. Its predictability for the external validation set was R² = 0.864, SE = 0.171 log units. Using the developed model, we identified the most important driving forces of the adsorption at the molecular level: Coulomb interaction of the anion, molecular volume, and H-bond acceptor and donor, which significantly affect the basic momentum of molecules on the surface of seaweed. Moreover, in silico calculated descriptors were applied to the prediction, and the results revealed reasonable predictability (R² of 0.944 and SE of 0.17 log units). Our approach provides an understanding of the adsorption process of seaweed for organic micropollutants and an efficient prediction method to estimate the adsorption affinities of seaweed and micropollutants in neutral and ionic forms.

Keywords: Adsorption; In silico; Micropollutants; Prediction; Removal; Seaweed.