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

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.

Modeling for the estimating the adsorption property of fruit waste-based biosorbents for the removal of organic micropollutants.

The enormous production of fruit waste and the generation of countless organic micropollutants are serious environmental problems. To solve the problems, the biowastes, i.e., orange, mandarin, and banana peels, were used as biosorbents to remove the organic pollutants. In this application, the difficult challenge is knowing the degree of adsorption affinity of biomass for each type of micropollutant. However, since there are numerous micropollutants, it requires enormous material consumption and labor to physically estimate the adsorbability of biomass. To address this limitation, quantitative structure-adsorption relationship (QSAR) models for the adsorption assessment were established. In this process, the surface properties of each adsorbent were measured with instrumental analyzers, their adsorption affinity values for several organic micropollutants were determined through isotherm experiments, and QSAR models for each adsorbent were developed. The results showed that the tested adsorbents had significant adsorption affinity for cationic and neutral micropollutants, while the anionic one had low adsorption. As a result of the modeling, it was found that the adsorption could be predicted for a modeling set with an R2 of 0.90-0.915, and the models were validated via the prediction of a test set that was not included in the modeling set. Also, using the models, the adsorption mechanisms were identified. It is speculated that these developed models can be used to rapidly estimate adsorption affinity values for other micropollutants.

Adsorption of organic micropollutants on yeast: Batch experiment and modeling.

Yeast is ubiquitous and may act as a solid phase in natural aquatic systems, which may affect the distribution of organic micropollutants (OMs). Therefore, it is important to understand the adsorption of OMs on yeast. Therefore, in this study, a predictive model for the adsorption values of OMs on the yeast was developed. For that, an isotherm experiment was performed to estimate the adsorption affinity of OMs on yeast (i.e., Saccharomyces cerevisiae). Afterwards, quantitative structure-activity relationship (QSAR) modeling was performed for the purpose of developing a prediction model and explaining the adsorption mechanism. For the modeling, empirical and in silico linear free energy relationship (LFER) descriptors were applied. The isotherm results showed that yeast adsorbs a wide range of OMs, but the magnitude of Kd strongly depends on the types of OMs. The measured log Kd values of the tested OMs ranged from -1.91 to 1.1. Additionally, it was confirmed that the Kd measured in distilled water is comparable to that measured in real anaerobic or aerobic wastewater (R2 = 0.79). In QSAR modeling, the Kd value could be predicted by the LFER concept with an R2 of 0.867 by empirical descriptors and an R2 of 0.796 by in silico descriptors. The adsorption mechanisms of yeast for OMs were identified in individual correlations between log Kd and each descriptor: Dispersive interaction, hydrophobicity, hydrogen-bond donor, and cationic Coulombic interaction of OMs attract the adsorption, while the hydrogen-bond acceptor and anionic Coulombic interaction of OMs act as repulsive forces. The developed model can be used as an efficient method to estimate OM adsorption to yeast at a low level of concentration.