Adsorptive removal of micropollutants by natural and faujasite zeolites: Structural effect of micropollutants on adsorption.

To effectively characterize natural zeolite powder (ZP) and faujasite zeolite (FAU) as adsorbents to remove a wide variety of organic micropollutants, quantitative structure-activity relationship (QSAR) models for the adsorption of zeolites were developed. For this purpose, batch isotherms were performed to measure the adsorption affinity (K) between zeolite and organic micropollutants, and the measured K values were used as a dependent variable in the QSAR modeling. In the modeling, the concept of a linear free energy relationship (LFER) was employed and used either empirically measured or in silico calculated descriptors.

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.

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.

Adsorption modeling of microcrystalline cellulose for pharmaceutical-based micropollutants.

Cellulose can be considered as a raw material for the production of filters and adsorbents for the removal of micropollutants, particularly in pharmaceutical-based products. To study its applications, it is important to estimate the adsorptive interaction of cellulose with the targeted chemicals, and develop predictive models for the expandable estimation into various types of micropollutants. Therefore, the adsorption affinity between cellulose and micropollutants was measured through isotherm experiments, and a quantitative structure-adsorption relationship model was developed using the linear free energy relationship (LFER) equation.