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• Chitin and chitosan adsorb several non-steroidal anti-inflammatory pharmaceuticals. • Adsorption of chitin for organic pollutants are correlated with that of chitosan. • Adsorption properties of chitin and chitosan can be predicted by LFER modeling. • Main adsorptive interactions of chitin and chitosan are H-bonding and ionic forces. • Effect of impurities in chitin on adsorption was negligible. Chitin and its derivative, chitosan, are common bioresources and possess well-developed functional groups; therefore, they have been used as adsorbents to remove micropollutants during wastewater treatment. To efficiently use them in the removal process, it is necessary to understand and predict the adsorption properties of chitin and chitosan for micropollutants because there are numerous types of micropollutants. Therefore, we measured the adsorption affinities of raw types of chitin and chitosan for approximately 30 types of micropollutants in neutral or ionic forms. Then, the measured properties were predicted using experimentally determined and theoretically calculated linear free energy relationship (LFER) descriptors. The experimental results revealed that the adsorption affinities depended on the chemical structures of the micropollutants, and of chitin and chitosan possess similar adsorption properties. Next, the relationships between the adsorption affinities of the adsorbents and quantitative structural properties of the adsorbates were analyzed using experimental and in silico calculated LFER descriptors. The results indicated that the experimental descriptors could predict the properties of chitin and chitosan within R2 of 0.832 and 0.827, respectively; moreover, the calculated descriptors could predict the properties of chitin and chitosan within R2 of 0.812 and 0.867, respectively. In addition, since chitin obtained from the red crab contains some impurities, the effect of impurities on adsorption after washing it with methanol and water was investigated. The results showed that the effect of impurities on adsorption was negligible. Using the developed models, based on the measured descriptors, it was identified that the main driving forces of chitin and chitosan were H-bonding donor moment and ionic electrostatic attraction. [ABSTRACT FROM AUTHOR]

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. The results indicate that microcrystalline cellulose has a remarkably high adsorption affinity with cationic micropollutants. Moreover, it has interactions with neutral and anionic micropollutants, although they have relatively lower affinities than those of cations. Through a modeling study, an LFER model - comprising of excess molar refraction, polar interaction, molecular volume, and charge-related terms - was developed, which could be used to predict the adsorption affinity values with an R2 of 0.895. To verify the robustness and predictability of the model, internal and external validation studies were performed. The results proved that the model was reasonable and acceptable, with an SE = 0.207 log unit. [Display omitted] • Non-pretreated microcrystalline cellulose adsorbs pharmaceutical-based emerging contaminants. • Adsorption of cellulose for micropollutants could be predicted using a LFER model. • In silico descriptors could be used to predict adsorption of cellulose. • Main driving force for adsorption of molecules on cellulose is the Coulombic interaction. [ABSTRACT FROM AUTHOR]

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 K d strongly depends on the types of OMs. The measured log K d values of the tested OMs ranged from −1.91 to 1.1. Additionally, it was confirmed that the K d measured in distilled water is comparable to that measured in real anaerobic or aerobic wastewater (R2 = 0.79). In QSAR modeling, the K d 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 K d 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. • Adsorption affinity between 42 organic micropollutants and yeast was determined. • A model for predicting the adsorption of yeast to organic micropollutants was developed. • Adsorption can be predicted with high accuracy using empirical and in silico parameters. • Adsorption of OMs on yeast was identified at the molecular level. [ABSTRACT FROM AUTHOR]