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.
Keywords: Biosorbent; Emerging contaminant; Ions; Prediction.
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