Abstract
This study investigated the effectiveness of ionic liquids (ILs) loaded onto the surface of a polymeric adsorbent (βCD-TDI) grafted with modified magnetic nanoparticles (MNPs) via an analysis of water treatment, which resulted in high removal of selected endocrine-disrupting chemicals (parabens). The syntheses of MNPs, MNP-βCD-TDI, and IL-MNP-βCD-TDI were characterised and compared using Fourier transform infrared (FT-IR) spectroscopy, carbon-hydrogen-nitrogen (CHN) analysis, vibrating sample magnetometry (VSM), scanning electron microscopy (SEM), transmission electron microscopy (TEM), the Brunauer-Emmett-Teller (BET) method, thermogravimetric analysis (TGA), and X-ray diffraction (XRD). The results of SEM and TEM indicated that the pore size distribution exhibited mesoporous characteristics with a small surface area (BET analysis: 42.95 m2 g-1). Furthermore, a preliminary sorption experiment demonstrated the ability of IL-MNP-βCD-TDI to enhance not only the sorption capacity, but also the removal of propyl paraben (PP), butyl paraben (BP), and benzyl paraben (ArP). The adsorption process appeared to be pH-dependent, and hence the optimum pH of 6 was selected for a subsequent batch adsorption study of all the studied parabens with an equilibrium time of 80 min. Next, in an attempt to investigate the interactions that occur between the adsorbent and the adsorbates, adsorption kinetics and isotherm studies were performed. All the studied parabens were found to best fit pseudo-second-order kinetics and the Freundlich isotherm with R 2 > 0.98 at room temperature (298 K). The interaction of the host-guest inclusion complex and the π-π interaction between βCD and a selected paraben compound (ArP) were identified by performing 1H nuclear magnetic resonance (NMR), together with ultraviolet-visible (UV-vis) spectroscopic analysis. Finally, the adsorption efficiency of the developed material was practically tested on tap water, drain water, and industrial wastewater, which revealed a significant removal of parabens of up to 60-90% in comparison with a prior analysis.