Experimental and Modeling of Dicamba Adsorption in Aqueous Medium Using MIL-101(Cr) Metal-Organic Framework

Drift deposition of emerging and carcinogenic contaminant dicamba (3,6-dichloro-2-methoxy benzoic acid) has become a major health and environmental concern. Effective removal of dicamba in aqueous medium becomes imperative. This study investigates the adsorption of a promising adsorbent, MIL-101(Cr) metal-organic framework (MOF), for the removal of dicamba in aqueous solution. The adsorbent was hydrothermally synthesized and characterized using N-2 adsorption-desorption isotherms, Brunauer, Emmett and Teller (BET), powdered X-ray diffraction (XRD), Fourier Transformed Infrared (FTIR) and field emission scanning electron microscopy (FESEM). Adsorption models such as kinetics, isotherms and thermodynamics were studied to understand details of the adsorption process. The significance and optimization of the data matrix, as well as the multivariate interaction of the adsorption parameters, were determined using response surface methodology (RSM). RSM and artificial neural network (ANN) were used to predict the adsorption capacity. In each of the experimental adsorption conditions used, the ANN gave a better prediction with minimal error than the RSM model. The MIL-101(Cr) adsorbent was recycled six times to determine the possibility of reuse. The results show that MIL-101(Cr) is a very promising adsorbent, in particular due to the high surface area (1439 m(2) g(-1)), rapid equilibration (similar to 25 min), high adsorption capacity (237.384 mg g(-1)) and high removal efficiency of 99.432%.

Automated summary

Drift deposition of the emerging and carcinogenic contaminant dicamba has raised concerns, leading to the need for effective removal methods. This study investigated the adsorption of MIL-101(Cr) metal-organic framework for dicamba removal in aqueous solution, showing promising results with high adsorption capacity and efficiency. The use of response surface methodology and artificial neural network allowed for accurate prediction of adsorption capacity.