%R 10.1016/j.poly.2020.114857
%V 192
%T Optimization studies and artificial neural network modeling for pyrene adsorption onto UiO-66(Zr) and NH2-UiO-66(Zr) metal organic frameworks
%J Polyhedron
%I Elsevier Ltd
%D 2020
%A Z.U. Zango
%A A. Ramli
%A K. Jumbri
%A N.S. Sambudi
%A H.A. Isiyaka
%A N.H.H. Abu Bakar
%A B. Saad
%X Optimization studies was conducted for the pyrene (PYR) adsorption onto Zr-based metal organic frameworks (MOFs), UiO-66(Zr) and NH2-UiO-66(Zr) in aqueous medium. Central composite design (CCD) model has shown good fittings of the coefficient of determination (R2) with non-significant lack of fit for both UiO-66(Zr) and NH2-UiO-66(Zr) MOFs. The optimized adsorption efficiency achieved by the UiO-66(Zr) and NH2-UiO-66(Zr) were 99.22 and 95.67 respectively. Artificial neural network (ANN) model was able to predict the experimental findings with high precision at topographic node of 5-4-2 structural layer. The kinetics and isotherms of the process was best described by pseudo-second-order Langmuir models respectively. The process was exothermic and spontaneous with the good reusability of the MOFs. © 2020 Elsevier Ltd
%O cited By 20
%L scholars12411