Optimization studies and artificial neural network modeling for pyrene adsorption onto UiO-66(Zr) and NH2-UiO-66(Zr) metal organic frameworks

Zango, Z.U. and Ramli, A. and Jumbri, K. and Sambudi, N.S. and Isiyaka, H.A. and Abu Bakar, N.H.H. and Saad, B. (2020) Optimization studies and artificial neural network modeling for pyrene adsorption onto UiO-66(Zr) and NH2-UiO-66(Zr) metal organic frameworks. Polyhedron, 192. ISSN 02775387

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Abstract

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

Item Type: Article
Additional Information: cited By 20
Depositing User: Mr Ahmad Suhairi UTP
Date Deposited: 10 Nov 2023 03:26
Last Modified: 10 Nov 2023 03:26
URI: https://khub.utp.edu.my/scholars/id/eprint/12411

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