@article{scholars12411,
             doi = {10.1016/j.poly.2020.114857},
       publisher = {Elsevier Ltd},
            note = {cited By 20},
           title = {Optimization studies and artificial neural network modeling for pyrene adsorption onto UiO-66(Zr) and NH2-UiO-66(Zr) metal organic frameworks},
            year = {2020},
          volume = {192},
         journal = {Polyhedron},
             url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-85093927924&doi=10.1016\%2fj.poly.2020.114857&partnerID=40&md5=a4d4aa27d3698cae58328dbed8eadaee},
          author = {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.},
            issn = {02775387},
        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. {\^A}{\copyright} 2020 Elsevier Ltd}
}