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- W3153791535 abstract "Today, a variety of pesticides are used to fight plant pests in the world. The entry of these resistant pollutants into water resources can have devastating effects on human health and the environment, hence their removal from the environment is a vital task. In the present work, the magnetic iron-based metal–organic framework (Fe3O4/MIL-101 (Fe)) was synthesized by a simple and feasible method and characterized by FT-IR, XRD, BET, FESEM, TEM, TGA, and VSM techniques. The synthesized nanocomposite was successfully applied for the removal of fenitrothion (FEN) pesticide from the aqueous solutions. The isothermal and kinetic models were also investigated. The Langmuir isotherm model (type I) and pseudo-second-order kinetic model were more consistent in the adsorption process. The thermodynamic parameters of fenitrothion sorption were also calculated. The results revealed that the adsorption of fenitrothion onto Fe3O4/MIL-101 (Fe) was spontaneous and endothermic under optimized conditions. Moreover, the removal efficiency of FEN was predicted using the developed fuzzy logic model. Four input variables including the initial concentration of FEN (mg·L−1), pH of the solution, adsorbent dosage (mg), and contact time (min) versus removal efficiency as output were fuzzified by the usage of an artificial intelligence-based method. The fuzzy subsets consisted of Triangular and Trapezoidal membership functions (MFs) with six levels and a total of 23 rules in IF-THEN format which was applied on a Mamdani inference system. The obtained coefficient of determination value (Rpred2=0.98205) proved the excellent accuracy of the fuzzy logic model as a powerful tool for the prediction of FEN removal efficiency." @default.
- W3153791535 created "2021-04-26" @default.
- W3153791535 creator A5028552039 @default.
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- W3153791535 date "2021-12-01" @default.
- W3153791535 modified "2023-09-23" @default.
- W3153791535 title "Modeling of removal of an organophosphorus pesticide from aqueous solution by amagnetic metal–organic framework composite" @default.
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- W3153791535 doi "https://doi.org/10.1016/j.cjche.2020.09.072" @default.
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