Vinayagam, Ramesh and Pai, Shraddha and Murugesan, Gokulakrishnan and Thivaharan, Varadavenkatesan and Selvaraju, Narayanaswamy and Selvaraj, Raja (2022) Magnetic activated charcoal/Fe2O3 nanocomposite for the adsorptive removal of 2,4-Dichlorophenoxyacetic acid (2,4-D) from aqueous solutions: Synthesis, characterization, optimization, kinetic and isotherm studies. Chemosphere, 286. ISSN 0045-6535
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Abstract
Magnetic activated charcoal/Fe2O3 nanocomposite (AC/Fe2O3NC) was fabricated using Spondias dulcis leaf extract by a facile method and used for the adsorptive removal of 2,4-Dichlorophenoxyacetic acid (2,4-D) from aqueous solutions for the first time. The nanocomposite was characterized by methods such as FE-SEM, EDS, XRD, FTIR, TGA, VSM, and BET to identify and confirm the surface morphology, elemental composition, crys�talline nature, functional groups, thermal stability, magnetic behavior, and surface area respectively. Box�Behnken Design (BBD) – an optimization method, which belongs to the Response surface methodology (RSM) and a modeling tool – Artificial Neural Network (ANN) were employed to design, optimize and predict the relationship between the input parameters (pH, initial concentration of 2,4-D, time and agitation speed) versus the output parameter (adsorption efficiency of 2,4-D). Adsorption efficiency of 98.12% was obtained at optimum conditions (pH: 2.05, initial concentration: 32 ppm, contact time: 100 min, agitation speed: 130 rpm, temper�ature: 30 ◦C, and dosage: 0.2 g/L). The predictive ability of the ANN was superior (R2 = 0.99) than the quadratic model, given by the RSM (R2 = 0.93). The equilibrium data were best-fitted to Langmuir isotherm (R2 = 0.9944) and the kinetics obeyed pseudo-second-order model (R2 = 0.9993) satisfactorily. Thermodynamic studies revealed the spontaneity and exothermic nature of adsorption. The maximum adsorption capacity, qm was found
Item Type: | Article |
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Uncontrolled Keywords: | Magnetic nanocomposite 2,4-Dichlorophenoxyacetic acid Spondias dulcis Response surface methodology Artificial neural network Adsorption |
Subjects: | Engineering > MIT Manipal > Biotechnology Engineering > MIT Manipal > Chemical |
Depositing User: | MIT Library |
Date Deposited: | 26 Mar 2022 08:55 |
Last Modified: | 26 Mar 2022 08:55 |
URI: | http://eprints.manipal.edu/id/eprint/158474 |
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