Abstract

Design and assembly of a novel nanocomposite with efficient incorporated functional groups as well as superior adsorption behavior is a challenging aspect and generally aimed in the research and application fields which are focused on removal of organic, inorganic and biological pollutants. Appreciable amounts of colored wastewater are produced due to the existence of some dyes in water even at very low concentration levels which are known as highly toxic and undesirable pollutants. Therefore, the current study is devoted to assemble a novel nanocomposite for removal Congo red (CR) dye based on the intercalation of graphene oxide (GO) with amino-nanomagnetite (Fe3O4-NH2) and amino-nanomanganese dioxide (MnO2-NH2) for the fabrication of NH2-Fe3O4-GO-MnO2-NH2 by microwave assisted synthesis technique. The as-prepared NH2-Fe3O4-GO-MnO2-NH2 nanocomposite was explored for removal of Congo red (CR, anionic dye) from aqueous matrices. The produced NH2-Fe3O4-GO-MnO2-NH2 along with its constituent counterparts was investigated by FT-IR, SEM, BET, XRD and TGA for characterization and structure confirmation. Experimental studies were performed to figure out the impact of pH and CR dye was found to reach the maximum removal efficiency at pH 2. The variation in NH2-Fe3O4-GO-MnO2-NH2 dosage, shaking time, temperature and initial dye concentration were also optimized to achieve the maximum removal percentages of CR. The best fitting of kinetic model was referred to pseudo-second order. The linear and nonlinear isotherm models to evaluate the CR removal performance by NH2-Fe3O4-GO-MnO2-NH2 were examined and found to best fitted to Langmuir model. Thermodynamic parameters, viz., ΔHo, ΔGo, and ΔSo were also estimated to confirm spontaneous and endothermic adsorption behaviors. Excellent CR dye extraction percentages (96–100%) were achieved from various water samples by applying the optimum conditions. Consequently, NH2-Fe3O4-GO-MnO2-NH2 nanocomposite is believed to exhibit high potential in water remediation for dye removal.

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