Abstract
The present research aims to optimize the removal of phosphate (PO4 3−) from aqueous solution by Fe3O4 stabilized zero-valent iron nanoparticles (Fe3O4–ZVINPs). A three-factor, three-level, Box–Behnken design combined with response surface methodology was applied to design the experiments, to develop a mathematical model, and for evaluating the individual and also the interactive effects of the operating variables like pH, temperature, and PO4 3− concentration on removal efficiency. The analysis of variance has been used to evaluate the adequacy of the developed mathematical model in order to predict the optimal conditions of independent process variables, and to get maximum removal efficiency. Three-dimensional response surface plots were constructed to visualize the simultaneous interactive effects between two process variables. All three factors had a significant impact on removal of PO4 3−. The predicted value of the model (166.0 mg g−1PO4 3−) was in good agreement with the experimental value (164.92 mg g−1 PO4 3−) under the optimum conditions of temperature 49.2 °C; pH 3.5; and PO4 3− concentration 79.8 mg L−1. The removal of PO4 3− in the presence of environmental matrix (other ions) was also investigated at optimum conditions as predicted by the model. The results suggest that the presence of these ions had no significant effect on PO4 3− removal. In addition, the adsorbed PO4 3− can be effectively desorbed at higher pH of the solution. The findings suggest that removal of PO4 3− from aqueous solution using Fe3O4–ZVINPs can be an effective method.
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Acknowledgments
The authors are grateful to the director, CSIR-Indian Institute of Toxicology Research, Lucknow (India) for his keen interest in this work and for providing the necessary facilities. Arun K. Singh gratefully acknowledges CSIR (Council of Scientific and Industrial Research) for awarding the senior research fellowship to him.
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Singh, A.K., Singh, K.P. Evaluation of phosphate removal capacity of Fe3O4–ZVINPs from aqueous solution: optimization using response surface analysis. Res Chem Intermed 42, 7397–7415 (2016). https://doi.org/10.1007/s11164-016-2543-6
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DOI: https://doi.org/10.1007/s11164-016-2543-6