Research on Chemical Intermediates

, Volume 42, Issue 3, pp 2247–2265 | Cite as

Response surface optimization of nitrite removal from aqueous solution by Fe3O4 stabilized zero-valent iron nanoparticles using a three-factor, three-level Box-Behnken design



The magnetite (Fe3O4) stabilized zero-valent iron nanoparticles (Fe3O4-ZVINPs) were synthesized and characterized by TEM, SEM, BET, and XRD techniques and used for removal of NO 2 from aqueous solution. Response surface methodology (RSM) combined with a three-level, three-variable, Box-Behnken design was used to optimize the individual and interactive effects of three different experimentally controlled factors like pH, temperature, and Fe3O4-ZVINPs dose on removal efficiency. The RSM uses a second-order polynomial quadratic model (SOPM) for predicting the optimum point. The analysis of variance has been employed to evaluate the significance of the polynomial model for predicting the optimal conditions of independent process variables to get maximum removal efficiency. Three-dimensional (3D) response surface plots were constructed to visualize the simultaneous interactive effects between two process variables. Regression analysis showed a good fit of the experimental data to the SOPM with a coefficient of determination (R 2) of 0.993 and Fisher F-value of 82.27. All the three factors had a significant impact on removal of NO 2 . The predicted value of model (94.54 mg g−1) was in good agreement with experimental value (93.78 mg g−1) under the optimum conditions of temperature 49.6 °C; pH 4; and dose 0.4 g L−1. The study demonstrated that Fe3O4 in combination with ZVINPs significantly accelerated the NO 2 removal. The removal of NO 2 from synthetic ground water was also investigated at optimum conditions to assess the effect of the other competing ions. The results of the study indicate that Fe3O4-ZVINPs have promising potential to cleanup NO 2 from contaminated water.


Zero-valent iron nanoparticles NO2 removal Box-Behnken design Response surface methodology Regression analysis 



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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Copyright information

© Springer Science+Business Media Dordrecht 2015

Authors and Affiliations

  1. 1.Academy of Scientific and Innovative ResearchNew DelhiIndia
  2. 2.Environmental Chemistry DivisionCSIR-Indian Institute of Toxicology ResearchLucknowIndia

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