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Optimization of the photocatalytic degradation of phenol using superparamagnetic iron oxide (Fe3O4) nanoparticles in
Edris Bazrafshan1,2, Leili Mohammadi3, Amin Allah Zarei1,2
1Health Sciences Research Center, Torbat Heydariyeh University of Medical Sciences Torbat Heydariyeh 33787 95196 Iran.
Abstract:
The present work was carried out to remove phenol from aqueous medium using a photocatalytic process with superparamagnetic iron oxide nanoparticles (Fe3O4) called SPIONs. The photocatalytic process was optimized using a central composite design based on the response surface methodology. The effects of pH (3-7), UV/SPION nanoparticles ratio (1-3), contact time (30-90 minutes), and initial phenol concentration (20-80 mg L-1) on the photocatalytic process were investigated. The interaction of the process parameters and their optimal conditions were determined using CCD. The statistical data were analyzed using a one-way analysis of variance. We developed a quadratic model using a central composite design to indicate the photocatalyst impact on the decomposition of phenol. There was a close similarity between the empirical values gained for the phenol content and the predicted response values. Considering the design, optimum values of pH, phenol concentration, UV/SPION ratio, and contact time were determined to be 3, 80 mg L-1, 3, and 60 min, respectively; 94.9% of phenol was eliminated under the mentioned conditions. Since high values were obtained for the adjusted R2 (0.9786) and determination coefficient (R2 = 0.9875), the response surface methodology can describe the phenol removal by the use of the photocatalytic process. According to the one-way analysis of variance results, the quadratic model obtained by RSM is statistically significant for removing phenol. The recyclability of 92% after four consecutive cycles indicates the excellent stability of the photocatalyst for practical applications. Our research findings indicate that it is possible to employ response surface methodology as a helpful tool to optimize and modify process parameters for maximizing phenol removal from aqueous solutions and photocatalytic processes using SPIONs.

