application of response surface methodology to optimize the process variables for fluoride ion removal using maghemite nanoparticles

Journal of Saudi Chemical Society (2013) xxx, xxx–xxx King Saud University Journal of Saudi Chemical Society www.ksu.edu.sa www.sciencedirect.com ORIGINAL ARTICLE Application of response surface methodology to optimize the process variables for fluoride ion removal using maghemite nanoparticles Ali Fakhri * Department of Chemistry, Shahre-Qods Branch, Islamic Azad University, Tehran, Iran KEYWORDS Adsorption; Fluoride; Box–Behnken design; Maghemite (c-Fe2O3) nanoparticles; Thermodynamics studies Abstract Adsorption of fluoride ion was done from its aqueous solution by using maghemite (c-Fe2O3) nanoparticles Effects of the major independent variables (temperature, adsorbent dose and pH) and their interactions during fluoride ion adsorption were determined by response surface methodology (RSM) based on three-level three-factorial Box–Behnken design (BBD) Optimized values of temperature, maghemite nanoparticle dose and pH for fluoride sorption were found as 313 K, 0.5 g/L, and 4, respectively In order to investigate the mechanism of fluoride removal, various adsorption isotherms such as Langmuir, Freundlich, Temkin and Florry–Huggins were fitted The experimental data revealed that the Langmuir isotherm gave a more satisfactory fit for fluoride removal The adsorption process was rapid and obeyed pseudo-second-order kinetics The values of thermodynamic parameters DG°, DH° and DS° indicated that adsorption was spontaneous and endothermic in nature ª 2013 King Saud University Production and hosting by Elsevier B.V All rights reserved Introduction Fluoride is a health affecting substance The acceptable fluoride concentration in drinking water is generally in the range of 0.5–1.5 mg/L [38] The natural presence of fluoride generally occurs through soil and rock formation in the form of fluorapatite, fluorspar and amphiboles, geochemical deposits, natural water systems and earth’s crust [29,32] In addition to this * Tel./fax: +98 (21)22873079 E-mail address: ali.fakhri88@yahoo.com Peer review under responsibility of King Saud University Production and hosting by Elsevier fluoride can also be found in various industrial activities, specially semiconductor, electroplating, glass, steel, ceramic and fertilizer industries [31] Therefore higher fluoride concentration causes severe harmful effects in aquatic life as well as in human bodies Excess intake of fluoride by human beings may lead to dental caries, bone fluorosis, and lesions of the thyroid, endocrine glands, and brain [32] Because of these reasons the pollution of water by fluoride has been a major concern The problems associated with fluoride ion pollution could be reduced or minimized by precipitation, ultra-filtration, electrode-deposition, reverse osmosis, etc., but these processes have high cost and poor removal efficiency Adsorption has been found to be an effective and economic method with high potential for the removal, recovery and recycle of fluoride ions from wastewater [4], although desorption is an issue In the past decade, the synthesis of spinel magnetite 1319-6103 ª 2013 King Saud University Production and hosting by Elsevier B.V All rights reserved http://dx.doi.org/10.1016/j.jscs.2013.10.010 Please cite this article in press as: A Fakhri, Application of response surface methodology to optimize the process variables for fluoride ion removal using maghemite nanoparticles, Journal of Saudi Chemical Society (2013), http://dx.doi.org/10.1016/j.jscs.2013.10.010 and maghemite nanoparticles has been intensively developed not only for its great fundamental scientific interest but also for many technological applications in biology [39,9], medical applications [23], bioseparation [8,16], separation and preconcentration of various anions and cations [33] and pollutant removal [34,36,37] due to their structural, electronic, magnetic and catalytic properties Nano-iron oxides, such as magnetite (Fe3O4) and maghemite (c-Fe2O3), and also different ferrite compounds have unique magnetic and electronic properties Due to their chemical stability, biocompatibility and heating ability, ferrofluids of maghemite nanoparticles can be used for ferrofluids hyperthermia in tumor treatment [15,30] Response surface modeling (RSM) is an empirical statistical technique that uses quantitative data obtained from appropriately designed experiments to determine regression model and operating conditions [20,6,18,7,12,26,28,40] RSM is a technique to design factorial experiments, in order to build mathematical models which allow one to assess the effects of several factors onto a desired response It is suitable for multi-factor experiments and searches the common relationship between various factors for the most favorable conditions of the processes This paper is mainly concerned with the investigation of a combined effect of various process parameters like adsorbent dose, temperature and pH of the solution on removal of fluoride ion from aqueous medium by maghemite nanoparticles using Box–Behnken model experimental design in Response Surface Methodology (RSM) by Design Expert Version 6.0.10 (Stat Ease, USA) Materials and methods 2.1 Raw materials Iron(II) chloride (FeCl2), Iron(III) chloride (FeCl3) and Sodium hydroxide were supplied by Sigma–Aldrich, Germany Sodium fluoride salt (NaF) (molecular weight, 41.98871 g/mol) and D-sorbitol were supplied by Merck, Germany (maximum purity available) Doubly distilled deionized water (HPLC grade 99.99% purity) was obtained from Sigma–Aldrich Co (Germany) 2.2 Preparation of maghemite nanoparticles There are various methods for the preparation of c-Fe2O3, using different reagents for the synthesis In this paper the nanoparticles were prepared by coprecipitation of ferrous ion (Fe2+) and ferric ion (Fe3+) with NaOH solution D-sorbitol was used to prevent the agglomeration between the nanoparticles [33] The iron solutions were strongly stirred in water, after adding NaOH solution The precipitates were separated by magnetic decantation or slow filtration after which it was washed several times with distilled water and ethanol The magnetite nanoparticles were dried into an oven at 60 °C In order to obtain maghemite (c-Fe2O3), the magnetite nanoparticle was heated at 200 °C, for h and finally, red–brown maghemite nanoparticles were collected Transmission electron microscopy (TEM, JEM-2100F HR, 200 kV) and X-ray diffractometer (XRD) Philips X’Pert were used to characterize the adsorbent for its morphological information A Fakhri 2.3 Adsorption experiment The adsorption of fluoride onto maghemite nanoparticles was investigated using batch experiments In these studies 1000 mg/L stock solution was prepared by dissolving g of NaF in 1000 mL distilled water Different concentrations (25–75 mg/L) of fluoride solutions were prepared by this stock solution Solutions were evacuated to flasks of 100 mL Then adsorbent in the range of dosage 0.25–0.75 g/L was added and placed in the water bath shaker after pH adjustments made in the range of 4–12 The suspensions were shaken at 2000 rpm for 12 at room temperature These experiments were conducted duplicate Samples from shaker were filtered with filter paper, and then remaining fluoride levels were measured using a fluoride electrode (Orion, 9606BNWP) The equilibrium adsorption capacity was calculated from the relationship qe ẳ Co Ce ịV W ð1Þ where, qe (mg/g) is the equilibrium adsorption capacity, Ce is the fluoride concentration at equilibrium (mg/L), V is the volume of solution (l) and W is the weight of adsorbent (g) 2.4 Response surface methodology The three-level Box–Behnken experimental design with categorical factor was employed to optimize the adsorption capacity of the maghemite nanoparticles for fluoride (response) The design was composed of three levels (low, medium and high) and a total of 17 runs were carried out to optimize the chosen variables, such as temperature, maghemite nanoparticle dosage and pH For the purpose of statistical computations, the three independent variables were denoted as x1, x2, and x3, respectively According to the preliminary experiments, the range and levels used in the experiments are selected and listed in Table The main effects and interactions between factors were determined The experimental design matrix by the Box–Behnken design is tabulated in Table For RSM, the most commonly used second-order polynomial equation developed to fit the experimental data and determine the relevant model terms can be written as: Y ẳ b0 ỵ k k k X X X bi xi ỵ bii x2i ỵ bij xi xj ỵ e iẳ1 iẳ1 2ị 16i6j where Y represents the predicted response, i.e the adsorption capacity for fluoride by the maghemite nanoparticles (mg/g), b0, the constant coefficient, bi, the ith linear coefficient of the input factor xi, bii, the ith quadratic coefficient of the input Table Factors and levels used in the factorial design Factor Low level (À1) Medium level (0) High level (+1) Temperature (X1) Maghemite dosage (X2) pH (X3) 283 K 0.25 g/L 297 K 0.50 g/L 313 K 0.75 g/L 12 Please cite this article in press as: A Fakhri, Application of response surface methodology to optimize the process variables for fluoride ion removal using maghemite nanoparticles, Journal of Saudi Chemical Society (2013), http://dx.doi.org/10.1016/j.jscs.2013.10.010 Application of response surface methodology Table BBD and results for the study of three experimental variables in coded units Runs X1 X2 X3 YExp (mg/g) 10 11 12 13 14 15 16 17 0 +1 +1 0 À1 À1 +1 À1 0 0 À1 +1 0 +1 0 0 +1 À1 À1 +1 +1 À1 À1 0 À1 0 +1 À1 +1 À1 À1 +1 22.88 22.75 31.04 45.70 22.53 22.89 31.58 32.49 37.75 25.32 25.51 26.65 22.49 33.57 39.85 28.37 34.45 factors xi, bij, the different interaction coefficients between input factors xi and xj (i = 1–3, j = 1–3 and i/ = j), and e, the error of the model [5] The equation expresses the relationship between the predicted response and independent variables in coded values according to Tables and Results and discussion 3.1 Characterization of maghemite nanoparticles Particle size distribution is calculated from TEM images by measuring the diameter of 100 particles randomly chosen From Fig 1A, it can be seen that the homemade maghemites have the most even particle Fig 1B shows that the fluoride molecules possible into the adsorbent surface are covered The XRD pattern of maghemite nanoparticles is shown in Fig 1C It shows that no other phases other than maghemite exist in the products The intensity change and peak broadening of the XRD patterns of maghemites are due to a change in particle size of the nanoparticles which is in good agreement with the particle size: the smaller the particle the weaker is the intensity and broader are the peaks 3.2 Statistical analysis The optimum conditions for adsorption of fluoride onto the surface of maghemite nanoparticles were determined by means of the BBD under RSM The results are displayed in Table In this way, the fluoride uptake by maghemite nanoparticles could be expressed using the following equation: Y ẳ 22:474 ỵ 2:476X1 ỵ 1:241X2 2:445X3 2:650X1 X2 ỵ0:147X1 X3 2:472X2 X3 þ 9:095X21 À 0:879X22 þ 6:873X23 ð3Þ Figure TEM images of maghemite nanoparticles (A), maghemite nanoparticles after adsorption (B) of fluoride ion and XRD pattern of maghemite nanoparticles (C) Please cite this article in press as: A Fakhri, Application of response surface methodology to optimize the process variables for fluoride ion removal using maghemite nanoparticles, Journal of Saudi Chemical Society (2013), http://dx.doi.org/10.1016/j.jscs.2013.10.010 A Fakhri Table Analysis of variance for the response of the adsorption capacity for fluoride ion Source Sum of squares df Mean square F Value P-value Prob > F Model Temperature (X1) Maghemite dosage (X2) pH (X3) Temp–maghemite dosage (X1X2) Temp–pH (X1X3) Maghemite dosage–pH (X2X3) Temperature–temperature ðX21 Þ Maghemite dosage–maghemite dosage ðX22 Þ pH–pH ðX23 Þ Residual Lack of fit Pure error Cor total 759.44 50.09 11.48 50.32 29.18 0.099 25.35 360.02 3.05 189.99 19.05 19.15 0.030 748.33 1 1 1 1 16 82.22 49.35 12.42 47.95 28.21 0.094 24.85 355.93 3.05 189.99 2.73 6.59 4.820E-003 29.84 17.81 4.53 17.38 10.22 0.038 8.88 128.02 1.21 73.01