Response surface modeling of lead (׀׀) removal by graphene oxide-Fe3O4 nanocomposite using central composite design
© Khazaei et al. 2016
Received: 27 September 2015
Accepted: 12 January 2016
Published: 22 January 2016
Magnetic graphene oxide (Fe3O4@SiO2-GO) nanocomposite was fabricated through a facile process and its application as an excellent adsorbent for lead (II) removal was also demonstrated by applying response surface methodology (RSM).
Fe3O4@SiO2-GO nanocomposite was synthesized and characterized properly. The effects of four independent variables, initial pH of solution (3.5–8.5), nanocomposite dosage (1–60 mg L−1), contact time (2–30 min), and initial lead (II) ion concentration (0.5–5 mg L−1) on the lead (II) removal efficiency were investigated and the process was optimized using RSM. Using central composite design (CCD), 44 experiments were carried out and the process response was modeled using a quadratic equation as function of the variables.
The optimum values of the variables were found to be 6.9, 30.5 mg L−1, 16 min, and 2.49 mg L−1 for pH, adsorbent dosage, contact time, and lead (II) initial concentration, respectively. The amount of adsorbed lead (II) after 16 min was recorded as high as 505.81 mg g−1 for 90 mg L−1 initial lead (II) ion concentration. The Sips isotherm was found to provide a good fit with the adsorption data (KS = 256 L mg−1, nS = 0.57, qm = 598.4 mg g−1, and R2 = 0.984). The mean free energy Eads was 9.901 kJ/mol which confirmed the chemisorption mechanism. The kinetic study determined an appropriate compliance of experimental data with the double exponential kinetic model (R2 = 0.982).
Quadratic and reduced models were examined to correlate the variables with the removal efficiency of Fe3O4@SiO2-GO. According to the analysis of variance, the most influential factors were identified as pH and contact time. At the optimum condition, the adsorption yield was achieved up to nearly 100 %.
KeywordsGraphene oxide Adsorption Lead (II) Optimization Central composite design
Effluents containing Lead and other toxic metals (׀׀) are increasingly discharged into the water supplies due to the expansion of industries . The maximum levels lower than 15 ppb for lead (׀׀) in drinking waters has been mandated by many environmental agencies and national standard organizations [2–4]. The strict limitations on discharging effluents contained lead (׀׀) to the natural water bodies are attributed to the lead (׀׀) potential health effects on children and adults .
Many processes such as precipitation, membrane filtration, adsorption, and ion exchange have been applied to remove lead (׀׀) and other toxic metals from the industrial effluents . Only a few methods such as using functionalized adsorbents and membrane technologies can be adopted to capture low concentrations around 1 mg L−1, which is commonly occurred in drinking water sources . Although, adsorption processes are useful in removal low concentrations of metal ions from aqueous solutions, but there are two main limitations regarding to the use of them; 1. low adsorption capacity [7, 8], and 2. difficult separation of adsorbent from treated water after the end of adsorption process [9–11].
Graphene oxide is an emerging carbon-based nonmaterial that has revealed the promising adsorptive properties [12, 13]. Graphene oxide (GO) creates a highly stable aqueous dispersion which prepares an excellent situation for effective contacts with target contaminants without needing to vigorous mechanical mixing . The GO flakes have high specific surface area ranging from 600 to 3500 m2 g−1 [15, 16]. The dispersibility property of GO is attributed to the plenty of hydrophilic functional groups on the GO flakes . The GO flake surface contains different functional groups including epoxide and hydroxide, whereas, the edge of flakes are mainly contained a hedge of carboxylic groups .
Using magnetic agents like Fe3O4 has been considered as a way to separate the GO nanosheets from aqueous solution when the adsorption process is finished [17, 18]. Some methods employed for the adding of Fe3O4 on the GO surface are generally led to form reduced GO (rGO) [19, 20]. Because of the elimination of functional groups during the reduction process, rGO represents weak dispersity . Hence, preserving the GO dispersibility in the aqueous solution as well as adding the magnetic property for separation purposes is under consideration.
Few literatures were reported applying non reduced Fe3O4/GO for the adsorption purposes [17, 21, 22]. Among them, some synthesis approaches have relied on the formation of covalent bonds between the GO sheets and Fe3O4 nanoparticles [17, 22] which has more stability than those methods based on physical attraction .
This research aimed to fabricate the covalent bond Fe3O4@SiO2-GO nanocomposite as a highly dispersible and easy separatable adsorbent for the elimination of lead (׀׀) from aqueous solution. Other purpose of the study was determining the optimal operational condition using response surface methodology (RSM) to achieve satisfactory lead (׀׀) removal. The conventional optimization method, which altered one variable at a time by keeping the other variables constant, is a time consuming and costly approach that can not consider the interactive effects between variables. RSM technique is an empirical statistical approach used to evaluate the relationship between a set of controlled experimental variables and observed results. It can be applied to optimize and identify the performance of adsorption process. Minimum experimental runs are achievable by using RSM. Applying RSM reduces the experiment runs and the reagents consumption. It also facilitates the execution of experiments necessary for the construction of the response surface.
Graphite powder (particle size ˂ 20 μm), tetraethyl orthosilicate (TEOS), (3-aminopropyl) triethoxysilane (APTES), n- hydroxysuccinimide (NHS) and 1- ethyl-3- (3-dimethyl aminopropyl) carbodiimide (EDC.HCl) were purchased from Sigma- Aldrich, Ltd. Co. All other chemicals such as sodium nitrate (NaNO3), potassium permanganate (KMnO4), sulfuric acid (H2SO4), hydrochloric acid (HCl), hydrogen peroxide aqueous solution (H2O2), iron chloride hexahydrate (FeCl3, 6 H2O), and iron chloride tetrahydrate (FeCl2, 4 H2O) were of reagent grade and used without further purification.
Preparation of graphene oxide (GO)
Graphene oxide was synthesized from the graphite powder by the modified Hummers et al. method . Briefly, 2.0 g of graphite powder and 2.0 g of NaNO3 were mixed with 92 mL of H2SO4 (98 %) in a flask and stirred in an ice bath vigorously for 0.5 h, and then 12.0 g of KMnO4 was added to the above solution slowly. After stirring for 0.5 h, the ice bath was removed and the solution was stirred in a water bath at 35 °C for 6 h. After that, 160 mL of the DI water was added slowly to the flask. Then, the obtained mixture was stirred at 900Cfor 2 h. Afterward 400 mL of DI water was added and followed by addition of 12 mL of H2O2 (30 %), Upon which the color of mixture turned to bright yellow. The obtained suspension was washed with 1:10 HCl solution (150 mL) and DI water several times to remove metal ions . The resultant dispersion was sonicated at 130 KHz for 2 h and centrifuged to obtain exfoliated graphene oxide .
Preparation of Fe3O4@SiO2-NH2
The Fe3O4 magnetic nanoparticles were synthesized using a coprecipitation method . For the synthesis of Fe3O4@SiO2- NH2, 1.0 g of the obtained Fe3O4MNPs was dispersed in a mixture of 40 mL ethanol and 10 mL of DI water using an ultrasonic water bath. After that, o.5 mL TEOS and 2 mL NH3.H2O (25 %) were added, and the mixture was stirred at 50 °C for 6 h. The solid product was collected by an external magnetic field, washed with ethanol and dried under vacuum. In the next step, 1 g of the obtained Fe3O4@SiO2 was dispersed in 25 mL dried toluene and treated with addition of 1 mL APTES . The mixture was refluxed for 24 h under nitrogen atmosphere. The product was washed with ethanol and then dried to obtain Fe3O4@SiO2-NH2 .
Preparation of Fe3O4@SiO2-GO
To confirm the stability of nanocomposite, concentration of Iron after the adsorption process was measured. As shown in Tables 2, and 3, the leaching of Iron into the aqueous solution after contact times was negligible.
The SEM images were taken with Hitachi- S4160 scanning microscope (Tokyo, Japan) to survey the morphological pattern and surface structural aspects of GO and Fe3O4@SiO2-GO nanocomposite. A Nanoscope V multimode atomic force microscope (Veeco Instruments, USA) were used to perform AFM measurements. The AFM images were taken from samples which prepared by deposition a dispersed GO/methanol solution (70 mg mL−1) onto a mica surface and allowing them to dry in air . The images were taken under ambient condition by adjusting the instrument on the tapping mode.
Batch adsorption experiments
Where, R (%) is the removal efficiency, C 0 and C t are the concentrations (as mg L−1) of lead (׀׀) at 0 and t minutes after the contact time, respectively.
Where, q e is the equilibrium capacity (mg g−1), x ads is the nanocomposite concentration in aqueous solution (mg L−1), and 1000 is converting factor (mg g−1).
Lead (׀׀) measurements in the aqueous solution were performed by using a Spectro Arcos ICP-optical emission spectrometer (SPECTRO Analytical Instruments, Kleve, Germany) based on radial plasma observation. The Spectro Arcos has a Paschen–Runge mount which equipped with 32 linear CCD detectors. The CCD detectors supply the ability of simultaneous monitoring of line intensities at wavelengths between 130 and 770 nm.
Isotherm and kinetic constants were obtained using the Solver “add-in” with Microsoft Excel spreadsheet program  according to the nonlinear forms of the equations.
The original and coded levels of independent variables
Fe3O4@SiO2-GO -X2 (mg/L)
Initial Pb2+ Concentration-X4 (ppm)
Observed and predicted values for the quadratic model (T = 298 K)a
Observed values (%)
Predicted values (%)
Where, Y represents the dependent variable (lead (׀׀) removal efficiency), b0 is a constant value, bi, bii, and bij refer to the regression coefficient for linear, second order, and interactive effects, respectively, Xi, and Xj are the independent variables, c denotes the error of prediction.
The above mentioned CCD analysis plus to the statistical analysis, such as ANOVA, F-test, and t-test were obtained using R software (version 3.0.3: 2014-03-06).
Results and discussion
Characterization of GO-Fe3O4 nanocomposite
The VSM curves (Fig. 4) shows that, the magnetic power of Fe3O4 nanoparticles were dropped into the one third of original state which was due to both the SiO2-NH2 coverage and the GO covalent bonds. But, the remaining 22.3 emu g −1 of saturation magnetization can still be considered as a powerful magnetic field to separate the nanocomposite from the aqueous solution, as shown in the Fig. 4d. Also, the coercivity and remanence were not observed after removing the magnetic field. From Fig. 4d, the yellow brown color of the GO dispersion revealed that the oxygenation of the graphene nanosheets has been effectively occurred during the synthesis [16, 34]. After 3 months from the GO preparation, there is no any visible sign of sedimentation which shows long-term dispersibility of GO in water.
As shown in Fig. 5c, the thickness of a random GO sheet measured using the height profile (Line 1) in the AFM image, is about 0.75 nm. This sub-nanometer thickness confirms producing the GO monolayer .
As can be seen from Fig. 6b, the spectrum of the Fe3O4 shows the Fe-O stretching vibration at 591 cm−1, and an intense OH band around 3400 cm−1. The OH band is attributed to the stretching vibrations of Fe-OH groups attached on the Fe3O4 surface and also can be assigned to the remaining water that was not eliminated from the surface of the Fe3O4 nanoparticles .
As depicted in Fig. 6c, the peak at 3401 cm−1 of Fig. 5c attributed to the –NH2 vibration. Comparing Fig. 6c with Fig. 6a, the peak at 1733 cm−1 was almost disappeared, and a new broad peak was emerged at 1641 cm-1 corresponding to C = O characteristic stretching band of the amide group. The stretching band of the amide C–N peak appears at 1230 cm−1 . Meanwhile, as shown in Fig. 6c, the peaks at 802 and 1110 cm−1 were obviously observed due to the Si–O vibrations. From these findings, it is recommended that APTES functionalized Fe3O4 was covalently bonded to GO through the amide linkage .
Response surface methodology model analysis
Observed and predicted values for the reduced cubic model (T = 298 K)a
Observed values (%)
Predicted values (%)
Analysis of variance (ANOVA) for the quadratic model
Model formula in rsm (X1,X2,X3,X4)
Sum of squares
Pure quadratic response
Lack of fit
Analysis of variance (ANOVA) for the reduced quadratic model
Sum of squares
pH × Adsorbent
pH × Time
Adsorbent × Time
The reduced quadratic model was applied by omitting the variables assigned to the P-values more than 0.05 in the quadratic model .
The values of the determination coefficient (multiple R2) shown in Tables 4 and 5 indicated that 87.9 and 86.3 % of the variability in the response could be explained by the quadratic and reduced quadratic models, respectively.
If there have been various terms in the model and also the sample size has not been very large, the adjusted correlation coefficient (adjusted R2) may represent values considerably smaller than the multiple correlation coefficients (Multiple R2) . In this experiment, the adjusted correlation coefficient value (adjusted R2 = 0.836) are also noticeable to support the high significance of the models and approves a satisfactory adjustment for the reduced quadratic model to the experimental data [45–47].
The values of the determination coefficient (multiple R2) shown in Tables 4 and 5 indicated that 87.9 and 86.3 % of the variability in the response could be explained by the quadratic and reduced quadratic models, respectively.
As revealed from Tables 4 and 5, the “lack of fit (LOF)” values were 0.337 and 0.355 for the quadratic and reduced quadratic models, respectively. The insignificant values of LOF (>0.05) and the significant P-values for both models prove that applying models is eligible to interpret the lead (׀׀) removal process and also, the reduced model is the better choice because of the higher adjusted R2 (0.836) and the higher value obtained for LOF (0.355) [43, 46].
Regression analysis for the reduced quadratic model
5.5 × 10−9
3.9 × 10−11
1.6 × 10−5
1.3 × 10−5
3.20 × 10−3
pH × Adsorbent
6.1 × 10−9
pH × Time
4.4 × 10−3
Adsorbent × Time
5.8 × 10−5
The effects of pH (X1) and contact time (X3) on lead (׀׀) removal are shown in Fig. 7b. The adsorbent dose and lead (׀׀) initial concentration were fixed constant at 30.5 mg/L and 2.49 mg/L, respectively. It is inferred from Fig. 7b that around the neutral pH, the lead (׀׀) removal process was almost completed during the contact time up to 10 min. But for pH values less than 4, after the contact time more than 30 min, the removal efficiency near 70 % was achieved.
The effects of Fe3O4@SiO2-GO dosage (X2) and contact time (X3) on lead (׀׀) removal can be observed in Fig. 7c. The pH and lead (׀׀) initial concentration were fixed constant at 6 and 2.49 mg/L, respectively. Figure 7b shows that, for the adsorbent doses more than 40 mg/L, regardless the contact time, the lead (׀׀) removal efficiency revealed the levels permanently beyond 95 %.
Lead (׀׀) removal showed to be very sensitive to changes in the pH both in low and high adsorbent dose. The removal capacity of Fe3O4@SiO2-GO nanocomposite was rapidly increased when the pH increased from 3.5 to 8.5; as it was also reported by Madadrang . pH influences both the surface charges of the functional groups on the surface of graphene oxide and also the species of lead ion in the aqueous solution .
Increasing the protonation of functional groups on graphene oxide surface would be happen at acidic conditions and electropositivity of Fe3O4@SiO2-GO surface would retard the adsorption rate, and finally, the removal efficiency of lead (׀׀) can be reduced . As inferred from Fig. 7a-b, when pH is less than 5, the lead (׀׀) removal efficiency was weak. However, the adsorption of lead (׀׀) was enhanced with the increasing pH from 5 to 8.5. Normally, the adsorption capacities of metal ions for most carbon based nanomaterials would increase with increases in the pH value. In this case, lead (׀׀) can be adsorbed onto the graphene oxide surface by reacting lead (׀׀) with − COOH and − OH groups [15, 49].
As shown in the contour plot exhibited in Fig. 7c, regardless the adsorbent dose, more than 75 % of lead (׀׀) adsorption was achieved during the contact time less than 10 min and the adsorption process was completely done after passing twenty minutes. These findings revealed that the fast adsorption rate of the lead (׀׀) can be attributed to the high affinity of lead (׀׀) ions to the hydroxide (−OH), epoxide (−O−) and carboxylic (−COOH) groups on the GO nanosheets [15, 50, 51].
The optimum values of pH, adsorbent dose, and contact time determined by applying the reduced quadratic model were 6.9, 30.49 mg/L, and 16.01 min, respectively. The optimum concentration of initial lead (׀׀) was not obtained from the reduced quadratic model because it was omitted from the model. But the results of quadratic model suggested 2.49 mg L−1 as the optimum value. Further studies such as isotherm and kinetic experiments were investigated according to the abovementioned optimum values obtained from the model.
Where, qe is the amount of lead (׀׀) adsorbed on the absorbent at equilibrium (mg g−1), Ce describes the equilibrium lead (׀׀) concentration (mg L−1), KL is the Langmuir adsorption constant (L mg−1) and qm denotes the maximum adsorption capacity attributeing to the complete monolayer coverage of the adsorbent (mg g−1). KF is the Freundlich constant related to the maximum sorption capacity (mg g−1), also, nF is the Frendlich constant related to the heterogeneity factor. KS (L g−1) is the affinity constant and nS denotes the surface heterogeneity. If nS value is equal to the unity, the Sips isotherm is turned to the Langmuir isotherm, and consequently, the homogeneous adsorption can be modeled. Also, any deviation of nS value from the unity (more than or less than unity) predicts the heterogeneous surface [52, 53].
As shown in Fig. 8b, the Sips isotherm model represents the higher correlation coefficient (R2 = 0.984) comparing with the Langmuir (R2 = 0.964) and Freundlich (R2 = 0.952) models.
The Sips model includes three parameters and has the capability to apply for both the homogeneous and heterogeneous systems . The Sips model (Eq. 5) integrates parameters from both the Langmuir and the Freundlich isotherm. The heterogeneous surface of adsorbent can be considered if the deviation of n S value from the unity be occurred [53, 55]. However, the Sips isotherm moves toward a constant level at high concentrations whereas a pattern of Freundlich model can be observed at low concentrations .
According to the experimental data, the maximum adsorption uptake (qm) was 505.8 mg g−1 which indicates the adsorption capacity higher than those reported by studies applying magnetic GO as lead (׀׀) adsorbent  and is comparable with the studies using pristine GO [15, 49, 57]. The maximum adsorption uptake (qm) obtained from Sips isotherm model was found to be 598.4 mg g−1 which was more than the values achieved both from the Langmuir model and the experimental data (qm.Langmuir = 497.8 mg g−1, qm,exp = 505.8 mg g−1). This indicates that the Sips model overestimates the qm value which can be due to the heterogeneity characteristic considered in the Sips model. As shown in Fig. 8b, the deviation of nS value from the unity (nS = 0.57) as well as the nF value more than unity (nF = 4.28) can be assigned to the crosslinking effects beside the amount of functionalities such as -COOH and -OH on the adsorbent surface (see FTIR-spectra in Fig. 6). The isotherm curves were L-shaped, which shows the high affinity of surface groups towards lead (׀׀) ions both at low and high concentrations . As revealed from Fig. 8b, the mean free energy Eads was 9.901 Kj/mol which seems to be the evidence of predomination the chemisorption mechanism .
Where, qt and qe are the sorption capacity (mg g−1) at time t and at the equilibrium time, respectively. k1 and k2 are the pseudo-first-order and pseudo-second-order rate constants, respectively. D 1 and D 2 (mg L−1) are the rapid and slow steps, K D1 and K D2 (min−1) are constants controlling the mechanism of slow and rapid phases, respectively. x ads is the adsorbent dosage (g L−1).
According to the regression coefficient values of kinetic models, it was found that the double exponential kinetic model (R2 = 0.982) obtains a better description to predict the kinetic data of lead (׀׀) than both pseudo-first-order and pseudo-second-order models.
The values of constant parameters of double-exponential kinetic model revealed that the both external diffusion and internal diffusion have substantial effects on the lead (׀׀) sorption using Fe3O4@SiO2-GO nanocomposite .
Magnetic Fe3O4@SiO2-GO nanocomposite was synthesized and applied to elimination the lead (׀׀) from aqueous solution. Due to the high loading capacity of GO for metal ions, Fe3O4@SiO2-GO revealed excellent performance in treatment the lead (׀׀) contaminated waters. The removal process was found to be quick and facile, and the lead (׀׀) adsorption process was almost completed up to 10 min contact time.
Main advantages of Fe3O4@SiO2-GO nanocomposite include quick separation performed by using an external magnetic field and the noticeable lead (׀׀) removal capacity (506 mg g−1).
A central composite design (CCD) was applied to investigate the effects of four adsorption variables, namely pH, adsorbent dose, contact time, and initial lead ion concentration on the removal efficiency of lead (׀׀).
Both the quadratic and reduced quadratic models were applied to correlate the variables to the response values. Results from the analysis of response surfaces indicated that pH, time, and the adsorbent dose were found to have significant effects on the removal efficiency of lead (׀׀). The optimization of process was performed and the experimental values were found to be agreed satisfactorily with the predicted values.
The adsorption isotherms and kinetics were also investigated. Equilibrium adsorption data had best fit by the Sips isotherm model and chemisorption mechanism was predominated. Kinetic studies indicated that the double-exponential kinetic model is the preferred model to explain the equilibrium adsorption over the time.
This research was part of a PhD dissertation of the first author and has been financially supported by a grant (NO, 28232-27-01-94) from Tehran University of Medical Sciences, Tehran, Iran. The authors would like to express their thanks to the Department of Environmental Health Engineering, School of Public Health, Tehran University of Medical Sciences for their collaboration.
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