Application of response surface methodology for optimization of fluoride adsorption from aqueous solution using MgO-based nanocomposites
Abbas Rahdar
1
(
Department of physics, university of zabol, zabol, iran
)
Somayeh Rahdar
2
(
1Department of Environmental Health, Zabol University of Medical Sciences, Zabol, Iran
)
laili Mohammadi
3
(
2Department of Environmental Health, Zahedan University of Medical Sciences, Zahedan, Iran
)
Saiedeh Sistani
4
(
4MSc of Environmental Health, Kerman University of Medical Sciences, Kerman, Iran
)
Shabnam Ahmadi
5
(
Department of Environmental Health, Zabol University of Medical Sciences, Zabol, Iran
)
Md. Abu Bin Hasan Susan
6
(
Department of Chemistry, University of Dhaka, Dhaka 1000, Bangladesh
)
Keywords: response surface methodology, Isotherm, kinetic, fluoride, MgO-FCN-NPs,
Abstract :
Fluoride at high concentrations in water is detrimental to human health. To find an efficient means of removal of fluoride from aqueous system, we synthesized magnesium oxide (MgO)- based iron-cobalt-manganese (MgO-FCN ) nanocomposites via co-precipitation. Fluoride adsorption process was optimized by standard software. The effect of independent parameters such as pH (3-11), initial dose of nanoparticle (0.02-0.1 g/L), initial concentration of fluoride (10-50 mg/L) and reaction time (30-180 min) were optimized to obtain the best responses of fluoride removal using statistical Central Composite Design (CCD) in the procedure of response surface modeling. The best conditions were optimized as pH=5, initial concentration of nanoparticle =0.05 g/L, initial concentration of fluoride =50 mg/L and the process time of 90 min. Under these conditions, the removal efficiency of the fluoride by MgO-based nanocomposites was achieved as 84.64%. High correlation coefficients for the proposed model was also obtained (adjusted R2=0.9993 and R2=0.9984). The equilibrium data were analyzed using Langmuir, Freundlich, Temkin and Dubinin-Radushkevich isotherm models. The Langmuir model was found to be describing the data best. Kinetic studies showed that the adsorption followed a pseudo-second order reaction.
Abstract
Fluoride at high concentrations in water is detrimental to human health. To find an efficient means of removal of fluoride from aqueous system, we synthesized magnesium oxide (MgO)- based iron-cobalt-manganese (MgO-FCN ) nanocomposites via co-precipitation. Fluoride adsorption process was optimized by standard software. The effect of independent parameters such as pH (3-11), initial dose of nanoparticle (0.02-0.1 g/L), initial concentration of fluoride (10-50 mg/L) and reaction time (30-180 min) were optimized to obtain the best responses of fluoride removal using statistical Central Composite Design (CCD) in the procedure of response surface modeling. The best conditions were optimized as pH=5, initial concentration of nanoparticle =0.05 g/L, initial concentration of fluoride =50 mg/L and the process time of 90 min. Under these conditions, the removal efficiency of the fluoride by MgO-based nanocomposites was achieved as 84.64%. High correlation coefficients for the proposed model was also obtained (adjusted R2=0.9993 and R2=0.9984). The equilibrium data were analyzed using Langmuir, Freundlich, Temkin and Dubinin-Radushkevich isotherm models. The Langmuir model was found to be describing the data best. Kinetic studies showed that the adsorption followed a pseudo-second order reaction.
Keywords: Fluoride, MgO-FCN-NPs, Response Surface Methodology, Isotherm, Kinetic
Introduction
Fluoride is introduced into groundwater and surface water resources through wastewater of iron, glass making, steel making, fertilizer, and aluminum making industries (1,2). Presence of fluorine at low concentrations in water resources has considerable effects in preserving the health of bones and teeth (3,4). World Health Organization (WHO) has limited the standard level of fluorine in drinking waters to 1.5 mg/L, with lower values stated for children (5,6). Fluoride in the human body, given the duration of exposure, leads to dental fluorosis and skeletal fluorosis. Dental fluorosis which is more specific to children leads to increased enamel porosity, diminished mineral content and vulnerability of teeth (7-9). Further, fluoride causes other diseases such as osteoporosis, infertility, etc. (10, 8-12). It also affects the metabolism of other elements such as calcium and potassium in the body (13). High fluoride concentrations in groundwater in 30 countries have caused more than 260 million people to experience health problems (14). Thus, use of different technologies for defluorination from water resources is essential (3). Various methods developed for fluoride removal are absorption, membrane processes, coagulation and chemical precipitation, nanofiltration, reverse osmosis, and ion exchange (15-18). Choice of the methods depends on the concentration, region, and water resources available (12). Coagulation and precipitation are cheaper, but they cause production of harmful materials. On the other hand, membrane processes such as reverse osmosis have a high maintenance cost, and the ion exchange is very expensive (19). Adsorption has been a widely used method for defluoridation which depends on ions (adsorbate) in liquid diffusing to the surface of a solid (adsorbent)(9).
Adsorption is environmentally friendly, inexpensive, efficient, and easy in operation (17,20,21). Adsorption for fluoride removal involves use of several adsorbents such as activated carbon, bentonite, red mud, iron oxide, zeolites, fly ash and charcoals (22-24). In the current study, nanoparticles of MgO-FCN were synthesized via co-precipitation method and structural and magnetic properties were studied. MgO-FCN NPs were investigated as a fluoride sorbent by considering combined effects of some significant parameters such as pH, adsorbent dose , contact time and F concentration by building a mathematical model that accurately describes the overall process. Fluoride removal by MgO-FCN-NPs were investigated using the most common and efficient design, central composite design (CCD) in response surface methodology. The advantages of reduction of consumption, the number of experiment and cost were exploited (25). The experimental data were analyzed by variance (ANOVA), R2 and lack of fit test to evaluate the significance of the model.
Materials and methods
2.1. Materials
Sodium fluoride (NaF), H2SO4 and NaOH were supplied by Sigma-Aldrich Co, Germany and Mn(NO3)2.6H2O (99%), Co (NO3)2 .6H2O (99%), Fe (NO3)3.6H2O (99%) NaOH (98%), HCl (37%) were purchased from Merck.
2.2. Synthesis of MgO-based nanocomposites
MgO-FCN-NPs were synthesized following method reported earlier with slight modification [26].
2.3. Characterization of MgO-based nanocomposites
SEM (Mira 3-XMU apparatus proficient of 700,000x magnifications) was used to study morphology of nanocomposites.
2.4. Batch study
Stock solution of fluoride (1000 mg/L) was prepared by dissolving sodium fluoride in distilled water, and certain concentrations of fluoride were obtained by further dilution. The pH of each last solution was adjusted by adding 0.1M H2SO4 and 0.1m NaOH solution and measuring pH by using an MT65 pH meter. 100 mL of fluorine solution with different pHs, absorbent doses, contact times, and initial fluorine concentration was poured in 250-mL flasks at room temperature. Once the contact time was over, the samples were filtered using Whatman filter paper 0.45 µm, and the final absorption level of the solution was monitored at the wavelength corresponding to absorption maximum of 570 nm using spectrophotometer UV/Vis (Shimadzu Model: CE-1021-UK), and the removal efficiency (Y) was determined according to Equation (1) (27-29).
Eq(1)
The amount of fluoride adsorbed per unit adsorbent (mg/g) was calculated according to a mass balance on the fluoride concentration using Equation (2).
A calibration curve was constructed using different concentrations of fluoride from a stock solution of 100 mg/L. 1 mL of zirconium acid reagent and 1 mL of spandex reagent were added to the samples, and after formation of color, measurements were conducted spectrophotometrically (3).
570 nm (1)
Eq(2)
2.5. Central Composite Design (CCD) method
Optimization of fluoride removal by the MgO-based nanocomposites was performed through surface response methodology via CCD model. The effects of four independent variables of pH, nanoparticle dose, contact time, and initial concentration were examined for defluorination, performed at five levels according to Table 1. The percentage removal of fluoride as a response to each experiments and their predicted results are shown in Table 2.
By applying RSM method, the following equation represents the experimental relationship between the tested variables and defluorination.
Y = b0 + b1A + b2B + b3C + b4D + b11A2+ b22B2+ b33C2+ b44D2+ b12AB+ b13AC + b14AD + b23BC + b24BD + b34CD
Where, Y is the response variable of each of the levels of factors (removal percentage) correlated to the set of regression coefficients (β) intercept (β0), linear (β1, β2, β3), quadratic coefficients (β11, β22, β33) and interaction (β12, β13, β23).
The individual and interaction effects of the variables were designed using two- and three-dimensional plots (30,31).The quality of the polynomial equation fitting was evaluated using the coefficients obtained including R2 and adjusted-R2 (32).
Table1. Coded and actual values of variable of the experimental design
Factor | Independent variables | Unit |
| Range and level of actual and coded values | |||||
|
|
| Low Actual | High Actual | Low Coded | Mean | High Coded | Std. Dev. | |
A | Dose | g/L | 0.02 | 0.1 | -1 | 0 | 1 | 0.031 | |
B | initial concentrations of fluoride | mg/L | 10 | 50 | -1 | 0 | 1 | 15.49 | |
C | pH |
| 3 | 11 | -1 | 0 | 1 | 3 | |
D | Contact time | min | 30 | 180 | -1 | 0 | 1 | 58.09 | |
Table2. Central Composite Design (CCD) of variables and their responses.
Run | Doseg/L | ConcentrationF | pH | Time | Flouride removal% |
1 | 0.1 | 30 | 7 | 105 | 85.13 |
2 | 0.1 | 50 | 11 | 180 | 98.95 |
3 | 0.06 | 30 | 7 | 105 | 86.36 |
4 | 0.02 | 10 | 3 | 30 | 69.58 |
5 | 0.02 | 50 | 3 | 30 | 73.95 |
6 | 0.02 | 10 | 11 | 180 | 93.35 |
7 | 0.1 | 10 | 11 | 180 | 93.18 |
8 | 0.06 | 30 | 7 | 180 | 99.47 |
9 | 0.1 | 10 | 3 | 30 | 68.18 |
10 | 0.06 | 30 | 7 | 105 | 86.01 |
11 | 0.02 | 10 | 11 | 30 | 66.6 |
12 | 0.06 | 30 | 7 | 105 | 86.01 |
13 | 0.06 | 30 | 3 | 105 | 86.53 |
14 | 0.02 | 50 | 3 | 180 | 100 |
15 | 0.06 | 30 | 11 | 105 | 86.01 |
16 | 0.06 | 10 | 7 | 105 | 82.86 |
17 | 0.02 | 10 | 3 | 180 | 96.32 |
18 | 0.02 | 50 | 11 | 30 | 73.25 |
19 | 0.1 | 50 | 3 | 30 | 73.07 |
20 | 0.02 | 30 | 7 | 105 | 86.01 |
21 | 0.06 | 30 | 7 | 30 | 72.72 |
22 | 0.06 | 30 | 7 | 105 | 86.18 |
23 | 0.06 | 50 | 7 | 105 | 87.41 |
24 | 0.02 | 50 | 11 | 180 | 99.65 |
25 | 0.06 | 30 | 7 | 105 | 86.18 |
26 | 0.1 | 50 | 3 | 180 | 99.3 |
27 | 0.1 | 10 | 3 | 180 | 95.62 |
28 | 0.1 | 10 | 11 | 30 | 65.9 |
29 | 0.1 | 50 | 11 | 30 | 72.55 |
30 | 0.06 | 30 | 7 | 105 | 86.01 |
Results and Discussions
3.1. Characterization of MgO-based nanocomposites
Morphology of nanocomposites was characterized by SEM tool and SEM image of MgO-based nanocomposites is shown in Figure 1.
Fig.1 SEM image of nanoparticles
The SEM image displays that the sample consists of nearly spherical nanostructures with changed sizes. The average particle size was 60 nm. The particles were distributed in almost uniform size.
FTIR spectra of fluoride and fluoride/nanoparticle are presented in Fig.2.
Figure 8. FTIR spectra of fluoride and fluoride/ MgO-based nanocomposites
The FTIR characterization of samples after adsorption is related to the addition of the MgO-based nanocomposites to the fluoride solution. The broad bands at around of 3463 cm-1 are attributable to the H-bonding. On the other hand, the band at around of 1642 cm-1 is related to after adsorption and can be attributed to the water association and the H-OH bending vibration due to presence of MgO-FCN nanoparticles. By comparing two FT-IR spectra, it is apparent that the intensity of the peak related to after adsorption (MgO-based nanocomposites/ fluoride) has increased compared with the peak related to before adsorption (fluoride solution) due to presence of MgO-based nanocomposites in the fluoride solution.
The MgO-based nanocomposites were characterized by XRD technique [26]. The cubic spinel phase of nanoparticles was confirmed by XRD pattern in compliance with JCPDS: 22-1086 [26].
3.2. RSM approach and statistical analysis
Statistical analysis of the results (RSM) lead to empirical relationship that mathematically expresses fluordie removal percentage as function of interaction and individual contribution of variables.
The coefficients of relation (12) were obtained using Minitab 16 software, whereby the following second order relation between response variable and resulting independent variables after removal of parameters with minimal impact was obtained.
Final equation in terms of coded factors is:
Fluoride removal =+123.36-0.58A+3.74B-1.08C+19.11D-0.047AB+0.17AC+0.078AD+0.73BC-0.23BD-0.016CD-1.17A2-1.80B2-0.17C2-0.42D2
The equation of fluoride removal indicates that the initial concentration of fluoride and contact time have had a positive effect and direct relationship on fluoride removal, and had a desirable effect on optimization. On the other hand, pH and absorbent dose have been negative, representing inverse relationship between the parameter and response (33).To analyze the significance and fitness of the obtained model, analysis of variance (ANOVA) was used. Table 3 presents the results of ANOVA (34).
R2 shows to what extent variability in the response variable can be explained by experimental factors and their interaction effects. R2 predicted for fluoride was equivalent to (0.0.9984), which had a logical proportion with R2ADJ (0.9993). Therefore, there was a good match between the values predicted by the model and the results obtained from the experiments. Furthermore, the obtained value (0.999) suggests that around 99.9% of the variability in the extent of removal has been justifiable by the independent variables. Comparison between the actual efficiency and predicted efficiency in the fluoride absorption process using MgO-based nanocomposites has been presented in Table 4, revealing that there is a good and acceptable relationship between the obtained experimental values and the predicted values given the value of R2.
Values of Prob>F at less than 0.05 showed the model terms are significant for fluoride removal. P-value for the model is less than 0.05, confirming that the present model can well predict experimental results. The results of second order regression indicated that that second order model with Fmodel=2856.91 and Prob>F<0.0001 was very significant (31). The lack-of-fit test compares the Pure error to residual error from replicated design points. The lack-of-fit F-value 0.0341 is significant as the P-value is < 0.05.
The coefficient of variations (CV) represents variability of the model results. A model is considered replicable if its CV is less than 10% (30). CV in this study has been 0.34, suggesting replicability of the results obtained from the model (35).The difference between the response obtained from the experiment and response fitted by the model is called the residual. The points obtained from the experiment should lie on a straight line, whereby it can be concluded that the residual has normal distribution (36). As can be observed in Fig.1, the residual has normal distribution.
Figure.1 The studentized residuals and normal % of probability residuals for defluoridation
Table3. ANOVA for Response Surface Quadratic Model
Source | SS* | df* | MS* | F- Value | P-value Prob > F |
|
Model | 3377.00 | 14 | 241.21 | 2856.91 | < 0.0001 | significant |
A-Dose | 2.58 | 1 | 2.58 | 30.59 | < 0.0001 |
|
B-Concentration | 120.14 | 1 | 120.14 | 1422.96 | < 0.0001 |
|
C-pH | 9.55 | 1 | 9.55 | 113.12 | < 0.0001 |
|
D-Time | 3200.93 | 1 | 3200.93 | 37911.43 | < 0.0001 |
|
AB | 0.000 | 1 | 0.000 | 0.000 | 1.0000 |
|
AC | 0.12 | 1 | 0.12 | 1.45 | 0.2475 |
|
AD | 0.12 | 1 | 0.12 | 1.45 | 0.2475 |
|
BC | 4.78 | 1 | 4.78 | 56.56 | < 0.0001 |
|
BD | 0.62 | 1 | 0.62 | 7.33 | 0.0162 |
|
CD | 7.641E-003 | 1 | 7.641E-003 | 0.090 | 0.7677 |
|
A^2 | 1.78 | 1 | 1.78 | 21.12 | 0.0003 |
|
B^2 | 4.16 | 1 | 4.16 | 49.24 | < 0.0001 |
|
C^2 | 0.044 | 1 | 0.044 | 0.52 | 0.4813 |
|
D^2 | 0.24 | 1 | 0.24 | 2.86 | 0.1116 |
|
Residual | 1.27 | 15 | 0.084 |
|
|
|
Lack of Fit | 1.16 | 10 | 0.12 | 5.72 | 0.0341 |
|
Pure Error | 0.10 | 5 | 0.020 |
|
|
|
Cor Total | 3378.26 | 29 |
|
|
|
|
DF: Degree of freedom, SS: Sum of Square, MS: Mean of Square.
Table4. Model summary of the experimental design for the removal of fluoride
Std. Dev. | 0.29 | R-Squared | 0.9996 |
Mean | 84.75 | Adj R-Squared | 0.9993 |
CV % | 0.34 | Pred R-Squared | 0.9984 |
PRESS | 5.48 | Adeq Precision | 165.732 |
3.3. Effect of interaction variales
The solution pH can affect dissociation of applied groups on active sites of nanoparticles, structure of pollutants, and bonds of the nanoparticles surface (37).
Fig 2. Illustrates the two-dimensional and three-dimensional diagrams of the extent of removal as a function of nanoparticle dose and pH for performing the process with a solution at constant concentration of fluoride 20 mg/L and contact time of 60 min, where the removal efficiency reached its maximum (99.9%) at pH=5 and nanoparticles dose of 0.05g/L.
With increase in pH, fluoride removal efficiency from aqueous solutions decreases. Further, with the rise of the nanoparticles dose, fluoride removal efficiency increased and reaches an optimal point. Thereafter, with increase in the nanoparticle content, the removal efficiency finds a descending trend. The optimal value of the nanoparticle dose was obtained as 0.05g/L.
At acidic pHs, H+ ions dominate the nanoparticle surface, whereby the nanoparticle surface finds a positive flux, the electrostatic attraction force between fluoride anions and nanoparticle surface grows, thereby enhancing the removal efficiency (27,38). At higher pHs and with accumulation of OH+ ion on the nanoparticle surface, the repulsive force between the nanoparticle surface and fluoride ion increases, causing diminished efficiency (27,39). Similar result was reported by Vardhan and Srimurali (18).
Another influential parameter affecting the efficiency of contaminant removal processes is the absorbent dose. Figs. 2, 3 and 5 demonstrate the effect of interactive parameter of MgO supported Fe-Co-Mn NP dose and other variables. Here, the defluoridation value first increased slightly with increase in the nanoparticle dose from 0.02 to 0.05g/L (98.5-99.9%), After which with the rise of the MgO-FCN-NPs dose from 0.05 to 0.1g/L, the repulsive force between the nanoparticles results in inaccessibility of all active sites of the nanoparticles for the fluoride ion (27,40), where there removal efficiency reaches 94%.
Fluoride ion has been initially absorbed on the free sites of the nanoparticles at the beginning of the process 90 min (99%) at a high rate, and over time the removal efficiency did not have a considerable increase and remained almost constant.
At the beginning of the process, the entire surface of the absorbent has been active and fluoride solution concentration has been high. Therefore, they are rapidly absorbed by the active sites of the nanoparticles where a layer of fluoride ion has formed on the external surface of nanoparticles which blocks the pores of the absorbent, and over time, absorption on the internal surface of nanoparticles becomes limited (41). With the rise in fluoride concentration, the removal efficiency grows due to the concentration of fluoride on the surface of nanoparticles and increase the probability of collisions between nanoparticles and fluoride (42). Viswanathan and Menakshi, (2008) reported that fluoride removal was rapid in the first 40 min, followed by a limiting value and an increase in fluoride uptake was marked by increasing fluoride concentration (43).
Figure2. 3D surface and 2D contour plot of the interactive effect of pH and dose on fluoride removal efficiency by synthesized MgO-based nanocomposites at constant fluoride concentration and time.
Figure3.3D surface and 2D contour plot of the interactive effect of dose and fluoride concentration on fluoride removal efficiency by synthesized MgO-based nanocomposites at constant pH and time.
Figure4.3D surface and 2D contour plot of the interactive effect of pH and fluoride concentration on fluoride removal efficiency by synthesized MgO-based nanocomposites at constant dose and time.
Figure5.3D surface and 2D contour plot of the interactive effect of time and dose on fluoride removal efficiency by synthesized MgO-capped Fe-Co-Mn nanoparticles at constant fluoride concentration and pH
Figure6.3D surface and 2D contour plot of the interactive effect of time and fluoride concentration on fluoride removal efficiency by synthesized MgO-capped Fe-Co-Mn nanoparticles at constant dose and pH
Figure7. 3D surface and 2D contour plot of the interactive effect of time and pH on fluoride removal efficiency by synthesized MgO-capped Fe-Co-Mn nanoparticles at constant fluoride concentration and dose.
3.4. Isotherm and kinetic modeling
Isotherm is a parameter representing the relationship between the concentration of the absorbate and the absorption capacity of the absorbent. In this study, Freundlich, Langmuir, and Temkin adsorption isotherms have been used for mathematical modeling of the fluoride absorption process. After considering thecorrelation coefficients, it can be concluded which type of absorption curve has a greater fit with experimental data of the absorption process.
Kinetic equations are used to describe the behavior of transfer of the molecules of the absorbate per unit of time on the surface of absorbent and investigating the variables affecting the reaction rate. In the present study, pseudo-first order and pseudo-second order plus interarticular effusion kinetic models have been used to investigate the factors affecting the reaction rate of fluoride absorption process on MgO-FCN-NPs.
The equations and parameters of isotherms and adsorption kinetics are presented in Table 1S, and the values of isotherm parameters and fluoride absorption kinetics by the MgO-FCN-NPs have been calculated and presented in Table 5. The experimental data fitted well with the Langmuir model.
Table 5: Kinetic and isotherm parameters for adsorption of F onto MgO-FCN-NPs for optimal condition
Isotherm model | Parameters | Kinetic model | Parameters | ||
Langmuir | (L/ mg) KL | 1.31 | Pseudo- first order | Kf (1/min) | 0.581277 |
(mg/g) qm | 15.59
| qe (mg/g) | 1.029201 | ||
| R2 | 0.894 |
| R2 | 0.834 |
Freundlich | Kf (mg/g) | 135.1 | Pseudo-second order | K2 (g/mg min) | 0.036364 |
n | 0.47 | qe (mg/g) | 50 | ||
R2 | 0.662 | R2 | 0.9999 | ||
Temkin | KT | 9.20196E-45 | intra-particle diffusion | K (mg/g/min 1/2) | 0.074 |
B | 0.01 | C (mg/g) | 48.81 | ||
R2 | 0.487 | R2 | 0.943 | ||
Dubinin-Radushkevich | β | 5.181 | Ritchie | Kr | 2.5 |
(mg/g) qm | 0.915 | qe (mg/g) | 50 | ||
R2 | 0.691 | R2 | 0.871 | ||
Fig 8. Adsorption isotherm Langmuir model in fluoride removal using synthesized MgO-capped Fe-Co-Mn nanoparticles
3.6. Optimization of the adsorption process in removing fluoride by MgO-FCN-NPs
In order to obtain the optimal conditions for fluoride removal using the adsorption process, the optimization was performed in a mixed search of level of variables at which the maximum fluoride removal occurs. Minitab software chooses the level of response during the best operational conditions within the range of operational variables of pH, nanoparticles dose, contact time, and initial concentration of input fluoride to the process and predicts them, as presented in Table 6.
To confirm the results obtained from prediction of the model, an experiment was performed under optimal conditions. The results of the experiment had a good match with the extent of fluoride removal predicted by the model under optimal conditions.
Table 6. Results of fluoride removal using synthesized MgO-capped Fe-Co-Mn nanoparticles
Response | Name | Units | Obs | Analysis | Min | Max | Mean | Std. Dev. | Ratio |
Y1 | Fluoride removal | % | 30 | Polynomial | 65.9 | 100 | 84.64 | 0.29 | 1.525 |
4. Conclusions
MgO-FCN-NPs could be successfully synthesized via co-precipitation method. The particle size of the NPs was 60 nm and they have cubic spinel structure. Fluoride adsorption process was optimized with focus on the effect of parameters such as pH, fluoride concentration, dose nanoparticle and time on adsorption of fluoride by MgO-FCN-NPs. The adsorption process can be well described by quadratic model using response surface methodology based central composite design. The best conditions for the removal of fluoride from aqueous media were obtained as 50 mg/L initial fluoride concentration, pH=5 for solution, and 0.05 g/L for dose MgO-FCN-NPs. The removal efficiency of 84.64% of the fluoride by MgO capped Fe-Co-Mn nanoparticles was achieved. The equilibrium data were analyzed using Langmuir, Freundlich, Temkin and Dubinin-Radushkevich isotherm models. The Langmuir model was found to be best describing the data. Kinetic studies showed that the adsorption followed a pseudo-second order reaction.
Acknowledgments
The authors are grateful to the Zabol University of Medical Sciences for the financial support of this study (Project No.IR.ZBMU.REC.1397.046).
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