Journal of Magnetism and Magnetic Materials 405 (2016) 88–96

Contents lists available at ScienceDirect

Journal of Magnetism and Magnetic Materials

http://d0304-88

n CorrE-m

m-kash

journal homepage: www.elsevier.com/locate/jmmm

Modeling and optimization of effective parameters on the size ofsynthesized Fe3O4 superparamagnetic nanoparticles by coprecipitationtechnique using response surface methodology

Mohammad Reza Ghazanfari a, Mehrdad Kashefi a,n, Mahmoud Reza Jaafari b

a Department of Materials Science and Engineering, Ferdowsi University of Mashhad, 9177948974 Mashhad, Iranb Biotechnology Research Center, Nanotechnology Research Center, School of Pharmacy, Mashhad University of Medical Sciences, Mashhad, Iran

a r t i c l e i n f o

Article history:Received 27 September 2015Received in revised form22 November 2015Accepted 7 December 2015Available online 15 December 2015

Keywords:Central composite designSuperparamagnetismCation ratioBioapplicationsNanostructuresChemical synthesis

x.doi.org/10.1016/j.jmmm.2015.12.03153/& 2015 Elsevier B.V. All rights reserved.

esponding author.ail addresses: Ghazanfari.mr@gmail.com (M.Refi@um.ac.ir (M. Kashefi).

a b s t r a c t

Generally, the statistical methods are defined as appropriate techniques to study the processes trends. Incurrent research, the Fe3O4 superparamagnetic nanoparticles were synthesized by coprecipitationmethod. In order to investigate the size properties of synthesized particles, the experimental design wasdone using central composite method (CCD) of response surface methodology (RSM) while the tem-perature, pH, and cation ratio of reaction were selected as influential factors. After particles synthesisbased on designed runs, the different responses such as hydrodynamic size of particles (both freeze driedand air dried), size distribution, crystallite size, magnetic size, and zeta potential were evaluated bydifferent techniques i.e. dynamic light scattering (DLS), X-ray diffraction (XRD), and vibrating samplemagnetometer (VSM). Based on these results, the quadratic polynomial model was fitted for each re-sponse that could predict the response amounts. In following, the study of factors effects was carried outthat showed the temperature, pH, and their interactions had higher effectiveness. Finally, by optimizing,it was clear that the minimum amounts of particle size (10.15 nm) and size distribution (13.01 nm) werereached in the minimum temperature (70 °C) and cation ratio (0.5) amounts and maximum pH amount(10.5). Moreover, the characterizations showed the particles size was about 10 nm while the amounts ofMs, Hc, and Mr were equal to 60 (emu/g), 0.2 (Oe) and 0.22 (emu/g), respectively.

& 2015 Elsevier B.V. All rights reserved.

1. Introduction

Generally, in recent years the magnetic nanoparticles especiallyFe3O4 and other stoichiometries of iron oxides are found ex-tremely new applications such as data storage systems, sealingprocess, wastewater treatment, efficiency improvement of oil andgas extraction, and biomedicine applications [1–3]. Although in allthese cases, the performance modality of particles strongly de-pends on their various properties, the dependency is more sig-nificant in medicine applications particularly in vivo ones [3–6].The magnetic, biocompatibility and structural properties likecrystallinity degree are the most important characteristic of par-ticles [7–16]. On the other hand, it can be seen that these prop-erties are inseparably related to the particles size [17–19]. In fact,by variation of surface to volume ratio of materials, their magneticbehaviors (in both surface and volume scales) are considerablyaltered [18–22]. For instance, by decrease particles size in

. Ghazanfari),

nanometric scales, the magnetic anisotropy parameters are chan-ged that is directly affected to the performance of some applica-tions like magnetic hyperthermia [5–7]. Furthermore, the reduc-tion of particles size to critical limit is caused to create the su-perparamagnetic behavior that can be utilized for in vivo appli-cations i.e. magnetic targeted drug delivery and hyperthermiaprocess due to ability to prevent the particles agglomeration underapplying magnetic field [5–7]. In addition, by size diminishing ofparticles, their surfaces can be contributed more actively in surfacebiological process like absorption of drugs and other agents [23].Moreover, for in vivo applications, the particles size is identified asan influential factor to improve the particles biocompatibilityowing to the detecting of particles larger than special size by im-mune system [24–26]. However, on the other hand, the in-appropriate size of particles can be led to degrade their differentproperties. Accordingly, it can be concluded that the control ofsynthesized particles size could be very important to achieve thesupreme choice of particles properties.

Heretofore, in order to synthesize Fe3O4 nanoparticles, differenttechniques are employed such as coprecipitation, microemulsion(normal and reverse), hydrothermal, sol–gel, thermal decomposi-tion, sonochemical, and mechanochemical [27–32]. Among these,

Table 1(a) Levels of selected factors for CCD experimental design.

Effective factor Symbol Levels

Low axial Low fractional Center High fractional High axial

Temperature (°C) A 60 70 80 90 100Cation ratio (Fe2þ/Fe3þ) B 0.45 0.5 0.55 0.6 0.65pH C 9 9.5 10 10.5 11

M.R. Ghazanfari et al. / Journal of Magnetism and Magnetic Materials 405 (2016) 88–96 89

the coprecipitation method has been further developed because ofits simplicity, suitable cost, nontoxic precursors, non-time-con-suming, and industrial scale yielding [33–35]. However, in thismethod, the insignificant desired control of particle size duringsynthesize process can be known as a main challenge [36]. Sincenow, although many efforts are focused on the synthesis of par-ticles with optimum properties, the simultaneous effects of dif-ferent parameters include temperature of process, pH of processenvironment, and cationic ratio of initial solutions on the particlessize has not been comprehensively studied.

One of the most common methods to study the simultaneouseffects of significant parameters is the statistical techniques suchas neural network, Taguchi, factorial design (full and fractional(FFD)), and response surface methodology (RSM) [37–40]. RSMtechnique can be used based on different design models like Box-Behnken (BB), factorial, D-optimal, and central composite design(CCD) [39–41]. By use of this method it can be optimized thespecific features of materials like particles size according to in-fluential factors and presented the related model equation [42–44]. In recent years, this method is successfully employed to op-timizing very properties of materials reactions and synthesisprocesses [42–45].

In the current work, the simultaneous effects of some criticalparameters include cation ratio, temperature, and pH of process onthe size characteristics (i.e. hydrodynamic size, size distribution,crystallite size, and magnetic size) of synthesized Fe3O4 nano-particles by coprecipitation method were investigated by use ofRSM technique based on CCD approach. Furthermore, in following,some statistical models were presented to predict each mentionedresponse. Finally, the size of particles and their size distributionwere optimized according to contributed factors by RSMprocedure.

Table 1(b) Selected responses to study the size properties of particles by CCD experimentaldesign.

Response Symbol

Hydrodynamic size of freeze dried particles R1Size distribution R2Crystallite size R3Magnetic size R4Hydrodynamic size of air dried particles R5Zeta potential R6

2. Material and methods

2.1. Materials

In order to synthesize Fe3O4 nanoparticles, some precursorswere utilized include FeCl2 �4H2O and FeCl3.6H2O (499%, Merck)as initial salts, NH4OH (Merck) as a reduction agent, Citric acid(499.5%, Merck) as a dispersant agent, and HCl (37%, Merck).Moreover, deionized water (DI) is selected as a solvent medium ofreactions.

2.2. Design of experiments

In this work, the RSM technique was used by DESIGN EXPERTsoftware (V 7.0) to evaluate the effects of important factors onfabricated particles size. The design of experiments was done byCCD approach that is performed based on design of experiments(runs) in 5 defined levels of influential parameters (factors) such as�α, �1, 0, þ1, and þα. In CCD approach, in order to study theeffects of three independent factor, it can be designed 20 runs ofexperiment that are divided to eight full factorial points, six axialpoints (that are selected in α distance from center point), and six

replications of center point (that are used to determine the errorsof design). Furthermore, it can be designed the factors in bothcoded and actual modes according to following equation [45].

( ) δ= − ( )x X X X/ 1i i 0

where xi, Xi, X0, and δX are the coded values of factor, actual valuesof factor, actual values of factor in center point, and variation stepsof factor, respectively. In this research, three significant factorsconsist of Fe2þ/Fe3þ cations ratio, pH, and temperature of processwere selected. Moreover, the hydrodynamic size of particles (bothgroups of freeze dried and air dried), their size distribution (freezedried), crystallite size (freeze dried), magnetic size (freeze dried) ofparticles, and zeta potential of their surfaces were defined as re-sponses. The selected factors and responses are listed in Table 1(a) and Table 1 (b), respectively. According to RSM technique, it canbe fitted the quadratic polynomial model for each response asfollows.

β β β β β β β

β β β β

= + + + + + +

+ + + + ( )

Y X X X X X X X X X

X X X X X X 2

0 1 1 2 2 3 3 12 1 2 13 1 3 23 2 3

11 12

22 22

33 32

123 1 2 3

where Y is the response amount, Xi is the actual values of factors,and Bi, Bii, and Biii are the first, second, and third degree coeffi-cients, respectively.

In following, by utilization of analyses of variance (ANOVA)table, the model accuracy and related details were evaluated.Additionally, in order to study the conformity of actual data topredicted amounts by models, the R2 (R-Squared) diagrams wereplotted. Moreover, the investigation of importance of each factorwas carried out by Pareto diagrams based on the following equa-tion [46, 47].

( )∑β β( ) = × ( ≠ ) ( )P n% 100 / If 0 3n n2 2

where Pn is the effectiveness percentage of each factor and β is amark of first order and/or quadratic coefficients of significantfactors while the higher order coefficients are assumed not-sig-nificant. Finally, based on CCD approach, the plots of interactioneffects were presented and the optimization of each response wasdone.

2.3. Synthesis of Fe3O4 nanoparticles

The synthesis of nanoparticles was developed by

Table 3The results of ANOVA analysis for the response model of R1.

Source Sum ofsquares

Degreeof free-dom

Meansquare

F-value P-value Remarks

Model 336.15 9 37.35 58.52 o0.0001 SignificantA 203.78 1 203.78 319.26 o0.0001 SignificantB 11.06 1 11.06 17.32 0.0019 SignificantC 64.4 1 64.4 100.9 o0.0001 SignificantAB 0.66 1 0.66 1.04 0.3327 Not significantAC 4.65 1 4.65 7.29 0.0223 SignificantBC 8.61 1 8.61 13.49 0.0043 SignificantA2 21.2 1 21.2 33.21 0.0002 SignificantB2 6.43 1 6.43 10.07 0.0099 SignificantC2 30.05 1 30.05 47.08 o0.0001 SignificantResidual 6.38 10 0.64Lack of fit 5.21 5 1.04 4.43 0.064 Not significantPure error 1.18 5 0.24

Table 4The results of ANOVA analysis for the response model of R2.

Source Sum ofsquares

Degreeof free-dom

Meansquare

F-value P-value Remarks

Model 365.62 9 40.62 36.35 o0.0001 SignificantA 203.06 1 203.06 181.69 o0.0001 SignificantB 7.56 1 7.56 6.77 0.0264 SignificantC 76.56 1 76.56 68.51 o0.0001 SignificantAB 0.13 1 0.13 0.11 0.745 Not significantAC 3.12 1 3.12 2.8 0.1254 Not significantBC 10.13 1 10.13 9.06 0.0131 SignificantA2 15.46 1 15.46 13.83 0.004 SignificantB2 7.17 1 7.17 6.42 0.0297 SignificantC2 59.17 1 59.17 52.95 o0.0001 SignificantResidual 11.18 10 1.12Lack of fit 7.84 5 1.57 2.35 0.1847 Not significantPure error 3.33 5 0.67

M.R. Ghazanfari et al. / Journal of Magnetism and Magnetic Materials 405 (2016) 88–9690

coprecipitation method that was started by mixing of specificamounts of Fe2þ and Fe3þ chloride salts (according to stoichio-metric ratio of each run) in 100 ml DI water at N2 atmosphere.Next, the critical amounts of NH4OH were added to the mixture tocontrol the solution pH according to design. After 60 min me-chanical stirring process, the specific amounts (fixed in all runs)citric acid (1 M) were added to the solution and then it was agedfor 1 h. Finally, the synthesized particles were washed with DIwater and ethanol for 5 times after separation by magnet andcentrifuge. Subsequently, particles were dried in two groups byfreezer drier and normal air drier mechanisms.

2.4. Characterization

The characterization of hydrodynamic size and size distributionof dispersed particles in aquatic solutions were done based ondynamic light scattering (DLS) technique by particle size analyzer(MALVERN, Zetasizer Nano-ZSP). Moreover, in order to investigatethe crystallite size, according to utilize X-ray diffraction (XRD)method that was carried out by diffractometer (XRD, Bruker Ad-vance 2) using a CuKα1,2 radiation set (at 40 kV and 40 mA at roomtemperature with a 2O- range of 20–80° with step size and rate of0.03° and 6 s, respectively), the XRD patterns were analyzed byMAUD (V. 2.2) and Reflect software based on the Rietveld struc-tures and Pseudo-Voigt (PV) method [48,49]. Furthermore, by useof magnetic properties like amounts of saturation magnetization(Ms) which were resulted from vibrating sample magnetometer(VSM, Meghnatis Daghigh Kavir Co., Iran) analyses, the magneticsize of particles (the active part of particles by assumption ofsphericity) was calculated based on following equations [50].

( )( ) = μ ( )M H N L H K T/ 4B

where M, N, m, L, H, KB, and T are the total magnetization, numberof particles, magnetization of each particle, Langevin function,magnetic field, Boltzmann constant, and temperature (K), respec-tively. Langevin function can be defined as follows [50–52].

( ) = (( ( )) − ( )) ( )L X h X X1/ tan 1/ 5

where

( ) ( )= μ ( )X M V K T. . / 3 6s B02

Table 2The experimental design of RMS for three factors and obtained results.

Run order Factors Responses

A (°C) B C R1 (nm) R

1 70 0.6 9.5 14.4 12 90 0.6 10.5 20 23 80 0.55 10 18.2 24 70 0.5 9.5 14.3 15 80 0.55 10 19.2 26 80 0.55 10 19 27 80 0.55 10 19.1 28 90 0.6 9.5 22.4 29 80 0.55 10 19.1 2

10 80 0.55 10 19.7 211 80 0.55 9 18.3 212 60 0.55 10 8.4 113 80 0.65 10 19 214 70 0.6 10.5 12.2 115 100 0.55 10 22.3 216 70 0.5 10.5 10.8 117 80 0.55 11 11 118 90 0.5 9.5 24 219 90 0.5 10.5 14.6 120 80 0.45 10 15 1

where m0, Ms, and V are the free space permeability, saturationmagnetization, and average volume of particles, respectively. Onthe other hand, because of impossibility of precise amounts of Nand m, these values were estimated from the following equation.

2 (nm) R3 (nm) R4 (nm) R5 (nm) R6 (mV)

8 10.2 12.2 26.2 �35.23 16 17.9 34 �33.82 14.9 17.4 30.3 �33.48 10.1 12 25.7 �28.13 15.4 17.4 32 �32.12 15 16.9 31.6 �30.34 16.1 18.5 31.7 �32.86 18.9 22 36.8 �33.62 15 17.3 32.2 �30.33 15.3 18.8 33.9 �31.20 14.5 16.6 30.3 �25.63 6.8 9.1 17.9 �33.32 14.9 17 31.5 �19.45 9.7 11.2 24.4 �306 18.5 21.8 36.4 �233 7.8 11.1 15 �21.53 8.9 12.5 16.9 �22.18 18.9 22.7 40.1 �24.88 10.2 13.1 26.9 �26.99 11.9 14.2 27.9 �28.9

Table 5The results of ANOVA analysis for the response model of R3.

Source Sum ofsquares

Degreeof free-dom

Meansquare

F-value P-value Remarks

Model 255.87 9 28.43 51.11 o0.0001 SignificantA 153.76 1 153.76 276.45 o0.0001 SignificantB 11.9 1 11.9 21.4 0.0009 SignificantC 40.96 1 40.96 73.64 o0.0001 SignificantAB 1.81 1 1.81 3.25 0.1018 Not significantAC 9.68 1 9.68 17.4 0.0019 SignificantBC 7.22 1 7.22 12.98 0.0048 SignificantA2 12.56 1 12.56 22.58 0.0008 SignificantB2 6.78 1 6.78 12.19 0.0058 SignificantC2 22.42 1 22.42 40.31 o0.0001 SignificantResidual 5.56 10 0.56Lack of fit 4.57 5 0.91 4.63 0.059 Not significantPure error 0.99 5 0.2

Table 6The results of ANOVA analysis for the response model of R4.

Source Sum ofsquares

Degreeof free-dom

Meansquare

F-value P-value Remarks

Model 276.5 9 30.72 24.57 o0.0001 SignificantA 186.32 1 186.32 149.02 o0.0001 SignificantB 6.25 1 6.25 5 0.0494 SignificantC 35.4 1 35.4 28.31 0.0003 SignificantAB 1.81 1 1.81 1.44 0.2572 Not significantAC 17.4 1 17.4 13.92 0.0039 SignificantBC 3.65 1 3.65 2.92 0.1186 Not significantA2 9.57 1 9.57 7.66 0.0199 SignificantB2 8.44 1 8.44 6.75 0.0265 SignificantC2 17.83 1 17.83 14.26 0.0036 SignificantResidual 12.5 10 1.25Lack of fit 9.68 5 1.94 3.42 0.1016 Not significantPure error 2.83 5 0.57

Table 7The results of ANOVA analysis for the response model of R5.

Source Sum ofsquares

Degreeof free-dom

Meansquare

F-value P-value Remarks

Model 824.95 9 91.66 54.63 o0.0001 SignificantA 435.77 1 435.77 259.71 o0.0001 SignificantB 27.3 1 27.3 16.27 0.0024 SignificantC 191.13 1 191.13 113.91 o0.0001 SignificantAB 4.65 1 4.65 2.77 0.1269 Not significantAC 1.53 1 1.53 0.91 0.3619 Not significantBC 46.56 1 46.56 27.75 0.0004 SignificantA2 33.32 1 33.32 19.86 0.0012 SignificantB2 6.63 1 6.63 3.95 0.0748 Not significantC2 104.49 1 104.49 62.28 o0.0001 SignificantResidual 16.78 10 1.68Lack of fit 10 5 2 1.48 0.3397 Not significantPure error 6.77 5 1.35

Fig. 1. The Pareto diagram of effectiveness percentages of different factors on theR1 model.

M.R. Ghazanfari et al. / Journal of Magnetism and Magnetic Materials 405 (2016) 88–96 91

= μ ( )M N. 7s

Furthermore, the amounts of zeta potential of samples weremeasured by potentiometer instrument (MALVERN, ZetasizerNano ZSP).

In final, to confirm the results, the particles size was analyzedby a transmission electron microscope (TEM, JEOL 2010).

3. Results and discussion

3.1. Model equation and fitting

According to CCD approach for three factors, the designed ex-periments were concluded 20 runs such as factorial, axial, andcenter points. Table 2 lists the amounts of factors and responsesand their variations in each run. In this work, five meaningfulresponses were described which are noted with R1–R5 based onTables 1 (a) and (b). By analyzing of these results using software, aquadratic polynomial equation was proposed for each responsebased on influential factors. The fitted model of R1 can be observedas follows.

= + + – + – +

– – – ( )R A B C AB AC BC

A B C

19.06 3.57 0.83 2.01 0.29 0.76 1.04

0.92 0.51 1.09 81

2 2 2

where R1 is an average hydrodynamic size of freeze dried particlesand A, B, and C are the defined factors include temperature, cationratio, and pH, respectively. Furthermore, the quadratic modelequations for R2, R3, R4, and R5 were found which are shown infollowing.

= + + – + – +

– – – ( )R A B C AB AC BC

A B C

22.68 3.56 0.69 2.19 0.13 0.62 1.13

0.78 0.53 1.53 92

2 2 2

where R2 is the hydrodynamic size distribution of freeze driedparticles,

= + + – + – +

– – – ( )R A B C AB AC BC

A B C

15.19 3.10 0.86 1.60 0.48 1.10 0.95

0.71 0.52 0.94 103

2 2 2

where R3 is the crystallite size of freeze dried samples,

= + + – + – +

– – – ( )R A B C AB AC BC

A B C

17.62 3.41 0.62 1.49 0.48 1.47 0.68

0.62 0.58 0.84 114

2 2 2

where R4 is the magnetic size of freeze dried particles, and

= + + – – – +

– – – ( )R A B C AB AC BC

A B C

32.05 5.22 1.31 3.46 0.76 0.44 2.41

1.15 0.51 2.04 125

2 2 2

where R5 is an average hydrodynamic size of air dried particles.Moreover, because of fitting no quadratic model on zeta po-

tential data, the suggested model for this response was cubicstructure (third order equation) while the P-values of model and“Lack of fit” part were equal to 0.0081 and 0.0344, respectively.Actually, considering the signification of lack of fit part, this model

Fig. 2. The diagrams of relationship between predicted and actual values of (a) R1 response, (b) R2 response, (c) R3 response, (d) R4 response, and (e) R5 response.

M.R. Ghazanfari et al. / Journal of Magnetism and Magnetic Materials 405 (2016) 88–9692

was completely useless. In fact, due to the inhomogeneous fluc-tuations of zeta potential amounts the components of quadraticpolynomial cannot describe the trend of zeta potential variations,completely; so, the cubic structure was the only validate model todescribe the variations of zeta potential amounts because of thecubic structure not only contains the one and two order coeffi-cients but also covers the third order components of equation likeABC, AB2, and A3. However, the third order equations are relativelyunsuitable for modeling and prediction of phenomena owing totheir complex coefficients. Accordingly, it can be concluded thatthe amounts of zeta potential show no meaningful dependency tosynthesis parameters. Indeed, zeta potential amounts rely only onthe stabilization process conditions.

The ANOVA tables of fitted models of R1 to R5 responses arepresented in Tables 3–7, respectively. As can be observed, allsuggested models had relatively high amounts of F-value and theirP-values were provided o0.0001 amounts; hence, these equa-tions were considered as strongly significant models. Based onANOVA tables, the significant factor terms of models were in-cluded A, B, C, AC, BC, A2, B2, and C2 for R1, A, B, C, BC, A2, B2, and C2

for R2, A, B, C, AC, BC, A2, B2, and C2 for R3, A, B, C, AC, A2, B2, and C2

for R4, and finally A, B, C, BC, A2, and C2 for R5. In fact, the variationsof these significant factors could logically effect on the relatedresponses. In addition, the lacks of fit amounts of presentedmodels were not significant (due to ineffective pure errors) whichwere caused to confirm the models qualification. In order to studythe effectiveness percentage of each factor (including main effectsand interactions), not only the amounts of F-value can helpful, butalso the Pareto diagram is appropriate. Fig. 1 illustrates the Paretodiagram of effectiveness percentages of different factors on R1model. As a result, the temperature and solution pH (factors A and

C) were identified as the most effective parameters.Furthermore, the “Adjusted R-squared” amounts of all sug-

gested models were 490% while the “Predicted R-squared” ofthese models (except R4) were higher than 85%. The low differencebetween these values is indicated the considerable matching ofmodels and experimental data. Accordingly, the R-squaredamounts of R1, R2, R3, and R5 models were achieved more than 97%that was very suitable limit. In R4 model, the adjusted R-squared,predicted R-squared, and model R-squared were equal to about 91,73, and 95%, respectively. Although these amounts were lowerthan other models, this model was absolutely acceptable. Thesedifferences were caused by calculation of magnetic size based onmagnetization amount that was affected by many parameters likeparticle size, crystallinity degree, and the measurement errorsduring VSM technique. For instance, owing to the using of rela-tively low applied magnetic field, the measured magnetizationamounts show some minor errors. Fig. 2 indicates the diagrams ofrelationship between predicted and actual values of R1–R5 re-sponses which show the considerable matching (high R-squaredamounts).

3.2. Study of interactions and RSM analyses

In this section, in order to accurately investigate the effective-ness quality of different factors, 3D graphs of response surfaces,which were plotted based on specified polynomial functions, havebeen employed; so, it can be studied the simultaneous effects oftwo independent factors on the response. At this point, due tocomparative similarity of factors effectiveness on the differentresponses of particles size (achieved by different methods), thefactors effectiveness on the R1 and R2 were presented, downright.

Fig. 3. The 3D surface plots of simultaneous effects on responses. (a) The effects of temperature and cation ratio on the R1 (pH¼10.5), (b) The effects of temperature and pHon the R1 (cation ratio¼0.5), (c) The effects of cation ratio and pH on the R1 (temperature¼70 °C), (d) The effects of temperature and cation ratio on the R2 (pH¼10.5), (e) Theeffects of temperature and pH on the R2 (cation ratio¼0.5), and (f) The effects of cation ratio and pH on the R2 (temperature¼70 °C).

M.R. Ghazanfari et al. / Journal of Magnetism and Magnetic Materials 405 (2016) 88–96 93

Fig. 3(a) shows the 3D plot of simultaneous effects of temperature(A) and cation ratio (B) on the R1. Based on this figure, as be ex-pected, the temperature augmentation was caused to increase theparticles size. Moreover, by intensification of cation ratio, theparticles size was increased gradually due to higher crystallinitydegree of synthesized particles [53–55]. Although, both factorsshowed the enhancing effect on response, it can be seen that thetemperature is more effective factor. Accordingly, the minimumamount of response was achieved in minimum amounts of bothfactors while the largest size was related to maximum amounts ofthese factors.

In Fig. 3(b), it can be seen the 3D plot of temperature and pH (Aand C) factors on the R1 amounts. The temperature increasing wasled to particles coarsening while the pH increasing was caused toreduce the size of particles. In fact, by enhance the pH amount,synthesis reaction of ferrite nanoparticles and their nucleationprocess were accelerated and the particles size could reduce [53–

55]. According to this plot, the temperature was more effectivethan pH factor, while the lower amount of response was attainedin minimum and maximum amounts of temperature and pH ofreaction, respectively. Fig. 3(c) demonstrates the 3D plot of pH andcation ratio effects on the R1 that indicates the particles size de-crement by cation ratio augmentation and pH reduction. Further-more, it can be observed that the effectiveness of pH was sig-nificantly more than cation ratio factor, while the minimum sizeamount of particles was presented in minimum cation ratio andmaximum pH amounts.

Fig. 3(d)–(f) illustrate the 3D plots of different factors effects onthe size distribution of particles (R2). The variation trends of fac-tors effectiveness were similar to R1 plots, so that the augmenta-tion of temperature and cation ratio amounts was caused to in-crease the size distribution broadening with higher and lowerintensity, respectively. By cation ratio increasing, the size dis-tribution was widened due to augmentation of time period of

Fig. 4. The variation contour plots of factors effects on the optimized R1 and R2. (a) The effects of temperature and cation ratio on the R1 (pH¼10.5), (b) The effects oftemperature and pH on the R1 (cation ratio¼0.5), (c) The effects of cation ratio and pH on the R1 (temperature¼70 °C), (d) The effects of temperature and cation ratio on theR2 (pH¼10.5), (e) The effects of temperature and pH on the R2 (cation ratio¼0.5), and (f) The effects of cation ratio and pH on the R2 (temperature¼70 °C).

Table 8The most favorable amounts of factors to attain the optimum states of R1 and R2.

Case Target A (°C) B C R1 (nm) R2 (nm)

R1 Minimize 70 0.5 10.5 10.13R2 Minimize 70 0.5 10.5 13.01R1 and R2 Minimize 70 0.5 10.5 10.13 13.01

M.R. Ghazanfari et al. / Journal of Magnetism and Magnetic Materials 405 (2016) 88–9694

particles nucleation process [53–54]. Moreover, the pH amountenhancement was led to shift the particle size distribution rangeto narrower scales owing to acceleration of nucleation process[55]. On the other hand, the R1 and R2 plots were different invariations slopes of factors effectiveness. Consequently, in R2 plots,

Fig. 5. The XRD pattern of optimum sample (run 16) with minimum amount

the slopes of temperature and pH effectiveness were sharpercompared to R1 plots, while the slope of cation ratio effect wasdramatically decreased, especially in Fig. 3(f). It can be expectedthat the factors effectiveness show fairly similar trends on theother responses.

3.3. Optimizing

In this section, using DESIGN EXPERT software, the optimizingof influential factors were done, in order to achieve the optimumamounts of R1 and R2 (smaller size and narrower size distribution).Fig. 4(a)–(f) indicates the variation contour plots of factors effectson the optimized R1 and R2. Accordingly, in can be considered theappropriate range of responses based on valuable parameters.

s of R1 and R2. This pattern shows the broad peaks of only Fe3O4 phase.

Fig. 6. The results of magnetic properties of optimum sample (run 16) withminimum amounts of R1 and R2. (a) M–H curve of sample that shows the super-paramagnetic behavior and relatively high Ms amount. (b) Magnification of M–Hcurve in zero point range that shows very low amounts of Mr and Hc as a indicationof perfect superparamagnetic properties.

Fig. 7. TEM micrograph of optimum sample (run 16) with minimum amounts of R1and R2. This image shows that the particles size is about 10 nm with the narrowdistribution.

M.R. Ghazanfari et al. / Journal of Magnetism and Magnetic Materials 405 (2016) 88–96 95

Moreover, the point prediction of responses could be carried out indifferent points. Table 8 presents the most favorable amounts offactors to attain the optimum states of R1 and R2. Based on theseresults, the minimum particles size and size distribution werereached in minimum amounts of temperature and cation ratio andmaximum pH value.

3.4. Characterization of optimal samples

In order to study the properties of optimum samples, thestructural properties of sample of run 16 (optimum factors) wasinvestigated by XRD that its pattern can be seen in Fig. 5. Ac-cordingly, the peaks broadening were relatively wide as a result oflow crystallites size, while the peaks of Fe3O4 were clearly ob-servable. In addition, it can be detected no peaks of undesirablephases such as hematite (α-Fe2O3), goethite (α-FeOOH), and otherintermediate hydroxide phases. Based on these results, it can beconcluded that the structure properties and crystallinity degree ofsample had suitable conditions.

Furthermore, Fig. 6(a) and (b) show the plots of VSM analysis ofoptimal sample. Based on these results, the M–H curve showedcompletely reversible trend indeed hysteresis loop owing to thesuperparamagnetic behavior of synthesized particles. Moreover,the amount of Ms was about 60 (emu/g) which was a mediumvalue. In fact, because of small size of particles and relatively lowamount of applied field, the sample may not be fully saturated.

Additionally, the amounts of Hc and Mr were equal to 0.2 (Oe) and0.22 (emu/g), respectively. The negligible amounts of these para-meters can be led to verify the creation of perfect super-paramagnetic structure.

Finally, in order to confirm the results of measured particlessize by different techniques, the TEM micrograph of optimalsample can be surveyed in Fig. 7. As a result, the particle size wasabout 10 nm while the size distribution was a moderately narrowstate. These results validate the successful process to control thesize of synthesized particles based on statistical method.

4. Conclusion

In this work, initially, the Fe3O4 magnetic nanoparticles weresuccessfully synthesized by coprecipitation method. Afterward,using RMS technique, the quadratic polynomial models were fittedon the different responses such as hydrodynamic size (R1 and R5),size distribution (R2), crystallite size (R3), and magnetic size (R4).Accordingly, it can be concluded some terms as follows.

1. Although, it can be presented the acceptable predictivemodels for responses R1–R5, there was no acceptable model topredict the zeta potential variations. In fact, this response onlydepends on parameters of stabilization process.

2. Based on results, the temperature, pH, and cation ratio weredefined as main factors while the temperature and cation ratio hadpositive effects and the pH had negative role. Moreover, the in-teraction factors such as AB (positive), AC (negative), and BC (po-sitive) were effective in models. The factors A2, B2, and especiallyC2 showed negative effects on equations.

3. According to Pareto diagram, it can be seen that the tem-perature, pH and their interactions had most effectiveness on theresponses, especially R1 and R2.

4. The hydrodynamic size of freeze dried particles was com-pletely smaller compared to hydrodynamic size of normal air driedparticles; so, it was clear that the drying techniques can effect onthe size properties.

5. According to optimizing process, in order to achieve

M.R. Ghazanfari et al. / Journal of Magnetism and Magnetic Materials 405 (2016) 88–9696

minimum particle size (10.15 nm) and narrowest size distribution(13.01 nm), the minimum amounts of temperature (70 °C) andcation ratio (0.5) and maximum amount of pH (10.5) were sug-gested as optimum factors.

6. Based on characterization, the size of optimal samples wasabout 10 nm while the Ms, Hc, and Mr were equal to 60 (emu/g),0.2 (Oe) and 0.22 (emu/g), respectively. In fact, these results con-firmed the perfect superparamagnetic behavior of samples.

References

[1] Z.P. Xu, Q.H. Zeng, G.Q. Lu, A.B. Yu, Chem. Eng. Sci. 61 (3) (2006) 1027–1040.[2] A.S. Lubbe, C. Bergemann, J. Brock, D.G. McClure, J. Magn. Magn. Mater. 194 (1–

3) (1999) 149–155.[3] H. Ai, C. Flask, B. Weinberg, X. Shuai, M.D. Pagel, D. Farrell, J. Duerk, J.M. Gao,

Adv. Mater. 17 (16) (2005) 1949–1952.[4] H. Lee, H.P. Shao, Y.Q. Huang, B. Kwak, IEEE Trans. Magn. 41 (10) (2005)

4102–4104.[5] R. Jurgons, C. Seliger, A. Hilpert, L. Trahms, S. Odenbach, C. Alexiou, J. Phys.:

Condens. Matter 18 (38) (2006) S2893–S2902.[6] Y. Qiang, J. Antony, A. Sharma, J. Nutting, D. Sikes, D. Meyer, J. Nanopart. Res. 8

(3–4) (2006) 489–496.[7] R.M. Cornell, U. Schwertmann, The Iron Oxides: Structure, Properties, Reac-

tions, Occurrences and Uses, second ed., Wiley-VCH, Weinheim, 2003.[8] E. Murad, in: J.W. Stucki, B.A. Goodman, U. Schwertmann (Eds.), Series C:

Mathematical and Physical Sciences, D. Reidel, Boston, 1985.[9] U. Meisen, H. Kathrein, J. Imaging Sci. Technol. 44 (6) (2000) 508–513.[10] R. Amini, M.R. Ghazanfari, M. Alizadeh, H. Ahmadi Ardakani, Structural, mi-

crostructural and thermal properties of lead-free bismuth–sodium–barium–

titanate piezoceramics synthesized by mechanical alloying, Mater. Res. Bull. 48(2013) 482–486.

[11] R. Amini, M.R. Ghazanfari, Effect of intermediate phases on the formation ofnanostructured lead-free (Bi0.5 Na0.5)0.94 Ba0.06 TiO3 piezoceramics by me-chanical alloying, J. Alloy. Compd. 587 (25) (2014) 520–526.

[12] H. Ahmadi Ardakani, M. Alizadeh, R. Amini, M.R. Ghazanfari, Dielectric prop-erties of CaCu3Ti4O12 improved by chromium/lanthanum co-doping, Ceram.Int. 38 (2012) 4217–4220.

[13] H. Ahmadi Ardakani, M. Alizadeh, R. Amini, M.R. Ghazanfari, M. Ghaffari,Structural and phase evolution in mechanically alloyed calcium copper tita-nate dielectrics, Ceram. Int. 39 (2013) 3307–3312.

[14] R.F. Elhajjar, S.S. Shams, Compression testing of continuous fiber reinforcedpolymer composites with out-of-plane fiber waviness and circular notches,Polym. Test. 35 (2014) 45–55.

[15] S.S. Shams, R.F. El-Hajjar, Effects of scratch damage on progressive failure oflaminated carbon fiber/epoxy composites, Int. J. Mech. Sci. 67 (2013) 70–77.

[16] S.S. Shams, R.F. El-Hajjar, Overlay patch repair of scratch damage in carbonfiber/epoxy laminated composites, Composites: Part A 49 (2013) 148–156.

[17] A.S. Teja, L.J. Holm, in: Y.-P. Sun (Ed.), Supercritical Fluid Technology in Ma-terials Science and Engineering: Synthesis, Properties, and Applications,Elsevier, 2002, pp. 327–349.

[18] A.S. Teja, P.Y. Koh, Synthesis, properties, and applications of magnetic ironoxide nanoparticles, Prog. Cryst. Growth Charact. Mater. 55 (1–2) (2009)22–45.

[19] H. Yin, H.P. Too, G.M. Chow, The effects of particle size and surface coating onthe cytotoxicity of nickel ferrite, Biomaterials 26 (29) (2005) 5818–5826.

[20] J. Lai, K.V.P.M. Shafi, A. Ulman, K. Loos, N.L. Yang, M.H. Cui, T. Vogt,C. Estournès, D.C. Locke, Mixed iron-manganese oxide nanoparticles, J. Phys.Chem. B 108 (39) (2004) 14876–14883.

[21] J.K. Oh, J.M. Park, Iron oxide-based superparamagnetic polymeric nanoma-terials: design, preparation, and biomedical application, Prog. Polym. Sci. 36(1) (2011) 168–189.

[22] D.S. Mathew, R.-S. Juang, An overview of the structure and magnetism ofspinel ferrite nanoparticles and their synthesis in microemulsions, Chem. Eng.J. 129 (1–3) (2007) 51–65.

[23] D.S. Mathew, R.S. Juang, An overview of the structure and magnetism of spinelferrite nanoparticles and their synthesis in microemulsions, Chem. Eng. J. 129(1–3) (2007) 51–65.

[24] R.E. Rosensweig, in: K.H.J. Buschow, W.C. Robert, C.F. Merton, I. Bernard, J.K. Edward, M. Subhash, V. Patrick (Eds.), Introduction, Encyclopedia of Ma-terials: Science and Technology, Elsevier, Oxford, 2001.

[25] A. Lascialfari, C. Sangregorio, Nanoparticles in biomedicine, Chim. Oggi/Chem.Today 29 (2) (2011) 20–23.

[26] B.D. Cullity, C.D. Graham, Introduction to magnetic materials, Wiley, NewJersey, USA, 2009.

[27] D.K. Kim, Y. Zhang, W. Voit, K.V. Rao, M. Muhammed, J. Magn. Magn. Mater.

225 (1–2) (2001) 30–36.[28] A. Tavakoli, M. Sohrabi, A. Kargari, Chem. Pap. 61 (3) (2007) 151–170.[29] Y.L. Hao, A.S. Teja, J. Mater. Res. 18 (2) (2003) 415–422.[30] U.T. Lam, R. Mammucari, K. Suzuki, N.R. Foster, Ind. Eng. Chem. Res. 47 (3)

(2008) 599–614.[31] R. Hao, R. Xing, Z. Xu, Y. Hou, S. Gao, S. Sun, Synthesis, functionalization, and

biomedical applications of multifunctional magnetic nanoparticles, Adv. Ma-ter. 22 (25) (2010) 2729–2742.

[32] C. Boyer, V. Bulmus, J. Liu, T.P. Davis, The design and utility of polymer-sta-bilized iron-oxide nanoparticles for nanomedicine applications, NPG AsiaMater. (2010) 23–30.

[33] E. Auzāns, R. Massart , Mn–Zn Ferrite Nanoparticles for Water-and Hydro-carbone-based Ferrofluids: Preparation and Properties, 1999.

[34] E. Auzans, D. Zins, E. Blums, R. Massart, Synthesis and properties of Mn–Znferrite ferrofluids, J. Mater. Sci. 34 (6) (1999) 1253–1260.

[35] R. Ahmadi, H.R. Madaah hosseini, A. Masoudi, Avrami behavior of magnetitenanoparticles formation in co-precipitation process, J. Min. Metall. 47 (2)(2011) 211–218.

[36] R. Massart, E. Dubois, V. Cabuil, E. Hasmonay, Preperation and properties ofmonodisperse magnetic fluid, J. Magn. Magn. Mater. 149 (1995) 1–5.

[37] V.A. Sakkas, M.A. Islam, C. Stalikas, T.A. Albanis, Photocatalytic degradationusing design of experiments: a review and example of the Congo red de-gradation, J. Hazard. Mater. 175 (2010) 33–44.

[38] M. Antonopoulou, I. Konstantinou, Optimization and modeling of the photo-catalytic degradation of the Insect repellent DEET in aqueous TiO2 suspen-sions, Clean – Soil Air Water 41 (2013) 593–600.

[39] A.R. Khataee, M.B. Kasiri, L. Alidokht, Application of response surface metho-dology in the optimization of photocatalytic removal of environmental pol-lutants using nanocatalysts, Environ. Technol. 32 (2011) 1669–1684.

[40] M. Ahmadi, F. Vahabzadeh, B. Bonakdarpour, E. Mofarrah, M. Mehranian,Application of the central composite design and response surface methodol-ogy to the advanced treatment of olive oil processing wastewater using Fen-ton’s peroxidation, J. Hazard. Mater. 123 (2005) 187–195.

[41] J.P. Wang, Y.Z. Chen, X.W. Ge, H.Q. Yu, Optimization of coagulation–floccula-tion process for a paper-recycling wastewater treatment using response sur-face methodology, Colloids Surf. A: Physicochem. Eng. Asp. 302 (2007)204–210.

[42] B. Ebrahimi, S. Shojaosadati, S. Ranaie, S. Mousavi, Optimization and evalua-tion of acetylcholine esterase immobilization on ceramic packing using re-sponse surface methodology, Process. Biochem. 45 (2010) 81–87.

[43] M.A. Bezerra, R.E. Santelli, E.P. Oliveira, L.S. Villar, L.A. Escaleira, Responsesurface methodology (RSM) as a tool for optimization in analytical chemistry,Talanta 76 (2008) 965–977.

[44] Y. Li, F. Cui, Z. Liu, Y. Xu, H. Zhao, Improvement of xylanase production byPenicillium oxalicum ZH-30 using response surface methodology, Enzym.Microb. Technol. 40 (2007) 1381–1388.

[45] S. Ahmadi, M. Manteghian, H. Kazemian, S. Rohani, J. Towfighi Darian,Synthesis of silver nanocatalyst by gel-casting using response surface meth-odology, Powder Technol. 228 (2012) 163–170.

[46] M. Zarei, A. Niaei, D. Salari, A. Khataee, Application of response surfacemethodology for optimization of peroxi-coagulation of textile dye solutionusing carbon nanotube–PTFE cathode, J. Hazard. Mater. 173 (2010) 544–551.

[47] L. Sun, S. Wan, Z. Yu, L. Wang, Optimization and modeling of preparationconditions of TiO2 nanoparticles coated on hollow glass microspheres usingresponse surface methodology, Sep. Purif. Technol. 125 (2014) 156–162.

[48] L. Lutterotti, P. Scardi, P. Maistrelli, LS1-A computer program for simultaneousrefinement of material structure and microstructure, J. Appl. Cryst. 25 (1992)459–462.

[49] R.A. Young, D.B. Wiles, Profile shape functions in rietveld refinements, J. Appl.Cryst. 15 (1982) 430–438.

[50] D.X. Chen, A. Sanchez, E. Taboada, A. Roig, N. Sun, H.C. Gu, Size determinationof superparamagnetic nanoparticles from magnetization curve, J. Appl. Phys.105 (2009) 083924.

[51] N.A. Spaldin, Magnetic Materials: Fundamentals and Device Applications,Cambridge University Press, New York, 2003.

[52] D. Gopman, E. Belogay, K. Sorge, Nanoparticles in the Mirror are Smaller thanthey Appear Sixth Annual Harriet L. Wilkes Honors College Symposium forResearch and Creative Projects Featuring the Chastain Honors SymposiumLecture Series, 2008.

[53] H. Meng, Z. Zhang, F. Zhao, T. Qiu, J. Yang, Orthogonal optimization design forpreparation of Fe3O4 nanoparticles via chemical coprecipitation, Appl. Surf.Sci. 280 (2013) 679–685.

[54] S.H. Kim, M.J. Kim, Y.H. Choa, Fabrication and estimation of Au-coated Fe3O4

nanocomposite powders for the separation and purification of biomolecules,Mater. Sci. Eng. A 449–451 (2007) 386–388.

[55] S. Wu, A. Sun, F. Zhai, J. Wang, W. Xu, Q. Zhang, A.A. Volinsky, Fe3O4 magneticnanoparticles synthesis from tailings by ultrasonic chemical co-precipitation,Mater. Lett. 65 (2011) 1882–1884.