See discussions, stats, and author profiles for this publication at: https://www.researchgate.net/publication/268527758 Inﬂuencing factors in the CO-precipitation process of superparamagnetic iron oxide nano particles: A model based study Article in Journal of Magnetism and Magnetic Materials · March 2015 DOI: 10.1016/j.jmmm.2014.10.074 CITATIONS READS 38 185 5 authors, including: Christian Roth Sebastian Patrick Schwaminger Drees & Sommer AG Technische Universität München 6 PUBLICATIONS 74 CITATIONS 23 PUBLICATIONS 149 CITATIONS SEE PROFILE SEE PROFILE Friedrich E. Wagner Sonja Berensmeier Technische Universität München Technische Universität München 123 PUBLICATIONS 1,516 CITATIONS 53 PUBLICATIONS 815 CITATIONS SEE PROFILE Some of the authors of this publication are also working on these related projects: Magnetic separation View project All content following this page was uploaded by Sonja Berensmeier on 02 November 2018. The user has requested enhancement of the downloaded file. SEE PROFILE Journal of Magnetism and Magnetic Materials 377 (2015) 81–89 Contents lists available at ScienceDirect Journal of Magnetism and Magnetic Materials journal homepage: www.elsevier.com/locate/jmmm Inﬂuencing factors in the CO-precipitation process of superparamagnetic iron oxide nano particles: A model based study Hans-Christian Roth a, Sebastian P. Schwaminger a, Michael Schindler a, Friedrich E. Wagner b, Sonja Berensmeier a,n a b Bioseparation Engineering Group, Technische Universität München, Boltzmannstraße 15, Garching d-85748, Germany Technische Universität München, Physics Department El5, Garching d-85748, Germany art ic l e i nf o a b s t r a c t Article history: Received 19 March 2014 Received in revised form 27 August 2014 Accepted 16 October 2014 Available online 22 October 2014 The study, presented here, focuses on the impact of synthesis parameters on the co-precipitation process of superparamagnetic iron oxide nanoparticles. Particle diameters between 3 and 17 nm and saturation magnetizations from 26 to 89 Am2 kg 1 were achieved by variation of iron salt concentration, reaction temperature, ratio of hydroxide ions to iron ions and ratio of Fe3 þ /Fe2 þ . All synthesis assays were conceived according to the “design of experiments” method. The results were ﬁtted to signiﬁcant models. Subsequent validation experiments could conﬁrm the models with an accuracy495%. The characterization of the chemical composition, as well as structural and magnetic properties was carried out using powder X-ray diffraction, transmission electron microscopy, Raman and Mössbauer spectroscopy and superconducting quantum interference device magnetometry. The results reveal that the particles' saturation magnetization can be enhanced by the employment of high iron salt concentrations and a molar ratio of Fe3 þ /Fe2 þ below 2:1. Furthermore, the particle size can be increased by higher iron salt concentrations and a hyperstoichiometric normal ratio of hydroxide ions to iron ions of 1.4:1. Overall results indicate that the saturation magnetization is directly related to the particle size. & Published by Elsevier B.V. Keywords: Iron oxide nanoparticles Design of experiment Magnetite Alkaline co-precipitation Mössbauer spectroscopy X-ray diffraction 1. Introduction Superparamagnetic nano-particles (MNP) have been studied intensively in the last decades, due to fundamental scientiﬁc interest and the wide range of applications. Particularly, their chemical, physical and especially magnetic properties predestine them as excellent materials for a variety of research ﬁelds. The applications range from wastewater treatment [1,2], to electrode material in lithium ion batteries [3–5], magnetic ﬂuids respectively ferroﬂuids [6–8], used e.g. as magnetic inks for jet printing [9,10], biosensing applications [11,12], medical applications, such as targeted drug delivery [13,14], contrast agents in magnetic resonance imaging [15–20], and also biotechnological processing [21–24] and carrier materials for biocatalysts , to sum up a few. All of these applications require high magnetization values and a narrow particle size distribution. Numerous synthesis methods have been developed to satisfy the demand for MNP with controlled properties including sol–gel syntheses , hydrothermal reactions , sonochemical procedures , hydrolysis and thermolysis of precursors , electrospray n Corresponding author. Tel.: +49 89 289 15750; fax: +49 89 289 15766. E-mail address: [email protected] (S. Berensmeier). http://dx.doi.org/10.1016/j.jmmm.2014.10.074 0304-8853/& Published by Elsevier B.V. synthesis  and the synthesis from microemulsions . However, one common and economic method for the controlled synthesis of large amounts of superparamagnetic MNPs, without any stabilizing surfactant agents, is the chemical co-precipitation of iron salts in an alkaline environment, [32–38] known as the Massart process [39,40]. The chemical reaction of dissolved iron salts to magnetite (Fe3O4) can be formulated according to Eq. (1) . Fe2 þ þ2Fe3 þ þ8OH -Fe3O4 þ 4H2O (1) As is well known, certain reaction conditions in this process and the dimensions of the nanocrystals have a signiﬁcant impact on the physical, chemical, structural and magnetic properties of the MNP [42–44]. Therefore, our motivation is to predict the effect of reaction conditions on particle properties, with this being the key to produce customized MNPs for a variety of applications. In this study, we proceeded with a statistical approach to describe and predict the impact of the main synthesis parameters in the Massart process on the properties of obtained MNP. We chose the Fe3 þ /Fe2 þ -ratio, the ratio of hydroxide to iron ions with respect to Eq. (1), the synthesis temperature and the concentration of iron salts as parameters of interest in our investigation. All syntheses were operated in a well-deﬁned and controlled reaction setup. Magnetic properties of MNP were characterized by a superconducting quantum interference device (SQUID), chemical 82 H.-C. Roth et al. / Journal of Magnetism and Magnetic Materials 377 (2015) 81–89 and structural properties by powder X-ray diffraction (XRD), electron microscopy (TEM) and Mössbauer- and Raman spectroscopy. All experiments were planned by the software Design Expert 8 (Stat-Ease, Inc. USA), based on the method “design of experiments” (DoE). Resulting models were conﬁrmed by validation experiments. 2. Experimental 2.1. Synthesis and characterization of MNPs All syntheses of iron oxide nanoparticles were processed by the co-precipitation of Fe2 þ and Fe3 þ aqueous salt solutions in alkaline environment as reported previously . Ferric chloride (FeCl3∙6H2O) and sodium hydroxide (NaOH) were purchased from AppliChem GmbH, Germany. Ferrous chloride (FeCl2∙4H2O) was purchased from Bernd Kraft GmbH, Germany. Aqueous solutions of iron salts and sodium hydroxide were prepared with degassed and deionized water. The precipitation of iron oxide nanoparticles was performed under nitrogen atmosphere in order to prevent oxidation of the precursors and the product. Process conditions, such as temperature, pH value, dosing rate and stirring speed, were controlled and monitored by the fully automated OptiMax™ synthesis station (Mettler–Toledo GmbH, Germany). The reaction setup was prepared as follows: The reactor vessel was ﬁlled with 500 mL of a sodium hydroxide solution. The desired reaction temperature was adjusted while the solution was stirred under a steady ﬂow of nitrogen. Ferric- and ferrous chloride solutions were prepared separately and mixed thoroughly in a ﬂask maintained under nitrogen atmosphere. Using a dosing pump, the iron salt mixture was added to the stirred reactor vessel with a constant volumetric ﬂow of 150 mL min 1. Instantly, a brown-black precipitate was formed and the reaction proceeded for 30 min under mechanical stirring. In order to lower the content of salt and residual sodium hydroxide, the precipitate was washed several times with degassed and deionized water-using a NdFeB permanent magnet to support separation of the precipitate from solution. The washing procedure was performed until the solution′ s conductivity dropped below 200 mS cm 1. A colloidal sample was stored under nitrogen atmosphere for transmission electron microscopy (TEM) imaging while the rest of the precipitate was lyophilized with an ALPHA 1-2LDplus (Martin Christ Gefriertrocknungsanlagen GmbH, Germany). Crystal structure and phase purity of the precipitates were examined at room temperature via powder X-ray diffraction (XRD) from the lyophilized samples. The Stadi-P (STOE & Cie GmbH, Germany), employed in this work, is equipped with a molybdenum source (Ge (111) monochromator, Kα1 radiation (λ ¼0.7093 Å) and a Mythen 1 K detector (DECTRIS Ltd., Switzerland). Data was collected in the range from 2° to 50° (2θ). The software package STOE WinXPOW (STOE & Cie GmbH, Germany) was used for indexing and reﬁnement purposes. For more detailed information about phase purity, the colloidal samples were frozen and analyzed via 57Fe Mössbauer spectroscopy at 4.2 K in a liquid He bath cryostat. Data was recorded with a source of 25 mCi of 57 Co in Rh in transmission geometry using a conventional spectrometer with sinusoidal velocity waveform. Both, the absorber and the source were held at 4.2 K throughout all measurements. The spectra were ﬁtted with a superposition of Lorentzian lines grouped into sextets for ferric and octets for ferrous iron ions using MOS90 software version 2.2. The ﬁtted components often show broadened lines and have to be considered as representing distributions of magnetic hyperﬁne ﬁelds. The chemical composition was also determined by Raman spectroscopy. A Senterra Raman spectrometer (Bruker Corporation, USA), equipped with a 785 nm laser, was used for the analysis of particles produced. Low laser powers were chosen in order to prevent oxidation of samples. A baseline correction was accomplished, applying the rubber band method, by the software OPUS for each spectrum measured by Raman spectroscopy. The mean diameters (dp) of the precipitated crystallites were estimated from XRD-data using the Scherrer equation employing a shape factor of 0.89 for spherical magnetite particles . Additionally, the particle dimensions were assessed by transmission electron microscopy (TEM) using a JEM 100-CX (JEOL GmbH, Germany). For the TEM measurements the colloidal samples were diluted in degassed and deionized water and sonicated in order to disperse any agglomerates. Dilution of the samples was controlled by visual light spectroscopy at 475 nm, measured in polystyrene cuvettes with 10 mm optical path length with the Inﬁnites 200 Pro (Tecan Group Ltd., Switzerland). Values of adsorption were adjusted from 0.2 to 0.25. All samples were repeatedly homogenized by sonication, without addition of any stabilizing surfactant agents, before placing a droplet of each 5 mL on a carbon coated copper grid (Quantifoil Micro Tools GmbH, Germany), respectively. The grids were allowed to dry under ambient conditions. In order to cover a representative imaging of the samples, each copper grid was evaluated with a number of three pictures from different locations. The pictures were manually processed in ImageJ (java based open source). A number of 100 particles per sample were measured in randomized order. Magnetic properties of the precipitates were characterized with a superconducting quantum interference device (SQUID) magnetometer MPMSs (Quantum Design Inc., USA) at a temperature of 300 K. The magnetic ﬂux density was varied from 5 T to þ5 T. 2.2. Experimental design The setup of reaction conditions was planned using “design of experiments”. Response surface methodology (RSM) was used to create a central composite face centered design (CCF). Four parameters, discussed in the literature [16,46,47] to have signiﬁcant inﬂuence on saturation magnetization (MS) and particle size (dp), were embedded into the design. Variables chosen for the design are the total amount of iron (A), synthesis temperature (B), the molar ratio of Fe3 þ to Fe2 þ (C) and the ratio of sodium hydroxide to iron salts (D). Factor D is deﬁned as a variable, which indicates whether the ratio of sodium hydroxide to iron ions is hypostoichiometric (D o1), stoichiometric (D ¼1) or sodium hydroxide is present in excess (D41) with respect to Eq. (1). The range and levels of investigated parameters are listed in Table 1 and were chosen to model the widest possible sector of reaction conditions within physically and technically reasonable borders. The inﬂuence was evaluated according to the CCF design with ﬁve repetitions at the center point (0 0 0 0) (Table 2). Saturation magnetization and mean particle diameter were chosen as response parameters for the design. In analogy to DoE, all assays were carried out in randomized order. Graphical analysis and regressions of obtained data were carried out with the software Design Expert 8. The DoE model was validated with three randomly chosen values within the range of the CCF design for the factors A, B, C and D, shown in Table 3. 3. Results and discussion Iron oxide nanoparticles were prepared from iron salts in alkaline environment, according to a modiﬁed Massart porcess [39,40]. Synthesis assays were processed with parameter combinations H.-C. Roth et al. / Journal of Magnetism and Magnetic Materials 377 (2015) 81–89 Table 1 Factor levels used according to the CCF design. Variable parameter Level A: total amount of iron (mol) B: temperature (°C) C: Fe3 þ /Fe2 þ ratio (mol mol 1) D: NaOH/Fe ratio 1 0 þ1 0.006 5 1.5 0.8 0.303 47 2.25 1.4 0.6 90 3 2 Table 2 Values for saturation magnetization (Ms) and mean particle diameter (dp) (XRD) according to the CCF design. Assay 01 02 03 04 05 06 07 08 09 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 Variable Response A B C D MS (Am2 kg 1) dp (nm) 0 1 1 1 1 1 1 1 1 1 0 0 0 0 0 0 0 0 0 0 0 þ1 þ1 þ1 þ1 þ1 þ1 þ1 þ1 þ1 0 þ1 þ1 þ1 1 1 1 þ1 1 0 0 0 0 0 1 þ1 0 0 0 0 0 þ1 þ1 0 1 þ1 1 þ1 1 1 0 þ1 þ1 0 0 þ1 þ1 1 1 0 1 0 0 0 0 0 þ1 0 0 0 0 þ1 þ1 0 1 1 1 1 þ1 þ1 0 1 þ1 þ1 þ1 þ1 1 1 1 0 0 0 1 0 0 0 0 0 þ1 0 0 þ1 1 0 þ1 1 1 þ1 1 þ1 89.19 33.01 33.58 79.37 66.95 26.76 2.06 67.27 40.51 65.08 82.48 56.15 63.79 71.40 75.42 71.36 61.77 74.53 76.69 73.23 77.06 60.67 59.52 81.22 70.45 78.93 73.26 79.31 16.70 59.18 15.2 6.3 6.4 12.5 9.0 3.3 n.a. 10.4 8.0 8.3 16.3 13.3 10.4 12.1 12.1 13.7 9.7 15.2 15.7 13.7 13.7 11.5 11.5 15.7 14.7 15.7 17.0 17.0 14.2 10.9 n.a.¼ not accessible Table 3 Factors used for validation of the DoE model. Variable parameter A: total amount of iron (mol) B: temperature (°C) C: Fe3 þ /Fe2 þ ratio (mol mol 1) D: NaOH/Fe ratio Validation assays V01 V02 V03 0.4 85 1.7 1.2 0.5 25 1.9 1.8 0.2 70 2.6 1.0 shown in Table 2. All samples were characterized by powder X-ray diffraction. Particle mean diameters were estimated by aid of the Scherrer equation and are presented in Table 2. The mean diameters of the iron oxide nano-particles range from 3 nm to 17 nm, which is in good agreement with values reported in literature [42,48]. Results of assays 7 and 29 are not included in the DoE model. Due to unfavorable synthesis parameters in assay 7, no precipitation occurred and a red salt solution remained. Therefore, neither powder XRD nor SQUID analyses could be performed on this assay. The results of assay 29 are discussed separately. 83 Powder XRD patterns of samples representing the change of one single synthesis condition from center point conﬁguration (0 0 0 0) are displayed in Fig. 1. Within the detection limit, peaks of all patterns could be attributed to the Bragg positions of magnetite given by the WinXPOW database. The decrease in peak width with increasing values for factor A, is evidenced by our measurements. This means an increase in particle size with higher iron salt concentrations in the synthesis assays. In classical nucleation theory [49,50] the formation of crystals in the co-precipitation process is described by two single steps, in which a short burst of nucleation from a supersaturated solution is followed by a slow growth of the crystals. Therefore, an increase of factor A is thought to enforce the nucleation process by elevating the relative supersaturation of the solvent. As a result, more nuclei are formed, leading to smaller particles. It is conceivable, that our reaction set up with a good homogenization of the reaction volume in combination with a deﬁned dosing of the iron salt solution leads to a decrease of relative supersaturation. Compared to an instantaneous addition of the iron salts, this might enforce the process of particle growth. This effect is described in literature, where the addition of the iron salt solution to alkaline solution is performed at deﬁned dosing rates [16,32]. Within our reaction setup, the particle size is barely affected by changes of factor B, tending to bigger particles at higher temperatures [16,41]. It is well known that the ratio of Fe3 þ /Fe2 þ in the co-precipitation method strongly affects the properties of iron oxide products [46,48]. The mean particle size rises with decreasing values of factor C . This effect can be explained by an analysis of the reaction pathway of iron oxides. Hence, primary nuclei, formed after super saturation of an iron salt solution, consist of Fe3 þ -species . Thus, higher values of factor C lead to a larger number of nuclei and therefore to more, but smaller particles for an equal amount of iron salts. The ratio of sodium hydroxide to the total amount of iron salt reduces the ﬁnal particle diameter for D ¼0.8. Taking into account, that the formation of magnetite takes place at pH values exceeding 8 , the formation of byproducts is likely, when the reaction has consumed the limited amount of base . As the byproducts are produced only at the very end of the synthesis, when the pH value drops below 8, their amount proves to be very low and might be lost in the magnetic washing procedure after synthesis. The reaction pathway of iron to magnetite switches to the formation of byproducts like lepidocrocite and goethite  for values of factor Do1, leading to smaller MNPs. There is no signiﬁcant inﬂuence of factor D on the particle diameter for D ¼1.4 and D¼ 2. This result is in good accordance with the data of Khalafalla and Reimers . According to the thermodynamics of the chemical reaction described in Eq. (1), the complete precipitation of Fe3O4 should be expected at a pH between 8 and 14, with a molar Fe3 þ /Fe2 þ -ratio of 2 in a non-oxidizing environment. . Reaction conditions that strongly differ from the ideal reaction parameters of magnetite can lead to various byproducts [52,54]. A good example is assay 29 (Table 2), where factor C is 3 mol mol 1 and factor D is 0.8. The powder XRD pattern of sample 29 in Fig. 2 can be attributed to iron oxyhydroxide species. Corresponding Bragg reﬂections are listed in Table 4. In order to conﬁrm XRD-data concerning phase and composition, Mössbauer spectroscopic analyses were carried out on selected samples. All spectra attest to the presence of a nanoscale magnetite phase, except for sample 29. Mössbauer patterns of assay 29 and the samples prepared with the center point parameters are presented in Fig. 3. Parameters of ﬁtted spectra are summarized in Table 5. The isomer shift was received from the experiment without further manipulation, i.e. with respect to the source of 57Co in Rh at 4.2 K. To refer them to metallic iron at ambient temperature, 0.24 mm s 1 have to be added. 84 H.-C. Roth et al. / Journal of Magnetism and Magnetic Materials 377 (2015) 81–89 Fig. 1. Powder X-ray diffractograms of magnetic iron oxide nano-particles synthesized with variation in a) total amount of iron salts, b) reaction temperature, c) Fe3 þ /Fe2 þ ratio, and d) NaOH/Fe ratio. All other parameters are based on the center point assay (Table 2). Table 4 Characteristic Bragg reﬂection angles (XRD) with corresponding lattice planes of iron oxides contributed to sample 29 (Fig. 2). Lepidocrocite Fig. 2. Powder X-ray diffractogram of sample 29 consisting of lepidocrocite and goethite. The Mössbauer data conﬁrms the powder XRD results concerning phase composition of sample 29. The ﬁtted parameters are in good accordance with literature and imply the presence of goethite, lepidocrocite and maghemite [55,56]. The presence of ferrous chloride (FeCl2) in sample 29 can be explained by the hypostoichiometric ratio of sodium hydroxide to iron salts with respect to Eq. 1. Once, the sodium hydroxide is consumed in the reaction, the excess amount of FeCl2 remains in sample 29. Although the washing procedure was carried out thoroughly, some residual FeCl2 could be detected by Mössbauer spectroscopy. An additional investigation towards the chemical composition of precipitated iron oxides was accomplished by Raman spectroscopy. Goethite Angle (2θ) Plane Angle (2θ) Plane 6.47 12.34 13.67 16.44 17.23 19.49 21.02 23.56 26.83 29.47 020 120 011 031 111 060 200 151 231 112 8.13 9.68 15.10 15.74 17.98 020 110 130 021 210 Data of all samples was acquired at a low laser power of 0.1 mW by a 785 nm laser source. All spectra but samples 7 and 29 evidence the presence of magnetite while maghemite and other iron oxides are not indicated. Raman spectra conﬁrm the Mössbauer and XRD data concerning the compositions of the samples investigated. Characteristic Raman shifts for magnetite could be detected and are demonstrated for a center point sample in Fig. 4b). Here the typical main band at 667 cm 1 and also minor peaks at 536 and 310 cm 1 can be observed, which are in good agreement with literature values [56–59]. Though, assay 29 shows characteristic iron oxide and iron oxyhydroxide peaks. What is more, the Mössbauer and XRD data is conﬁrmed by Raman spectroscopy as consisting of goethite, lepidocrocite and maghemite which can be detected here. The Raman shifts 385 and 299 cm 1 indicate goethite while H.-C. Roth et al. / Journal of Magnetism and Magnetic Materials 377 (2015) 81–89 85 Fig. 3. Mössbauer spectra of a) assay 29 and b) center point. Table 5 Parameters of Mössbauer spectra displayed in Fig. 3. Sample Iron oxide ΔEQ δ Bhf Area (%) 29 Goethite Lepidocrocite Maghemite (A) Maghemite (B) Ferrous chloride Magnetite (A) Magnetite (B1) 0.171 0.018 0.015 0.000 3.285 0.029 0.057 0.278 2.126 0.631 0.513 1.104 0.26 0.25 0.10 0.3 1.09 0.15 0.32 0.24 0.80 0.79 0.82 0.96 49.5 45.4 50.7 51.5 – 50.6 52.8 50.0 48.8 49.2 44.5 39.2 31.1 38.1 14.0 15.2 1.6 37.6 18.2 15.8 12.2 6.5 6.0 3.7 Center point Magnetite (B2) Quadrupole splittings (ΔEQ) and isomer shifts (δ) given in mm s 1; magnetic hyperﬁne ﬁelds (Bhf) in T. the bands at 250 and 528 cm 1 demonstrate the presence of lepidocrocite and the band at 730 cm 1 can be referred to maghemite [56–58]. Magnetic properties of all samples were characterized by a SQUID magnetometer at 300 K in hysteresis mode, varying the magnetic ﬂux density from 5 to 5 T. Values obtained for saturation magnetization MS of the samples range from 16.7 to 89.19 Am2 kg 1, which is in good agreement with iron oxide nanoparticles synthesized by co-precipitation . All samples tend to show superparamagnetic behavior, characteristic for iron oxide particles with a mean diameter smaller than 20 nm [60,61]. Values of remanence vary from 0.03 to 1.67 Am2 kg 1 and indicate the superparamagnetic character of the MNPs. Hysteresis loops of magnetization (M) versus magnetic ﬂux density (B) for samples, a b intensity (a.u.) intensity (a.u.) 100 representing the change of one single synthesis condition from the center point conﬁguration, are displayed in Fig. 5. Results show that values for MS of the iron oxide nano-particles can be tailored from 65 to 81 Am2 kg 1 by changing factor A from 0.006 to 0.6 mol, respectively. The variation of factor B barely modiﬁes MS in our reaction setup. Therefore, MS can be elevated from 62 to 82 Am2 kg 1 by reducing values of factor C from 3 to 1.5 mol mol 1, respectively. A similar impact on MS by the Fe3 þ /Fe2 þ -ratio is reported in literature [46,51]. The increase of factor D from 0.8 to 2 leads to an increase of MS from 64 to 77 Am2 kg 1. Iron oxide particles synthesized at D¼1.4 and 3 barely differ in their values of MS. Particles prepared at D¼0.8 show a signiﬁcant drop in MS which can be attributed to byproducts with lower saturation magnetization than magnetite occurring at pH valueso8 . Magnetic properties of iron oxide nanoparticles strongly depend on crystallinity and particle size [63,64]. Values of saturation magnetization reported for nanoscale material (65 to 70 Am2 kg 1) range below the values of bulk material (92 to 100 Am2 kg 1) . The discrepancy in MS between the bulk and the nanoscale material can be explained by the higher number of lattice defects with increasing surface to volume ratio for smaller particles. This correlation is visualized in Fig. 6, where MS is plotted versus the corresponding particle diameter of the iron oxide nanoparticles prepared in this work. Data sets of samples containing other materials than magnetite are not included. Though, all samples prepared in this study are proven to be nanoscaled by XRD measurements, high saturation magnetizations close to those reported for bulk material could be achieved for assay 01, 11 and 24. The decrease of MS from about 83 Am2 kg 1 to about 27 Am2 kg 1 at particle diameters from 16 nm to 3 nm in this study suggests a linear dependence of MS on particle size. 300 500 700 900 Raman shift (cm-1) 1100 1300 1500 100 300 500 700 900 Raman shift (cm-1) Fig. 4. Raman Spectra of a) assay 29 and b) center point at 0.1 mW laser power. 1100 1300 1500 86 H.-C. Roth et al. / Journal of Magnetism and Magnetic Materials 377 (2015) 81–89 a 100 40 B = 90°C B = 47.5°C B = 5°C 80 60 M (Am2 kg-1) M (Am2 kg-1) 60 100 b A = 0.6 mol A = 0.303 mol A = 0.006 mol 80 20 0 -20 -40 40 20 0 -20 -40 -60 -60 -80 -80 -100 -100 -6 -4 -2 0 2 4 6 -6 -4 -2 B (T) c 100 40 2 4 6 2 4 6 D=2 D = 1.4 D = 0.8 80 60 M (Am2 kg-1) M (Am2 kg-1) 60 100 d C = 3 mol mol-1 C = 2.25 mol mol-1 C = 1.5 mol mol-1 80 0 B (T) 20 0 -20 -40 -60 40 20 0 -20 -40 -60 -80 -80 -100 -100 -6 -4 -2 0 2 4 6 B (T) -6 -4 -2 0 B (T) Fig. 5. SQUID measurements of MNPs synthesized with variation in a) the total amount of iron salts, b) the reaction temperature, c) the ratio of Fe3 þ /Fe2 þ and d) the ratio of NaOH/Fe. Other parameters are based on the center point assay (Table 2). Fig. 6. Correlation of saturation magnetization of iron oxide nano-particles with the particle diameter. Geometric properties of the produced iron oxide nanoparticles were investigated by TEM imaging to conﬁrm the particle size estimated from powder XRD-data via the Scherrer equation. Assay conditions were selected randomly. Particle size was determined manually using the software ImageJ. TEM images and determined particle size distributions of sample 10 and 19 are shown in Fig. 7. All TEM images show a roughly gaussian-shaped particle size distribution. The mean particle diameter of sample 19 and sample 10 is determined as 15.1 nm and 8.2 nm, respectively, which is in very good agreement with the particle diameters determined from powder XRD data. A more detailed discussion referring to the correlation of colloidal particle sizes obtained from XRD and TEM can be found in . TEM images show that most particles, with a diameter smaller than 10 nm, have a spherical shape, while bigger particles often have a distinct octahedral shape. All data from powder XRD and SQUID analysis was incorporated in Design Expert 8 and computed to a model with a quadratic approach. Manual optimization was carried out by purging the model of insigniﬁcant terms and interactions (P o0.3). Table 6 summarizes the results of the statistical analyses for the response parameters dp and MS. In our model, each of the investigated synthesis parameters proves to affect the saturation magnetization of the particles prepared with a statistical signiﬁcance above 96.8% conﬁdence level. Interaction effects of the factors BC, BD and CD and the quadratic inﬂuence B2 were excluded from the quadratic model as their signiﬁcance is below 95% conﬁdence level. The ﬁnal equation, in terms of coded factors suggested for our model, is given by Eq. (2). MS (Am2 kg 1) ¼74.92 þ10.14A þ5.07B 12.23C þ4.31D 3.79AB þ 4.41AC 4.08AD 4.53B2 5.56C2 7.44D2 (2) The model is highly signiﬁcant with a value of P o0.0001, as can be seen in the analysis of variance (ANOVA) in Table 7. The model′s coefﬁcient of determination is R2 ¼0.91. The predicted R2 of 0.85 is in good agreement with the adjusted R2 of 0.88. In regard to the mean particle diameter, all factors except D show no statistical signiﬁcance at less than 99.2%. Although the effect of factor D (P ¼0.1478) on dp is not signiﬁcant, the factor is included in the model due to the models hierarchical structure. The ﬁnal equation in terms of coded factors suggested for our model is given in Eq. (3). H.-C. Roth et al. / Journal of Magnetism and Magnetic Materials 377 (2015) 81–89 87 120 frequency (%) 100 80 60 40 20 0 1 3 5 7 9 11 13 15 17 19 21 23 25 27 29 particle size (nm) a 120 frequency (%) 100 80 60 40 20 0 1 3 5 7 9 11 13 15 17 19 21 23 25 27 29 particle size (nm) b Fig. 7. TEM images of a) Assay 19 and b) Assay 10. Table 6 Estimated coefﬁcients and standard errors for mean particle diameter (dp) and saturation magnetization (MS) using the CCF design. Factor Intercept A: total amount of iron (mol) B: temperature (°C) C: Fe3 þ /Fe2 þ ratio (mol mol 1) D: Fe/NaOH ratio AB AC AD A2 C2 D2 Table 8 Analysis of variance (ANOVA) for the representative model of the particle mean diameter in the studied range of parameters. MS (Am2 kg 1) dp (nm) Source SS d.f. MS F-value P Coefﬁcient Standard error Coefﬁcient Standard error Model Residual Lack of ﬁt Pure error Total 306.08 31.85 24.78 7.05 337.92 6 21 16 5 27 51.1 1.52 1.55 1.41 33.64 o 0.0001 1.10 0.5013 74.92 10.14 2.19 1.85 13.43 3.19 0.36 0.31 5.07 12.23 1.84 1.84 0.94 2.66 0.32 0.32 R2 ¼ 0.86; C.V. ¼ 10.16%; SS ¼ sum of squares; d.f. ¼ degrees of freedom; MS ¼ mean square 4.31 3.79 4.41 4.08 4.53 5.56 7.44 1.84 1.98 1.98 1.98 4.24 4.24 4.24 0.48 0.61 – – – 2.80 – 0.32 0.33 – – – 0.48 – The determination coefﬁcient of the model is R2 ¼0.86. The predicted R2 of 0.74 is in good agreement with the adjusted R2 of 0.77 and the model is signiﬁcant at a conﬁdence level greater than 99.99% (Po 0.0001). The resulting models are valuable tools for the prediction of factor interactions and their inﬂuence on the response parameters within the studied area of design. This can be seen, when the results of the validation experiments are compared to the forecasted values by the models. It is remarkable that the predicted values meet the reality with a conﬁdence greater than 95%. Results are summarized in Table 9. Contour plots indicating the predicted parameter interactions are displayed in Fig. 8 with actual factors corresponding with the center points. The model (Fig. 8a)–c)) predicts a strong inﬂuence of factor A on MS with an increase of MS for higher values of factor A. This prediction is in good agreement with the SQUID measurements presented in Fig. 5a). Elevated magnetization properties at higher synthesis temperatures are suggested by several authors due to improvement in crystal formation [16,47]. This effect of factor B is predicted by our model (Fig. 8a)), even though the inﬂuence is rather slight. The predicted interaction of factor A and factor C (Fig. 8b)) suggests a decrease of MS with increasing values of factor C, which is in good analogy with literature  and our SQUID data (Fig. 5c)). Formation of byproducts with hypostoichiometric Table 7 Analysis of variance (ANOVA) for the representative model of saturation magnetization in the studied range of parameters. Source SS d.f. MS F-value P Model Residual lack of ﬁt pure error Total 5195.69 861.90 296.54 565.36 6057.59 10 17 12 5 27 519.57 50.70 24.71 113.07 10.25 o0.0001 0.22 0.9856 R2 ¼0.91; C.V¼10.72%; SS ¼ sum of squares; d.f. ¼degrees of freedom; MS¼ mean square. MS (Am2 kg 1)¼13.43 þ3.19A þ0.94B 2.66C þ0.48D 0.16AB 7.44C2 (3) The analysis of variance (ANOVA) is displayed in Table 8. 88 H.-C. Roth et al. / Journal of Magnetism and Magnetic Materials 377 (2015) 81–89 Table 9 Conﬁrmation of the models with data of the validation experiments. V001 Predicted 95% PIlow 95% PIhigh Experimental V002 V003 MS (Am2 kg 1) dp (nm) MS (Am2 kg 1) dp (nm) MS (Am2 kg 1) dp (nm) 84.23 67.61 100.86 80.53 16.6 13.8 19.4 15.8 79.19 62.98 95.39 73.34 15.6 12.9 18.3 16.3 59.50 43.33 75.66 74.50 11.0 8.3 13.8 13.3 PIlow ¼ lower boundary of prediction interval; PIhigh ¼ upper boundary of prediction interval. Fig. 8. Contour plots of factor interaction a) AB, b) AC and c) AD for saturation magnetization and d) factor interaction AB for mean particle diameters. addition of base, with respect to the total amount of iron ions, results in decreasing MS with lower values of factor D (Fig. 8c)). At high values of factor A, an optimum value of 1.4 is predicted for factor D. This effect might be explained by the decrease of mean particle size with an increase of the relative supersaturation of the synthesis solution . The correlation in Fig. 6 suggests that smaller particles lead to decreasing values of MS. In the co-precipitation process, formation of byproducts is favored at pH o 8 and therefore leads to a decrease in MS. These two negative effects of factor D in reaction pathways seem to have their minimum inﬂuence on MS at a value of D¼1.4. Our model suggests an increase in dp with increasing values of factor A (Fig. 8d)) and sustains the data received from powder XRD and literature [16,32]. The contour plot in Fig. 8d) indicates a trend to bigger particles with increasing values of factor B. Powder XRD analysis of our samples (Fig. 1b)) supports this indication. An explanation for this effect might be provided by the nucleation pathways according to Lamer (1950) . The solubility of the iron salts improves  while the pH decreases with increasing temperatures . Thus, the relative supersaturation of the reaction medium is reduced . Furthermore the critical nucleus size increases with temperature according to thermodynamic equilibrium . Nuclei that are smaller than the critical nucleus size tend to undergo Ostwald ripening and favor the growth of bigger particles . The burst of nucleation at high temperatures therefore leads to a smaller amount of bigger crystals, facilitating the process of crystal growth. Summarizing the results of the models, maximum values of MS can be achieved at the highest temperatures, the highest iron salt concentrations, the lowest ratios of Fe3 þ /Fe2 þ within the range and levels of investigated parameters and a value of 1.4 for the introduced normality factor D. H.-C. Roth et al. / Journal of Magnetism and Magnetic Materials 377 (2015) 81–89 4. Conclusions In this study the statistical method based on “design of experiments” (DoE) was used to plan a reaction setup for the coprecipitation process of deﬁned iron oxide nanoparticles. The focus lies on the inﬂuence of reaction conditions regarding size and saturation magnetization of the product. Iron oxide nanoparticles with mean particle diameters from 3 to 17 nm and saturation magnetizations from 16.7 to 89.19 Am2 kg 1 were synthesized and fully characterized by powder XRD, Raman- and Mössbauer spectroscopy. The results distinctly show that magnetite is the only iron oxide species for the majority of reaction conditions. Samples prepared at hypostoichiometric ratios of hydroxide ions with respect to iron ions in combination with a high Fe3 þ /Fe2 þ -ratio tend to lead to a mixture of lepidocrocite and goethite. Based on the vast number of experiments, the correlation of particle size with saturation magnetization could be depicted. Employing the software Design Expert 8, statistically representative models could be generated, which enable the prediction of particle size and saturation magnetization within the studied range of synthesis parameters with adequate accuracy. The validity of the models was conﬁrmed with three independent experiments with a conﬁdence level4 95%. Acknowledgements The authors would like to gratefully thank Prof. Dr. T. Nilges for his support with powder XRD and Dr. M. Hanzlik for performing a share of the TEM measurements. Furthermore I would like to express my very great appreciation to Dr. P. Fraga and the TUM Graduate School, Technische Universität München for proof reading this work. Also I am particularly grateful for the ﬁnancial support of this work by the Federal Ministry of Education and Research (Grant numbers 1340/68351/3/11 and 031A173A) and Clariant. References  M. Franzreb, Magnettechnologie in der Verfahrenstechnik wässriger Medien, FZKA (2003).  Ra Williams, Colloid and Surface Engineering: Applications in the Process Industries, Butterworth-Heinemann Limited, 1994.  A. Brandt, A. Balducci, Electrochim. Acta 108 (2013) 219–225.  D.-W. Jung, S.-W. Han, B.-S. Kong, E.-S. Oh, J. Power Sources 242 (2013) 357–364.  L. Wang, L.-C. Zhang, J.-X. Cheng, C.-X. Ding, C.-H. Chen, Electrochim. Acta 102 (2013) 306–311.  S. Odenbach, Colloidal magnetic ﬂuids, Springer, 2009.  D. Shi, NanoScience in Biomedicine, Springer, 2010.  L. Vékás, M.V. Avdeev, D. Bica, Magnetic nanoﬂuids: synthesis and structure, in: D. Shi (Ed.), NanoScience in Biomedicine, Springer, Berlin Heidelberg, 2009, pp. 650–728.  K. Johns, Tribol. Int. 31 (1998) 485–490.  S.W. Charles, J. Popplewell, Endeavour 6 (1982) 153–161.  Y. Wang, Q. Liu, Q. Qi, J. Ding, X. Gao, Y. Zhang, Y. Sun, Electrochim. Acta 111 (2013) 31–40.  M.M. Miller, G.A. Prinz, S.F. Cheng, S. Bounnak, Appl. Phys. Lett. 81 (2002) 2211–2213.  T.K. Jain, M.A. Morales, S.K. Sahoo, D.L. Leslie-Pelecky, V. Labhasetwar, Mol. Pharm. 2 (2005) 194–205.  I. Chourpa, L. Douziech-Eyrolles, L. Ngaboni-Okassa, J.F. Fouquenet, S. CohenJonathan, M. Souce, H. Marchais, P. Dubois, Analyst 130 (2005) 1395–1403.  S. Boutry, S. Laurent, L. Vander Elst, R.N. Muller, Contrast Media Mol. I. 1 (2006) 15–22.  L. Babes, B. Denizot, G. Tanguy, J.J. Le Jeune, P. Jallet, J. Colloid Interface Sci. 212 (1999) 474–482.  M.M.J. Modo, J.W.M. Bulte, Molecular and Cellular MR Imaging, Taylor & Francis, 2007. View publication stats 89  F. Sonvico, C. Dubernet, P. Colombo, P. Couvreur, Curr. Pharm. Des. 11 (2005) 2091–2105.  C. Corot, P. Robert, J.-M. Idée, M. Port, Adv. Drug Deliv. Rev. 58 (2006) 1471–1504.  C. Burtea, S. Laurent, A. Roch, L. Vander Elst, R.N. Muller, J. Inorg. Biochem. 99 (2005) 1135–1144.  S. Berensmeier, Appl. Microbiol. Biotechnol. 73 (2006) 495–504.  M. Franzreb, M. Siemann-Herzberg, T.J. Hobley, O.R.T. Thomas, Microbiol. Appl. Biotechnol. 70 (2006) 505–516.  M. Heyd, M. Franzreb, S. Berensmeier, Biotechnol. Prog. 27 (2011) 706–716.  A. Meyer, S. Berensmeier, M. Franzreb, React. Funct. Polym. 67 (2007) 1577–1588.  H. Motejadded, B. Kranz, S. Berensmeier, M. Franzreb, J. Altenbuchner, Appl. Biochem. Biotechnol. 162 (2010) 2098–2110.  C. Albornoz, S.E. Jacobo, J. Magn. Magn. Mater. 305 (2006) 12–15.  J. Wan, X. Chen, Z. Wang, X. Yang, Y. Qian, J. Cryst. Growth 276 (2005) 571–576.  E.H. Kim, H.S. Lee, B.K. Kwak, B.K. Kim, J. Magn. Magn. Mater. 289 (2005) 328–330.  M. Kimata, D. Nakagawa, M. Hasegawa, Powder Technol. 132 (2003) 112–118.  S. Basak, D.-R. Chen, P. Biswas, Chem. Eng. Sci. 62 (2007) 1263–1268.  A.B. Chin, I.I. Yaacob, J. Mater. Process Technol. 191 (2007) 235–237.  I. Martínez-Mera, M.E. Espinosa-Pesqueira, R. Pérez-Hernández, J. ArenasAlatorre, Mater. Lett. 61 (2007) 4447–4451.  Y.K. Sun, M. Ma, Y. Zhang, N. Gu, Colloid Surf. A 245 (2004) 15–19.  S.J. Lee, J.R. Jeong, S.C. Shin, J.C. Kim, J.D. Kim, J. Magn. Magn. Mater. 282 (2004) 147–150.  J. Qiu, R. Yang, M. Li, N. Jiang, Mater. Res. Bull. 40 (2005) 1968–1975.  S.A. Morrison, C.L. Cahill, E.E. Carpenter, S. Calvin, V.G. Harris, J. Nanosci. Nanotechnol. 5 (2005) 1323–1344.  M.E. Silvestre, M. Franzreb, Fakultät für Chemieingenieurwesen und Verfahrenstechnik, Universität Fridericiana Karlsruhe, Karlsruhe, 2009.  S.E. Khalafalla, G.W. Reimers, IEEE Trans. Magn. 16 (1980) 178–183.  R. Massart, Google Patents (1982).  R. Massart, IEEE Trans. Magn. 17 (1981) 1247–1248.  M. Fang, V. Strom, R.T. Olsson, L. Belova, K.V. Rao, Nanotechnology 23 (2012).  M.D. Carvalho, F. Henriques, L.P. Ferreira, M. Godinho, M.M. Cruz, J. Solid State Chem. 201 (2013) 144–152.  F. Mei, S. Valter, T.O. Richard, B. Lyubov, K.V. Rao, Nanotechnology 23 (2012) 145601.  J. Baumgartner, A. Dey, P.H.H. Bomans, C. Le Coadou, P. Fratzl, N.A.J. M. Sommerdijk, D. Faivre, Nat. Mater. 12 (2013) 310–314.  H.P.K. Alexander, L.E. Alexander, X-Ray Diffraction Procedures for Polycrystalline and Amorphous Materials, Chapman & Hall: London, New York, 1954.  D.-D. Herea, H. Chiriac, N. Lupu, J. Nanopart. Res. 13 (2011) 4357–4369.  N.M. Gribanov, E.E. Bibik, O.V. Buzunov, V.N. Naumov, J. Magn. Magn. Mater. 85 (1990) 7–10.  G. A. Salazar Alvarez, K.T.H. Materialvetenskap, K.T.H. Stockholm, Stockholm 2004.  V.K. Lamer, R.H. Dinegar, J. Am. Chem. Soc. 72 (1950) 4847–4854.  O. Bomati-Miguel, P. Tartaj, M.P. Morales, P. Bonville, U. Golla-Schindler, X.Q. Q. Zhao, S. Veintemillas-Verdaguer, Small 2 (2006) 1476–1483.  W.G. Yu, T.L. Zhang, X.J. Qiao, J.G. Zhang, L. Yang, Mater. Sci. Eng. B-Solid 136 (2007) 101–105.  T. Ahn, J.H. Kim, H.M. Yang, J.W. Lee, J.D. Kim, J. Phys. Chem. C 116 (2012) 6069–6076.  J.-P. Jolivet, C. Chaneac, E. Tronc, Chem. Commun. (2004) 481–483.  R.M. Cornell, U. Schwertmann, The Iron Oxides: Structure, Properties, Reactions, Occurrences and Uses, Wiley, 2006.  D.E. Madsen, M.F. Hansen, C.B. Koch, S. Morup, J. Phys.: Condens. Matter 20 (2008).  S.J. Oh, D.C. Cook, H.E. Townsend, Hyperﬁne Interact 112 (1998) 59–65.  D.L.A.V.S. de Faria, S. de Oliviera, M.T, J. Raman Spectrosc. 28 (1997) 873–878.  M. Hanesch, Geophys. J. Int. 177 (2009) 941–948.  G. Gouadec, P. Colomban, Prog. Cryst. Growth Charact. Mater. 53 (2007) 1–56.  G. Bate, J.K. Alstad, IEEE Trans. Magn. 5 (1969) 821–839.  A.-H. Lu, E.L. Salabas, F. Schüth, Angew. Chem. 119 (2007) 1242–1266.  R.M. Cornell, U. Schwertmann, The Iron Oxides: Structure, Properties, Reactions, Occurrences and Uses, Wiley, 2003.  A. Demortiere, P. Panissod, B.P. Pichon, G. Pourroy, D. Guillon, B. Donnio, S. Begin-Colin, Nanoscale 3 (2011) 225–232.  J. Park, K.J. An, Y.S. Hwang, J.G. Park, H.J. Noh, J.Y. Kim, J.H. Park, N.M. Hwang, T. Hyeon, Nat. Mater. 3 (2004) 891–895.  B. Luigjes, S.M.C. Woudenberg, R. de Groot, J.D. Meeldijk, H.M.T. Galvis, K.P. de Jong, A.P. Philipse, B.H. Erne, J. Phys. Chem. C 115 (2011) 14598–14605.  F. Haseidl, N.C. Jacobsen, K.-O. Hinrichsen, Chem-Ing-Tech 85 (2013) 540–549.  M.A. Blesa, N.M. Figliolia, A.J.G. Maroto, A.E. Regazzoni, J. Colloid Interface Sci. 101 (1984) 410–418.  L. Vayssieres, C. Chaneac, E. Tronc, J.P. Jolivet, J. Colloid Interface Sci. 205 (1998) 205–212.