Maghemite Composition and Core–Shell

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C: Physical Processes in Nanomaterials and Nanostructures

Determining Magnetite/Maghemite Composition and Core-Shell Nanostructure from Magnetization Curve for Iron Oxide Nanoparticles Hamed Sharifi Dehsari, Vadim Ksenofontov, Angela Möller, Gerhard Jakob, and Kamal Asadi J. Phys. Chem. C, Just Accepted Manuscript • DOI: 10.1021/acs.jpcc.8b06927 • Publication Date (Web): 19 Nov 2018 Downloaded from http://pubs.acs.org on December 1, 2018

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The Journal of Physical Chemistry

Determining Magnetite/Maghemite Composition and Core-Shell Nanostructure from Magnetization Curve for Iron Oxide Nanoparticles Hamed Sharifi Dehsari1, Vadim Ksenofontov 2, Angela Möller2, Gerhard Jakob3,*, Kamal Asadi1,* 1

Max-Planck Institute for Polymer Research, Ackermannweg 10, 55128, Mainz, Germany

2

Institute of Inorganic and Analytical Chemistry, Johannes Gutenberg-University, Duesberg

Weg 10-14, 55099 Mainz, Germany 3

Institute of Physics, Johannes Gutenberg-University, Staudinger Weg 7, 55099 Mainz,

Germany

KEYWORDS: Magnetic nanoparticles, thermal decomposition, magnetic domain, multi-domain, superparamagnetic

1 Environment ACS Paragon Plus

The Journal of Physical Chemistry 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60

ABSTRACT: Iron oxide magnetic nanoparticles produced by chemical synthesis are usually composed of both magnetite and maghemite phases. Information about the phase composition is typically obtained using Mössbauer spectroscopy. A method that can provide information about the magnetite versus maghemite phase composition of the nanoparticles, and the organization of the phases simply from magnetization curve is still missing. Here we present a simple and elegant method that for nanoparticles with a known size distribution can give information about the magnetite and maghemite phase composition and suggests a magnetite core, maghemite shell structure for all the nanoparticles sizes. The method is based on fitting of the room temperature magnetization curve using a Brillouin function, while considering dipolar interactions. The model predicts that the nanoparticles are composed of single magnetic domain for sizes below 14 nm. The model is validated by Mössbauer spectroscopy.


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Introduction The magnetic properties of iron-oxide nanoparticles (IONPs) strongly depend on the particle size, shape and composition.1-11 As the size reduces, below a certain value, the IONPs change from ferrimagnet to superparamagnet;1-2, 12-19 a general behavior that was predicted by Frenkel and Dorfman.16 Size-controlled IONPs are usually produced by chemical synthesis, and have a distribution in size. The size and the respective distribution are tunable by altering different reaction parameters.7, 20-28 Moreover, the synthesis method influences the stoichiometry of the IONPs i.e. the magnetite (Fe3O4) vs. maghemite (γ-Fe2O3) phase composition.22 The chemical phase composition of the IONPs, and structural organization, e.g. random clusters of one phase in the other versus core-shell structure, can change with the size of the IONPs.29-31 Dedicated techniques such as Mössbauer spectroscopy22, 30 and Lorentz microscopy are employed to gain information on the magnetite vs. maghemite composition and the magnetic domain size, respectively.32-34 Magnetization curves, M(H), of the IONPs are usually measured in powder form using conventional magnetometry techniques for an ensemble of particles with a size distribution. Measured magnetization, M, in an applied field, H, is a cumulative response of a large number of particles. The measured M(H) curve is therefore a statistical sum of the magnetization response of individual IONPs with different domains and different magnetite and maghemite phases composition.30-31,

35

Depending on the packing density, size and magnetization of the

nanoparticles, dipolar interactions modify the magnetization curves of the IONP ensemble. 36-39 Extracting information about the domains and magnetite versus maghemite phase composition from the experimentally measured room temperature M(H) magnetization curve is still an open question.



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There are attempts to numerically invert the experimentally measured M(H) curve to a nanoparticle size distribution. Chantrell et al.40 determined the mean value of the magnetic particle diameter by convoluting a size distribution function using a log-normal distribution with the expected size dependent magnetic response. O’Grady and Mahmoud have improved the method further by using a Gaussian distribution41 and a flat topped distribution,42 respectively, with the latter being the most frequently followed methodology. 7, 30-31, 35, 43 Recently promising schemes have been developed wherein based on the size-dependence of the experimentally measured blocking temperature, a magnetic particle size (distribution) has been obtained. 44-47 To do so however, usually a fixed magnetization for the nanoparticles is assumed. As a result, the calculated diameter of the magnetic particle is smaller than the actual particle diameter. Hence, a magnetically dead layer is artificially assumed to compensate for lost material. 22,

30-31, 35, 48-51

Furthermore, due to the possible existence of several magnetic domains in one particle, the number of the magnetic particles calculated from this method is typically larger than the physical number of particles in the measured ensemble. 30-31, 35 Numerical inversion of the M(H) curves to particle size is however a mathematically ill-conditioned problem. Despite the great progress, a model that accurately describes the magnetic behavior of the nanoparticles and that further can give predication on the phase and structural composition, is still lacking. Here, we present a model that, despite its simplicity, provides information on the magnetic domain size and the Fe3O4 versus Fe2O3 phase composition in IONPs with a good accuracy. The model takes room-temperature magnetization and actual particle size distribution into account as the experimental inputs and provides information about magnetic domains and magnetic phase composition. To verify the model, nanoparticles of different sizes are synthesized using thermal decomposition routes by altering the reaction parameters. 20-21,


52-53

The calculation accurately

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describes the experimentally measured M(H) curves for different nanoparticle sizes by taking into account the dipolar interactions and decomposition of the magnetic into a multi-domain state. We derive from the model a critical particle size of 11 nm at which transition from single domain to multi domain takes place. The observed transition matches closely to the evolution of the coercive fields at low temperature. The model derives from the magnetization curve the magnetite/maghemite phase composition using a magnetite/maghemite core/shell structure of the nanoparticles. The model predictions were corroborated with Mössbauer spectroscopy measurements. 22, 30-31, 35, 54

Experiments Iron oxide nanoparticles were synthesized by thermal decomposition following previously reported procedure.5, 20-21, 28, 49, 52-53, 55 Iron acetylacetonate (97 %) was used as precursor that was mixed with oleic acid (OAC, technical grade, 90 %), oleylamine (OAM, > 70 %), and 1,2 hexadecandiol (99 %), and dissolved in benzyl ether. Iron (III) acetylacetonate (99 %), oleylamine (OAM, > 70 %), benzyl ether (BE, technical grade 99 %), oleic acid (OAC, technical grade 90 %), Toluene, ethanol and acetone were all purchased from Sigma Aldrich. 1,2Hexadecanediol (99 %) was purchased from TCI. All chemicals were used as received. The mixture was stirred at 110 °C, under vacuum for 60 min. While purging with N 2, the temperature was raised to 180 °C and remained at that temperature for 120 min. Then, the solution was heated to 294 °C with a constant heating rate and kept at this temperature for 1 h. After cooling to room temperature, the NPs were washed four times by addition of ethanol/acetone and centrifugation (6000 rpm, 10 minutes) and were finally suspended in toluene. To tune the nanoparticle size, we changed the heating rate of the reaction and precursor concentration as



detailed previously.20-21 We synthesized eleven different batches of nanoparticles of (A1-A11). Table S1 supplementary information (SI) gives details of the syntheses. Transmission electron microscopy (JEOL 1400) was used to determine nanoparticles’ size and the size distribution. A population of larger than 2000 nanoparticles was analyzed to obtain a reliable size distribution histogram, from which average particle size, DTEM, and the standard deviation, σTEM, was obtained. The crystallinity of the nanoparticles was probed using X-ray diffraction (XRD).56 Subsequently, we performed Fourier transform infrared (FTIR) spectroscopy. As synthesized nanoparticles were coated with a layer of surfactant, which is magnetically inactive, but contributes to the total particle mass. To determine the mass of the surfactant shell, thermogravimetric analysis (TGA) was performed under N 2 from 20 to 800 °C at 10 °C/min. Magnetization measurements of the powder samples were performed using a Quantum Design SQUID magnetometer. The nanoparticles were filled in gelatin capsules. Magnetization hysteresis loops M(H) were measured at room temperature (300 K) and 2 K. The hysteresis curves were subsequently corrected by subtracting the M(H) response of the empty capsule from the data. Mössbauer experiments (57Fe) were performed at two different temperatures, above the Verwey transition at 124 K and below at 5 K, in transmission geometry in a closed cycle cryostat (Montana Inst.) with a custom built sample holder.

Results and discussion I. Particle size analysis All samples have gone through TEM, XRD, and magnetic characterization. Of the eleven batches, three sets (A3, A7 and A9) are used for additional Mössbauer analysis. Figure 1 shows


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the TEM images of nanoparticles of A3, A7 and A9 batches. The TEM images of other IONP batches along with their respective size distribution histograms are shown in Figure S1. For processing of TEM images, we assume spherical shapes for the nanoparticles. The size histograms are well described with a normal distribution. The mean particle diameter, DTEM, and standard deviation, σTEM, are given in Figure 1 and Figure S1. The monodispersity, defined as the width of the normal distribution, is typically below 10 %. We note that, high resolution TEM (HRTEM) micrographs of the nanoparticles, given in Figure S2, show absence of lattice defects such as dislocations or stacking faults. However, the nanoparticles show a surface distorted layer of much less than a nanometer.23

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Figure 1. Representative TEM images and respective size distribution histograms of small, medium and large nanoparticles from (a) A3, (b) A7 and (c) A9 batch respectively. TEM images of other samples are given in the Figure S1.



II. Structural analysis Representative XRD patterns of samples A3, A7 and A9 of Figure 1, are shown in Figure 2a. The diffraction peaks are indexed in the fcc inverse spinel structure for magnetite. The mean crystalline diameter of the nanoparticles is calculated using Scherrer equation for the (311) reflection.10, 56 The result is shown in supplementary information in Table S1. The evolution of the inter-planar d311 distances as a function of nanoparticle size is given in Figure 2b. For comparison, d311 of the bulk stoichiometric magnetite Fe3O4 (0.8396 nm) (JCPDS file 19-629) and maghemite γ-Fe2O3 (0.8346 nm) (JCPDS file 39-1346) are given as red, and blue dashed lines, respectively.21-22,

30-31

The value of the lattice constant increases with

increasing the nanoparticle size, changing from (more) maghemite for the smallest particles to (more) stoichiometric magnetite for the largest particles.

311 A9

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Figure 2. a) Typical XRD diffractograms of nanoparticles A3, A7, and A9 and their corresponding fit (black line). The orange line is a guide to see a shift in the peak position of 311. b) Evolution of lattice parameter as a function of average diameter from the smallest A1 to the largest A11 nanoparticles.


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FTIR, survey of the surface of the nanoparticles, given in Figure 3a, shows two groups of strong bands 3000 cm-1-2800 cm-1 and 1300 cm-1-1650 cm-1, which are assigned to methyl groups, and the asymmetric-symmetric COO¯ bands of oleate, respectively. Hence, the ligand adsorbed on the particle surface is oleate. 7, 22, 57 To estimate the weight of the oleate shell, TGA is performed (Figure 3b). TGA curves show three weight loss plateaus.7,

20-21, 58

The slight weight-loss below 150 °C is attributed to the

evaporation of adsorbed water molecule and solvent remainders in the powder. The second loss between 200–500 °C is due to removing of the oleate from the surface of the particles. The third loss between 600-750 °C is due to particle decomposition or the formation of carbonates by the reorganization of organic matter.7, 57

57

From TGA, we obtain organic mass fraction as function

of particle size, as shown in the inset of Figure 3b. The nanoparticles give a weight loss between 28.2 % and 10.3 % from the smallest, A1, to largest, A11, particle. The larger weight loss for the smaller particles is due to their larger surface/volume ratio. Modeling of the observed trend in weight loss (the solid line in inset of Figure 3b) using oleate density of  = 0.9 g/cm3 gives a surfactant layer thickness of 1.4 nm. 59-60

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Figure 3. (a) FTIR survey of A8 nanoparticle indicating attachment of oleate to the surface. Inset shows FTIR survey of A1, A4, A8 and A11 of the peak around Fe-O absorption. (b) TGA of nanoparticles. The inset shows weight loss as a function of particle diameter.

III. Magnetic measurements The saturation magnetization increases with increasing nanoparticle size. 20-21 The magnetization hysteresis M(H) curves of the A3, A7, and A9 particles measured at 300 K and at 2 K are shown in Figure 4a and b, respectively. The M(H) curves for all the IONPs are shown in Figure S3. The room temperature M(H) curves show a superparamagnetic like hysteresis-free behavior, whereas curves at 2 K the M(H) curves show ferrimagnetic hysteretic behavior. For all IONP samples, the values of MS increase as a function of d at both 300 K and 2 K and are shown in Figure S4a. We note that the M(H) curves were measured for nanoparticles covered with oleate. The TGA-corrected MS values are given in Figure S4b. Interestingly, low temperature MS values for the largest nanoparticles approach 98 emu/g, close to the theoretical bulk value for pure single crystalline bulk magnetite at low temperature. 54,

61

The reduction in MS for small

IONP size is frequently attributed to the presence of a magnetically disordered (dead) layer. (see the discussion for Figure S4).22, 35, 62-64 We note our magnetization model does not require a dead-layer. 560 6 0

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Figure 4. Magnetization loops of A3, A7 and A9 nanoparticle batches at (a) 300 K and (b) 2 K. Magnetization curves of other samples are shown in Figure S3. (c) HC at T = 2 K (squares) as a function of size (determined by TEM).

The coercive field (HC) at 2 K increases with the IONP size up to the nanoparticle size of 10.5 ± 1 nm and then reduces upon further increase in size, as shown in Figure 4c. The behavior of HC with size is commonly attributed to shifting of the particle from a magnetically single-domain to a multi-domain regime.

2, 14, 23, 65-66

We shall treat the subjects later by the magnetization

model.

IV. Magnetite versus maghemite phase composition determination Mössbauer spectroscopy of

57

Fe is performed on three different nanoparticle batches with

nominal small (A3), intermediate (A7) and large (A9) sizes with diameters that amount to 8.6 ± 0.9, 12.7 ± 1.2, and 14.5 ± 1.5 nm, respectively. We obtain from Lorentzian site analysis of the experimental data (Recoil software)67 three Fe-sextets which we ascribe to the magnetic hyperfine splitting of iron-sites on tetrahedral and octahedral positions as present in magnetite and maghemite. The spectral intensities associated with the charge ordering of Fe2+ in octahedral and tetrahedral coordination is used as an indicator for the fraction of magnetite. Furthermore, paramagnetic Fe-contributions are absent in all Mössbauer spectra of this series. Figure 5 shows the Mössbauer spectra for sample A9 at two different temperatures T = 124 K and 5K above and below Verwey transition, respectively. The Mössbauer spectra for sample A3 and A7 are given in the Figure S5. All the fitting parameters of the Mössbauer data have been included in the SI as Table S2. The fitted three sextets at 5 K are assigned to (i) Fe 3+ octahedral (chemical shift 0.52(1) mm/s, hyperfine field 527.9(3) kOe), (ii) Fe2+ and Fe3+ in tetrahedral coordination (chemical shift 0.41(1) mm/s, hyperfine field 506.7(2) kOe), and (iii) Fe 2+ in octahedral 11 Environment ACS Paragon Plus


coordination (chemical shift 0.88(2) mm/s, hyperfine field 472(1) kOe). We observe corresponding spectral fractions of 29 : 49 : 22 at 5 K and 19 : 36 : 45 at 124 K for sample A9 and conclude that the changes stem from charge ordering across the Verwey transition of the magnetite fraction. The spectra for the first, second and third sextets, give a magnetite percentage of ~ 68 % for sample A9. Mössbauer spectroscopic data show an increase in magnetite phase fraction upon nanoparticle size increase. We shall compare the Mössbauer spectroscopic results later with the model developed for describing the magnetization response of the nanoparticles.

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0.95 T = 124 K 1.00

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v (mm/s) Figure 5. Mössbauer spectra for sample A9 taken at a) 124 K and b) 5K fitted as a superposition of three magnetic sextets.


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V. Magnetization model We develop a model to fit the experimentally measured M(H) curves. The model employs the known experimental facts for the IONPs ensemble i.e. the experimentally determined size distribution histogram from the TEM analysis and the mass after TGA correction. The model calculates the magnetization of a single nanoparticle of a certain size, magnetic domain state and magnetite/maghemite composition. The measured M(H) curve is then reproduced by summing the magnetizations of individual particles according to the experimentally determined size distribution histograms. For an individual particle of size d, the contribution to the total M(H) curve from the particle, MJ(H), is: (1)

𝑀𝐽 = 𝐽𝑔𝜇𝐵 ⋅ 𝐵𝐽

where J is the quantum mechanical angular momentum number, g is the g-factor of the nanoparticle, 𝜇𝐵 is the Bohr magneton, and BJ is the Brillouin function. For one single monodomain nanoparticle, MJ(H) is uniquely determined by the temperature dependent Brillouin function, BJ(J,T,B),: (3)

𝐵𝐽 (𝐽, 𝑔, 𝑇, 𝐵) =

2𝐽 + 1 2𝐽 + 1 𝐽𝑔𝜇𝐵 𝐵 1 1 𝐽𝑔𝜇𝐵 𝐵 coth ( ) − coth ( ) 2𝐽 2𝐽 𝑘𝐵 𝑇 2𝐽 2𝐽 𝑘𝐵 𝑇

where 𝑘𝐵 , T and B are Boltzmann constant, temperature, and magnetic induction, respectively. We note that usually the Langevin function is used which is just the classical high temperature limit of the quantum mechanical Brillouin function. We further note, the Brillouin function has also been used in nanoparticle research, although less frequently. 68 Alignment of the magnetic moment of the individual IONPs in an external field introduces inter-particle dipolar interactions, where the dipolar fields of all neighboring particles oppose the



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alignment of the particle in the external field. To consider the dipolar interaction, renormalization of the temperature has been suggested. 71-74 The direction of the magnetic dipole moment of the superparamagnetic IONPs fluctuate fast above the blocking temperature. Therefore, the effect of the fluctuating dipolar field is represented by an enhanced temperature with: (4) 𝑇 → 𝑇 + 𝑇 ∗

with

𝜇2

𝑘𝐵 𝑇 ∗ = 𝛼 𝑑 3

where  is the magnetic moment, d is the distance between the fluctuating dipoles, and  is a dimensionless proportionality constant that depending on the actual spatial distribution of the IONPs varies in the range between unity and some tens. 71 The bulk M(H) curves are calculated for all nanoparticle ensembles of A1, to A11 using the experimentally determined size histograms (given in Figures 1 and S1) and the absolute nanoparticle mass after TGA correction (Figure 3). We use the room temperature saturation magnetization values of 74.3 emu/g (=2.134 μB/f.u.) and 92.8 emu/g (=3.89 μB/f.u.) for pure bulk maghemite and magnetite, respectively. 69 The value of the g-factor is constant with g = 2. To calculate M of an ensemble, we first determine the total angular momentum J and its contribution to MJ for one single particle. The J value changes with the size of the nanoparticles and the magnetite/maghemite composition. The chemical phase composition of the IONPs is determined from the absolute value of the measured saturation magnetization. The shape (curvature) of the calculated M(H) curve gives information about domain structure in an IONP ensemble.

VI. Model results VI.1. Phase-pure vs. mixed-phase INOPs.


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To determine the chemical phase maghemite versus magnetite composition of the nanoparticles, we need to know two decisive experimental measurables: a) the mass of the IONP samples after TGA correction and b) the absolute value of the saturation magnetization. A representative experimental and three calculated M(H) curves for sample A1 are given in Figure 6a. When assuming 100% phase purity for maghemite with 74.3 emu/g (=2.13 μB/f.u.) or magnetite with 93.8 emu/g (=3.89 μB/f.u.) the model respectively underestimates or overestimates the measured saturation magnetizations. Hence, the nanoparticles are composed of both magnetite and maghemite phases, which is in agreement with our Mössbauer spectroscopic data, and the experimental reports.22, 30-31, 35 We determine the volume fraction70 of magnetite x and maghemite (1-x) phases

𝑉𝑁𝑃 = 𝑥 ⋅ 𝑉𝐹𝑒3𝑂4 + (1 − 𝑥 ) ⋅ 𝑉𝐹𝑒2𝑂3 from the saturation

magnetization assuming that each phase has its bulk magnetization. The model, as shown in Figure 6a, describes the measured M(H) curve very well. Using the procedure explained above, we determine the magnetite phase composition for all IONPs of A1 to A11 batches. The magnetite phase fraction as a function of particle diameter is shown in Figure 7a. The model predicted an increasing trend in magnetite phase fraction with increasing IONPs diameter. To corroborate model’s predictions, we have plotted on the same curve, the experimentally determined magnetite content obtained by Mössbauer spectrometry for three IONPs with nominal diameters of small, medium and large. A good agreement is obtained between model’s prediction and the experiment, as shown in Figure 7a.

VI.2. The case of dead-layer. We have obtained a good description of the experimental M(H) curves for all nanoparticles assuming no dead-layer for all the IONPs. The fits for three nominal nanoparticle sizes, of A1, A5 and A8 are shown in Figure 6a-c. Furthermore, we have achieved a good agreement between the 15 Environment ACS Paragon Plus


magnetite volume fraction from the modeling and the Mössbauer spectroscopy, as shown in Figure 7a. The slight deviation in the model’s prediction and the Mössbauer data for small IONPs can be due to the errors in estimation of the weight loss obtained from TGA, which is used as an input for the model.

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Figure 6. Room temperature magnetization measurements for magnetic nanoparticles (symbols) and the respective Brillouin fits (solid lines) for a) small nanoparticles of 6.3 nm (A1) diameter, b) large nanoparticles of 16.2 nm (A11) diameter and c) intermediate nanoparticles of 10.5 nm (A5) diameter. d) A zoom-in of the fit is given that shows the improvement by calculating with a fraction of the particles in a multi-domain state. The lines are calculated based on the histograms presented in Figures 1 and S1.


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Furthermore, we note that by including a dead-layer, the calculated magnetite volume ratio will shift systematically upwards. For example, a dead-layer of 0.1 nm thick increases the magnetite fraction from 15% to 27.5% for A1. For larger IONPs, the influence of the dead-layer on estimated magnetite fraction is weaker and increases the magnetite fraction from 75% to 80% for A11.

a)

100

b)

Model Mössbauer

80

Fe3O4 Core radius:

6

Model Mössbauer Fe2O3 Shell thickness: Model

Mössbauer

60

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Fe3O4 (%)

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60


40

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Figure 7. a) Volume fraction of magnetite x(Fe3O4) as function of the nanoparticle diameter obtained from Mössbauer measurements and magnetic data. The width of the particle size distribution (horizontal bars) size has no influence on the precision of the model because the actual measured statistical distribution is used as input for the model. b) Calculated magnetite core radius and maghemite shell thickness for the different size of IONPs from Mössbauer measurements and magnetic data. The dashed lines are guides to the eye.

VI.3. The core-shell structure. For the volume partition between the phases, we consider a core-shell structure. Two situations may occur; I) maghemite-core magnetite-shell and II) magnetite-core maghemite-shell structures. Due to the higher oxidation state of magnetite we assume a magnetite-core maghemite-shell in agreement with the literature. 22, 30-31, 71 The calculated sizes for the magnetitecore maghemite-shell as a function of the nanoparticle size are shown in Figure 7b. Interestingly



we find out that as the IONP size grows, the radius of the magnetite core linearly increases, whereas the thickness of the maghemite shell remains constant at about 1 nm. The calculated sizes for the magnetite-core and maghemite-shell agree very well with the results of Mössbauer spectroscopy.

VI.4. Single vs. multi-domain. Earlier we stated that the shape (curvature) of the calculated M(H) curve gives information about domain structure in an IONP ensemble. Here we explain in more detail how to obtain good fits to the curvature using Eq. 3. Due to the different magnetizations of magnetite and maghemite the total J of a nanoparticle depends on its composition. Correspondingly, the J values of the IOPNs have been calculated with the volume fractions deduced from the saturation magnetization. Assuming a single-domain state for all nanoparticles results in a steady increase in J values ranging from 3800 h for small particles of 6.3 nm to huge J values of 58000 h for the largest IONPs with diameter of 16.2 nm. The very large J values lead to a “rectangular” shape for the calculated room temperature M(H) curve which deviates strongly from the curvature of the measured M(H) curve in Figure 6b. Moreover, experimental observation of a maximum in coercivity is commonly attributed to the decomposition of single-domain to multi-domain within the nanoparticles. Therefore, assuming that the IONPs stay single-domain for all investigated particle sizes, contradicts the experimentally observed maximum in the coercive field at low temperatures (Figure 4c). Furthermore, assuming a single magnetic domain and using the actual temperature, the model is able to describe only the experimental M(H) curves of small IONPs, as


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shown in Figure 6, whereas for the largest particles, 16.2 nm, the calculated M(H) curve, green curve in Figure 6b, strongly deviates from the experiment. Getting the curvature right: The curvature of the M(H) curve changes in three different ways: a) by only changing the magnetic moment J and ignoring the dipolar interactions, b) by only changing the temperature T ignoring the domains or c) by changing a combination of J and J/T such that both domains and dipolar interactions are taken into account. All three possibilities lead to identical calculated M(H) curves. However, only the last approach captures the physical reality because both J and the ratio J/T enter in the argument of the Brillouin function. We have discussed in detail a) and b) in the supporting information. For completeness sake, we have provided here just a brief discussion. a)

Only J varies, and dipolar interactions are not playing a role. The temperature, T, is at the

actual room-temperature value. Hence, we introduce an upper cut-off threshold, Jcut-off in the model for the maximum size of a single magnetic domain within a IONP and adapt this cut-off value for each batch to have the best agreement between measured and calculated shape of the magnetization curves. Despite good fits to M(H) curves, this approach suffers from exclusion of dipolar interactions. b)

Only T varies, and dipolar interactions are dominant. Using an effective temperature, Teff

= T + T* in the Brillouin function, we achieve practically the same fit as in the former case. The resulting values of T* (Table S3 of the supporting information) show an increase proportional to the fourth power of the average IONP diameter, which is stronger than the predicted third power for single domain IONPs. With only dipolar interactions, we cannot explain observation of the maxima in HC.



c)

The dipolar interaction model b) is more natural but it does not explain easily why there

is a maximum in the coercive field as function of size of the IONPs, and why the renormalization temperature scales stronger than the third power with diameter. On the other hand, the limiting domain size model a) introduces a cut-off for the domain size by hand, while neglecting dipolar interactions. This is in so far self-consistent as dipolar interactions between multi-domain particles are expected to be weak. We expect the truth to be a combination of both models, i.e. the decay into several domains will happen for larger nanoparticles than when neglecting dipolar interactions, but it must happen for particles smaller than our largest investigated size of 16.2 nm in order to reduce the scaling for T* with diameter from fourth to third power. Thus, we looked for a critical domain size, for which the dipolar scaling has the correct power law. If we set the Jcut-off = 38000, i.e. corresponding to a critical particle diameter of 14.5 nm, for all batches the dipolar interaction strength that is characterized by T* is weaker than for the pure single-domain case. The newly derived values of T* are shown in Figure 8a together with a straight line that indicates 𝑇 ∗ ∝ 𝑑 3 . We calculate the fraction of IONPs in a batch that is monodomain from the histogram data for Jcut-off = 38000, and plot it in Figure 8b together with the normalized strength of the coercive field, i.e. 𝐻𝑐 (𝑑)/𝐻𝑐 (𝑑 = 10.5 nm). Remarkably, the drop of coercive field strength starts as soon as the fraction of single-domain particles deviates from 100%. Note that for a batch with an average d =14.5 nm, equivalent to the critical size for a single-domain particle, we would expect only approximately 50% single-domain fraction.


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a)

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b) T * (K)

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Figure 8. a) The fraction of the particles that are single-domain in a batch of mean diameter d for a Jcut-off = 38000 (black squares) and the low temperature coercive field normalized to the maximum observed value (red circles). b) The dipolar interaction strength characterized by T* for the batches of diameter d. The straight line gives a slope 𝑇 ∗ ∝ 𝑑3 as a guide to the eye.

CONCLUSION We have developed a method that provides information about the magnetic phase composition and the structure of the iron-oxide nanoparticle. The method takes into account (I) the experimentally obtained size distribution histograms of the nanoparticles, (II) the mass of the



nanoparticles, (III) room-temperature magnetization curves, and (VI) allows formation of magnetic multi domain states. The model described the magnetic behavior of the nanoparticles with size ranging from 6.3 nm to 16.2 nm very well, and predicts that (I) the nanoparticles are composed of both magnetite and maghemite phases, (II) with a magnetite-maghemite core-shell (III) and have single magnetic domain for sizes below 14 nm. The model predictions were confirmed by Mössbauer spectroscopy. The methodology developed here offers a new route that helps to retrieve compositional and structural information about the superparamagnetic IONPs provided that a well- characterized size distribution and magnetization curve of the nanoparticles is available.

ASSOCIATED CONTENT Supporting Information A table summarizing synthesis condition, TEM and HR-TEM images of samples, calculation the crystalline size via Scherrer’s formula, Magnetization curve obtained by SQUID, saturation magnetization (MS) as a function of average diameter, calculation of magnetic dead layer from magnetic measurement, Mössbauer spectra of samples A3 and A7, tables summarizing Mössbauer, and model fitting parameters, details of the pure multi-domain vs. the pure dipolar interaction model, cut-off angular momentum for the pure multi-domain model without dipolar interactions as a function of average diameter, and remarks on determining nanoparticle size distributions from magnetization measurements. AUTHOR INFORMATION Corresponding Author *Gerhard Jakob, *Kamal Asadi 22 Environment ACS Paragon Plus

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Max-Planck Institute for Polymer Research, Ackermannweg 10, 55128, Mainz, Germany [email protected], [email protected] Author Contributions K.A. designed the experiment and supervised the work. H.S.D performed the experiments. V.K. and A.M. carried out the Mössbauer measurements and evaluation. G.J. performed the calculations and developed the magnetic model. All authors co-wrote the manuscript. All authors have given approval to the final version of the manuscript. ACKNOWLEDGMENT The authors acknowledge the financial support from the Max-Planck Institute for Polymer Research (Mainz, Germany). The authors would like to acknowledge Prof. W. Tremel and Anielen Halda Ribeiro for fruitful discussions, Frank Keller, Michelle Beuchel, Christian Bauer and Verona Maus for their technical helps. K.A. acknowledges the Alexander von Humboldt Foundation for funding provided in the framework of the Sofja Kovalevskaja Award endowed by the Federal Ministry of Education and Research, Germany.

REFERENCES 1. Kolhatkar, A. G.; Jamison, A. C.; Litvinov, D.; Willson, R. C.; Lee, T. R., Tuning the magnetic properties of nanoparticles. Int. J. Mol. Sci. 2013, 14, 15977-16009. 2. Leslie-Pelecky, D. L.; Rieke, R. D., Magnetic properties of nanostructured materials. Chem. Mater. 1996, 8, 1770-1783. 3. Park, J.; Lee, E.; Hwang, N. M.; Kang, M.; Kim, S. C.; Hwang, Y.; Park, J. G.; Noh, H. J.; Kim, J. Y.; Park, J. H., One‐nanometer‐scale size‐controlled synthesis of monodisperse magnetic Iron oxide nanoparticles. Angew. Chem. 2005, 117, 2932-2937. 4. Guardia, P.; Labarta, A.; Batlle, X., Tuning the size, the shape, and the magnetic properties of iron oxide nanoparticles. J. Phys. Chem. C 2010, 115, 390-396.



5. Guardia, P.; Riedinger, A.; Nitti, S.; Pugliese, G.; Marras, S.; Genovese, A.; Materia, M. E.; Lefevre, C.; Manna, L.; Pellegrino, T., One pot synthesis of monodisperse water soluble iron oxide nanocrystals with high values of the specific absorption rate. J. Mater. Chem. B 2014, 2, 4426-4434. 6. Guardia, P.; Batlle-Brugal, B.; Roca, A.; Iglesias, O.; Morales, M.; Serna, C.; Labarta, A.; Batlle, X., Surfactant effects in magnetite nanoparticles of controlled size. J. Magn. Magn. Mater. 2007, 316, e756-e759. 7. Castellanos-Rubio, I.; Insausti, M.; Garaio, E.; de Muro, I. G.; Plazaola, F.; Rojo, T.; Lezama, L., Fe 3 O 4 nanoparticles prepared by the seeded-growth route for hyperthermia: electron magnetic resonance as a key tool to evaluate size distribution in magnetic nanoparticles. Nanoscale 2014, 6, 7542-7552. 8. Zhou, Z.; Zhu, X.; Wu, D.; Chen, Q.; Huang, D.; Sun, C.; Xin, J.; Ni, K.; Gao, J., Anisotropic shaped iron oxide nanostructures: controlled synthesis and proton relaxation shortening effects. Chem. Mater. 2015, 27, 3505-3515. 9. Chalasani, R.; Vasudevan, S., Form, content, and magnetism in iron oxide nanocrystals. J. Phys. Chem. C 2011, 115, 18088-18093. 10. Eom, Y.; Abbas, M.; Noh, H.; Kim, C., Morphology-controlled synthesis of highly crystalline Fe 3 O 4 and CoFe 2 O 4 nanoparticles using a facile thermal decomposition method. RSC Adv. 2016, 6, 15861-15867. 11. Liu, S.; Yu, J.; Jaroniec, M., Anatase TiO2 with dominant high-energy {001} facets: synthesis, properties, and applications. Chem. Mater. 2011, 23, 4085-4093. 12. Qu, Y.; Yang, H.; Yang, N.; Fan, Y.; Zhu, H.; Zou, G., The effect of reaction temperature on the particle size, structure and magnetic properties of coprecipitated CoFe 2 O 4 nanoparticles. Mater. Lett. 2006, 60, 3548-3552. 13. Gajbhiye, N. S.; Sharma, S.; Nigam, A. K.; Ningthoujam, R. S., Tuning of single to multi-domain behavior for monodispersed ferromagnetic cobalt nanoparticles. Chem. Phys. Lett. 2008, 466, 181-185. 14. Jeong, U.; Teng, X.; Wang, Y.; Yang, H.; Xia, Y., Superparamagnetic colloids: controlled synthesis and niche applications. Adv. Mater. 2007, 19, 33-60. 15. Kittel, C., Physical theory of ferromagnetic domains. Rev. Mod. Phys. 1949, 21, 541. 16. Frenkel, J.; Dorfman, J., Spontaneous and induced magnetisation in ferromagnetic bodies. Nature 1930, 126, 274-275. 17. Kittel, C., Theory of the structure of ferromagnetic domains in films and small particles. Phys. Rev. 1946, 70, 965. 18. Aharoni, A., Brown’s “fundamental theorem” revisited. J. Appl. Phys. 2001, 90, 46454650. 19. Krishnan, K. M., Biomedical nanomagnetics: a spin through possibilities in imaging, diagnostics, and therapy. IEEE Trans. Magn. 2010, 46, 2523-2558. 20. Sharifi Dehsari, H.; Ribeiro, A. H.; Ersöz, B.; Tremel, W.; Jakob, G.; Asadi, K., Effect of precursor concentration on size evolution of iron oxide nanoparticles. CrystEngComm 2017, 19, 6694-6702. 21. Sharifi Dehsari, H.; Heidari, M.; Halda Ribeiro, A.; Tremel, W.; Jakob, G.; Donadio, D.; Potestio, R.; Asadi, K., Combined Experimental and Theoretical Investigation of Heating Rate on Growth of Iron Oxide Nanoparticles. Chem. Mater. 2017, 29, 9648-9656. 22. Baaziz, W.; Pichon, B. P.; Fleutot, S.; Liu, Y.; Lefevre, C.; Greneche, J.-M.; Toumi, M.; Mhiri, T.; Begin-Colin, S., Magnetic iron oxide nanoparticles: reproducible tuning of the size


Page 24 of 34

Page 25 of 34 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60


and nanosized-dependent composition, defects, and spin canting. J. Phys. Chem. C 2014, 118, 3795-3810. 23. Hufschmid, R.; Arami, H.; Ferguson, R. M.; Gonzales, M.; Teeman, E.; Brush, L. N.; Browning, N. D.; Krishnan, K. M., Synthesis of phase-pure and monodisperse iron oxide nanoparticles by thermal decomposition. Nanoscale 2015, 7, 11142-11154. 24. Meledandri, C. J.; Stolarczyk, J. K.; Ghosh, S.; Brougham, D. F., Nonaqueous magnetic nanoparticle suspensions with controlled particle size and nuclear magnetic resonance properties. Langmuir 2008, 24, 14159-14165. 25. Bronstein, L. M.; Huang, X.; Retrum, J.; Schmucker, A.; Pink, M.; Stein, B. D.; Dragnea, B., Influence of iron oleate complex structure on iron oxide nanoparticle formation. Chem. Mater. 2007, 19, 3624-3632. 26. Vreeland, E. C.; Watt, J.; Schober, G. B.; Hance, B. G.; Austin, M. J.; Price, A. D.; Fellows, B. D.; Monson, T. C.; Hudak, N. S.; Maldonado-Camargo, L., Enhanced nanoparticle size control by extending LaMer’s mechanism. Chem. Mater. 2015, 27, 6059-6066. 27. Huang, J.-H.; Parab, H. J.; Liu, R.-S.; Lai, T.-C.; Hsiao, M.; Chen, C.-H.; Sheu, H.-S.; Chen, J.-M.; Tsai, D.-P.; Hwu, Y.-K., Investigation of the growth mechanism of iron oxide nanoparticles via a seed-mediated method and its cytotoxicity studies. J. Phys. Chem. C 2008, 112, 15684-15690. 28. Sharifi Dehsari, H.; Harris, R. A.; Ribeiro, A. H.; Tremel, W.; Asadi, K., Optimizing the Binding Energy of the Surfactant to Iron-Oxide Yields Truly Monodisperse Nanoparticles. Langmuir 2018, 22, 6582-6590. 29. Hergt, R.; Dutz, S.; Röder, M., Effects of size distribution on hysteresis losses of magnetic nanoparticles for hyperthermia. J. Phys.: Condens. Matter 2008, 20, 385214-385226. 30. Frison, R.; Cernuto, G.; Cervellino, A.; Zaharko, O.; Colonna, G. M.; Guagliardi, A.; Masciocchi, N., Magnetite–Maghemite Nanoparticles in the 5–15 nm Range: Correlating the Core–Shell Composition and the Surface Structure to the Magnetic Properties. A Total Scattering Study. Chem. Mater. 2013, 25, 4820-4827. 31. Santoyo Salazar, J.; Perez, L.; de Abril, O.; Truong Phuoc, L.; Ihiawakrim, D.; Vazquez, M.; Greneche, J.-M.; Begin-Colin, S.; Pourroy, G., Magnetic iron oxide nanoparticles in 10− 40 nm range: composition in terms of magnetite/maghemite ratio and effect on the magnetic properties. Chem. Mater. 2011, 23, 1379-1386. 32. Zweck, J., Imaging of magnetic and electric fields by electron microscopy. J. Phys.: Condens. Matter 2016, 28, 403001-403033. 33. Dunin-Borkowski, R.; McCartney, M.; Kardynal, B.; Smith, D. J., Magnetic interactions within patterned cobalt nanostructures using off-axis electron holography. J. Appl. Phys. 1998, 84, 374-378. 34. Dunin‐Borkowski, R. E.; Kasama, T.; Wei, A.; Tripp, S. L.; Hÿtch, M. J.; Snoeck, E.; Harrison, R. J.; Putnis, A., Off‐axis electron holography of magnetic nanowires and chains, rings, and planar arrays of magnetic nanoparticles. Microsc. Res. Tech. 2004, 64, 390-402. 35. Unni, M.; Uhl, A. M.; Savliwala, S.; Savitzky, B. H.; Dhavalikar, R.; Garraud, N.; Arnold, D. P.; Kourkoutis, L. F.; Andrew, J. S.; Rinaldi, C., Thermal decomposition synthesis of iron oxide nanoparticles with diminished magnetic dead layer by controlled addition of oxygen. ACS nano 2017, 11, 2284-2303. 36. Allia, P.; Coisson, M.; Tiberto, P.; Vinai, F.; Knobel, M.; Novak, M.; Nunes, W., Granular Cu-Co alloys as interacting superparamagnets. Phys. Rev. B 2001, 64, 144420.



37. Knobel, M.; Nunes, W.; Brandl, A.; Vargas, J.; Socolovsky, L.; Zanchet, D., Interaction effects in magnetic granular systems. Physica B: Condensed Matter 2004, 354, 80-87. 38. Tartaj, P.; Gonzalez-Carreno, T.; Bomati-Miguel, O.; Serna, C.; Bonville, P., Magnetic behavior of superparamagnetic Fe nanocrystals confined inside submicron-sized spherical silica particles. Phys. Rev. B 2004, 69, 094401. 39. Can, M. M.; Coşkun, M.; Fırat, T., Domain state–dependent magnetic formation of Fe 3 O 4 nanoparticles analyzed via magnetic resonance. J. Nanopart. Res. 2011, 13, 5497. 40. Chantrell, R.; Popplewell, J.; Charles, S., Measurements of particle size distribution parameters in ferrofluids. IEEE Trans. Magn. 1978, 14, 975-977. 41. O'grady, K.; Bradbury, A., Particle size analysis in ferrofluids. J. Magn. Magn. Mater. 1983, 39, 91-94. 42. Mahmood, S. H., Magnetic anisotropy in fine magnetic particles. J. Magn. Magn. Mater. 1993, 118, 359-364. 43. Caruntu, D.; Caruntu, G.; O'Connor, C. J., Magnetic properties of variable-sized Fe3O4 nanoparticles synthesized from non-aqueous homogeneous solutions of polyols. J. Phys. D: Appl. Phys. 2007, 40, 5801-5809. 44. Schmidt, D.; Eberbeck, D.; Steinhoff, U.; Wiekhorst, F., Finding the magnetic size distribution of magnetic nanoparticles from magnetization measurements via the iterative Kaczmarz algorithm. J. Magn. Magn. Mater. 2017, 431, 33-37. 45. Berkov, D.; Görnert, P.; Buske, N.; Gansau, C.; Mueller, J.; Giersig, M.; Neumann, W.; Su, D., New method for the determination of the particle magnetic moment distribution in a ferrofluid. J. Phys. D: Appl. Phys. 2000, 33, 331-337. 46. Bender, P.; Bogart, L.; Posth, O.; Szczerba, W.; Rogers, S. E.; Castro, A.; Nilsson, L.; Zeng, L.; Sugunan, A.; Sommertune, J., Structural and magnetic properties of multi-core nanoparticles analysed using a generalised numerical inversion method. Sci. Rep. 2017, 7, 45990-46004. 47. Plouffe, B. D.; Nagesha, D. K.; DiPietro, R. S.; Sridhar, S.; Heiman, D.; Murthy, S. K.; Lewis, L. H., Thermomagnetic determination of Fe3O4 magnetic nanoparticle diameters for biomedical applications. J. Magn. Magn. Mater. 2011, 323, 2310-2317. 48. Luigjes, B.; Woudenberg, S. M.; de Groot, R.; Meeldijk, J. D.; Torres Galvis, H. M.; de Jong, K. P.; Philipse, A. P.; Erné, B. H., Diverging geometric and magnetic size distributions of iron oxide nanocrystals. J. Phys. Chem. C 2011, 115, 14598-14605. 49. Levy, M.; Quarta, A.; Espinosa, A.; Figuerola, A.; Wilhelm, C.; García-Hernández, M.; Genovese, A.; Falqui, A.; Alloyeau, D.; Buonsanti, R., Correlating magneto-structural properties to hyperthermia performance of highly monodisperse iron oxide nanoparticles prepared by a seeded-growth route. Chem. Mater. 2011, 23, 4170-4180. 50. Gonzales-Weimuller, M.; Zeisberger, M.; Krishnan, K. M., Size-dependant heating rates of iron oxide nanoparticles for magnetic fluid hyperthermia. J. Magn. Magn. Mater. 2009, 321, 1947-1950. 51. van Rijssel, J.; Kuipers, B. W.; Erné, B. H., Bimodal distribution of the magnetic dipole moment in nanoparticles with a monomodal distribution of the physical size. J. Magn. Magn. Mater. 2015, 380, 325-329. 52. Sun, S.; Zeng, H., Size-controlled synthesis of magnetite nanoparticles. J. Am. Chem. Soc. 2002, 124, 8204-8205. 53. Sun, S.; Zeng, H.; Robinson, D. B.; Raoux, S.; Rice, P. M.; Wang, S. X.; Li, G., Monodisperse MFe2O4 (M= Fe, Co, Mn) nanoparticles. J. Am. Chem. Soc. 2004, 126, 273-279.


Page 26 of 34

Page 27 of 34 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60


54. Gorski, C. A.; Scherer, M. M., Determination of nanoparticulate magnetite stoichiometry by Mossbauer spectroscopy, acidic dissolution, and powder X-ray diffraction: A critical review. Am. Mineral. 2010, 95, 1017-1026. 55. Guardia, P.; Perez-Juste, J.; Labarta, A.; Batlle, X.; Liz-Marzán, L. M., Heating rate influence on the synthesis of iron oxide nanoparticles: the case of decanoic acid. Chem. Commun. 2010, 46, 6108-6110. 56. Klug, H. P.; Alexander, L. E., X-ray diffraction procedures. Wiely: New York, 1954. 57. Yang, K.; Peng, H.; Wen, Y.; Li, N., Re-examination of characteristic FTIR spectrum of secondary layer in bilayer oleic acid-coated Fe3O4 nanoparticles. Appl. Surf. Sci. 2010, 256, 3093-3097. 58. Moya, C.; Batlle, X.; Labarta, A., The effect of oleic acid on the synthesis of Fe 3− x O 4 nanoparticles over a wide size range. Phys. Chem. Chem. Phys. 2015, 17, 27373-27379. 59. Zhang, L.; He, R.; Gu, H.-C., Oleic acid coating on the monodisperse magnetite nanoparticles. Appl. Surf. Sci. 2006, 253, 2611-2617. 60. Puntes, V. F.; Krishnan, K. M.; Alivisatos, A. P., Colloidal nanocrystal shape and size control: the case of cobalt. Science 2001, 291, 2115-2117. 61. Jiles, D., Introduction to magnetism and magnetic materials. CRC press: New York, 2015. 62. Kodama, R.; Berkowitz, A.; McNiff Jr, E.; Foner, S., Surface spin disorder in ferrite nanoparticles. J. Appl. Phys. 1997, 81, 5552-5557. 63. Morales, M. d. P.; Veintemillas-Verdaguer, S.; Montero, M.; Serna, C.; Roig, A.; Casas, L.; Martinez, B.; Sandiumenge, F., Surface and internal spin canting in γ-Fe2O3 nanoparticles. Chem. Mater. 1999, 11, 3058-3064. 64. Yang, H.; Ogawa, T.; Hasegawa, D.; Takahashi, M., Synthesis and magnetic properties of monodisperse magnetite nanocubes. J. Appl. Phys. 2008, 103, 07D526-07D529. 65. Song, Q.; Zhang, Z. J., Correlation between spin− orbital coupling and the superparamagnetic properties in magnetite and cobalt ferrite spinel nanocrystals. J. Phys. Chem. B 2006, 110, 11205-11209. 66. Wu, L.; Mendoza-Garcia, A.; Li, Q.; Sun, S., Organic phase syntheses of magnetic nanoparticles and their applications. Chem. Rev. 2016, 116, 10473-10512. 67. Lagarec, K.; Rancourt, D., Extended Voigt-based analytic lineshape method for determining N-dimensional correlated hyperfine parameter distributions in Mössbauer spectroscopy. Nuclear Instruments and Methods in Physics Research Section B: Beam Interactions with Materials and Atoms 1997, 129, 266-280. 68. Liu, J.; Bin, Y.; Matsuo, M., Magnetic behavior of Zn-doped Fe3O4 nanoparticles estimated in terms of crystal domain size. J. Phys. Chem. C 2011, 116, 134-143. 69. Dunlop, D. J.; Özdemir, Ö., Rock magnetism: fundamentals and frontiers. Cambridge university press: 2001; Vol. 3. 70. Daou, T.; Greneche, J.; Pourroy, G.; Buathong, S.; Derory, A.; Ulhaq-Bouillet, C.; Donnio, B.; Guillon, D.; Begin-Colin, S., Coupling agent effect on magnetic properties of functionalized magnetite-based nanoparticles. Chem. Mater. 2008, 20, 5869-5875. 71. Signorini, L.; Pasquini, L.; Savini, L.; Carboni, R.; Boscherini, F.; Bonetti, E.; Giglia, A.; Pedio, M.; Mahne, N.; Nannarone, S., Size-dependent oxidation in iron/iron oxide core-shell nanoparticles. Phys. Rev. B 2003, 68, 195423-195431.




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1. Kolhatkar, A. G.; Jamison, A. C.; Litvinov, D.; Willson, R. C.; Lee, T. R., Tuning the magnetic properties of nanoparticles. Int. J. Mol. Sci. 2013, 14, 15977-16009. 2. Leslie-Pelecky, D. L.; Rieke, R. D., Magnetic properties of nanostructured materials. Chem. Mater. 1996, 8, 1770-1783. 3. Park, J.; Lee, E.; Hwang, N. M.; Kang, M.; Kim, S. C.; Hwang, Y.; Park, J. G.; Noh, H. J.; Kim, J. Y.; Park, J. H., One‐nanometer‐scale size‐controlled synthesis of monodisperse magnetic Iron oxide nanoparticles. Angew. Chem. 2005, 117, 2932-2937. 4. Guardia, P.; Labarta, A.; Batlle, X., Tuning the size, the shape, and the magnetic properties of iron oxide nanoparticles. J. Phys. Chem. C 2010, 115, 390-396. 5. Guardia, P.; Riedinger, A.; Nitti, S.; Pugliese, G.; Marras, S.; Genovese, A.; Materia, M. E.; Lefevre, C.; Manna, L.; Pellegrino, T., One pot synthesis of monodisperse water soluble iron oxide nanocrystals with high values of the specific absorption rate. J. Mater. Chem. B 2014, 2, 4426-4434. 6. Guardia, P.; Batlle-Brugal, B.; Roca, A.; Iglesias, O.; Morales, M.; Serna, C.; Labarta, A.; Batlle, X., Surfactant effects in magnetite nanoparticles of controlled size. J. Magn. Magn. Mater. 2007, 316, e756-e759. 7. Castellanos-Rubio, I.; Insausti, M.; Garaio, E.; de Muro, I. G.; Plazaola, F.; Rojo, T.; Lezama, L., Fe 3 O 4 nanoparticles prepared by the seeded-growth route for hyperthermia: electron magnetic resonance as a key tool to evaluate size distribution in magnetic nanoparticles. Nanoscale 2014, 6, 7542-7552. 8. Zhou, Z.; Zhu, X.; Wu, D.; Chen, Q.; Huang, D.; Sun, C.; Xin, J.; Ni, K.; Gao, J., Anisotropic shaped iron oxide nanostructures: controlled synthesis and proton relaxation shortening effects. Chem. Mater. 2015, 27, 3505-3515. 9. Chalasani, R.; Vasudevan, S., Form, content, and magnetism in iron oxide nanocrystals. J. Phys. Chem. C 2011, 115, 18088-18093. 10. Eom, Y.; Abbas, M.; Noh, H.; Kim, C., Morphology-controlled synthesis of highly crystalline Fe 3 O 4 and CoFe 2 O 4 nanoparticles using a facile thermal decomposition method. RSC Adv. 2016, 6, 15861-15867. 11. Liu, S.; Yu, J.; Jaroniec, M., Anatase TiO2 with dominant high-energy {001} facets: synthesis, properties, and applications. Chem. Mater. 2011, 23, 4085-4093. 12. Qu, Y.; Yang, H.; Yang, N.; Fan, Y.; Zhu, H.; Zou, G., The effect of reaction temperature on the particle size, structure and magnetic properties of coprecipitated CoFe 2 O 4 nanoparticles. Mater. Lett. 2006, 60, 3548-3552. 13. Gajbhiye, N. S.; Sharma, S.; Nigam, A. K.; Ningthoujam, R. S., Tuning of single to multi-domain behavior for monodispersed ferromagnetic cobalt nanoparticles. Chem. Phys. Lett. 2008, 466, 181-185. 14. Jeong, U.; Teng, X.; Wang, Y.; Yang, H.; Xia, Y., Superparamagnetic colloids: controlled synthesis and niche applications. Adv. Mater. 2007, 19, 33-60. 15. Kittel, C., Physical theory of ferromagnetic domains. Rev. Mod. Phys. 1949, 21, 541. 16. Frenkel, J.; Dorfman, J., Spontaneous and induced magnetisation in ferromagnetic bodies. Nature 1930, 126, 274-275. 17. Kittel, C., Theory of the structure of ferromagnetic domains in films and small particles. Phys. Rev. 1946, 70, 965. 18. Aharoni, A., Brown’s “fundamental theorem” revisited. J. Appl. Phys. 2001, 90, 46454650.



19. Krishnan, K. M., Biomedical nanomagnetics: a spin through possibilities in imaging, diagnostics, and therapy. IEEE Trans. Magn. 2010, 46, 2523-2558. 20. Sharifi Dehsari, H.; Ribeiro, A. H.; Ersöz, B.; Tremel, W.; Jakob, G.; Asadi, K., Effect of precursor concentration on size evolution of iron oxide nanoparticles. CrystEngComm 2017, 19, 6694-6702. 21. Sharifi Dehsari, H.; Heidari, M.; Halda Ribeiro, A.; Tremel, W.; Jakob, G.; Donadio, D.; Potestio, R.; Asadi, K., Combined Experimental and Theoretical Investigation of Heating Rate on Growth of Iron Oxide Nanoparticles. Chem. Mater. 2017, 29, 9648-9656. 22. Baaziz, W.; Pichon, B. P.; Fleutot, S.; Liu, Y.; Lefevre, C.; Greneche, J.-M.; Toumi, M.; Mhiri, T.; Begin-Colin, S., Magnetic iron oxide nanoparticles: reproducible tuning of the size and nanosized-dependent composition, defects, and spin canting. J. Phys. Chem. C 2014, 118, 3795-3810. 23. Hufschmid, R.; Arami, H.; Ferguson, R. M.; Gonzales, M.; Teeman, E.; Brush, L. N.; Browning, N. D.; Krishnan, K. M., Synthesis of phase-pure and monodisperse iron oxide nanoparticles by thermal decomposition. Nanoscale 2015, 7, 11142-11154. 24. Meledandri, C. J.; Stolarczyk, J. K.; Ghosh, S.; Brougham, D. F., Nonaqueous magnetic nanoparticle suspensions with controlled particle size and nuclear magnetic resonance properties. Langmuir 2008, 24, 14159-14165. 25. Bronstein, L. M.; Huang, X.; Retrum, J.; Schmucker, A.; Pink, M.; Stein, B. D.; Dragnea, B., Influence of iron oleate complex structure on iron oxide nanoparticle formation. Chem. Mater. 2007, 19, 3624-3632. 26. Vreeland, E. C.; Watt, J.; Schober, G. B.; Hance, B. G.; Austin, M. J.; Price, A. D.; Fellows, B. D.; Monson, T. C.; Hudak, N. S.; Maldonado-Camargo, L., Enhanced nanoparticle size control by extending LaMer’s mechanism. Chem. Mater. 2015, 27, 6059-6066. 27. Huang, J.-H.; Parab, H. J.; Liu, R.-S.; Lai, T.-C.; Hsiao, M.; Chen, C.-H.; Sheu, H.-S.; Chen, J.-M.; Tsai, D.-P.; Hwu, Y.-K., Investigation of the growth mechanism of iron oxide nanoparticles via a seed-mediated method and its cytotoxicity studies. J. Phys. Chem. C 2008, 112, 15684-15690. 28. Sharifi Dehsari, H.; Harris, R. A.; Ribeiro, A. H.; Tremel, W.; Asadi, K., Optimizing the Binding Energy of the Surfactant to Iron-Oxide Yields Truly Monodisperse Nanoparticles. Langmuir 2018, 22, 6582-6590. 29. Hergt, R.; Dutz, S.; Röder, M., Effects of size distribution on hysteresis losses of magnetic nanoparticles for hyperthermia. J. Phys.: Condens. Matter 2008, 20, 385214-385226. 30. Frison, R.; Cernuto, G.; Cervellino, A.; Zaharko, O.; Colonna, G. M.; Guagliardi, A.; Masciocchi, N., Magnetite–Maghemite Nanoparticles in the 5–15 nm Range: Correlating the Core–Shell Composition and the Surface Structure to the Magnetic Properties. A Total Scattering Study. Chem. Mater. 2013, 25, 4820-4827. 31. Santoyo Salazar, J.; Perez, L.; de Abril, O.; Truong Phuoc, L.; Ihiawakrim, D.; Vazquez, M.; Greneche, J.-M.; Begin-Colin, S.; Pourroy, G., Magnetic iron oxide nanoparticles in 10− 40 nm range: composition in terms of magnetite/maghemite ratio and effect on the magnetic properties. Chem. Mater. 2011, 23, 1379-1386. 32. Zweck, J., Imaging of magnetic and electric fields by electron microscopy. J. Phys.: Condens. Matter 2016, 28, 403001-403033. 33. Dunin-Borkowski, R.; McCartney, M.; Kardynal, B.; Smith, D. J., Magnetic interactions within patterned cobalt nanostructures using off-axis electron holography. J. Appl. Phys. 1998, 84, 374-378.


Page 30 of 34

Page 31 of 34 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60


34. Dunin‐Borkowski, R. E.; Kasama, T.; Wei, A.; Tripp, S. L.; Hÿtch, M. J.; Snoeck, E.; Harrison, R. J.; Putnis, A., Off‐axis electron holography of magnetic nanowires and chains, rings, and planar arrays of magnetic nanoparticles. Microsc. Res. Tech. 2004, 64, 390-402. 35. Unni, M.; Uhl, A. M.; Savliwala, S.; Savitzky, B. H.; Dhavalikar, R.; Garraud, N.; Arnold, D. P.; Kourkoutis, L. F.; Andrew, J. S.; Rinaldi, C., Thermal decomposition synthesis of iron oxide nanoparticles with diminished magnetic dead layer by controlled addition of oxygen. ACS nano 2017, 11, 2284-2303. 36. Allia, P.; Coisson, M.; Tiberto, P.; Vinai, F.; Knobel, M.; Novak, M.; Nunes, W., Granular Cu-Co alloys as interacting superparamagnets. Phys. Rev. B 2001, 64, 144420. 37. Knobel, M.; Nunes, W.; Brandl, A.; Vargas, J.; Socolovsky, L.; Zanchet, D., Interaction effects in magnetic granular systems. Physica B: Condensed Matter 2004, 354, 80-87. 38. Tartaj, P.; Gonzalez-Carreno, T.; Bomati-Miguel, O.; Serna, C.; Bonville, P., Magnetic behavior of superparamagnetic Fe nanocrystals confined inside submicron-sized spherical silica particles. Phys. Rev. B 2004, 69, 094401. 39. Can, M. M.; Coşkun, M.; Fırat, T., Domain state–dependent magnetic formation of Fe 3 O 4 nanoparticles analyzed via magnetic resonance. J. Nanopart. Res. 2011, 13, 5497. 40. Chantrell, R.; Popplewell, J.; Charles, S., Measurements of particle size distribution parameters in ferrofluids. IEEE Trans. Magn. 1978, 14, 975-977. 41. O'grady, K.; Bradbury, A., Particle size analysis in ferrofluids. J. Magn. Magn. Mater. 1983, 39, 91-94. 42. Mahmood, S. H., Magnetic anisotropy in fine magnetic particles. J. Magn. Magn. Mater. 1993, 118, 359-364. 43. Caruntu, D.; Caruntu, G.; O'Connor, C. J., Magnetic properties of variable-sized Fe3O4 nanoparticles synthesized from non-aqueous homogeneous solutions of polyols. J. Phys. D: Appl. Phys. 2007, 40, 5801-5809. 44. Schmidt, D.; Eberbeck, D.; Steinhoff, U.; Wiekhorst, F., Finding the magnetic size distribution of magnetic nanoparticles from magnetization measurements via the iterative Kaczmarz algorithm. J. Magn. Magn. Mater. 2017, 431, 33-37. 45. Berkov, D.; Görnert, P.; Buske, N.; Gansau, C.; Mueller, J.; Giersig, M.; Neumann, W.; Su, D., New method for the determination of the particle magnetic moment distribution in a ferrofluid. J. Phys. D: Appl. Phys. 2000, 33, 331-337. 46. Bender, P.; Bogart, L.; Posth, O.; Szczerba, W.; Rogers, S. E.; Castro, A.; Nilsson, L.; Zeng, L.; Sugunan, A.; Sommertune, J., Structural and magnetic properties of multi-core nanoparticles analysed using a generalised numerical inversion method. Sci. Rep. 2017, 7, 45990-46004. 47. Plouffe, B. D.; Nagesha, D. K.; DiPietro, R. S.; Sridhar, S.; Heiman, D.; Murthy, S. K.; Lewis, L. H., Thermomagnetic determination of Fe3O4 magnetic nanoparticle diameters for biomedical applications. J. Magn. Magn. Mater. 2011, 323, 2310-2317. 48. Luigjes, B.; Woudenberg, S. M.; de Groot, R.; Meeldijk, J. D.; Torres Galvis, H. M.; de Jong, K. P.; Philipse, A. P.; Erné, B. H., Diverging geometric and magnetic size distributions of iron oxide nanocrystals. J. Phys. Chem. C 2011, 115, 14598-14605. 49. Levy, M.; Quarta, A.; Espinosa, A.; Figuerola, A.; Wilhelm, C.; García-Hernández, M.; Genovese, A.; Falqui, A.; Alloyeau, D.; Buonsanti, R., Correlating magneto-structural properties to hyperthermia performance of highly monodisperse iron oxide nanoparticles prepared by a seeded-growth route. Chem. Mater. 2011, 23, 4170-4180.



50. Gonzales-Weimuller, M.; Zeisberger, M.; Krishnan, K. M., Size-dependant heating rates of iron oxide nanoparticles for magnetic fluid hyperthermia. J. Magn. Magn. Mater. 2009, 321, 1947-1950. 51. van Rijssel, J.; Kuipers, B. W.; Erné, B. H., Bimodal distribution of the magnetic dipole moment in nanoparticles with a monomodal distribution of the physical size. J. Magn. Magn. Mater. 2015, 380, 325-329. 52. Sun, S.; Zeng, H., Size-controlled synthesis of magnetite nanoparticles. J. Am. Chem. Soc. 2002, 124, 8204-8205. 53. Sun, S.; Zeng, H.; Robinson, D. B.; Raoux, S.; Rice, P. M.; Wang, S. X.; Li, G., Monodisperse MFe2O4 (M= Fe, Co, Mn) nanoparticles. J. Am. Chem. Soc. 2004, 126, 273-279. 54. Gorski, C. A.; Scherer, M. M., Determination of nanoparticulate magnetite stoichiometry by Mossbauer spectroscopy, acidic dissolution, and powder X-ray diffraction: A critical review. Am. Mineral. 2010, 95, 1017-1026. 55. Guardia, P.; Perez-Juste, J.; Labarta, A.; Batlle, X.; Liz-Marzán, L. M., Heating rate influence on the synthesis of iron oxide nanoparticles: the case of decanoic acid. Chem. Commun. 2010, 46, 6108-6110. 56. Klug, H. P.; Alexander, L. E., X-ray diffraction procedures. Wiely: New York, 1954. 57. Yang, K.; Peng, H.; Wen, Y.; Li, N., Re-examination of characteristic FTIR spectrum of secondary layer in bilayer oleic acid-coated Fe3O4 nanoparticles. Appl. Surf. Sci. 2010, 256, 3093-3097. 58. Moya, C.; Batlle, X.; Labarta, A., The effect of oleic acid on the synthesis of Fe 3− x O 4 nanoparticles over a wide size range. Phys. Chem. Chem. Phys. 2015, 17, 27373-27379. 59. Zhang, L.; He, R.; Gu, H.-C., Oleic acid coating on the monodisperse magnetite nanoparticles. Appl. Surf. Sci. 2006, 253, 2611-2617. 60. Puntes, V. F.; Krishnan, K. M.; Alivisatos, A. P., Colloidal nanocrystal shape and size control: the case of cobalt. Science 2001, 291, 2115-2117. 61. Jiles, D., Introduction to magnetism and magnetic materials. CRC press: New York, 2015. 62. Kodama, R.; Berkowitz, A.; McNiff Jr, E.; Foner, S., Surface spin disorder in ferrite nanoparticles. J. Appl. Phys. 1997, 81, 5552-5557. 63. Morales, M. d. P.; Veintemillas-Verdaguer, S.; Montero, M.; Serna, C.; Roig, A.; Casas, L.; Martinez, B.; Sandiumenge, F., Surface and internal spin canting in γ-Fe2O3 nanoparticles. Chem. Mater. 1999, 11, 3058-3064. 64. Yang, H.; Ogawa, T.; Hasegawa, D.; Takahashi, M., Synthesis and magnetic properties of monodisperse magnetite nanocubes. J. Appl. Phys. 2008, 103, 07D526-07D529. 65. Song, Q.; Zhang, Z. J., Correlation between spin− orbital coupling and the superparamagnetic properties in magnetite and cobalt ferrite spinel nanocrystals. J. Phys. Chem. B 2006, 110, 11205-11209. 66. Wu, L.; Mendoza-Garcia, A.; Li, Q.; Sun, S., Organic phase syntheses of magnetic nanoparticles and their applications. Chem. Rev. 2016, 116, 10473-10512. 67. Lagarec, K.; Rancourt, D., Extended Voigt-based analytic lineshape method for determining N-dimensional correlated hyperfine parameter distributions in Mössbauer spectroscopy. Nuclear Instruments and Methods in Physics Research Section B: Beam Interactions with Materials and Atoms 1997, 129, 266-280. 68. Liu, J.; Bin, Y.; Matsuo, M., Magnetic behavior of Zn-doped Fe3O4 nanoparticles estimated in terms of crystal domain size. J. Phys. Chem. C 2011, 116, 134-143.


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69. Dunlop, D. J.; Özdemir, Ö., Rock magnetism: fundamentals and frontiers. Cambridge university press: 2001; Vol. 3. 70. Daou, T.; Greneche, J.; Pourroy, G.; Buathong, S.; Derory, A.; Ulhaq-Bouillet, C.; Donnio, B.; Guillon, D.; Begin-Colin, S., Coupling agent effect on magnetic properties of functionalized magnetite-based nanoparticles. Chem. Mater. 2008, 20, 5869-5875. 71. Signorini, L.; Pasquini, L.; Savini, L.; Carboni, R.; Boscherini, F.; Bonetti, E.; Giglia, A.; Pedio, M.; Mahne, N.; Nannarone, S., Size-dependent oxidation in iron/iron oxide core-shell nanoparticles. Phys. Rev. B 2003, 68, 195423-195431.



Table of Content Graphic:

M

H

Magnetite fraction (%)

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60

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100 80 60 40 20 0 4

6

8

10

12

14

16

18

Iron-oxide nanoparticle diameter (nm)


Maghemite Composition and Core–Shell

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