Supplementary Information. Optimizing the Binding Energy of the Surfactant to. Iron-Oxide Yields Truly Monodisperse

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1 Supplementary Information Optimizing the Binding Energy of the Surfactant to Iron-Oxide Yields Truly Monodisperse Nanoparticles Hamed Sharifi Dehsari 1, Richard Anthony Harris 2, Anielen Halda Ribeiro 1, Wolfgang Tremel 3, Kamal Asadi 1 * 1 Max Planck Institute for Polymer Research, Ackermannweg 10, Mainz, Germany 2 Department of Physics, University of the Free State, Bloemfontein 9300, Republic of South Africa. 3 Department of Chemistry, Johannes Gutenberg University of Mainz, Mainz, Germany * Corresponding Author: asadi@mpip-mainz.mpg.de 1

2 Theory and simulation method The adsorption locator module of the Materials Studio 6.0 (MS6.0) software package was used to simulate the initial adsorptions. Different ratios of the number of OAC / OAM- molecules were used and the molecules were simultaneously adsorbed onto the nanoparticle surface. These ratios were produced by first keeping the sum of OAC and OAM constant (for an array of different surfactant concentrations), as the number of OAC and OAM was varied. Possible adsorption configurations were identified by carrying out Monte Carlo searches of the configurational space of the substrate-adsorbate system as the temperature was slowly decreased according to a simulated annealing schedule. For the initial adsorption, a universal forcefield was used and the charges were assigned using the QEq charge equilibration method. As a check, the same experiments were repeated using the charge consistent valence forcefield (cvff) with charges assigned by the forcefield. The same results were generated. The summation method for the electrostatics and the Van der Waals interactions were both atom based and the quality of the calculation was set to ultra-fine. The experiment was repeated 15 times for each ratio. Geometry Optimization Molecular mechanics were used to do geometry optimization for each of the resulting OAC, OAM nanoparticle systems. The Discover module of MS6.0 was employed for this task. The cvff forcefield was used with charges assigned by the forcefield. Settings were applied to both Van der Waals and Coulomb forces with an atom based -summation method. A smart minimization algorithm was employed which uses combinations of the steepest descent, conjugate gradient and newton methods. The Fletcher-Reeves algorithm was used for the 2

3 conjugate gradient method and for the newton method the Broyden-Fletcher-Goldfarb-Shanno (BFGS) algorithm was used. Molecular Dynamics simulations The resulting geometries from the molecular mechanics geometry optimization calculations were used as input to the molecular dynamics simulations to arrive at the final, energy-optimized systems. A constant temperature, constant volume ensemble (NVT) at room temperature was used with a time step of 1.0 femtoseconds and a dynamic time of 20.0 picoseconds. An Anderson-thermostat was used with a collision ratio of 1.0 and the number of simulation steps were selected as Binding Energy To calculate the binding energy between the surfactants (E s ) (OAC and OAM) and the nanoparticle (E np ) surface, the total energy of the system (E totalsystem ) is the sum of the total energy of each separate system plus the interaction (binding) energy between the nanoparticle and the surfactants. Therefore, the binding energy (E b ) is calculated according to: E b = (E totalsystem ) E np E s (1) After the optimum configuration of the nanoparticle-ligand systems were determined, the nanoparticle was removed from the system. A single point energy calculation was carried out, from which the total energy of the surfactant was determined. To calculate the total energy of the nanoparticle, the surfactants were removed and a single point energy calculation was carried out to obtain the total energy of the nanoparticle. 3

4 Series B Figure S1. TEM images of samples (a) B 1, (b) B 2, (c) B 3, (d) B 4, (e) B 5, and (f) B 6. Particle size distributions obtained by fitting TEM histograms are also shown. The image of (b) is also given in before. 4

5 Series C Figure S2. TEM images of samples (a) C 1, (b) C 2, (c) C 3, (d) C 4, (e) C 5, (f) C 6, (g) C 7 and (f) C 8. Particle size distributions obtained by fitting TEM histograms are also shown. The image of (d) and (f) are also given in before. Calculation the graft density: The exemplary TGA and DTA graph of sample A 7 is shown in Figure S3. The graph shows three main weight loss plateaus. 1-3 The first one below 200 C is due to the evaporation of adsorbed water and/or solvent molecules. The second and the third plateaus between C and C attributed to the decomposition of the surfactant molecules on the nanoparticles. 5

6 Figure S3. TGA and dw/dt graph of standard samples between 50 C to 800 C. The amount of grafting was calculated from the TGA and dw/dt data, according to the following equations: Number of surfactants (molecules) N (2) Number of NPs (3) Graft density (molecules/nm 2 ).. (4) Where m 1 and m 3 are the amount of mass in the first and third dw/dt peaks, ρ is the density of NPs (~ 5.18 g/cm3), M is the molecular weight of capped surfactant ( g/mol), V is the volume of the each individual NP and A is the average are of single NP (we assumed spherical shape for all nanoparticles). 6

7 Calculation the crystalline size via Scherrer s formula: The crystallite size of Nanoparticles for the most intense, (311) plane was determined from the X-ray data using Debye Scherrer formula: 4-6 D = Kλ/βCosϴ (5) Where λ is the wavelength of X-ray (λ= Å); β the full width at half maximum (obtained from fitting the 311 peak); ϴ is the Bragg s diffraction angle (around 35 -obtained from fitting the 311 peak) and K is the shape factor which is normally taken as for ferrites. 4-5 The interplaner distances d hkl (Å) were calculated using Bragg s law and then the lattice constant of the samples was calculated using the relation: 4 nλ 2dsinƟ (6) a = d hkl h + + (7) Where a is lattice constant; (hkl) is the indexing plane of atoms which can be obtained from X-ray diffraction data. 7

8 Figure S4. TEM images of samples obtained with different heating rate and different ratio of 1 (a) and (b), 3 (c) and (d). 8

9 Figure S5. Temperature dependence of magnetization measured after zero-field cooling (ZFC) at 100 Oe for NPs with different size at ratio 3 of OAC/OAM. The magnetization data are normalized with respect to the value at the maximum of the ZFC magnetization M(T B ) for each sample. References: 1. Moya, C.; del Puerto Morales, M.; Batlle, X.; Labarta, A., Tuning the magnetic properties of Coferrite nanoparticles through the 1, 2-hexadecanediol concentration in the reaction mixture. Phys. Chem. Chem. Phys. 2015, 17 (19), Dehsari, H. S.; 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 (44), 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 9

10 resonance as a key tool to evaluate size distribution in magnetic nanoparticles. Nanoscale 2014, 6 (13), 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 (19), Klug, H. P.; Alexander, L. E., X-ray diffraction procedures: for polycrystalline and amorphous materials. X-Ray Diffraction Procedures: For Polycrystalline and Amorphous Materials, 2nd Edition, by Harold P. Klug, Leroy E. Alexander, pp ISBN Wiley-VCH, May , 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 (22),