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C: Physical Processes in Nanomaterials and Nanostructures

Novel Nanofluids Based on Magnetite Nanoclusters and Investigation on Their Cluster Size-Dependent Thermal Conductivity Vinodha Ganesan, Cindrella Louis, and Shima Porumpathparambil Damodaran J. Phys. Chem. C, Just Accepted Manuscript • DOI: 10.1021/acs.jpcc.7b12043 • Publication Date (Web): 12 Mar 2018 Downloaded from http://pubs.acs.org on March 12, 2018

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The Journal of Physical Chemistry

Novel Nanofluids Based on Magnetite Nanoclusters and Investigation on Their Cluster Size-Dependent Thermal Conductivity Vinodha Ganesan1, Cindrella Louis1 and Shima Porumpathparambil Damodaran1* 1

Department of Chemistry, National Institute of Technology, Tiruchirappalli-620 015, Tamil Nadu, India *E-Mail: [email protected], [email protected] Fax: +91-431-2500133 Tel: +91-9447956884

1 ACS Paragon Plus Environment

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

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Abstract To probe the effect particle size on thermal conductivity (k) enhancement in nanofluids, especially in very large particle size range, we study the cluster size-dependent k in novel nanofluids containing magnetite (Fe3O4) nanoclusters. The Fe3O4 nanoclusters in the size range of 115 to 530 nm were synthesised by a facile and cost-effective solvothermal approach. The structural, surface, and magnetic characteristics of Fe3O4 nanoclusters were investigated by powder X-ray diffraction (XRD), transmission electron microscopy (TEM), Fourier transform infrared spectroscopy (FT-IR), thermogravimetric analysis (TGA) and vibrating sample magnetometer (VSM). Thermal conductivity studies in diethylene glycol (DEG)-based Fe3O4 cluster nanofluids showed an enhancement in k with increase in nanocluster size. With a fixed volume fraction (φ) = 0.0193, the k enhancement was about 5.3% and 12.6%, respectively, for nanofluids having cluster size of 115 and 530 nm. The observed increase in nanofluid k with increase in cluster size being contrary to microconvection hypothesis, confirm the less prominent role of Brownian motion-induced microconvection on the k enhancements of nanofluids. The increase in nanofluid k with increase in cluster size is attributed to the growth of clusters into fractal like aggregates in the suspensions which was confirmed by optical microscopy, dynamic light scattering (DLS) and atomic force microscopy (AFM) studies. Furthermore, the experimental k data falls within the upper and lower Maxwell bounds for homogeneous systems, confirming the classical nature of thermal conduction in nanofluids. The nanofluids developed in the present study are promising candidates for heat transfer applications because of their improved thermal conductivity and long term stability. The present study can provide new insights for engineering efficient nanofluids containing nanoclusters with superior thermal conductivity for heat transfer applications.


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1. INTRODUCTION Magnetic nanomaterials have received extensive scientific attention because of their fascinating properties and potential applications for wide range of fields such as targeted drug delivery 1, data storage supercapacitors

2,3

, magnetic fluids

10,11

, Li-ion batteries

12,13

4,5

6,7

, magnetic resonance imaging

, catalysis

and environmental remediation

14,15

8,9

,

. Among

different magnetic nanoparticles, Fe3O4 nanoparticles have been considered as ideal candidate for many technical applications because of its superior magnetic properties, biocompatibility, chemical stability and low toxicity 16. Several robust synthesis protocols have been developed for synthesizing Fe3O4 nanoparticles having superior magnetic properties with a precise control over size, shape, uniformity and surface properties

17

. Superior

saturation magnetization and superparamagnetic behaviour are two key factors that statute the technical applications of Fe3O4 nanoparticles. The Fe3O4 nanoparticles in small size range (> 20 nm) exhibit superparamagnetic behaviour which enables them to rapidly respond to an external magnetic field with insignificant remanence and coercivity. Increasing the size of Fe3O4 nanoparticle will increase its saturation magnetization value, but will also induce the superparamagnetic to ferromagnetic transition 18. Assembling small Fe3O4 nanoparticles into nanoclusters can produce secondary structures that possesses a much higher saturation magnetization value while retaining the original superparamagnetic behaviour of their primary nanoparticle constituents even though the overall cluster size exceeds the critical size (~ 26 nm) for ferromagnetic to superparamagnetic transition for Fe3O4 19. On account of this unique feature, researchers have fabricated Fe3O4 nanoclusters with various sizes and exploited them for numerous applications such as magnetic separation 20, magnetic resonance imaging

21

, drug delivery

22

, photothermal therapy

23

, Li-ion batteries

24

, environmental

remediation 25, magnetic hyperthermia and colour display 26.



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In this paper, we demonstrate yet another fascinating application of Fe3O4 nanoclusters for thermal management. Nanofluids have been a topic of intense research recently because of their interesting thermo-physical properties and anticipated applications in heat transfer

27

.

There has been a gush on nanofluid thermal conductivity research activities after the first report of remarkably high k enhancement for copper (Cu) nanofluids even at very low particle loading 28. Subsequently, several theoretical and experimental studies were done on k of nanofluids to get further perceptions on this fascinating phenomenon. Many mechanisms have been proposed to account for the k enhancement in nanofluids such as liquid layering around nanoparticle surface, ballistic phonon transport, Brownian motion induced microconvection and aggregation of nanoparticles 29. Experimental studies show that the k of nanofluids depend on many factors such as volume fraction (ϕ), nanoparticle size (d), nanoparticle morphology, presence additives, pH, temperature, nature of base fluid and nanoparticle material 30. The size of the dispersed particles is an important parameter that dictate thermal properties of nanofluids and literature data on the effect of particle size on the k of nanofluids are limited and controversial. Many experimental and theoretical studies show an increase in k with decrease in particle size

31-45

. However, some reports show an enhancement in nanofluid k

with increase in particle size 46-58. An enhancement in k/kf with decrease in nanoparticle size is reported for water-based alumina (Al2O3) nanofluids 32,35,37,40,41, water and ethylene glycol (EG)-based Al2O3 nanofluids 36, EG+water-based zinc oxide (ZnO) nanofluids EG-based Al2Cu and Ag2Al alloy nanofluids titania (TiO2) nanofluids

42

39,59,60

38

water and

, EG-based ZnO nanofluids 42, EG-based

, water-based ZnO nanofluids

43

, water-based TiO2 nanofluids

44

and EG-based Al95Zn05 alloy nanofluids 45. However, a decrease in k with decrease in particle size is reported for water and EG-based silicon carbide (SiC) nanofluids

49,53,61

, water-based

gold (Au) nanofluids 50, water, EG, water+EG- based Al2O3 nanofluids 51, water-based ceria


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(CeO2) nanofluids nanofluids

48

, water and EG-based Al2O3 nanofluids

54,55

, water-based silica (SiO2) nanofluids

56

46

, water and EG-based SiC

and EG-based silver (Ag) nanofluids

57

. In our previous report, to probe the effect particle size on k enhancement, especially in

very small particle size range, we studied the size-dependant thermal conductivity in model nanofluids having long term stability 58. Our thermal conductivity studies in stable kerosenebased nanofluids containing oleic acid coated Fe3O4 nanoparticles of average particle size in the range of 2.8-9.5 nm showed an increase in k with increase in particle size. To obtain a better insight into the effect particle size on thermal conductivity enhancement of nanofluids, especially in very large particle size range, herein, we investigate the cluster sizedependant k in novel nanofluids containing Fe3O4 nanoclusters. The Fe3O4 nanoclusters were synthesized by a facile and cost-effective solvothermal approach. By changing the concentration of sodium citrate during the solvothermal synthesis, the size of Fe3O4 nanoclusters were varied in the range of 115 to 530 nm. Stable nanofluids were fabricated by dispersing Fe3O4 nanoclusters in DEG and we studied their thermal properties as a function of cluster size. The optical microscopy, DLS and AFM studies were carried out to validate the thermal conductivity results. To the best of our knowledge, there has been no previous k studies in nanocluster-based nanofluids. 2. EXPERIMENTAL METHODS Materials Iron (III) chloride hexahydrate (FeCl3.6H2O), trisodium citrate dihydrate, urea, EG, DEG, sodium hydroxide (NaOH), polyacrylic acid (PAA, MW=6000) and ethanol were purchased from Merck, India. All the chemicals were of analytical grade and directly used without further treatment. Unless otherwise specified, deionized water was used throughout the experiment.



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Preparation of Fe3O4 nanoclusters Monodisperse Fe3O4 nanoclusters were synthesised using a modified solvothermal method 62. In a typical synthetic procedure, ferric chloride hexahydrate (0.12 M), trisodium citrate (0.001 M, 0.01 M, 0.075 M, or 0.1 M) and urea (1.0 M) were dissolved in 150 mL of EG. The mixture was vigorously stirred for 30 min to form a homogeneous yellow solution which was then transferred into a teflon-lined stainless-steel autoclave and heated at 220 0C for 7 h. The autoclave was then cooled to room temperature and the obtained black precipitate was washed with ethanol and deionized water several times and finally dried in a vacuum oven at 70 0C for 7 h. Characterization The X-ray powder diffraction was performed using Bruker AXS D8 instrument in the 2θ values from 5 to 80° using Cu Kα radiation (λ value is 1.5418 Å). TEM images and selected area electron diffraction (SAED) patterns were taken on a JEM-2010 (JEOL, Japan) transmission electron microscope at an accelerating voltage of 200 kV. The samples for TEM were prepared by placing a drop of ethanol dispersion onto a carbon-coated copper grid and drying at room temperature. The magnetic properties of the nanoparticles were measured using Lakeshore VSM 7410 model. The measurements were performed in the field range of 15 to +15 kOe at room temperature. The FT-IR studies were done using Thermo Nicolet, Avatar 370 instrument in the spectral range of 4000-400 cm−1. For FT-IR studies, the dried samples were mixed thoroughly with powdered KBr and pressed to form a transparent pellet. Thermogravimetric analysis (TGA) was performed in nitrogen atmosphere using a Shimadzu TGA - 51 thermogravimetric analyser at a heating rate of 10 0C/min from room temperature to 900 0C. The thermal conductivity of the samples was measured by using a hot wire probe as per the procedure discussed earlier 63. The probe was standardized first by measuring the


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thermal conductivity of three standard liquids, namely water, EG, and kerosene; the measured values were in good agreement with literature values. The accuracy in the k measurement is within 5%. All the k measurements have been carried out at 25 0C. A Carl Zeiss inverted phase contrast microscope was used to obtain images of nanofluids in the presence and absence of magnetic field. Agglomeration and the particle size distribution in nanofluids were determined by DLS technique using Zeta nanosizer (Malvern ZEN3600). The AFM images were obtained using a Park NX10 equipment in non-contact mode with a Si tip. 3. RESULTS AND DISCUSSION Size and morphological studies Monodisperse Fe3O4 nanoclusters were synthesised by a modified solvothermal route by reducing FeCl3.6H2O at 220 0C using urea as precipitating agent, sodium citrate as the coordinative ligand and EG functioning as both the solvent and the reducing agent. The sodium citrate concentration was varied during the synthesis and Fe3O4 nanoclusters synthesised with 0.001 M, 0.01 M, 0.075 M and 0.1 M sodium citrate concentrations were named as sample S1, S2, S3 and S4 respectively. The morphology and size of as-synthesized Fe3O4 nanoclusters were examined by TEM. Figure 1 shows the representative TEM images and SAED pattern of Fe3O4 nanoclusters synthesized at different sodium citrate concentrations. The TEM analysis reveal that the clusters exhibited uniform size and a spherical shape. The average cluster size was ~ 530 nm (Figure 1a,b), 370 nm (Figure 1d,e), 200 nm (Figure 1g,h), and 115 nm (Figure 1j,k) for samples synthesized with 0.001 M, 0.01 M, 0.075 M and 0.1 M sodium citrate concentrations, respectively. The high magnification TEM images reveal that the individual Fe3O4 clusters are composed of many interconnected small nanocrystals. With increasing sodium citrate concentration, the primary nanocrystal size also increased and the cluster structure changed from a dense to looser structure. For



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Fe3O4 nanoclusters synthesised at low sodium citrate concentrations (sample S1 and S2), the size of primary nanoparticles was > 15 nm, while the primary nanoparticle size was < 10 nm for Fe3O4 nanoclusters synthesised at high sodium citrate concentrations (sample S3 and S4). The SAED pattern of sample S1 (cluster size ~ 530 nm) reveals single crystal-like diffraction pattern where the diffraction spots are widened into narrow arcs because of slight misalignments among the primary nanocrystals composing the cluster (Figure 1c)

64

. The

SAED patterns of sample S2 (Figure 1f), S3 (Figure 1i), and S4 (Figure 1l) show clear Debye-Scherrer rings corresponding to the cubic spinel structure of magnetite. The growth of Fe3O4 nanoclusters follows a two-stage growth model where the primary nanocrystals nucleate first in a supersaturated solution followed by aggregation into larger secondary clusters.

During the formation of magnetite nanoclusters, sodium citrate

molecules will be adsorbed on the surface of the primary nanocrystals which will enable them to remain well-dispersed in the solution by electrostatic stabilization. But at the same time, high surface energy of the small nanocrystals will induce the oriented aggregation of primary nanocrystals so as to reduce their surface energy. The aggregation of primary nanocrystals to form Fe3O4 nanoclusters depend on the balance of these two opposing forces. The schematic illustration of the formation of Fe3O4 nanoclusters is shown in

Figure 2. At low sodium

citrate concentration, a small number of citrate molecules will be adsorbed on the surface of primary nanocrystals and thus the electrostatic repelling force between neighbouring nanocrystals will be weak. Consequently, bigger-sized primary nanocrystals will be formed and they will subsequently aggregate to form large Fe3O4 clusters. On the other hand, at high sodium citrate concentration, more citrate molecules will be adsorbed on the surface of the primary nanocrystals which will cause a strong electrostatic repelling force between neighbouring nanocrystals. Accordingly, the primary nanocrystals will tend to disperse in the reaction medium which will result in the formation of nanoclusters of reduced cluster size 8 ACS Paragon Plus Environment

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with smaller-sized primary nanocrystals. The variation of Fe3O4 nanocluster size as function of sodium citrate concentration is shown in Figure S1. Crystallite size and structural analysis The XRD measurements confirm the secondary structure of Fe3O4 nanoclusters. Figure 3 shows the XRD patterns of the as-prepared Fe3O4 nanoclusters which shows a series of diffraction peaks at 2θ = 30.1°, 35.2°, 43.1°, 53.7°, 57.1° and 62.5° corresponding to the reflections from the (220), (311), (400), (422), (511), and (440) crystal planes of the cubic spinel structure of Fe3O4 (JCPDS no. 19-0629). The broad diffraction peaks confirm the nanocrystalline structure of the Fe3O4 clusters. The average crystallite size of primary nanocrystals in the Fe3O4 nanoclusters were obtained from the most intense peak of (311) by using the Debye-Scherrer formula [d = (0.9λ)/(β cos θ), where d is the crystallite size, β is the full width at half-maximum, λ is the incident Cu Kα wavelength of 1.5418 Å, and θ is the maximum peak position]. Figure S1 shows the variation of primary nanocrystal size as a function of sodium citrate concentration. The average nanocrystal sizes were 25 nm, 16 nm, 7 nm and 5 nm for Fe3O4 nanoclusters synthesized with 0.001 M, 0.01 M, 0.075 M and 0.1 M sodium citrate concentrations, respectively. The crystallite size is comparable with the average nanocrystal size from the TEM images. Magnetic behaviour of Fe3O4 nanoclusters The magnetic properties of as-prepared Fe3O4 nanoclusters were studied using vibrating sample magnetometer at room temperature in the field range of -15 kOe < H < +15 kOe. Figure 4a show the magnetization hysteresis loops measured for Fe3O4 nanoclusters. The saturation magnetization (Ms) values of Fe3O4 nanoclusters increased with increase in nanocluster size (Figure 4b). The Ms values were found to be 66.9, 57.8, 49.0, and 41.4 emu/g for sample S1 (cluster size 530 nm), S2 (cluster size 370 nm), S3 (cluster size 200 9 ACS Paragon Plus Environment


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nm), and S4 (cluster size 115 nm), respectively. The observed saturation magnetization values are smaller than the reported value for bulk Fe3O4 (92 emu/g)

65

. The surface of

magnetic nanomaterials will exhibit lower magnetic properties compared to the bulk due to the lack of structural periodicity and resulting non-collinear spin configuration at the surface. With decreasing nanoparticle size, the reduced coordination and hence broken superexchange bonds between surface spins will cause a reduction of the average net moment 18. Magnetic nanoclusters can exhibit either superparamagnetic (single magnetic domains) or ferromagnetic (multi-domains) behaviour depending on the size of primary nanocrystals. The increase in the primary nanoparticle size will result an increase in the saturation magnetization value of nanoclusters and will also induce superparamagnetic to ferromagnetic transition 66. The estimated superparamagnetic critical size (DP) for spherical Fe3O4 particles is ~ 26 nm 67. Figure 4b shows the variation of primary nanocrystal size as function of cluster size for as-synthesized Fe3O4 nanoclusters. nanocrystal

size

less

than

the

The samples S2, S3, and S4 with primary

superparamagnetic

critical

size

(DP)

exhibited

superparamagnetic behaviour with zero remanence and coercivity. The primary nanocrystal size was ~ 25 nm for sample S1, which is close to the crossover of superparamagnetic to ferromagnetic transition limit for Fe3O4. Thus, the sample S1 exhibited soft ferromagnetic behaviour with negligible coercivity (Hc) of ~ 41 Oe and remanence (Mr) of 1.30 emu/g (Figure 4a inset). FT-IR and TGA analysis Figure 5 shows the FT-IR spectra of as-prepared Fe3O4 nanoclusters. The characteristic peak corresponding to the stretching vibration of Fe-O bond was observed at 566 cm-1. The broad band at around 3388 cm-1 corresponds to O-H stretching vibration of citrate and absorbed water molecules. The characteristic peaks at 1642 and 1400 cm-1 correspond to the symmetric and antisymmetric stretching vibrations of −COO- groups of sodium citrate, respectively 22,26. 10 ACS Paragon Plus Environment

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It is known that sodium citrate will adsorb on the surface of magnetic nanoclusters through the covalent interaction between the −COO- groups of citrate and the Fe atoms present on the surface of the nanoclusters 68-70. The amount of sodium citrate present on Fe3O4 nanoclusters is examined by TGA. The Fe3O4 nanoclusters show a two-step weight loss as shown in Figure S2. The major weight loss in the temperature range of 200 to 700 0C is attributed to the decomposition of sodium citrate bonded on the surface of the Fe3O4 nanoclusters 26. The mass fractions of the bonded citrate were 3.23%, 4.56%, 10.07% and 15.76% for Fe3O4 nanoclusters synthesised with 0.001 M, 0.01 M, 0.075 M and 0.1 M sodium citrate concentrations, respectively. Another weight loss that occurred below 200 0C correspond to the evaporation of the physically adsorbed water. Functionalization of Fe3O4 clusters Stability of nanofluids is a crucial factor that dictate the feasibility nanofluids for practical application. In the present study, sodium citrate was used as a functional ligand during the solvothermal synthesis to enhance the dispersibility of the magnetite nanoclusters. As shown in Figure 6a, the carboxylate group (–COO-) of sodium citrate can strongly coordinate with Fe atom of Fe3O4 nanoclusters and the uncoordinated –COO- groups can extend towards the aqueous medium, creating a high degree of dispersibility by electrostatic interactions. The samples synthesised at high citrate concentrations (S3 and S4) were readily dispersible in water and other polar solvents. However, sample S1 and S2 having bigger cluster size and less amount of adsorbed sodium citrate as evident from the TGA data, were not readily dispersible in water. In order to improve the dispersion stability, sample S1 and S2 were further surface functionalized using PAA with the support of Fe3+ cations

71

. For surface

modification, Fe3O4 nanoclusters were first dispersed in 0.001 M NaOH solution with the help of ultrasonication. About 1 ml of FeCl3.6H2O (0.04 M) and 10 ml of PAA (20 g/L) were mixed to form an orange solution. The Fe3O4 nanocluster suspension was then slowly added 11 ACS Paragon Plus Environment


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into the FeCl3 + PAA mixture under mechanical stirring. The resultant suspension was continued to stir for 60 min to ensure the adsorption of PAA onto the surface of Fe3O4 nanoclusters. The product was then collected by magnetic separation and washed with deionised water. During surface functionalization, Fe3+cations will get preferentially adsorbed on the surface of Fe3O4 nanoclusters. As shown in Figure 6b, the carboxyl groups on PAA chain can coordinate with Fe3+ cations on the surface of Fe3O4 nanoclusters and the non-coordinated carboxylate groups of PAA will extend outward into the aqueous/polar medium, providing high degree of dispersibility to the nanoclusters

71

. The modification of

Fe3O4 nanoclusters with PAA was characterized by FT-IR analysis. As shown in Figure S3, the PAA-functionalized Fe3O4 nanoclusters showed characteristic peaks corresponding to the asymmetric (1555 cm-1 for S1 and 1560 cm-1 for S2) and symmetric (1415 cm-1 for S1 and 1410 cm-1 for S2) stretching modes of the carboxylate (−COO-) group 72. Furthermore, there was no peak corresponding to the carbonyl stretch, confirming that carboxylate functional groups of PAA were fully ionized and that PAA was bonded to the Fe3O4 nanocluster surface through the carboxylate groups 73. The peak at 1647 cm-1 and 1644 cm-1 for sample S1 and S2 can be attributed to the symmetric stretching vibrations of −COO- groups of sodium citrate initially adsorbed on the nanocluster surface during the solvothermal synthesis. The PAAstabilized Fe3O4 nanoclusters (S1 and S2) were readily dispersible in water and other polar solvents. The nanoclusters (PAA-stabilized S1 & S2, S3 and S4) were dispersed in DEG to prepare stable nanofluids for thermal conductivity studies. The nanofluids containing Fe3O4 nanoclusters prepared in the present study showed no sign of sedimentation and remained stable for days as shown in Figure 7a,b. Furthermore, nanofluids remained stable without phase separation even under the influence of a strong magnetic field (Figure 7c). Thermal conductivity studies


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Figure 8 shows the variation of thermal conductivity ratio with base fluid (k/kf) and % of k enhancement as a function of cluster size for DEG-based Fe3O4 cluster nanofluids at a particle loading of φ=0.0193. Here, the base fluid k includes the surfactant contribution. In order to take into consideration the contribution of surfactant present in the base fluid on k, we use the surfactant + base fluid thermal conductivity while taking the thermal conductivity ratio. As shown in Figure 8, an increase in k/kf with increasing in cluster size is observed in the present study. The enhancement was about 5.3% and 12.6%, respectively, for nanofluids containing Fe3O4 nanoclusters of 115 and 530 nm size. For a nanofluid containing noninteracting spherical nanoparticles, the Maxwell effective medium or mean field theory (EMT) for the thermal conductivity of nanofluid is given by 74:

k 1 + 2βφ = k f 1 − βφ

(1)

Where φ is the nanofluid volume fraction, k and kf are the thermal conductivities of the nanofluid and the base fluid, respectively. The term β is given by (kp-kf)/(kp+ 2kf), where kp is the thermal conductivity of nanoparticle. To account the interfacial thermal resistance Rb, kf is modified as kf → kf +αkp, where α = 2Rbkf /d and ‘d’ is the nanoparticle size. As shown in Figure 8, the experimental results were beyond the predictions of Maxwell’s model with Rb=0 and Rb>0. For nanofluids, the k enhancements beyond the prediction of Maxwell’s EMT are often regarded to be anomalous. Many mechanisms have been hypothesized to account for the anomalous k enhancement in nanofluids. Two mostly debated mechanism among them being Brownian motion induced convection and effective conduction through percolating nanoparticle paths 75. In the microconvection mechanism hypothesized to explain the anomalous k enhancement in nanofluids, it was assumed that convection currents caused by the Brownian motion of the nanoparticles can enhance the heat transfer between the nanoparticles and the base fluid, and hence the nanofluid k. By taking into consideration the 13 ACS Paragon Plus Environment


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interfacial thermal resistance and the mixing of convection currents from multiple nanoparticles, the ratio k/kf according to microconvection model is 76:

k 1 + 2βφ = 1 + A Reγ Pr 0.333 φ kf 1 − βφ

(

)

(2)

where γ is a system-specific exponent, A is constant with values as large as 4×104, Re is the Reynolds number of the particle and Pr

the Prandlt number of the base fluid. In the

microconvection hypothesis, it is assumed that Re = vN d/η, where vN is the convection velocity, η is the viscosity and ‘d’ is the particle size. The convection velocity is taken as the root-mean-square velocity (vN) of the nanoparticle and is defined as:

υN =

18 k B T πρ d 3

(3)

where kB is the Boltzmann constant, T is the temperature and is the density of particles. The predictions of microconvection model is shown in Figure 8 along with the experimental data. An increase in thermal conductivity with decrease in particle diameter is predicted by microconvection model. However, the experimental results are contrary to the predictions of microconvection model as shown in Figure 8. Thus, our results confirm the less prominent role of Brownian motion-induced microconvection on the k enhancement in nanofluids. The observed increase in nanofluid k with increase in cluster size may be originating from the kinetic growth of clusters into fractal like aggregates in the suspensions. The cryo-TEM studies demonstrated the existence of zero-field dipolar structures in colloidal dispersions of magnetic nanoparticles even when the particles were coated with thick surfactant layer to minimize the effect of Van der Waals attractions

77,78

.

The above studies further

demonstrated that the zero field dipolar structures in dispersion of magnetic nanoparticles are highly dependent on particle size. By systematically increasing the particle size, the cryo-


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TEM images showed a transition from individual particles to randomly oriented linear nanoparticle aggregates. As evident from the TEM images, the individual Fe3O4 clusters in the present study are composed of many interconnected small nanocrystals. The magnetic dipole moment of a single-domain sphere of radius r and bulk saturation magnetization Ms is given by = 4 /3. The dipole–dipole interaction between two magnetic particles at contact, scales as / ∝ / , where σ is the effective hard-sphere diameter, including the core diameter and the thickness of the surfactant layer, so that zero-field microstructures in dispersions of magnetic nanoparticles will be very sensitive to particle size

79

. The

saturation magnetization and primary nanocrystal size of as-synthesized Fe3O4 nanoclusters increased with the increase in cluster size. Thus the zero-field microstructure formation in assynthesized nanofluids will be highly sensitive to size of the dispersed nanoclusters. To obtain macroscopic evidence of the aggregation process, the phase contrast microscopic images of nanofluids were taken. Figure 9 shows the optical microscopy images of DEGbased Fe3O4 cluster nanofluid in the absence of magnetic field. As evident from the microscopy images, the extent of aggregation was least in nanofluids containing smallest nanoclusters (size ~ 115 nm). The size of the aggregates increased with the increase in the dispersed cluster size (Figure 9c,e). The optical microscopy studies of nanofluids showed that Fe2O3 nanoparticles (particle size < 50 nm) form some kind of alignment spontaneously in water even without an external magnetic field

80,81

. However, the aggregates present in as-

synthesized nanofluids have random fractal dimensions and any kind of alignment could hardly be seen in zero field. However, in presence of an applied magnetic field, when the magnetic dipolar interaction energy dominates over the thermal energy, the nanoclusters align in the direction of magnetic field resulting in the formation of thick elongated nanoparticle chains as shown in Figure 9b,d,f. Dynamic light scattering is often used to determine the size distribution of nanoparticles in dispersions and is a powerful tool to study the stability and



aggregation process in nanofluids

Page 16 of 39

50,52,82

. The DLS studies provided additional evidence for

the aggregation process in as-synthesized Fe3O4 cluster nanofluids. Dilute samples were used for DLS in order to avoid multiple scattering. As shown in Figure 10, the nanofluid containing smallest nanoclusters (S4; size ~ 115 nm), showed a single peak matching with the original nanocluster size. For sample S1 which contains biggest sized Fe3O4 clusters (size ~ 530 nm), the intensity-weighted size distribution showed a bimodal distribution; a smaller peak was observed near the nominal nanocluster size, and a bigger, broader peak at thousands of nanometers which corresponds to the aggregates. The AFM analysis of a coating obtained by drying of diluted nanofluid (sample S2) is given in Figure 11. The 2D and 3D AFM images of nanofluid confirms that the particles are aggregated and the aggregates are of few micrometres in dimension. To sum up, the optical microscopy, DLS and AFM analysis confirm the aggregation process in as-synthesized nanofluids that are highly dependent on particle size. It was demonstrated that aggregation in stable nanofluids can significantly increase the k higher than that predicted using classical theories of well-dispersed particles 52,80,83-86

.

The simplest and the most instinctive mean-field models are the series and parallel modes of thermal conduction

75

. In the former, the conducting paths through the base fluid and the

nanoparticles are assumed to be in series, and in the latter, the conducting paths are assumed to be in parallel. The effective thermal conductivity of the nanofluid in the series mode is given by 1⁄ = (1 − )⁄ + ⁄ . The effective thermal conductivity of the nanofluid in the parallel mode is given by ∥ = (1 − ) + . In the dilute limit, k=/kf is a function of volume fraction alone and is given by k=/kf = 1+φ; while k||/kf is a function of the constituent thermal conductivities and volume fraction, and is given by k||/kf = 1+φ(kp/kf). Since parallel mode has the geometric configuration that allows the most efficient means of heat propagation, extremely large k enhancement is possible with parallel modes

75

. In our 16

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previous report, by controlling the linear nanoparticle aggregation in a magnetically polarizable nanofluid, the mode of heat conduction through nanoparticles and base fluid was varied from series to parallel mode, and accordingly, thermal conductivity of the nanofluid was precisely tuned from low to very high values 63. The classical Maxwell theory has two limiting bounds, also known as the Hashin and Shtrikman (H-S) mean-field bounds which correspond to two geometrical configurations of the nanoparticles. The Maxwell (H-S) bounds for nanofluid thermal conductivity is given by 87:

   3(1 − φ )(k p − k f )  3φ (k p − k f ) k f 1 +  ≤ k ≤ k p 1 −   3k f + (1 + φ )(k p − k f )   3k p − φ (k p − k f ) 

(4)

Physically, the lower limit corresponds to a set of well-dispersed nanoparticles in a fluid matrix while the upper limit corresponds to large pockets of fluid separated by linked or chain-like nanoparticles 75. In the lower H-S configuration, the nanoparticles are always welldispersed, and the effective k will be biased towards the conduction paths in the surrounding fluid and in the upper H-S configuration, thermal conductivity will be biased towards the conduction paths along the dispersed nanoparticles. Thus, the lower H-S limit lies closer to the series mode of conduction, while the upper H-S limit lies closer to the parallel mode. If the nanofluid configuration is neither favouring the series nor the parallel mode, then the effective k will lie in between lower and upper H-S bounds. The anomalous k of nanofluids beyond the EMT predictions thus originate from the assumption of well-dispersed nanoparticles since all experimentally tested nanofluids will have some form of aggregation extending from a well-dispersed state to linear chain-like structures. Figure 12 shows the experimental data together with four mean-field bounds; series, lower H-S, upper H-S and parallel. The thermal conductivity of nanofluids is reliant on whether the nanoparticles stay dispersed in the base fluid, form linear chain-like structures, or adopt an intermediate conformation. While linear, percolating clusters can enhance the nanofluid k dramatically 88, 17 ACS Paragon Plus Environment


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large nanoparticle clusters without linear chain-forming morphologies can be detrimental to the k enhancement

89

. Since, the experimental data are enclosed between upper and lower

Maxwell (H-S) mean-field bounds, the Fe3O4 nanoclusters will have an intermediate conformation in between well-dispersed state and linear chain-like structures; which was confirmed by the optical microscopy, DLS and AFM studies. Even if aggregates are present, the as-synthesized nanofluids are stable enough and do not phase separate with time (Figure 7) making them ideal candidates for practical applications. 4. CONCLUSIONS In summary, we report a facile and cost-effective synthesis of novel nanofluids containing Fe3O4 nanoclusters and their cluster size-dependent thermal conductivity. The Fe3O4 nanoclusters in the size range of 115 to 530 nm were synthesised by a facile and costeffective solvothermal approach. The structural, surface, and magnetic characteristics of Fe3O4 nanoclusters were investigated by TEM, XRD, FT-IR, TGA and VSM techniques. The TEM analysis showed that the clusters exhibited uniform size and spherical shape. The high magnification TEM images revealed that the individual Fe3O4 clusters are composed of many interconnected small nanocrystals. The XRD analysis confirms the cubic spinel structure and nanocrystalline of magnetite Fe3O4 nanoclusters. The VSM analysis showed that the saturation magnetization values of Fe3O4 nanoclusters increased with increase in nanocluster and primary nanocrystal size. Thermal conductivity studies in stable DEG-based Fe3O4 cluster nanofluids showed an enhancement in k with increase in nanocluster size. With a fixed particle loading of φ=0.0193, the k enhancement was about 5.3% and 12.6%, respectively, for nanofluids with cluster size of 115 and 530 nm. The enhancement in k with increase in cluster size confirm the less prominent role of Brownian motion-induced microconvection on the thermal conductivity enhancements of nanofluids.

The optical

microscopy, DLS and AFM analysis confirms the aggregation process in as-synthesized 18 ACS Paragon Plus Environment

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nanofluids that are highly dependent on particle size. Thus the observed increase in nanofluid k with increase in cluster size is attributed to the growth of clusters into fractal like aggregates in the suspensions. Furthermore, the experimental data falls within the upper and lower Maxwell bounds for homogeneous systems, confirming the classical nature of thermal conduction in nanofluids. The nanofluids developed in the present study are promising candidates for heat transfer applications because of their improved thermal conductivity and long term stability. In addition, such nanofluids can act as smart nanofluids since its transport properties can be controlled by a magnet. The present study can provide new insights for engineering efficient nanofluids containing nanoclusters with superior heat transfer properties for thermal management. ACKNOWLEDGMENTS: Authors would like to thank the SAIF - CUSAT for XRD, TEM & FT-IR analysis; SAIF IIT Madras for VSM analysis and SMARTS, MMG, IGCAR for thermal conductivity, DLS and optical microscopy studies. SPD also thanks Department of Science and Technology, Govt.

of

India

for

the

INSPIRE

Faculty

Award

and

research

grant

(DST/INSPIRE/04/2014/001995). SUPPORTING INFORMATION: The variation of Fe3O4 cluster size and primary nanocrystal size as function of sodium citrate concentration, TGA curves of Fe3O4 nanoclusters synthesised at different of sodium citrate concentrations, and FT-IR spectra of PAA modified of Fe3O4 nanoclusters. REFERENCES (1) Yu, M. K.; Jeong, Y. Y.; Park, J.; Park, S.; Kim, J. W.; Min, J. J.; Kim, K.; Jon, S. Drug-loaded superparamagnetic iron oxide nanoparticles for combined cancer imaging and therapy in vivo. Angew. Chem. Int. Ed. 2008, 47, 5362–5365. (2) Terris, B. D.; Thomson, T. Nanofabricated and self-assembled magnetic structures as data storage media. J. Phys. D: Appl. Phys. 2005, 38, R199-R222. (3) Ethirajan, A.; Wiedwald, U.; Boyen, H. G.; Kern, B.; Han, L.; Klimmer, A.; Weigl, F.; Kästle, G.; Ziemann, P.; Fauth, K., et al. A micellar approach to magnetic 19 ACS Paragon Plus Environment


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List of figures



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Figure 1. TEM images and corresponding SAED pattern of Fe3O4 nanoclusters synthesized at different of sodium citrate concentrations: (a-c) 0.001 M; (d-f) 0.01 M; (g-i) 0.075 M; (j-l) 0.1 M.


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Figure 2. Schematic illustration showing the formation of Fe3O4 nanoclusters.

.



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Figure 3. The XRD patterns of Fe3O4 nanoclusters synthesised at different of sodium citrate concentrations: (S1) 0.001 M; (S2) 0.01 M; (S3) 0.075 M; (S4) 0.1 M.


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Figure 4. (a) Magnetic hysteresis loops of Fe3O4 nanoclusters synthesised at different of sodium citrate concentrations: (S1) 0.001 M; (S2) 0.01 M; (S3) 0.075 M; (S4) 0.1 M. (b) Variation of primary nanocrystal size and saturation magnetization as function of Fe3O4 cluster size.



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Figure 5. The FT-IR spectra of Fe3O4 nanoclusters synthesised at different of sodium citrate concentrations: (S1) 0.001 M; (S2) 0.01 M; (S3) 0.075 M; (S4) 0.1 M.


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Figure 6. (a) Schematic illustration of binding of sodium citrate on Fe3O4 nanocluster surface. (b) Schematic illustration of surface modification of Fe3O4 nanocluster with PAA with the aid of Fe3+ cations.



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Figure 7. Photographs (a&b) of nanofluids containing Fe3O4 clusters at different time intervals. (c) The ferrofluidic behaviour of Fe3O4 cluster dispersions in DEG in the presence of an external magnet.


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Figure 8. (a) Variation of k/kf and % of k enhancement as a function of Fe3O4 cluster size along with microconvection model with Rb = 2:5×10-8 Km2W-1, γ = 2:5, A = 4×104 (dash lines), Maxwell model with Rb = 0 (dotted line) and Rb > 0 (solid line). (b) Schematic representation of heat transfer in nanofluids containing smaller and bigger Fe3O4 nanoclusters.



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Figure 9. The phase contrast microscopy images for nanofluids containing Fe3O4 clusters in presence & absence of magnetic field [S4: (a) & (b), S3: (c) & (d) and S2: (e) & (f)].


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Figure 10. The DLS size distributions of particles/agglomerates in nanofluids (S1 and S4) containing Fe3O4 clusters.



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Figure 11. The 2D (a) and 3D (b) AFM images of dried nanofluid (S2) on a micro-glass slide.


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Figure 12. Variation of k/kf and % of k enhancement as a function of φ for nanofluids containing Fe3O4 clusters along with the series & parallel modes and the upper & lower H-S mean-field bounds.



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TOC Graphic


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