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An experimental study on fundamental mechanisms of ferro-fluidics for an electromagnetic energy harvester Mohammad Khairul Alam, Elham Doroodchi, Reza Azizian, and Behdad Moghtaderi Ind. Eng. Chem. Res., Just Accepted Manuscript • DOI: 10.1021/acs.iecr.6b03161 • Publication Date (Web): 14 Nov 2016 Downloaded from http://pubs.acs.org on November 19, 2016

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Industrial & Engineering Chemistry Research

An experimental study on fundamental mechanisms of ferro-fluidics for an electromagnetic energy harvester M.A. Khairul1, Elham Doroodchi2, Reza Azizian3, Behdad Moghtaderi1, 1

Priority Research Centre for Frontier Energy Technologies and Utilisation, Chemical Engineering, Faculty of Engineering and Built Environment, The University of Newcastle, Callaghan, NSW 2308, Australia. 2

3

Center for Advanced Particle Processing, Chemical Engineering, Faculty of Engineering and Built Environment, The University of Newcastle, Callaghan, NSW 2308, Australia.

Nuclear Science and Engineering Department, Massachusetts Institute of Technology (MIT), 77 Massachusetts Ave., Cambridge 02139, MA, USA.

ABSTRACT Ferrofluids are a unique class of colloidal liquids made of ferromagnetic or ferrimagnetic nanoparticles suspended in a carrier fluid. Ferrofluids have drawn considerable attention due to the possibility of tuning their heat transfer and flow properties through the application of an external magnetic field. They can also be utilised to improve the performance of an energy harvester, which can supply power and enhance the capability, assertion and lifespan of those devices where batteries or direct electricity are currently used as the primary source of power. Electromagnetic ferrofluid based energy harvesters convert the ferrofluids sloshing movement into electromotive force (EMF), therefore it is necessary to estimate the feasibility, stability and efficacy of ferrofluids through several physico-chemical studies. The objective of this work is to prepare a stable polymer coated Fe3O4 nanofluid with the aim of applying it in a novel energy harvester device currently under development at the University of Newcastle. The one-step chemical precipitation method was complied with to produce Fe3O4/DI-water nanofluids, and thermogravimetric analysis (TGA) and differential thermal gravimetry (DTG) have been carried out to determine the chemical and physical changes of



Corresponding author: Tel: +61 2 4033-9062, Fax: +61 2 4033-9095

Email: [email protected] (Attn: Professor Behdad Moghtaderi)

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ferrofluids due to thermal effect. Besides, X-ray diffraction (XRD) was employed to identify the crystal structure. The stability and physico-chemical properties of Fe3O4 particles were also investigated. The synthesised ferrofluids were found to be visually stable for more than two months. The results showed that the synthesised nanoparticles were magnetite, and a linear relationship was found among the dosing rate of ammonium hydroxide, nanoparticle size distribution, viscosity and thermal conductivity. Higher dosing rates of ammonium hydroxide reagent during the preparation of nanoparticles resulted in increasing average particle size. Keywords: Ferrofluid, Fe3O4, energy harvester, X-ray diffraction, thermogravimetric analysis and differential thermal gravimetry. 1. Introduction Magnetic fluids or ferrofluids are a colloidal suspension consisting of magnetic nanoparticles (i.e. Magnetite (Fe3O4) or maghemite (  -Fe2O3)) suspended in carrier fluids. Apart from thermal properties improvement, these types of fluids possess both magnetic properties like other magnetic materials as well as flow-ability features similar to other fluids. Such unique features of magnetic fluids make them ideal for controlled heat transfer, fluid flow and particle movement under the application of an external magnetic field and as a result, they have great potential for practical applications in different areas such as energy harvesting, mechanical engineering, aerospace, electronics, bioengineering, drag delivery, solar collectors as well as magnetic resonance imaging (MRI).1-4 Energy harvesting is the process of converting an ambient energy, from the environment, into electricity at a small-scale. The source of energy in energy harvesters is present as an ambient background; therefore, freely available such as vibrational energy, wind energy, wave energy, and thermal temperature gradients. An energy harvester can be used to supply power to different sensors such as sensors implemented in buildings, medical implants, wireless sensors, sensors employed in 2 ACS Paragon Plus Environment

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military applications, monitoring of the environment, sensors that may provide different information about the maintenance requirements of various industrial equipment and sensors for structural monitoring are just a few of the many examples.5-7 Nowadays, electromagnetic energy harvesters utilise the sloshing movement of ferrofluid columns to harvest vibrational energy. A ferrofluid is a stable colloidal suspension made up of nanoscale permanent magnetic dipoles1, which are capable of changing their shape freely to generate an electromotive force (EMF) from very small vibrations.8 A limited number of studies have been published to date on ferrofluid based electromagnetic energy harvesters. Bibo et al.1 studied the use of a ferrofluid in an electromagnetic micro-power generator and determined the effect of the ferrofluid quantity, magnetic field strength and acceleration level on the output voltage of the harvester. Oh et al.9 analysed the effects of different strengths of the permanent magnet and ferrofluid volume concentrations on the induced EMF of the energy harvester. Furthermore, Wang et al.10 concluded that the ferrofluid liquid spring can attain a relatively low resonant frequency while engaging small volumes of liquid; and it can also survive and maintain a uniform performance under high input acceleration. Thus far, most of the studies have been carried out on the EMF and power output augmentation of the energy harvester by optimising the fluid quantity, shape and size of the fluid container, magnetic field induction, vibration speed etc. However, studies related to optimisation of the performance of energy harvesters by modifying the ferrofluids physico-chemical properties (in terms of nanofluid concentration, polymer coating, particle size distribution, zeta potential, thermal conductivity and viscosity), which are generally considered passive methods or referred to as one of the potential techniques in heat transfer augmentation are very limited in the open literature.11-15 Investigations on magnetic nanofluids (MNFs) in the presence and absence of magnetic fields demonstrate that their thermo-physical properties (thermal conductivity and viscosity) are affected by various parameters such as particle size 3 ACS Paragon Plus Environment


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distribution, volume fraction of nanoparticles, base fluid properties, chemical composition of the magnetic nanoparticles, temperature, particle coating layer, and so forth. In general terms, when the focus was not the development of an energy harvesting ferrofluid system, the majority of studies assessed the effects of temperature and concentration of nanoparticles on the properties of MNFs. Syam Sundar et al.16 investigated the effective thermal conductivity and viscosity of Fe3O4/water nanofluids experimentally. Experiments were conducted in the concentration range of 0 to 2% and the temperature range of 20 ºC to 60 ºC. They concluded that the thermal conductivity was enhanced with an increase in particle concentration and temperature. It was also demonstrated that the nanofluids exhibit Newtonian behavior under the tested concentration range. Regarding the importance of the size of magnetic nanoparticles in the base fluid on the properties of MNFs, some researchers have investigated the effect of nanoparticle size. 2, 17 Tural et al.2 studied the physico-chemical properties of polymer coated superparamagnetic magnetite nanoparticles by investigating the effects of process temperature, polymer content, and amount of surfactant addition on the cluster size of the superparamagnetic magnetite particles. They concluded that both the amount of polymer content as well as the mixture temperature had minimal effect on the agglomerate size of magnetite particles. The particle size distribution of ferrofluids is directly related on the stability of the fluids. Preparation of stable and homogeneous nanofluid suspensions remains a big challenge in nanofluid research18, including those focused on ferrofluids for energy harvesting applications. Different methods have been introduced and reported for magnetite nanoparticles production.19 If magnetite comes in contact with oxygen, it may transform to maghemite.2 Applying a polymer coating on magnetite nanoparticles is one of the best ways to make stable nanoparticles and avoid their transformation into maghemite.20 Moreover, aggregation among the magnetic nanoparticles is unavoidable due to the attraction of van der Waals 4 ACS Paragon Plus Environment

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forces between them. Therefore, various coatings are introduced at the surface of nanoparticles with different types of stabilisers, such as poly(glycerol monoacrylate) or poly(methacrylic acid) (PMAA) and oleic acid, in order to obtain a stable colloidal suspension.21 In this study, a polymer coating was employed to enhance the magnetite nanoparticles stability as a result of its electrostatic and steric (electrosteric) stabilisation. It is important to control both the cluster and primary particle size of magnetite particles in order to obtain an optimum nanoparticle.22 The shape and size of the final particles can be controlled by tuning the experimental variables, such as temperature, reaction time, surfactant and solvent.23 Though a number of studies have been conducted on the physico-chemical properties of MNFs, there is still a great deal of scope for research in this area. More specifically, studies on the effect of the shapes and sizes of monodispersed Fe3O4 nanoparticles through tuning the amount of solvent addition is limited in the current literature. As discussed earlier, ferrofluids are promising for energy harvesting applications, thus the focus was not only to develop a stable polymer coated ferrofluid but also ultimately its implementation in an electromagnetic ferrofluid based energy harvester. The ultimate objective is to synthesise a ferrofluid with an optimum properties which can produce higher EMF with less vibration. Fe3O4 nanoparticles were prepared by one-step precipitation method at various dosing rates of ammonium hydroxide under a range of nanoparticles weight concentrations. The crystalline structure of the synthesised Fe3O4 nanoparticles was confirmed by XRD analysis. Moreover, thermal analysis of the nanofluids has been completed by thermogravimetric analysis (TGA) and the release and absorption of heat due to the chemical and physical changes were detected using differential thermal gravimetry (DTG). The stability and physico-chemical properties of Fe3O4 particles were investigated through a range of analytical methods such as TGA, DTG as well as dynamic light scattering techniques. 5 ACS Paragon Plus Environment


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2. Nanofluids preparation Stability of nanofluids, in general, has multiple variables.24 The smaller particles have less tendency to settle over the time due to the smaller gravitational body force. Besides, Brownian motion of the nanoparticles might be strong enough to keep the nanoparticles apart from each other. Though, the small size of nanoparticles greatly increases the surface to volume ratio of the system. The optimum stability of MNFs is only attained by the minimisation of the surface area as well as the separation of the two phases. It was found that, the van der Waals attraction depends on the dielectric constants of various base fluids.25 Hence, the overall stability of the ferrofluids or colloids relies significantly on the value of the dielectric constant as demonstrated in Figure 1. The values of the dielectric constant for various common working fluids are given in Table 1. The dispersion stability is maximum when the normalised settling rate is minimum; and the normalised settling rate = [(observed settling rate



viscosity of solvent)/(density of particle



density of solvent)].

Using various surfactants in a base fluid may have an inverse consequence depending on the value of the dielectric constant.24 Figure 1 shows that the fluids with very high or moderate dielectric constants are highly stable. For this reason, water has been used as base fluid in order to obtain a more stable solution in this study.


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Water 80 Ethylene Glycol 37 Methanol 32.7 Ethanol 24.5 Isopropanol 17.9 FC-72 1.7 Table 1. Dielectric constants of different common fluids24

Figure 1. Stability of bare alumina particles in various common fluids25 The ferrofluid preparation method used in this study was chemical precipitation, as introduced by Professor T.A. Hatton’s group at MIT.26 The process for the production of the Fe3O4 nanofluid is described as follows: As dissolved oxygen in water may change the hydrophobic or hydrophilic character of the mineral surface, it is essential to remove the dissolved oxygen from water, particularly when the experiment is carried out with redox sensitive reagents. In this process, firstly, nitrogen was bubbled through deionised (DI)-water to remove the dissolved oxygen from the water. Purging with nitrogen is a very prominent and quick technique to remove dissolved oxygen from DI-water.27 Butler et al.27 concluded that the most effective oxygen removal method is at nitrogen flow rate of 25 ml/s for 20-40 mins. They found that the final concentration of dissolved oxygen was 0.2-0.4 ppm, and recommended that it is not possible to completely eliminate oxygen from DI-water using any methods. Hence, a constant flow rate of nitrogen at 25 ml/s was maintained for 30 min in this study using a rotameter (calibrated before the experiment, see Appendix A). Deoxygenation of DI-water was performed in a gas wash 7 ACS Paragon Plus Environment


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bottle with a Drechsel pattern head, with a filter disc porosity of P1, supplied by SigmaAldrich. This apparatus was used as the kinetics of the degassing process relies on gas/liquid interface area, i.e. the elimination of dissolved oxygen is faster when nitrogen is introduced as small bubbles. Then, a 1:2 molar ratio of iron (III) chloride hexahydrate (FeCl3·6H2O) and iron (II) chloride tetrahydrate (FeCl2·4H2O) were added to the deoxygenated water, into a flat-bottom reaction flask. The flask was equipped with a thermocouple to monitor the reaction temperature. The mixture was heated to 80ºC and stirred, and until it became yellowish in colour. This was followed by the addition of 4-styrenesulfonic acid-co-maleic acid (polymer coating) to the mixture until foaming occurred. Fe3+ and Fe2+ ions are usually precipitated in solutions of sodium hydroxide (NaOH), ammonium hydroxide (NH4OH) or potassium hydroxide (KOH).28 As mentioned by Hong et al.29, sodium hydroxide and ammonium hydroxide both can be used as a precipitator, however, ammonium hydroxide has better crystallinity, smaller size and higher magnetic saturation. Therefore, in order to precipitate the particles in this study, a 10 ml ammonium hydroxide solution, 30% w/w (i.e. contains 30% ammonia by weight) was added to the mixture at different flow rates using a syringe pump while stirring vigorously. As a result, the solution instantly turned black, which is an indication that iron oxide nanoparticles formation has occurred inside the mixture. The nanometer sized particles was created due to the equilibrium between the amounts of materials in the mixture. The rapid adsorption of the polymer on the nanoparticles surfaces prevents the magnetic particles growing into larger macro particles. Azizian26 synthesised magnetite nanoparticles on the basis of different reaction temperatures and concluded that the smallest particle size can be achieved at a temperature of 80ºC. Thus, the solution has been allowed to stir and fully react at 80ºC for 30 mins. 8 ACS Paragon Plus Environment

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The overall reaction stoichiometry is written as; FeCl2.4H2O + 2FeCl3.6H2O + 8 NH4OH

Fe3O4 + 8NH4Cl + 14H2O

Finally, the synthesised nanoparticles were washed several times with acetone and separated from the solution utilising an electromagnetic force. Finally, the excess acetone was removed via heating and the nanoparticles were dispersed in DI-water. The final dispersions have been found to be stable for more than six months. Details of the different chemical materials used in the precipitation method are provided in Table 2. The methodology for iron oxide (Fe3O4) nanoparticle production is presented in Figure 2. Table 2. Different chemical materials used in the chemical precipitation method Iron (II) chloride tetrahydrate Molecular formula FeCl2 4H2O Molecular weight 198.81 (g/mol) Appearance Yellow to green crystalline powder, crystals Manufacturer/Supplier Acros Organics Iron (III) chloride hexahydrate Molecular formula FeCl3.6H2O Molecular weight 270.32 (g/mol) Appearance Yellow to brown crystalline powder Manufacturer/Supplier Scharlab, S.L. Poly (4-styrenesulfonic acid-co-maleic acid) sodium salt Molecular formula [CH2CH(C6H4SO3R)]x[CH(CO2R)CH(CO2R)]y, R=H or Na Molecular weight 20000 (g/mol) Appearance White powder Manufacturer/Supplier Sigma-Aldrich Pty. Ltd. Ammonium hydroxide Molecular formula NH4OH Molecular weight 35.5 (g/mol) Appearance Colourless and clear liquid Manufacturer/Supplier Chem-Supply Pty Ltd



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Deoxygenated Water 28 ml

FeCl2 4H2O 1.72 g

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FeCl3 6H2O 4.70 g

Mix

Poly sodium salt 2.0 g

Stir for 10 min till foaming

Ammonium hydroxide 10 ml (at different flow rate)

Stir for 30 min

Add acetone to precipitate the particles

Wash the samples with acetone and separate them using a permanent magnet

Heat it up to remove the excess acetone and nanoparticles dispersed in DI-water Figure 2. Methodology of ferrofluids production 3. Characterisation 3.1. X-Ray diffraction (XRD) Characterisation of nanoparticles is vital especially for magnetite nanoparticles, as the Fe3O4 nanoparticles have been synthesised through the chemical precipitation method. Ensuring that chemically synthesised nanoparticles are magnetite is the first step. X-ray diffraction (XRD) methods were used to verify and confirm the composition of the magnetite nanoparticles. XRD is a widely used method to examine the molecular and atomic crystallographic 10 ACS Paragon Plus Environment

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structure, and chemical composition of samples. X-ray patterns of pure substances are considered as a fingerprint for that substance. The interaction of the incident rays with the sample produces constructive interference (and a diffracted ray) when conditions satisfy Bragg's Law (n  2d sin  ) . 3.2. Thermogravimetric analysis (TGA) Thermogravimetric analysis (TGA) is one of the thermal analysis methods used to measure the variations of the materials’ chemical and physical properties as a function of temperature at constant heating rate, or as a function of time at constant mass loss or constant temperature. TGA is widely employed to determine the characteristics of materials that demonstrate either a gain or loss in mass because of oxidation, decomposition or loss of volatiles. TGA of ferrofluids provides an opportunity to analyse different components of the ferrofluid by weight. TGA has been found to be a simple and quick method of measuring particulate loading of a system.30 The “cooking off” of constituents allows one to see surfactant weight loading, as well as, the particle loading. TGA requires a very small amount of sample which can be heated under a highly sensitive balance in a high temperature furnace. The combination of knowledge regarding weight vs time as well as boiling point of the compounds in the sample helps in identifying the composition of the sample. In this study the samples used were usually less than 20 mg, placed in a platinum crucible and during the measurement the samples were heated from 20°C to 950°C at a rate of 10°C/min in a nitrogen saturated atmosphere. The weighting precision of Q50 TGA (TA Instruments) is ±0.01%, and the baseline dynamic drift is less than 50 µg (from 50 to 1000˚C at 20˚C/min using empty platinum pans, no baseline/blank subtraction).



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3.3. Differential thermal gravimetry (DTG) Differential thermal gravimetry (DTG) is derived from TGA data to determine the absorption or release of heat, which is related with the variation of physical and chemical properties of materials due to heating or cooling. 3.4. Dynamic light scattering (DLS) DLS theory is a broadly used technique for the measurement of particle size in the range of a few nanometers to a few microns.31 The working principle of DLS is established on the theory of Brownian motion.32 The particle size distribution and zeta potential of Fe3O4/DIwater nanofluids were examined using a Zetasizer Nano ZS. All measured electrophoretic mobilities were converted into zeta potential applying Smoluchowski’s formula.33, 34 It should be noted that the measured particle size by DLS is the particles’ hydrodynamic radius and it may differ from the actual size. The measurement duration setting can affect the accuracy of the reported size and with an automatic selected measurement, the equipment software automatically regulates the most appropriate measurement duration, therefore, measurement accuracy is improved. In this study, the automatic measurements were chosen. Each of the nanofluid samples was allowed to equilibrate at 298 K for 5 minutes to confirm the homogeneity of the temperature, and then 10 runs were carried out in a disposable folded capillary cell (DTS 1070). Each data point shown in this study is the average of three consequent sample measurements. The standard deviations for particle size distribution and zeta potential obtained from Zetasizer Nano ZS were less than 5 nm and 1.5 mV, respectively (Appendix C). Besides, the standard deviation of the mean or standard error for particle size distribution and zeta potential were 2.9 nm and 0.9 mV, accordingly.


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4. Measurements of thermal conductivity and viscosity 4.1. Thermal conductivity measurement A KD2-Pro (Decagon, USA) portable thermal conductivity measurement device was used to measure the thermal conductivity of samples tested in this study. The KD2-Pro works based on the transient hot wire (THW) principle. Before each thermal conductivity measurement, the setup was calibrated with fluids such as DI-water and glycerine which are of well-known thermal conductivity. Samples prepared at different particle volume fractions were placed in a water bath to maintain a constant temperature of 298 K. The thermal conductivity of each ferrofluid was measured five times and the average value is reported here. The accuracy of the equipment is ±0.001% for measurement within the range of 0.02–2.00 W/m.K. The uncertainty of the thermal conductivity was calculated from the standard deviation of experimental results. The maximum standard deviation for the thermal conductivity of magnetite nanofluids at different concentrations was 0.005 W/m.K and standard deviation of the mean was 0.002 W/m.K (Appendix C). 4.2. Viscosity measurement The dynamic viscosity of magnetite nanofluids was measured using an AR-G2 rotational rheometer (TA Instruments) at atmospheric pressure and a constant temperature of 298 K. A plate-cone, of the geometry 1º cone and 60 mm diameter, was mounted.35 Before taking measurements, the non-zero moment of inertia of the rheometer spindle as well as the measurement geometry and the friction of the instrument were calibrated. The viscosity measurements were carried out with different shear rates (from 5 to 1050 1/s) at constant temperature and each measurement point is the average of three successive measurements at constant temperature. The temperature was controlled by a Peltier plate in the rheometer, with an accuracy of ± 0.1 K. For the employed geometry approximately 1.0 ml of sample was considered ideal. The reported instrument accuracy is 5%. The standard deviation of the 13 ACS Paragon Plus Environment


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dynamic viscosity for magnetite nanofluids varies with shear rate, the maximum standard deviation was found 0.026 Pa.s at low shear rates from 5 to 40 1/s, and for shear rate values higher than 40 1/s, the standard deviation values were negligible (i.e. 3 orders of magnitude less than the maximum standard deviation, Appendix C). 5. Results and discussion 5.1. XRD analysis of Fe3O4 nanoparticles The XRD pattern for the synthesised Fe3O4 nanoparticles with different reagent dosing rates is shown in Figure 3. It was found that the characteristic peaks match with the conventional Bragg reflection plane of the magnetite cubic crystal structure (reference number: 04-0059786, magnetite), confirming that the produced nanoparticles’ were magnetite; and the absence of any other peak indicate the high samples purity. Figure 3 shows the widened or extended peaks for the Fe3O4 phase, which indicates that the crystallite size of the particles are in the nano-meter scale. The XRD patterns presented in this study are in good agreement with widely-known standard XRD patterns for Fe3O4 nanoparticles (magnetite).26


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Fe3O4 NH4Cl

Figure 3. XRD patterns for magnetite nanoparticles synthesised by chemical precipitation at different ammonium hydroxide addition rates.

5.2. TGA and DTG analyses of Fe3O4 nanoparticles TGA and DTG were carried out on the different Fe3O4 nanoparticles at various ammonium hydroxide flow rates. Before conducting measurement, the nanoparticles were washed and separated with acetone and a magnet respectively and finally dried in a furnace. Figure 4 and Figure 5 show the TGA and DTG curves for the Fe3O4 nanoparticles at ammonium hydroxide flow rates 5 ml/min and 15 ml/min, accordingly.



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1

3 2

Figure 4. Thermogravimetric analysis (TGA) and differential thermal gravimetry (DTG) curves for the magnetite nanoparticles at 5 ml/min ammonium hydroxide adding rate.

1

3 2

Figure 5. Thermogravimetric analysis (TGA) and differential thermal gravimetry (DTG) curves for the magnetite nanoparticles at 15 ml/min ammonium hydroxide adding rate. 16 ACS Paragon Plus Environment

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In Figures 4 and 5, the peaks in the DTG plots indicate the losses of mass in the TGA curve. The DTG curves show three peaks, peak number 1 suggests the loss of water as well as hydroxyl groups from the surface of the nanoparticles; they constitute nearly 11% of the total nanoparticle mass. Approximate 2.5% loss of mass is associated with the peak 2, and that point appears around the decomposition temperature and melting point of NH4Cl. Therefore, it is proposed that some of the NH4Cl salt molecules are chemically bonded and physically adsorbed to the surface of the nanoparticles and may escape when the temperature approaches the decomposition and melting point. In this study, the samples are heated to over 900 ºC. The results indicate that, the polymer surfactant in the Fe3O4 nanofluid did not entirely “cook off” until it reached reasonably high temperature, approximately 700 or 800 ºC.24 Peak number 3 indicates the phase transition from Fe3O4 to FeO within the nanoparticles as well as the complete cook off of the polymer at a temperature of about 850°C. 5.3. Analyses of nanoparticle size distribution and zeta potential



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(a)

(b)


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(c) Figure 6. Effect of various dosing rates of NH4OH on the size distribution of nanoparticles for (a) 0.24 wt%, (b) 0.55 wt% and (c) 1.4 wt% of Fe3O4/DI-water ferrofluids.

The ammonium hydroxide addition rate was measured as a crucial parameter in this study. Initially, the addition rate of the ammonium hydroxide was quick at 15 ml/min, whilst a significant amount of brown depositions appeared in the mixture; and finally the deposition colour changed into black while the value of pH attained a certain value.36 Figure 6 demonstrates the nanoparticles size distribution under various addition rates of ammonium hydroxide. The average nanoparticle diameter of different samples changes from 48.68 to 108.90 nm with the increase of addition rate. Moreover, the size distribution of nanoparticles widens severely. At 5 ml/min addition rate, the value of pH of the solution is uniform and homogeneous due to the stable and slow addition rate. Hence, it can be seen in Figure 6 (a)(c) that the average particle size of the 5 ml/min Fe3O4/DI-water nanofluid was the lowest and in most instances the particle size distribution narrowed down as a result of uniformity in 19 ACS Paragon Plus Environment


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nanoparticle size. Therefore, Figure 6 indicates that the nanoparticles average size is increased with the increment of ammonium hydroxide addition rate as well as with the enhancement of weight concentration of nanoparticles.

Figure 7. Effect of weight fraction and NH4OH addition rate on the zeta potential of Fe3O4/DI-water nanofluids

Zeta potential measurements were performed in order to determine the surface charge on the nanoparticles. The general zeta potential threshold between unstable and stable suspensions is usually considered as either +30 mV or -30 mV. This highlights that a particle with a zeta potential higher than +30 mV or lower than -30 mV is believed to be stable suspension.37 The stability of magnetite nanoparticles in base fluids is directly linked to their electrokinetic properties. Substantially well-dispersed nanofluids can be found with a large surface charge density to create strong repulsive forces. Thus, the electrophoretic behaviour study by taking measurements of particle size and zeta potential become crucial for gaining better understanding of nanoparticles distribution behaviour in a base fluid.38, 20 ACS Paragon Plus Environment

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The greater

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magnitude zeta potential indicates a smaller aggregate size in the base fluid, and in return a more stable nanofluid. Figure 7 shows the variation of zeta potential as a function of NH4OH addition rate for magnetite nanofluids of three different weight fractions. Figure 7 shows that the nanofluids had a negative zeta potential at all weight concentrations analysed. From these observations (Figures 6 and 7) it is apparent that the magnitude of zeta potential is maximum   50 mV and average particle size is minimum D  48.68 nm at an optimised value of

ammonium hydroxide addition rate of 5 ml/min and 0.24 wt% of nanoparticles. Therefore, in the region of a 5 ml/min ammonium hydroxide addition rate, the dispersion behaviour of Fe3O4 nanoparticles is superior in comparison to the other two dosing rates at all weight fractions of nanoparticles examined. 5.4. Analysis of nanoparticles weight percentage in DI-water Measurement of particle weight fraction is relatively easy for the two step production method given that a known quantity of particles is added to the base-fluid. However, in this study, the one step chemical precipitation method was used for Fe3O4/DI-water ferrofluid preparation. TGA is an effective technique to determine the exact weight fraction of nanoparticle in a base fluid24, hence, this technique has been used to determine the weight fraction of magnetite nanoparticles.



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(a) 0.24 wt%

(b) 0.55 wt% 22 ACS Paragon Plus Environment

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(c) 1.4 wt% Figure 8. Weight percentage of nanoparticles in the magnetic fluids for 5 ml/min addition rate of NH4OH measured using TGA. Figure 8 represents the TGA measurement results and indicates that by increasing the temperature, different phases such as water and polymer evaporate leaving the solid phase behind. It can be seen that around a temperature of 450oC, the weight percent stays constant and does not change with further temperature increase. This observation confirms that the weight concentration of the magnetite nanoparticles in the original sample must have been 0.24, 0.55 and 1.4 wt% for Figure 8 (a), (b), (c) accordingly. It was concluded that the nanoparticle weight fraction observed via TGA may be slightly higher than the actual value. The extra weight may be attributed to leftover hydroxide groups on the particles surface. This situation can be avoided or reduced by keeping the samples at a very high temperature for a longer time period.



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5.5. Analyses of viscosity and thermal conductivity It appeared from the previous studies that ferrofluids’ convective heat transfer properties depend on the variation of three different parameters, namely thermal conductivity, boundary layer thickness and viscosity of the working fluids.40,

41

There is an enhancement in the

thermal conductivity because of the effect of additional accumulation of the particles at or near the wall, which may reduce the thickness of the boundary layer; and as a result the convective heat transfer is improved. Nevertheless, rise in the viscosity and decrease in the velocity of ferrofluids at or near the wall may introduce a reduction in heat transfer.42,

43

Thus, the rivalry among the aforementioned factors such as the thermal boundary layer thickness, thermal conductivity, and viscosity influences the overall performance of a heat transfer system.

(a)


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(b)

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Figure 9. Viscosity as a function of shear rate for different ammonium hydroxide addition rates at 298K for (a) 0.24 wt%, (b) 0.55 wt%, and (c) 1.40 wt% of Fe3O4/DI-water nanofluids.

(a)


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(b)

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Figure 10. Viscosity as a function of shear rate for different weight concentration of ferrofluids at 298K for (a) 5 ml/min, (b) 10 ml/min, and (c) 15 ml/min ammonium hydroxide addition rates. The viscosity of the ferrofluids were measured at three different ammonium hydroxide addition rates as well as different nanoparticle weight concentrations. Figures 9 and 10 present the viscosity of Fe3O4/water nanofluids as a function of shear rate. From the similarity of experimental data found in Figure 9(a)-9(c) and Figure 10(a)-10(c), it is clear that the value of viscosity is dependent on the weight fraction of Fe3O4 nanoparticles and NH4OH dosing rate in the suspensions. It was also observed that the viscosity of the 1.4 wt% of Fe3O4/water nanofluid, precipitated using 15 ml/min of ammonium hydroxide, was approximately 31% higher than the viscosity of the 0.24 wt% ferrofluid with a 5 ml/min ammonium hydroxide addition rate. A low dosing rate of ammonium hydroxide ensured the uniform dispersion of the Fe3O4 nanoparticles in the DI-water. This observation clearly confirms that the stability of the nanofluids has a direct relation with the viscosity, in that better dispersion correlated to a lower viscosity of the nano-suspensions. Thus, utilising the optimal addition rate of NH4OH can result in lower viscosity values of the nanofluid, which in turn makes the nanofluid more favourable for practical applications. From Figures 9 and 10, the magnetite nanofluids rheological behaviour showed a non-linear trend, which is akin to the properties of a shear thinning liquid. The shear thinning behaviour was obtained from shear rates of 5-550 1/s. In addition, an increase in viscosity was observed for increases in nanoparticle weight concentration. This indicates that the value of viscosity of the nanofluids can be hampered by changing the nanoparticle concentration. Because, the nanofluids with higher concentrations may exhibit enhanced particle to particle interactions, changing the intra-molecular forces, altering the observed viscosity.44


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From Figures 9 and 10 it was concluded that the lowest value of viscosity occurred at a nanoparticle concentration of 0.24 wt% and 5 ml/min NH4OH addition rate. At that same point, the lowest particle size distribution and zeta potential of highest magnitude were found (Figures 6 and 7) which indicate the most stable nanofluids. If nanoparticles settle out, this causes variation in hydrodynamic size, morphology as well as the weight concentration. This results in a higher viscosity of the colloidal system as was observed. Derjaguin-Landau-Verwey- Overbeek (DLVO) theory45 predicts that as charge separation in double electric layer increases, shrinkage of double layer occurs (Debye length→0) and at small separations between nanoparticles the van der Waals attraction win over the double layer repulsion and particle agglomeration occurs (observed as a viscosity increase). Due to the statistics of random clustering, nanoparticle agglomerates are usually elongated46 and behave similar to high aspect ratio particles, restricting both rotational and translational motions. This could be the possible reason behind the shear thinning behaviour and higher viscosities of suspensions with respects to agglomerates.



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(a)

(b) Figure 11. Thermal conductivity of Fe3O4/DI-water nanofluids with various weight fractions of nanoparticles at a constant temperature of 298 K as a function of (a) NH4OH dosing rate and (b) average particle size 30 ACS Paragon Plus Environment

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Figure 11 presents the thermal conductivity of the ferrofluids as the function of ammonium hydroxide dosing rate, particle size as well as nanoparticle weight fraction. From Figure 11(a), a rapid dosing rate of NH4OH precipitator into the system resulted in a slight decrease in the value of thermal conductivity. The average thermal conductivity enhancement was about 2% for all nanoparticle weight fractions and a 5 ml/min precipitator addition rate compared to the corresponding weight concentration of ferrofluids at 15 ml/min NH4OH addition rate. Moreover, it can be seen that the thermal conductivity of the ferrofluids increased linearly with increasing weight concentration of nanoparticles, with a maximum increase of 3% for 1.4 wt% when compare to 0.24 wt% of ferrofluid with a 15 ml/min ammonium hydroxide addition rate at constant room temperature. It may due to the fact that the distance among the particles decreases as the weight concentration of nanoparticles increases. The thermal conductivity may increase with the increment of particle to particle interactions at higher weight fractions. Figure 11(b) shows that the value of thermal conductivity is improved as particle size is decreased. It is noted that, the enhancement in thermal conductivity with the reduction in particle size is surprising and controversial within previous studies.47-49 However, recently some studies reported similar results.50-53 This observation could be described in terms of nanoparticles’ Brownian motion, which is considered as one of the important factors in the thermal transport of nanofluids.54 The micro-convection and Brownian motion of liquid molecules are aggravated because of the reduction in particle size and therefore, the thermal conductivity significantly improves.55 As a result, smaller particles associating more violent Brownian motion, which may lead to an inverse relationship between thermal conductivity and particle size.54



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When the particles are dispersed into a base fluid, the overall particle-base fluid interaction behaviour depends on the characteristics of the particle surface. In the case of adding NH4OH as a precipitator at different dosing rates, the change in zeta potential results in weaker repulsion and lower surface charge between the particles by Brownian motions, which may lead to stronger agglomeration. As noticed by the measurement of zeta potential, when its value reaches the highest magnitude56, the surface charge rises due to more regular attacking of potential-determining ions ( H



, OH



and phenyl sulfonic group) against the surface of

phenyl sulfonic groups and hydroxyl groups. This may lead to an enhancement in the electrostatic repulsion force between nanoparticles, and the ferrofluids show notably decreased agglomeration and improved mobility, eventually boosting the heat transport method, to which Lee et al.56 reached to the same conclusion. 6. Conclusion Ferrofluids have novel properties such as fluidity and magnetic properties, which make them potentially useful in energy harvesters.8 It is vital to have a clear understanding of the particle size, purity, stability, and morphology of both Fe3O4 nanoparticles and Fe3O4/DI-water nanofluids prior to their application in a ferrofluid based electromagnetic energy harvester. In the current study, the prepared Fe3O4 nanoparticles were characterised using different methods such as TGA, DLS and XRD. The TGA data indicated that physical adsorption and chemical bonding occur at the DI-water/Fe3O4 nanoparticle interface and the XRD pattern confirmed the purity of the Fe3O4 samples. Moreover, the effect of nanoparticle weight concentration and reagent dosing rate on zeta potential, particle size distribution, viscosity and thermal conductivity of the ferrofluids were studied in detail. The values of zeta potential and particles size distribution in the base fluid varied with changing nanoparticle weight concentration as well as of ammonia reagent addition rate; and as a result the stability of the nanofluids was modified. 32 ACS Paragon Plus Environment

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At a low reagent dosing rates, the colloidal particles become more stable and ultimately modify the thermal conductivity and viscosity values of the nanofluids. Therefore, the lowest viscosity (at any shear rate from 5 to 1050 1/s) and highest thermal conductivity values were obtained at the lowermost value of ammonia reagent dosing rate for ferrofluids of three different nanoparticle concentrations. Hence, it appears logical to conclude that having the optimum charge on the nanoparticles surface leads to stablised nanofluids with ideal thermophysical properties. As shown here, the role of aggregation in terms of zeta potential, particle size distribution, as well as viscosity and thermal conductivity are tied together and is an important parameter for efficiency improvement in an energy harvester. Suggested future work Nanoparticle weight percentage analysis has been done using TGA, but it may not be obvious what constituents are producing the final weight results. If the nanofluids have severe contamination, this could be incorrectly seen as additional particles in the final weight loading. Therefore, it is recommended to use both inductively coupled plasma (ICP) spectroscopy and TGA in order to fully understand the loading of the nanofluid. Reactions were not performed under a nitrogen atmosphere, since, the oxygen in the environment may dissolve with the mixture and may change the hydrophobic or hydrophilic character of the mineral surface. Hence, it is suggested that further reactions should be carried out under an oxygen free environment in the future. Acknowledgements The authors gratefully acknowledge the financial support provided by the University of Newcastle (Australia), Granite Power Pty Ltd and the Australian Research Council through the ARC-Linkage grant LP100200871, for the present study. The authors also would like to thank Dr Kalpit Shah for his valuable advice and supervision. The authors’ sincerer thanks



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also go to Dr. Priscilla Tremain for the technical advice and ideas. The authors are thankful to the editor and reviewers for having patiently gone through the draft of this study and provided useful recommendations and suggestions, which significantly improved its presentation. Supporting information Appendix A: Rotameter calibration procedure, Appendix B: Random error analysis, and Appendix C: Supplemental data. References (1) Bibo, A.; Masana, R.; King, A.; Li, G.; Daqaq, M., Electromagnetic ferrofluid-based energy harvester. Phys. Lett. A 2012, 376, (32), 2163-2166. (2) Tural, B.; Özkan, N.; Volkan, M., Preparation and characterization of polymer coated superparamagnetic magnetite nanoparticle agglomerates. J. Phys. Chem. Solids 2009, 70, (5), 860-866. (3) Bahiraei, M.; Hangi, M., Flow and heat transfer characteristics of magnetic nanofluids: A review. J. Magn. Magn. Mater. 2015, 374, 125-138. (4) Lin, M.; Zhang, D.; Huang, J.; Zhang, J.; Xiao, W.; Yu, H.; Zhang, L.; Ye, J., The antihepatoma effect of nanosized Mn-Zn ferrite magnetic fluid hyperthermia associated with radiation in vitro and in vivo. Nanotechnology 2013, 24, (25), 255101. (5) Sari, I.; Balkan, T.; Kulah, H., An electromagnetic micro power generator for wideband environmental vibrations. Sens. Actuators A Phys. 2008, 145–146, (0), 405-413. (6) Glynne-Jones, P.; Tudor, M. J.; Beeby, S. P.; White, N. M., An electromagnetic, vibration-powered generator for intelligent sensor systems. Sens. Actuators A Phys. 2004, 110, (1–3), 344-349. (7) Azizian, R.; Doroodchi, E.; Moghtaderi, B., Effect of Nanoconvection Caused by Brownian Motion on the Enhancement of Thermal Conductivity in Nanofluids. Ind. Eng. Chem. Res. 2012, 51, (4), 1782-1789. (8) Elborai, S. M. Ferrofluid surface and volume flows in uniform rotating magnetic fields. Ph.D. Dissertation, Massachusetts Institute of Techn. USA. 2006. (9) Oh, D. W.; Sohn, D. Y.; Byun, D. G.; Kim, Y. S. Analysis of electromotive force characteristics and device implementation for ferrofluid based energy harvesting system. 17th IEEE International Conference on Electrical Machines and Systems (ICEMS). 2014; 20332038.


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