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An experimental study on the effect of ultrasonication on thermal conductivity of ferrofluid loaded with carbon nanotubes
Thermochimica Acta 617 (2015) 102–110
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An experimental study on the effect of ultrasonication on thermal conductivity of ferrofluid loaded with carbon nanotubes A. Shahsavar a , M.R. Salimpour a,∗ , M. Saghafian a , M.B. Shafii b a b
Department of Mechanical Engineering, Isfahan University of Technology, Isfahan 84156-83111, Iran Department of Mechanical Engineering, Sharif University of Technology, Tehran, Iran
a r t i c l e
i n f o
Article history: Received 19 November 2014 Received in revised form 18 August 2015 Accepted 22 August 2015 Available online 24 August 2015 Keywords: Ferrofluid Carbon nanotubes Thermal conductivity Ultrasonication Hybrid nanofluid
a b s t r a c t Nanofluids containing Fe3 O4 and carbon nanotubes nanoparticles emulsified and dispersed using gum arabic (GA) and tetramethylammonium hydroxide (TMAH) were made and characterized for potential use as heat transfer fluids. Due to the interaction between the TMAH and GA molecules, the magnetic nanoparticles and CNTs were physically adsorbed. This paper reports an experimental work on the effect of ultrasonication on thermal conductivity of this aqueous suspension. The characterization and surface morphology of the dried samples were studied by using XRD and TEM measurements. Experiments were conducted in the magnetic nanoparticles mass concentration range 0.494–2.428%, CNT mass concentration range 0.0–1.535% and the temperature range 25–55 ◦ C. Results show that thermal conductivity of the studied nanofluids is affected by ultrasonication time, increased first and then decreased after an optimum sonication time. Additionally, results show that addition of GA coated CNT nanofluid increases the thermal conductivity of the aqueous nanofluid containing TMAH coated Fe3 O4 nanoparticles. © 2015 Elsevier B.V. All rights reserved.
1. Introduction Conventional heat transfer fluids, such as water, oils and ethylene glycol have inherently low thermal conductivities that greatly limit the performance of many engineering equipment. Many research activities have been carried out attempting to improve the thermal conductivity of the heat transfer fluids by adding nanoparticles of more thermally conductive solids into liquids. These so-called nanofluids, exhibit substantially higher thermal conductivities than those of the corresponding base fluids [1]. A ferrofluid is a stable colloidal suspension of sub-domain magnetic particles (magnetite, ferric oxide, iron nickel oxide, etc.) with a particle size in the range of 5–15 nm dispersed in a liquid carrier. Each particle is coated with surfactants such as oleic acid, TMAH, citric acid and GA to prevent from aggregation. In spite of the name, ferrofluids are not themselves magnetic, but only display magnetic properties when they are in the presence of a strong magnetic field. Numerous studies have been carried out on the enhancement of thermal conductivity of ferrofluids. The thermal conductivity of a number of ferrofluids consisting of colloidally dispersed Fe3 O4 particles in diester, hydrocarbon, water and fluorocarbon carriers were measured by Popplewell et al. [2]. They showed that the
∗ Corresponding author. E-mail address: [email protected] (M.R. Salimpour). http://dx.doi.org/10.1016/j.tca.2015.08.025 0040-6031/© 2015 Elsevier B.V. All rights reserved.
application of a magnetic field of 0.1 T had no noticeable effect on the thermal conductivity of ferrofluids. Fertman et al. [3] investigated temperature and concentration dependent thermal conductivity of hydrocarbon-based magnetic fluids containing colloidal Fe3 O4 particles coated with oleic acid at 0.01–0.2% volume concentration and in the temperature range of 20–80 ◦ C. Li et al. [4] investigated the thermal conductivity of the aqueous magnetic fluids in either the absence or the presence of the external magnetic field. They analyzed the effects of the volume fraction of the suspended magnetic particles, concentration of surfactants and the external magnetic field strength as well as its orientation on the thermal conductivity of the magnetic fluid. The experimental results showed that the thermal conductivity of the sample magnetic fluids was larger than that of pure fluids in both the absence and presence of the external magnetic field. Almost no change in the thermal conductivity of the sample magnetic fluid was found in the magnetic field perpendicular to the temperature gradient. The thermal conductivity of the magnetic fluid increases with the strength of the applied magnetic field being parallel to the temperature gradient. Xuan et al. [5] investigated the mesoscaled structure of ferrofluids consisting of magnetic nanoparticles and a carrier fluid as well as some surfactant using the Lattice–Boltzmann (LB) method. They simulated the distribution of suspended magnetic nanoparticles and morphology of the ferrofluid in both cases of the absence and the presence of an external magnetic field. Additionally, they discussed the effects of the dipole–dipole
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interaction energy and the thermal energy on the aggregation structures of the magnetic nanoparticles. Gubin et al. [6] described the key methods for the preparation of ferrofluid samples, experimental data of ferrofluid properties and the main theoretical views on the magnetism of magnetic nanoparticles. Hong et al. [7] studied the effect of clustering of nanoparticles on the thermal conductivity of nanofluids. They observed a large enhancement of the thermal conductivity in Fe nanofluids sonicated with high powered pulses. The experimental results showed that the reduction of the thermal conductivity of nanofluids is directly related to the agglomeration of nanoparticles. Philip et al. [8] experimentally measured the thermal conductivity of the magnetic nanoparticles coated by oleic acid suspended in hexadecane under magnetic field. They observed a dramatic enhancement of thermal conductivity (300%) at a particle loading of 6.3% volume fraction under the influence of an applied magnetic field. Parekh and Lee [9] studied the thermal conductivity of magnetite nanoparticles dispersed in kerosene as a function of transverse magnetic field and temperature. They found almost 30% enhancements in thermal conductivity for 4.7% volume fraction under transverse magnetic field. Additionally, the temperature dependent conductivity showed no enhancement in the temperature region of 25–65 ◦ C. Yu et al. [10] studied the thermal conductivity of the kerosene based Fe3 O4 nanofluids and found 34.0% enhancement in thermal conductivity for 1.0% volume fraction under a temperature range of 10–60 ◦ C. Gavili et al. [11] investigated the thermal conductivity of ferrofluid containing Fe3 O4 nanoparticles suspended in de-ionized water under magnetic field. They observed a 200% thermal conductivity enhancement at 5.0% volume concentration. Krichler and Odenbach [12] investigated the effect of magnetically driven structure formation on heat flux in ferrofluids on the basis of thermal conductivity measurements in variation of an external magnetic field. Therefore, an improved measuring device based on the plane heat source instead of the standard hot wire method was used to enable both parallel and perpendicular orientation of magnetic field and heat flux. Thermal conductivity measurements were carried out in variation of strength and direction of an external magnetic field relative to heat flux. Unlike former experimental investigations, for the first time the results showed qualitative consistency with theoretical predictions for both orientations. Carbon nanotubes (CNTs) are allotropes of carbon with a cylindrical nanostructure. Since their discovery in 1991 [13], they have attracted considerable attention due to their unique structural, electrical and mechanical properties. But in order to take full advantages of their properties and to extend the scope of their application spectrum, CNTs often need to be modified and integrated with other materials [14]. Recent applications as magnetic materials have drawn attention on filling the CNTs with magnetic wires, nanoparticles or coating them with a magnetic shell, especially the preparation of CNTs coated with magnetic iron oxide nanocomposites [15]. Zhang et al. [16] reported a cheap and green in situ synthesis method to prepare CNTs/Fe3 O4 hybrids, which eliminated both harsh treatment conditions and the need for organic solvent. Wang et al. [17] fabricated the CNTs decorated with magnetite nanoparticles on their external surface by in situ solvothermal method. They found that the amount of mag-
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netite nanoparticles deposited on the CNTs could be controlled by adjusting the initial mass ratio of ferrocence to CNTs. Kim and Park [18] synthesized Fe3 O4 nanoparticle-dispersed multiwalled carbon nanotubes to evaluate their potential applicability to supercapacitor electrodes. The results showed that the Fe3 O4 /CNT electrode had typical pseudo-capacitive behavior in a 1 molar Na2 SO3 solution and a significantly greater specific capacitance than that of the pure multiwalled carbon nanotubes electrode. Prolongo et al. [19] studied the main properties of epoxy composites reinforced with aligned carbon nanotubes. The alignment was carried out in a specific designed device applying a weak magnetic field with permanent magnets. CNTs were modified with magnetite nanoparticles (Fe3 O4 ) functionalized. The convective heat transfer coefficient and friction factor for fully developed turbulent flow of MWCNT–Fe3 O4 /water hybrid nanofluids flowing through a uniformly heated at constant heat flux circular tube were estimated by Sundar et al. [20]. The results indicated a maximum of 31.10% enhancement in Nusselt number with a penalty of 1.18times increase of pumping power for the particle loading of 0.3% at a Reynolds number of 22,000 as compared to base fluid data. There are very few examples of incorporating magnetically sensitive metal or metal oxide nanoparticles in a CNT containing fluid. Reches and Gazit [21] used a simple technique for making the aromatic dipeptide nanotubes (ADNTs) ferromagnetic by assembling the diphenylalanine monomers in the presence of magnetite nanoparticles. While the ADNTs assembled into tubular structures, the magnetic nanoparticles formed a non-covalent coating layer of magnetic nanoparticles. As this coating also occurs around the walls of the non-charged nanotubes, the magnetite particles adhere to the walls by hydrophobic interactions. Hong et al. [22] studied the thermal conductivity of nanofluid containing 0.01 wt% nanotube and 0.02 wt% Fe2 O3 in water under the different magnetic strength. They dispersed CNTs, Fe2 O3 magnetic particles and chemical surfactant sodium dodecylbenzene sulfonate in de-ionized water via ultrasonication. Parmar et al. [23] used a homogeneous mixture of magnetic nanoparticles coated with double surfactant, both oleic acid and sodium dodecyl sulphate coated carbon nanofibers. Under the application of magnetic very low magnetic field (500 Oe), the physically adsorbed magnetic nanoparticles with carbon nanofibers were spatially aligned. The external magnetic field aligned the magnetic moment of the particles and consequent body forces aligned the carbon nanofibers. This study is concerned with a homogeneous mixture of magnetic nanoparticles coated with TMAH and GA coated carbon nanotubes. The interaction between the two surfactants causes the physical attachment of magnetic particles with carbon nanotubes. The objective of present investigation is to determine experimentally the effect of ultrasonication time on thermal conductivity of this magnetic fluid.
2. Experimental A new concept of incorporating CNTs in a Fe3 O4 containing ferrofluid is introduced here with an aim to increase the thermal conductivity of the fluid. Such fluids with enhanced thermal con-
Table 1 Sample table. Chemical name
Chemical formula
Source
Initial mass fraction purity
Ferric chloride Ferrous chloride Ammonium hydroxide Tetramethylammonium hydroxide Multiwalled carbon nanotube Gum arabic
FeCl3 ·6H2 O FeCl2 ·4H2 O NH4 OH C4 H13 NO C C12 H7 ClN2 O3
Merck, Germany Merck, Germany Merck, Germany Merck, Germany Research Institute of Petroleum Industry (RIPI), Iran Biochemical, Iran
97% 99% 25% in H2 O 25% in H2 O 99% 98%
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Table 2 Characteristics of the studied ferrofluid samples at temperature T = 298.15 K and experimental pressure P = 0.1 MPa. Sample name
Fe3 O4 mass fraction (%)
TMAH mass fraction (%)
0.494%FF 1.47%FF 2.428%FF
0.494 1.47 2.428
0.046 0.138 0.227
Standard uncertainties u are u(T) = 0.1 K, u(p) = 10 kPa, u(mass fraction) = 5 × 10−6 .
ductivity are useful for a variety of applications such as heat transfer coolants and lubricants [22]. 2.1. Materials The materials used to prepare the water-based hybrid nanofluid containing TMAH coated Fe3 O4 nanoparticles and GA coated carbon nanotubes are listed in Table 1, along with their specifications. All the materials were reagent grade and used without further purification. 2.2. Sample preparation 2.2.1. Ferrofluid The ferrofluid used in this research were synthesized by the method explained by Berger et al. [24] and Mohammadi et al. [25]. The chemical equation for the process of synthesizing of ferrofluids is: FeCl2(aq) + 2FeCl3(aq) + 8NH3(aq) + 4H2 O(l) → Fe3 O4(s) + 8NH4 Cl(aq)
(1)
First, 1.0 mL of stock FeCl2 solution and 4.0 mL of stock FeCl3 solution are mixed together. Afterward, 50 mL of 0.7 M aqueous NH3 solution is slowly added. During this process, black magnetite precipitates are produced. After evacuating the excess liquid, the remaining solution is centrifuged. As a result, a dark sludge remains at the bottom of the tube. Subsequently, 8 mL of 25% TMAH solution is added and stirred till the thorough suspension of solid in the liquid happens. Finally, by magnetically stirring the solution for about 30 min, the extra ammonia was evacuated [24,25]. The characteristics of the ferrofluid samples prepared for this study are given in Table 2. 2.2.2. Multi-wall carbon nanotube-based aqueous nanofluid (CNT nanofluid) In order to produce carbon nanotube-based aqueous nanofluid, de-ionized (DI) water, GA and multiwalled carbon nanotubes (MWCNT) were required. The nanotubes produced by chemical vapor deposition, with 10–30 nm in diameter and 10 m in length, were purchased from Research Institute of Petroleum Industry (RIPI, Tehran, Iran). Fig. 1(a) and (b) shows respectively an SEM image and a TEM image of the sample. Garg et al. [26] found that the optimum sonication time for a 1 wt% MWCNT and 0.25 wt% GA mixture was 40 min using a 130 W, 20 kHz ultrasonicator. In the current study, a similar approach was followed using an ultrasonicator that had an operating frequency of 20 kHz and a maximum power output of 130 W. 2.2.3. Hybrid nanofluid In order to prepare the hybrid nanofluid, the aqueous mixture of ferrofluid and CNTs is homogenized using ultrasonication technique. Due to the interaction between the TMAH coated magnetic nanoparticles and GA on the surface of nanotubes they are physicadsorbed. Nine 400 mL samples of hybrid nanofluid were prepared with different mass fraction of Fe3 O4 and CNT nanoparticles, which
Fig. 1. Images of carbon nanotubes as provided by the producer company: (a) SEM image of carbon nanotubes and (b) TEM image of carbon nanotubes.
are presented in Table 3. Then, Four 100 mL samples were prepared using each above mentioned samples and ultrasonicated for 2.5, 5, 7.5 and 10 min. 2.3. Characterization The crystal structure of the prepared samples was examined using Siemens D-500 diffractometer equipped with a rotating anode and a CuK␣ radiation source ( = 0.15148 nm). TEM (CM120, 120 kV) is used for determination of the surface morphology and size of the synthesized samples. Fig. 2(a) shows the X-ray diffraction (XRD) pattern of uncoated Fe3 O4 nanoparticles. This pattern is quite identical to pure magnetite and no peaks, which are characteristic of impurities, are observed [27,28]. The average crystallite size of sample can be calculated from the XRD line broadening using the Scherrer formula [29]: d=
0.9 ˇ cos
(2)
where d is the average crystalline size (nm), is the angle at maximum peak, corresponds to the wavelength of the incident X-ray and ˇ denotes the full width at the half maximum intensity. The average diameter of sample evaluated according to (3 1 1) reflection by utilizing Scherrer’s equation is approximately 13 nm. Fig. 2(b) shows the TEM images of Fe3 O4 nanoparticles. According to the TEM image the average diameter of nanoparticles is on the order of 10 nm, which is in good agreement with the XRD analysis. TEM result, photograph of the synthesized hybrid nanofluid and its response to a magnet are shown in Fig. 3(a), (b) and (c), respectively. The black powder is attracted toward the magnet in a few seconds, demonstrating high magnetic sensitivity.
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Table 3 Characteristics of the studied hybrid nanofluid samples at temperature T = 298.15 K and experimental pressure P = 0.1 MPa. Sample name
Fe3 O4 mass fraction (%)
TMAH mass fraction (%)
CNT mass fraction (%)
GA mass fraction (%)
Ultrasonication time (min)
0.494%FF + 0.105%CNT 0.494%FF + 0.21%CNT 0.494%FF + 0.315%CNT 1.47%FF + 0.315%CNT 1.47%FF + 0.625%CNT 1.47%FF + 0.935%CNT 2.428%FF + 0.515%CNT 2.428%FF + 1.025%CNT 2.428%FF + 1.535%CNT
0.494 0.494 0.494 1.47 1.47 1.47 2.428 2.428 2.428
0.046 0.046 0.046 0.138 0.138 0.138 0.227 0.227 0.227
0.105 0.21 0.315 0.315 0.625 0.935 0.515 1.025 1.535
0.026 0.053 0.079 0.079 0.156 0.234 0.129 0.256 0.384
2.5, 5, 7.5, 10
Standard uncertainties u are u(T) = 0.1 K, u(p) = 10 kPa, u(mass fraction) = 5 × 10−6 .
Fig. 2. (a) X-ray diffraction of synthesized Fe3 O4 particles and (b) TEM image of nanoparticles.
2.4. Measurement of thermal conductivity In the present work, the thermal conductivity was measured using a KD2-pro instrument (Decagon devices, Inc., USA), which is based on the transient hot wire method. The instrument had a probe of 60 mm length and a 1.3 mm diameter (KS-1) and closely approximates the infinite line heat source which gives least disturbance to the sample during measurements. The instrument uses a heating element, a thermo-resistor and a microprocessor to control and measure the conduction in the probe with a specified accuracy of 5%. The samples were maintained at several temperatures using a temperature-controlled chiller. After the temperature of the sample reached the required temperature, the sample was kept at that temperature for further 30 min to ensure temperature equilibrium before each measurement. The thermal conductivity of the samples are measured in the temperature range 25–55 ◦ C with 5 ◦ C intervals. 3. Results and discussion The variation of thermal conductivity with temperature for 0.494%FF + 0.105%CNT, 0.494%FF + 0.21%CNT and 0.494%FF + 0.315%CNT are presented in Fig. 4(a)–(c). Fig. 5(a)–(c) shows the temperature dependence of the thermal conductivity of 1.47%FF + 0.315%CNT, 1.47%FF + 0.625%CNT and 1.47%FF + 0.935%CNT, respectively. Additionally, the variation of thermal conductivity with temperature for 2.428%FF + 0.515%CNT, 2.428%FF + 1.025%CNT and 2.428%FF + 1.535%CNT are demonstrated in Fig. 6(a)–(c). Figs. 4–6 also show the variation of thermal
conductivity of water (base fluid) with temperature. The thermal conductivity data shown in Figs. 4–6 are also tabulated in Appendix A. It is observed that the thermal conductivity of the hybrid nanofluid is affected by ultrasonication time, reaching an optimum for sample which is sonicated for 5 min. In cases with 2.5 min sonication time, there is not sufficient time or energy to physical attachment of magnetic particles with carbon nanotubes and obtain a homogeneous mixture. On the other hand, the sonication energy received by the samples with 5 min sonication time is adequate to physical attachment of magnetic particles with carbon nanotubes and acquires a homogeneous mixture. Further increase in the sonication time causes reduction of thermal conductivity of the hybrid nanofluid. The exact reason for this observation is unclear. It could be suggested that the excess sonication energy causes the bond between TMAH and GA molecules to break. Additionally, further sonication leads to reduction of aspect ratio and lower quality of three-dimensional network of CNTs, resulting in a decrease in thermal conductivity enhancement [26,30,31]. It is clear from Figs. 4–6 that the thermal conductivity of all the samples increases with temperature. Also, Figs. 4–6 show that the thermal conductivity of the pure ferrofluid is higher than the base fluid and the thermal conductivity of the hybrid nanofluid is higher than the ferrofluid with the same Fe3 O4 mass fraction. A thermal conductivity enhancement of 4.92% to 7.87%, 10.66% to 18.06% and 13.93% to 23.92% is observed at 0.494%, 1.47% and 2.428% mass fraction of Fe3 O4 , respectively, in the temperature range of 25–55 ◦ C compared to the base fluid. In addition, a thermal conductivity enhancement of 13% and 15.59% is observed at 0.494%FF + 0.105%
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Fig. 3. (a) TEM image of hybrid nanofluid, (b) photograph of the synthesized hybrid nanofluid and (c) its response to a magnet.
CNT with 5 min sonication time and an enhancement of 26.07% and 34.26% is obtained at 2.428% FF + 1.535%CNT with 5 min sonication time in the temperature range of 25–55 ◦ C compared to the base fluid. The variation of percentage enhancement in thermal conductivity of ferrofluid and hybrid nanofluid samples ((KFForFF+CNT − KDIwater ) × 100/kDIwater ) with temperature are presented in Table 4. It should be noted that only hybrid nanofluid samples with 5 min sonication time are considered in this table. It is clear that the addition of CNT nanofluid increases the thermal conductivity of the ferrofluid. In addition, the thermal conductivity increment depends on the concentration of the Fe3 O4 and CNT nanoparticles. The higher the particle concentration, the more remarkable such an increment is. Though considerable progress has been made in tailoring efficient nanofluids with enhanced thermal conductivities, the science behind the exact mechanism of heat conduction in nanofluids is poorly understood [8]. Keblinski et al. [32] and Eastman et al. [33] suggested four possible mechanisms responsible for enhancement in the thermal conductivity of the nanofluids, e.g., Brownian motion of nanoparticles, liquid layering at liquid/particle interface, nature of heat transport in nanoparticles and nanoparticle clustering. The Brownian motion refers to the random movement of particles suspended in a liquid or gas, caused by collisions with molecules of the surrounding medium. The Brownian motion could contribute to the thermal conductivity enhancement in two ways, namely, the direct contribution due to motion of nanoparticles that transports heat (diffusion of nanoparticles) and the indirect contribution due to the so-called micro-convection of fluid surrounding individual nanoparticles [34]. Keblinski et al. [32], Eastman et al. [33] and Evans et al. [35] found that the movement of nanoparticles due to Brownian motion was too slow to transport significant amounts of heat through a nanofluid. Additionally, they found that the indirect contribution of Brownian motion could have an important role in producing particle clustering, which, as it will be described, could significantly enhance thermal conductivity [32]. On the other hand, Prasher et al. [36] performed an order of magnitude analysis and showed that convection caused by the Brownian movement of the nanoparticles was primarily responsible for the enhancement in thermal conductivity of the nanofluids. Layering of the fluid at the solid interface, by which the atomic structure of the liquid layer is significantly more ordered than that
of bulk liquid. Given that, crystalline solids (which are obviously ordered) display much better thermal transport than liquids, such liquid layering at the interface would be expected to lead to a higher thermal conductivity [32,37]. According to Keblinski et al. [32] and Xue et al. [38], thermal transport in a layered liquid may not be adequate to explain the increased thermal conductivity of nanoparticles. The carrier of heat in the crystalline solid is phonon, i.e., propagation of lattice vibration. Keblinski et al. [32] used the Debye theory to demonstrate that the generally accepted diffusive heat transport mechanism is not valid at the nano-scale and a theoretical treatment based on ballistic phonon transport is required. In particular, if the ballistic phonons initiated in a particle can persist in the liquid and get transmitted to another solid particle, the heat transport can significantly increase. Since particles are in constant motion due to Brownian motion, and move close together even at low packing factor, the coherent phonon transfer is possible even for low particle concentrations [32]. Prasher et al. [36] showed that the effect of crystalline-like phonon modes in thermal conductivity enhancement of the nanofluids is negligible due to the very small number of nanoparticles per unit volume in the system. Keblinski et al. [32] considered clustering of suspended nanoparticles into percolating pattern and showed that it is possible for these clusters to create paths of lower thermal resistance. The effective volume of a cluster, that is, the volume from which other clusters are excluded, can be much larger than the physical volume of the particles. Since within such clusters, heat can move very rapidly, the volume fraction of highly conductive phase is larger than the volume of solid which is significantly increase thermal conductivity [32]. This type of model may be appropriate if the particles are in the form of nanotubes or if they are not finely dispersed [39]. The effect of particle clustering on the thermal conductivity is also investigated by Philip et al. [40], Zhu et al. [41] and Jiang et al. [42]. They concluded that the clustering was mainly responsible for the thermal conductivity enhancement of the studied nanofluids. Finally, it can be said that among the mechanisms proposed for conventional nanofluids, convection caused by the Brownian movement of the nanoparticles and particle clustering are the two much important mechanism. 4. Nanofluid stability A stable nanofluid is characterized by three factors: low agglomeration, low sedimentation and high structural integrity. The most
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Table 4 Variation of percentage enhancement in thermal conductivity of ferrofluid and hybrid nanofluid samples with temperature at experimental pressure P = 0.1 MPa. Sample
0.494%FF 0.494%FF + 0.105%CNT 0.494%FF + 0.21%CNT 0.494%FF + 0.315%CNT 1.47%FF 1.47%FF + 0.315%CNT 1.47%FF + 0.625%CNT 1.47%FF + 0.935%CNT 2.428%FF 2.428%FF + 0.515%CNT 2.428%FF + 1.025%CNT 2.428%FF + 1.535%CNT
Temperature (◦ C) 25
30
35
40
45
50
55
4.92 13.00 13.06 13.11 10.66 20.30 21.11 21.48 13.93 23.59 25.49 26.07
6.00 13.86 13.91 13.97 13.78 21.89 22.70 23.18 17.50 25.56 27.45 27.71
6.25 14.29 14.35 14.41 15.22 23.15 23.97 24.68 19.07 27.09 28.99 29.65
6.81 14.82 14.88 14.95 16.01 23.89 24.72 25.04 20.13 28.19 30.10 31.06
7.22 15.22 15.29 15.36 16.80 24.90 25.90 26.53 21.66 29.67 31.59 32.53
7.62 15.22 15.30 15.37 17.42 25.12 25.96 26.28 22.86 30.82 31.73 33.59
7.87 15.59 15.67 15.75 18.06 25.91 26.76 27.16 23.92 31.68 33.62 34.26
Standard uncertainties u are u(T) = 0.1 K, u(p) = 10 kPa, u(mass fraction) = 5 × 10−6 , u(k) = 0.05k.
Fig. 5. Variation of thermal conductivity with temperature for 1.47%FF + 0.315%CNT, (b) 1.47%FF + 0.625%CNT and (c) 1.47%FF + 0.935%CNT. Fig. 4. Variation of thermal conductivity with temperature for (a) 0.494%FF + 0.105%CNT, (b) 0.494%FF + 0.21%CNT and (c) 0.494%FF + 0.315%CNT.
(a)
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Fig. 7. Reproducibility of thermal conductivity data of 0.494%FF, 1.47%FF, 2.428%FF, 0.494%FF + 0.315%CNT,1.47%FF + 0.935%CNT and 2.428%FF + 1.535%CNT at 25 ◦ C over 30 days.
of nanofluids are almost constant along with time. If there were increased particle agglomeration, particle sedimentation and damaged structural integrity, the stability of nanofluid and hence the reproducibility of thermal conductivity data would have been seriously affected, which are different from what was observed in the test runs. Therefore, it is verified that studied nanofluids are stable and their thermal conductivity data are reproducible for at least over 30 days. 5. Conclusions
Fig. 6. Variation of thermal conductivity with temperature for (a) 2.428%FF + 0.515%CNT, (b) 2.428%FF + 1.025%CNT and (c) 2.428%FF + 1.535%CNT.
frequently used method to evaluate the stability of nanofluids is the static position method. In this method, all samples were left standing for 30 days, and the distance or color difference in sedimentation between nanofluids was observed by the naked eye [43]. In this work, naked eye observation reveals no sedimentation at the bottom of the container with hybrid nanofluid samples. In order to further investigate the stability of studied nanofluids, the thermal conductivity of the 0.494%FF, 1.47%FF, 2.428%FF, 0.494%FF + 0.315%CNT, 1.47%FF + 0.935%CNT and 2.428%FF + 1.535%CNT was measured for 30 days and the results are presented in Fig. 7. It is seen that the thermal conductivities
In this paper, the effect of ultrasonication on the thermal conductivity of water-based hybrid nanofluid containing TMAH coated Fe3 O4 nanoparticles and GA coated carbon nanotubes is investigated experimentally. For this purpose, the aqueous mixture of ferrofluid having an average diameter of 13 nm and CNT nanofluid is homogenized using ultrasonication technique. Experimental results indicate that there is an optimum sonication time which gives the maximum enhancement in the thermal conductivity of the hybrid nanofluids. The experimental results show that addition of CNT nanofluid increases the thermal conductivity of the aqueous ferrofluid. The thermal conductivity enhancement of hybrid nanofluid purely depends on the temperature and Fe3 O4 and CNT concentration. Higher concentrations of nanofluid exhibit higher thermal conductivities. A thermal conductivity enhancement of 13% and 15.59% is observed at 0.494%FF + 0.105%CNT with 5 min sonication time and an enhancement of 26.07% and 34.26% is obtained at 2.428%FF + 1.535%CNT with 5 min sonication time in the temperature range of 25–55 ◦ C compared to the base fluid. Acknowledgments The authors would like to acknowledge Dr. Yadollah Mortazavvi and Catalyst & Nano-Structures laboratory assistants for their helpful advice on various technical issues examined in this paper.
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Appendix A. Variation of thermal conductivity with temperature for ferrofluid and hybrid nanofluid samples at experimental pressure P = 0.1 MPa Temperature (◦ C)
Sample
Sonication time
25
30
35
40
45
50
55
Water
–
0.610
0.617
0.624
0.631
0.637
0.643
0.648
0.494%FF
–
0.640
0.654
0.663
0.674
0.683
0.692
0.699
0.494%FF + 0.105%CNT
2.5 5 7.5 10
0.675 0.689 0.669 0.664
0.685 0.703 0.679 0.673
0.701 0.713 0.692 0.688
0.713 0.725 0.705 0.698
0.717 0.734 0.709 0.707
0.729 0.741 0.723 0.715
0.728 0.749 0.725 0.722
0.494%FF + 0.21%CNT
2.5 5 7.5 10
0.660 0.690 0.645 0.638
0.670 0.703 0.655 0.647
0.682 0.714 0.667 0.660
0.692 0.725 0.676 0.669
0.701 0.735 0.685 0.679
0.705 0.742 0.693 0.680
0.720 0.750 0.706 0.691
0.494%FF + 0.315%CNT
2.5 5 7.5 10
0.677 0.690 0.669 0.680
0.691 0.703 0.680 0.687
0.701 0.714 0.693 0.691
0.711 0.725 0.703 0.704
0.721 0.735 0.713 0.715
0.726 0.742 0.719 0.724
0.734 0.750 0.727 0.734
1.47%FF
–
0.675
0.702
0.719
0.732
0.744
0.755
0.765
1.47%FF + 0.315%CNT
2.5 5 7.5 10
0.724 0.734 0.707 0.687
0.734 0.752 0.728 0.707
0.744 0.768 0.742 0.718
0.759 0.782 0.755 0.731
0.774 0.796 0.766 0.747
0.786 0.805 0.783 0.758
0.798 0.816 0.796 0.766
1.47%FF + 0.625%CNT
2.5 5 7.5 10
0.702 0.739 0.702 0.693
0.730 0.757 0.717 0.710
0.743 0.774 0.733 0.722
0.760 0.787 0.748 0.738
0.772 0.802 0.762 0.746
0.776 0.810 0.771 0.757
0.788 0.821 0.777 0.764
1.47%FF + 0.935%CNT
2.5 5 7.5 10
0.710 0.741 0.695 0.683
0.743 0.760 0.724 0.715
0.759 0.778 0.742 0.727
0.774 0.789 0.754 0.740
0.781 0.806 0.768 0.753
0.797 0.812 0.781 0.767
0.802 0.824 0.787 0.774
2.428%FF
–
0.695
0.725
0.743
0.758
0.775
0.790
0.803
2.428%FF + 0.515%CNT
2.5 5 7.5 10
0.731 0.754 0.725 0.711
0.750 0.775 0.745 0.730
0.770 0.793 0.764 0.751
0.784 0.809 0.781 0.766
0.801 0.826 0.795 0.780
0.816 0.841 0.810 0.791
0.828 0.853 0.826 0.813
2.428%FF + 1.025%CNT
2.5 5 7.5 10
0.727 0.765 0.736 0.725
0.748 0.786 0.756 0.750
0.767 0.805 0.770 0.763
0.773 0.821 0.781 0.765
0.796 0.838 0.805 0.796
0.806 0.847 0.819 0.810
0.825 0.866 0.830 0.823
2.428%FF + 1.535%CNTa
2.5 5
0.731 0.769
0.752 0.788
0.764 0.809
0.782 0.827
0.799 0.844
0.814 0.859
0.829 0.870
Standard uncertainties u are u(T) = 0.1 K, u(p) = 10 kPa, u(mass fraction) = 5 × 10−6 , u(k) = 0.05k. a The results of cases with sonication time of 7.5 and 10 min are not presented.
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Thermochimica Acta journal homepage: www.elsevier.com/locate/tca
An experimental study on the effect of ultrasonication on thermal conductivity of ferrofluid loaded with carbon nanotubes A. Shahsavar a , M.R. Salimpour a,∗ , M. Saghafian a , M.B. Shafii b a b
Department of Mechanical Engineering, Isfahan University of Technology, Isfahan 84156-83111, Iran Department of Mechanical Engineering, Sharif University of Technology, Tehran, Iran
a r t i c l e
i n f o
Article history: Received 19 November 2014 Received in revised form 18 August 2015 Accepted 22 August 2015 Available online 24 August 2015 Keywords: Ferrofluid Carbon nanotubes Thermal conductivity Ultrasonication Hybrid nanofluid
a b s t r a c t Nanofluids containing Fe3 O4 and carbon nanotubes nanoparticles emulsified and dispersed using gum arabic (GA) and tetramethylammonium hydroxide (TMAH) were made and characterized for potential use as heat transfer fluids. Due to the interaction between the TMAH and GA molecules, the magnetic nanoparticles and CNTs were physically adsorbed. This paper reports an experimental work on the effect of ultrasonication on thermal conductivity of this aqueous suspension. The characterization and surface morphology of the dried samples were studied by using XRD and TEM measurements. Experiments were conducted in the magnetic nanoparticles mass concentration range 0.494–2.428%, CNT mass concentration range 0.0–1.535% and the temperature range 25–55 ◦ C. Results show that thermal conductivity of the studied nanofluids is affected by ultrasonication time, increased first and then decreased after an optimum sonication time. Additionally, results show that addition of GA coated CNT nanofluid increases the thermal conductivity of the aqueous nanofluid containing TMAH coated Fe3 O4 nanoparticles. © 2015 Elsevier B.V. All rights reserved.
1. Introduction Conventional heat transfer fluids, such as water, oils and ethylene glycol have inherently low thermal conductivities that greatly limit the performance of many engineering equipment. Many research activities have been carried out attempting to improve the thermal conductivity of the heat transfer fluids by adding nanoparticles of more thermally conductive solids into liquids. These so-called nanofluids, exhibit substantially higher thermal conductivities than those of the corresponding base fluids [1]. A ferrofluid is a stable colloidal suspension of sub-domain magnetic particles (magnetite, ferric oxide, iron nickel oxide, etc.) with a particle size in the range of 5–15 nm dispersed in a liquid carrier. Each particle is coated with surfactants such as oleic acid, TMAH, citric acid and GA to prevent from aggregation. In spite of the name, ferrofluids are not themselves magnetic, but only display magnetic properties when they are in the presence of a strong magnetic field. Numerous studies have been carried out on the enhancement of thermal conductivity of ferrofluids. The thermal conductivity of a number of ferrofluids consisting of colloidally dispersed Fe3 O4 particles in diester, hydrocarbon, water and fluorocarbon carriers were measured by Popplewell et al. [2]. They showed that the
∗ Corresponding author. E-mail address: [email protected] (M.R. Salimpour). http://dx.doi.org/10.1016/j.tca.2015.08.025 0040-6031/© 2015 Elsevier B.V. All rights reserved.
application of a magnetic field of 0.1 T had no noticeable effect on the thermal conductivity of ferrofluids. Fertman et al. [3] investigated temperature and concentration dependent thermal conductivity of hydrocarbon-based magnetic fluids containing colloidal Fe3 O4 particles coated with oleic acid at 0.01–0.2% volume concentration and in the temperature range of 20–80 ◦ C. Li et al. [4] investigated the thermal conductivity of the aqueous magnetic fluids in either the absence or the presence of the external magnetic field. They analyzed the effects of the volume fraction of the suspended magnetic particles, concentration of surfactants and the external magnetic field strength as well as its orientation on the thermal conductivity of the magnetic fluid. The experimental results showed that the thermal conductivity of the sample magnetic fluids was larger than that of pure fluids in both the absence and presence of the external magnetic field. Almost no change in the thermal conductivity of the sample magnetic fluid was found in the magnetic field perpendicular to the temperature gradient. The thermal conductivity of the magnetic fluid increases with the strength of the applied magnetic field being parallel to the temperature gradient. Xuan et al. [5] investigated the mesoscaled structure of ferrofluids consisting of magnetic nanoparticles and a carrier fluid as well as some surfactant using the Lattice–Boltzmann (LB) method. They simulated the distribution of suspended magnetic nanoparticles and morphology of the ferrofluid in both cases of the absence and the presence of an external magnetic field. Additionally, they discussed the effects of the dipole–dipole
A. Shahsavar et al. / Thermochimica Acta 617 (2015) 102–110
interaction energy and the thermal energy on the aggregation structures of the magnetic nanoparticles. Gubin et al. [6] described the key methods for the preparation of ferrofluid samples, experimental data of ferrofluid properties and the main theoretical views on the magnetism of magnetic nanoparticles. Hong et al. [7] studied the effect of clustering of nanoparticles on the thermal conductivity of nanofluids. They observed a large enhancement of the thermal conductivity in Fe nanofluids sonicated with high powered pulses. The experimental results showed that the reduction of the thermal conductivity of nanofluids is directly related to the agglomeration of nanoparticles. Philip et al. [8] experimentally measured the thermal conductivity of the magnetic nanoparticles coated by oleic acid suspended in hexadecane under magnetic field. They observed a dramatic enhancement of thermal conductivity (300%) at a particle loading of 6.3% volume fraction under the influence of an applied magnetic field. Parekh and Lee [9] studied the thermal conductivity of magnetite nanoparticles dispersed in kerosene as a function of transverse magnetic field and temperature. They found almost 30% enhancements in thermal conductivity for 4.7% volume fraction under transverse magnetic field. Additionally, the temperature dependent conductivity showed no enhancement in the temperature region of 25–65 ◦ C. Yu et al. [10] studied the thermal conductivity of the kerosene based Fe3 O4 nanofluids and found 34.0% enhancement in thermal conductivity for 1.0% volume fraction under a temperature range of 10–60 ◦ C. Gavili et al. [11] investigated the thermal conductivity of ferrofluid containing Fe3 O4 nanoparticles suspended in de-ionized water under magnetic field. They observed a 200% thermal conductivity enhancement at 5.0% volume concentration. Krichler and Odenbach [12] investigated the effect of magnetically driven structure formation on heat flux in ferrofluids on the basis of thermal conductivity measurements in variation of an external magnetic field. Therefore, an improved measuring device based on the plane heat source instead of the standard hot wire method was used to enable both parallel and perpendicular orientation of magnetic field and heat flux. Thermal conductivity measurements were carried out in variation of strength and direction of an external magnetic field relative to heat flux. Unlike former experimental investigations, for the first time the results showed qualitative consistency with theoretical predictions for both orientations. Carbon nanotubes (CNTs) are allotropes of carbon with a cylindrical nanostructure. Since their discovery in 1991 [13], they have attracted considerable attention due to their unique structural, electrical and mechanical properties. But in order to take full advantages of their properties and to extend the scope of their application spectrum, CNTs often need to be modified and integrated with other materials [14]. Recent applications as magnetic materials have drawn attention on filling the CNTs with magnetic wires, nanoparticles or coating them with a magnetic shell, especially the preparation of CNTs coated with magnetic iron oxide nanocomposites [15]. Zhang et al. [16] reported a cheap and green in situ synthesis method to prepare CNTs/Fe3 O4 hybrids, which eliminated both harsh treatment conditions and the need for organic solvent. Wang et al. [17] fabricated the CNTs decorated with magnetite nanoparticles on their external surface by in situ solvothermal method. They found that the amount of mag-
103
netite nanoparticles deposited on the CNTs could be controlled by adjusting the initial mass ratio of ferrocence to CNTs. Kim and Park [18] synthesized Fe3 O4 nanoparticle-dispersed multiwalled carbon nanotubes to evaluate their potential applicability to supercapacitor electrodes. The results showed that the Fe3 O4 /CNT electrode had typical pseudo-capacitive behavior in a 1 molar Na2 SO3 solution and a significantly greater specific capacitance than that of the pure multiwalled carbon nanotubes electrode. Prolongo et al. [19] studied the main properties of epoxy composites reinforced with aligned carbon nanotubes. The alignment was carried out in a specific designed device applying a weak magnetic field with permanent magnets. CNTs were modified with magnetite nanoparticles (Fe3 O4 ) functionalized. The convective heat transfer coefficient and friction factor for fully developed turbulent flow of MWCNT–Fe3 O4 /water hybrid nanofluids flowing through a uniformly heated at constant heat flux circular tube were estimated by Sundar et al. [20]. The results indicated a maximum of 31.10% enhancement in Nusselt number with a penalty of 1.18times increase of pumping power for the particle loading of 0.3% at a Reynolds number of 22,000 as compared to base fluid data. There are very few examples of incorporating magnetically sensitive metal or metal oxide nanoparticles in a CNT containing fluid. Reches and Gazit [21] used a simple technique for making the aromatic dipeptide nanotubes (ADNTs) ferromagnetic by assembling the diphenylalanine monomers in the presence of magnetite nanoparticles. While the ADNTs assembled into tubular structures, the magnetic nanoparticles formed a non-covalent coating layer of magnetic nanoparticles. As this coating also occurs around the walls of the non-charged nanotubes, the magnetite particles adhere to the walls by hydrophobic interactions. Hong et al. [22] studied the thermal conductivity of nanofluid containing 0.01 wt% nanotube and 0.02 wt% Fe2 O3 in water under the different magnetic strength. They dispersed CNTs, Fe2 O3 magnetic particles and chemical surfactant sodium dodecylbenzene sulfonate in de-ionized water via ultrasonication. Parmar et al. [23] used a homogeneous mixture of magnetic nanoparticles coated with double surfactant, both oleic acid and sodium dodecyl sulphate coated carbon nanofibers. Under the application of magnetic very low magnetic field (500 Oe), the physically adsorbed magnetic nanoparticles with carbon nanofibers were spatially aligned. The external magnetic field aligned the magnetic moment of the particles and consequent body forces aligned the carbon nanofibers. This study is concerned with a homogeneous mixture of magnetic nanoparticles coated with TMAH and GA coated carbon nanotubes. The interaction between the two surfactants causes the physical attachment of magnetic particles with carbon nanotubes. The objective of present investigation is to determine experimentally the effect of ultrasonication time on thermal conductivity of this magnetic fluid.
2. Experimental A new concept of incorporating CNTs in a Fe3 O4 containing ferrofluid is introduced here with an aim to increase the thermal conductivity of the fluid. Such fluids with enhanced thermal con-
Table 1 Sample table. Chemical name
Chemical formula
Source
Initial mass fraction purity
Ferric chloride Ferrous chloride Ammonium hydroxide Tetramethylammonium hydroxide Multiwalled carbon nanotube Gum arabic
FeCl3 ·6H2 O FeCl2 ·4H2 O NH4 OH C4 H13 NO C C12 H7 ClN2 O3
Merck, Germany Merck, Germany Merck, Germany Merck, Germany Research Institute of Petroleum Industry (RIPI), Iran Biochemical, Iran
97% 99% 25% in H2 O 25% in H2 O 99% 98%
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Table 2 Characteristics of the studied ferrofluid samples at temperature T = 298.15 K and experimental pressure P = 0.1 MPa. Sample name
Fe3 O4 mass fraction (%)
TMAH mass fraction (%)
0.494%FF 1.47%FF 2.428%FF
0.494 1.47 2.428
0.046 0.138 0.227
Standard uncertainties u are u(T) = 0.1 K, u(p) = 10 kPa, u(mass fraction) = 5 × 10−6 .
ductivity are useful for a variety of applications such as heat transfer coolants and lubricants [22]. 2.1. Materials The materials used to prepare the water-based hybrid nanofluid containing TMAH coated Fe3 O4 nanoparticles and GA coated carbon nanotubes are listed in Table 1, along with their specifications. All the materials were reagent grade and used without further purification. 2.2. Sample preparation 2.2.1. Ferrofluid The ferrofluid used in this research were synthesized by the method explained by Berger et al. [24] and Mohammadi et al. [25]. The chemical equation for the process of synthesizing of ferrofluids is: FeCl2(aq) + 2FeCl3(aq) + 8NH3(aq) + 4H2 O(l) → Fe3 O4(s) + 8NH4 Cl(aq)
(1)
First, 1.0 mL of stock FeCl2 solution and 4.0 mL of stock FeCl3 solution are mixed together. Afterward, 50 mL of 0.7 M aqueous NH3 solution is slowly added. During this process, black magnetite precipitates are produced. After evacuating the excess liquid, the remaining solution is centrifuged. As a result, a dark sludge remains at the bottom of the tube. Subsequently, 8 mL of 25% TMAH solution is added and stirred till the thorough suspension of solid in the liquid happens. Finally, by magnetically stirring the solution for about 30 min, the extra ammonia was evacuated [24,25]. The characteristics of the ferrofluid samples prepared for this study are given in Table 2. 2.2.2. Multi-wall carbon nanotube-based aqueous nanofluid (CNT nanofluid) In order to produce carbon nanotube-based aqueous nanofluid, de-ionized (DI) water, GA and multiwalled carbon nanotubes (MWCNT) were required. The nanotubes produced by chemical vapor deposition, with 10–30 nm in diameter and 10 m in length, were purchased from Research Institute of Petroleum Industry (RIPI, Tehran, Iran). Fig. 1(a) and (b) shows respectively an SEM image and a TEM image of the sample. Garg et al. [26] found that the optimum sonication time for a 1 wt% MWCNT and 0.25 wt% GA mixture was 40 min using a 130 W, 20 kHz ultrasonicator. In the current study, a similar approach was followed using an ultrasonicator that had an operating frequency of 20 kHz and a maximum power output of 130 W. 2.2.3. Hybrid nanofluid In order to prepare the hybrid nanofluid, the aqueous mixture of ferrofluid and CNTs is homogenized using ultrasonication technique. Due to the interaction between the TMAH coated magnetic nanoparticles and GA on the surface of nanotubes they are physicadsorbed. Nine 400 mL samples of hybrid nanofluid were prepared with different mass fraction of Fe3 O4 and CNT nanoparticles, which
Fig. 1. Images of carbon nanotubes as provided by the producer company: (a) SEM image of carbon nanotubes and (b) TEM image of carbon nanotubes.
are presented in Table 3. Then, Four 100 mL samples were prepared using each above mentioned samples and ultrasonicated for 2.5, 5, 7.5 and 10 min. 2.3. Characterization The crystal structure of the prepared samples was examined using Siemens D-500 diffractometer equipped with a rotating anode and a CuK␣ radiation source ( = 0.15148 nm). TEM (CM120, 120 kV) is used for determination of the surface morphology and size of the synthesized samples. Fig. 2(a) shows the X-ray diffraction (XRD) pattern of uncoated Fe3 O4 nanoparticles. This pattern is quite identical to pure magnetite and no peaks, which are characteristic of impurities, are observed [27,28]. The average crystallite size of sample can be calculated from the XRD line broadening using the Scherrer formula [29]: d=
0.9 ˇ cos
(2)
where d is the average crystalline size (nm), is the angle at maximum peak, corresponds to the wavelength of the incident X-ray and ˇ denotes the full width at the half maximum intensity. The average diameter of sample evaluated according to (3 1 1) reflection by utilizing Scherrer’s equation is approximately 13 nm. Fig. 2(b) shows the TEM images of Fe3 O4 nanoparticles. According to the TEM image the average diameter of nanoparticles is on the order of 10 nm, which is in good agreement with the XRD analysis. TEM result, photograph of the synthesized hybrid nanofluid and its response to a magnet are shown in Fig. 3(a), (b) and (c), respectively. The black powder is attracted toward the magnet in a few seconds, demonstrating high magnetic sensitivity.
A. Shahsavar et al. / Thermochimica Acta 617 (2015) 102–110
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Table 3 Characteristics of the studied hybrid nanofluid samples at temperature T = 298.15 K and experimental pressure P = 0.1 MPa. Sample name
Fe3 O4 mass fraction (%)
TMAH mass fraction (%)
CNT mass fraction (%)
GA mass fraction (%)
Ultrasonication time (min)
0.494%FF + 0.105%CNT 0.494%FF + 0.21%CNT 0.494%FF + 0.315%CNT 1.47%FF + 0.315%CNT 1.47%FF + 0.625%CNT 1.47%FF + 0.935%CNT 2.428%FF + 0.515%CNT 2.428%FF + 1.025%CNT 2.428%FF + 1.535%CNT
0.494 0.494 0.494 1.47 1.47 1.47 2.428 2.428 2.428
0.046 0.046 0.046 0.138 0.138 0.138 0.227 0.227 0.227
0.105 0.21 0.315 0.315 0.625 0.935 0.515 1.025 1.535
0.026 0.053 0.079 0.079 0.156 0.234 0.129 0.256 0.384
2.5, 5, 7.5, 10
Standard uncertainties u are u(T) = 0.1 K, u(p) = 10 kPa, u(mass fraction) = 5 × 10−6 .
Fig. 2. (a) X-ray diffraction of synthesized Fe3 O4 particles and (b) TEM image of nanoparticles.
2.4. Measurement of thermal conductivity In the present work, the thermal conductivity was measured using a KD2-pro instrument (Decagon devices, Inc., USA), which is based on the transient hot wire method. The instrument had a probe of 60 mm length and a 1.3 mm diameter (KS-1) and closely approximates the infinite line heat source which gives least disturbance to the sample during measurements. The instrument uses a heating element, a thermo-resistor and a microprocessor to control and measure the conduction in the probe with a specified accuracy of 5%. The samples were maintained at several temperatures using a temperature-controlled chiller. After the temperature of the sample reached the required temperature, the sample was kept at that temperature for further 30 min to ensure temperature equilibrium before each measurement. The thermal conductivity of the samples are measured in the temperature range 25–55 ◦ C with 5 ◦ C intervals. 3. Results and discussion The variation of thermal conductivity with temperature for 0.494%FF + 0.105%CNT, 0.494%FF + 0.21%CNT and 0.494%FF + 0.315%CNT are presented in Fig. 4(a)–(c). Fig. 5(a)–(c) shows the temperature dependence of the thermal conductivity of 1.47%FF + 0.315%CNT, 1.47%FF + 0.625%CNT and 1.47%FF + 0.935%CNT, respectively. Additionally, the variation of thermal conductivity with temperature for 2.428%FF + 0.515%CNT, 2.428%FF + 1.025%CNT and 2.428%FF + 1.535%CNT are demonstrated in Fig. 6(a)–(c). Figs. 4–6 also show the variation of thermal
conductivity of water (base fluid) with temperature. The thermal conductivity data shown in Figs. 4–6 are also tabulated in Appendix A. It is observed that the thermal conductivity of the hybrid nanofluid is affected by ultrasonication time, reaching an optimum for sample which is sonicated for 5 min. In cases with 2.5 min sonication time, there is not sufficient time or energy to physical attachment of magnetic particles with carbon nanotubes and obtain a homogeneous mixture. On the other hand, the sonication energy received by the samples with 5 min sonication time is adequate to physical attachment of magnetic particles with carbon nanotubes and acquires a homogeneous mixture. Further increase in the sonication time causes reduction of thermal conductivity of the hybrid nanofluid. The exact reason for this observation is unclear. It could be suggested that the excess sonication energy causes the bond between TMAH and GA molecules to break. Additionally, further sonication leads to reduction of aspect ratio and lower quality of three-dimensional network of CNTs, resulting in a decrease in thermal conductivity enhancement [26,30,31]. It is clear from Figs. 4–6 that the thermal conductivity of all the samples increases with temperature. Also, Figs. 4–6 show that the thermal conductivity of the pure ferrofluid is higher than the base fluid and the thermal conductivity of the hybrid nanofluid is higher than the ferrofluid with the same Fe3 O4 mass fraction. A thermal conductivity enhancement of 4.92% to 7.87%, 10.66% to 18.06% and 13.93% to 23.92% is observed at 0.494%, 1.47% and 2.428% mass fraction of Fe3 O4 , respectively, in the temperature range of 25–55 ◦ C compared to the base fluid. In addition, a thermal conductivity enhancement of 13% and 15.59% is observed at 0.494%FF + 0.105%
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Fig. 3. (a) TEM image of hybrid nanofluid, (b) photograph of the synthesized hybrid nanofluid and (c) its response to a magnet.
CNT with 5 min sonication time and an enhancement of 26.07% and 34.26% is obtained at 2.428% FF + 1.535%CNT with 5 min sonication time in the temperature range of 25–55 ◦ C compared to the base fluid. The variation of percentage enhancement in thermal conductivity of ferrofluid and hybrid nanofluid samples ((KFForFF+CNT − KDIwater ) × 100/kDIwater ) with temperature are presented in Table 4. It should be noted that only hybrid nanofluid samples with 5 min sonication time are considered in this table. It is clear that the addition of CNT nanofluid increases the thermal conductivity of the ferrofluid. In addition, the thermal conductivity increment depends on the concentration of the Fe3 O4 and CNT nanoparticles. The higher the particle concentration, the more remarkable such an increment is. Though considerable progress has been made in tailoring efficient nanofluids with enhanced thermal conductivities, the science behind the exact mechanism of heat conduction in nanofluids is poorly understood [8]. Keblinski et al. [32] and Eastman et al. [33] suggested four possible mechanisms responsible for enhancement in the thermal conductivity of the nanofluids, e.g., Brownian motion of nanoparticles, liquid layering at liquid/particle interface, nature of heat transport in nanoparticles and nanoparticle clustering. The Brownian motion refers to the random movement of particles suspended in a liquid or gas, caused by collisions with molecules of the surrounding medium. The Brownian motion could contribute to the thermal conductivity enhancement in two ways, namely, the direct contribution due to motion of nanoparticles that transports heat (diffusion of nanoparticles) and the indirect contribution due to the so-called micro-convection of fluid surrounding individual nanoparticles [34]. Keblinski et al. [32], Eastman et al. [33] and Evans et al. [35] found that the movement of nanoparticles due to Brownian motion was too slow to transport significant amounts of heat through a nanofluid. Additionally, they found that the indirect contribution of Brownian motion could have an important role in producing particle clustering, which, as it will be described, could significantly enhance thermal conductivity [32]. On the other hand, Prasher et al. [36] performed an order of magnitude analysis and showed that convection caused by the Brownian movement of the nanoparticles was primarily responsible for the enhancement in thermal conductivity of the nanofluids. Layering of the fluid at the solid interface, by which the atomic structure of the liquid layer is significantly more ordered than that
of bulk liquid. Given that, crystalline solids (which are obviously ordered) display much better thermal transport than liquids, such liquid layering at the interface would be expected to lead to a higher thermal conductivity [32,37]. According to Keblinski et al. [32] and Xue et al. [38], thermal transport in a layered liquid may not be adequate to explain the increased thermal conductivity of nanoparticles. The carrier of heat in the crystalline solid is phonon, i.e., propagation of lattice vibration. Keblinski et al. [32] used the Debye theory to demonstrate that the generally accepted diffusive heat transport mechanism is not valid at the nano-scale and a theoretical treatment based on ballistic phonon transport is required. In particular, if the ballistic phonons initiated in a particle can persist in the liquid and get transmitted to another solid particle, the heat transport can significantly increase. Since particles are in constant motion due to Brownian motion, and move close together even at low packing factor, the coherent phonon transfer is possible even for low particle concentrations [32]. Prasher et al. [36] showed that the effect of crystalline-like phonon modes in thermal conductivity enhancement of the nanofluids is negligible due to the very small number of nanoparticles per unit volume in the system. Keblinski et al. [32] considered clustering of suspended nanoparticles into percolating pattern and showed that it is possible for these clusters to create paths of lower thermal resistance. The effective volume of a cluster, that is, the volume from which other clusters are excluded, can be much larger than the physical volume of the particles. Since within such clusters, heat can move very rapidly, the volume fraction of highly conductive phase is larger than the volume of solid which is significantly increase thermal conductivity [32]. This type of model may be appropriate if the particles are in the form of nanotubes or if they are not finely dispersed [39]. The effect of particle clustering on the thermal conductivity is also investigated by Philip et al. [40], Zhu et al. [41] and Jiang et al. [42]. They concluded that the clustering was mainly responsible for the thermal conductivity enhancement of the studied nanofluids. Finally, it can be said that among the mechanisms proposed for conventional nanofluids, convection caused by the Brownian movement of the nanoparticles and particle clustering are the two much important mechanism. 4. Nanofluid stability A stable nanofluid is characterized by three factors: low agglomeration, low sedimentation and high structural integrity. The most
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Table 4 Variation of percentage enhancement in thermal conductivity of ferrofluid and hybrid nanofluid samples with temperature at experimental pressure P = 0.1 MPa. Sample
0.494%FF 0.494%FF + 0.105%CNT 0.494%FF + 0.21%CNT 0.494%FF + 0.315%CNT 1.47%FF 1.47%FF + 0.315%CNT 1.47%FF + 0.625%CNT 1.47%FF + 0.935%CNT 2.428%FF 2.428%FF + 0.515%CNT 2.428%FF + 1.025%CNT 2.428%FF + 1.535%CNT
Temperature (◦ C) 25
30
35
40
45
50
55
4.92 13.00 13.06 13.11 10.66 20.30 21.11 21.48 13.93 23.59 25.49 26.07
6.00 13.86 13.91 13.97 13.78 21.89 22.70 23.18 17.50 25.56 27.45 27.71
6.25 14.29 14.35 14.41 15.22 23.15 23.97 24.68 19.07 27.09 28.99 29.65
6.81 14.82 14.88 14.95 16.01 23.89 24.72 25.04 20.13 28.19 30.10 31.06
7.22 15.22 15.29 15.36 16.80 24.90 25.90 26.53 21.66 29.67 31.59 32.53
7.62 15.22 15.30 15.37 17.42 25.12 25.96 26.28 22.86 30.82 31.73 33.59
7.87 15.59 15.67 15.75 18.06 25.91 26.76 27.16 23.92 31.68 33.62 34.26
Standard uncertainties u are u(T) = 0.1 K, u(p) = 10 kPa, u(mass fraction) = 5 × 10−6 , u(k) = 0.05k.
Fig. 5. Variation of thermal conductivity with temperature for 1.47%FF + 0.315%CNT, (b) 1.47%FF + 0.625%CNT and (c) 1.47%FF + 0.935%CNT. Fig. 4. Variation of thermal conductivity with temperature for (a) 0.494%FF + 0.105%CNT, (b) 0.494%FF + 0.21%CNT and (c) 0.494%FF + 0.315%CNT.
(a)
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Fig. 7. Reproducibility of thermal conductivity data of 0.494%FF, 1.47%FF, 2.428%FF, 0.494%FF + 0.315%CNT,1.47%FF + 0.935%CNT and 2.428%FF + 1.535%CNT at 25 ◦ C over 30 days.
of nanofluids are almost constant along with time. If there were increased particle agglomeration, particle sedimentation and damaged structural integrity, the stability of nanofluid and hence the reproducibility of thermal conductivity data would have been seriously affected, which are different from what was observed in the test runs. Therefore, it is verified that studied nanofluids are stable and their thermal conductivity data are reproducible for at least over 30 days. 5. Conclusions
Fig. 6. Variation of thermal conductivity with temperature for (a) 2.428%FF + 0.515%CNT, (b) 2.428%FF + 1.025%CNT and (c) 2.428%FF + 1.535%CNT.
frequently used method to evaluate the stability of nanofluids is the static position method. In this method, all samples were left standing for 30 days, and the distance or color difference in sedimentation between nanofluids was observed by the naked eye [43]. In this work, naked eye observation reveals no sedimentation at the bottom of the container with hybrid nanofluid samples. In order to further investigate the stability of studied nanofluids, the thermal conductivity of the 0.494%FF, 1.47%FF, 2.428%FF, 0.494%FF + 0.315%CNT, 1.47%FF + 0.935%CNT and 2.428%FF + 1.535%CNT was measured for 30 days and the results are presented in Fig. 7. It is seen that the thermal conductivities
In this paper, the effect of ultrasonication on the thermal conductivity of water-based hybrid nanofluid containing TMAH coated Fe3 O4 nanoparticles and GA coated carbon nanotubes is investigated experimentally. For this purpose, the aqueous mixture of ferrofluid having an average diameter of 13 nm and CNT nanofluid is homogenized using ultrasonication technique. Experimental results indicate that there is an optimum sonication time which gives the maximum enhancement in the thermal conductivity of the hybrid nanofluids. The experimental results show that addition of CNT nanofluid increases the thermal conductivity of the aqueous ferrofluid. The thermal conductivity enhancement of hybrid nanofluid purely depends on the temperature and Fe3 O4 and CNT concentration. Higher concentrations of nanofluid exhibit higher thermal conductivities. A thermal conductivity enhancement of 13% and 15.59% is observed at 0.494%FF + 0.105%CNT with 5 min sonication time and an enhancement of 26.07% and 34.26% is obtained at 2.428%FF + 1.535%CNT with 5 min sonication time in the temperature range of 25–55 ◦ C compared to the base fluid. Acknowledgments The authors would like to acknowledge Dr. Yadollah Mortazavvi and Catalyst & Nano-Structures laboratory assistants for their helpful advice on various technical issues examined in this paper.
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Appendix A. Variation of thermal conductivity with temperature for ferrofluid and hybrid nanofluid samples at experimental pressure P = 0.1 MPa Temperature (◦ C)
Sample
Sonication time
25
30
35
40
45
50
55
Water
–
0.610
0.617
0.624
0.631
0.637
0.643
0.648
0.494%FF
–
0.640
0.654
0.663
0.674
0.683
0.692
0.699
0.494%FF + 0.105%CNT
2.5 5 7.5 10
0.675 0.689 0.669 0.664
0.685 0.703 0.679 0.673
0.701 0.713 0.692 0.688
0.713 0.725 0.705 0.698
0.717 0.734 0.709 0.707
0.729 0.741 0.723 0.715
0.728 0.749 0.725 0.722
0.494%FF + 0.21%CNT
2.5 5 7.5 10
0.660 0.690 0.645 0.638
0.670 0.703 0.655 0.647
0.682 0.714 0.667 0.660
0.692 0.725 0.676 0.669
0.701 0.735 0.685 0.679
0.705 0.742 0.693 0.680
0.720 0.750 0.706 0.691
0.494%FF + 0.315%CNT
2.5 5 7.5 10
0.677 0.690 0.669 0.680
0.691 0.703 0.680 0.687
0.701 0.714 0.693 0.691
0.711 0.725 0.703 0.704
0.721 0.735 0.713 0.715
0.726 0.742 0.719 0.724
0.734 0.750 0.727 0.734
1.47%FF
–
0.675
0.702
0.719
0.732
0.744
0.755
0.765
1.47%FF + 0.315%CNT
2.5 5 7.5 10
0.724 0.734 0.707 0.687
0.734 0.752 0.728 0.707
0.744 0.768 0.742 0.718
0.759 0.782 0.755 0.731
0.774 0.796 0.766 0.747
0.786 0.805 0.783 0.758
0.798 0.816 0.796 0.766
1.47%FF + 0.625%CNT
2.5 5 7.5 10
0.702 0.739 0.702 0.693
0.730 0.757 0.717 0.710
0.743 0.774 0.733 0.722
0.760 0.787 0.748 0.738
0.772 0.802 0.762 0.746
0.776 0.810 0.771 0.757
0.788 0.821 0.777 0.764
1.47%FF + 0.935%CNT
2.5 5 7.5 10
0.710 0.741 0.695 0.683
0.743 0.760 0.724 0.715
0.759 0.778 0.742 0.727
0.774 0.789 0.754 0.740
0.781 0.806 0.768 0.753
0.797 0.812 0.781 0.767
0.802 0.824 0.787 0.774
2.428%FF
–
0.695
0.725
0.743
0.758
0.775
0.790
0.803
2.428%FF + 0.515%CNT
2.5 5 7.5 10
0.731 0.754 0.725 0.711
0.750 0.775 0.745 0.730
0.770 0.793 0.764 0.751
0.784 0.809 0.781 0.766
0.801 0.826 0.795 0.780
0.816 0.841 0.810 0.791
0.828 0.853 0.826 0.813
2.428%FF + 1.025%CNT
2.5 5 7.5 10
0.727 0.765 0.736 0.725
0.748 0.786 0.756 0.750
0.767 0.805 0.770 0.763
0.773 0.821 0.781 0.765
0.796 0.838 0.805 0.796
0.806 0.847 0.819 0.810
0.825 0.866 0.830 0.823
2.428%FF + 1.535%CNTa
2.5 5
0.731 0.769
0.752 0.788
0.764 0.809
0.782 0.827
0.799 0.844
0.814 0.859
0.829 0.870
Standard uncertainties u are u(T) = 0.1 K, u(p) = 10 kPa, u(mass fraction) = 5 × 10−6 , u(k) = 0.05k. a The results of cases with sonication time of 7.5 and 10 min are not presented.
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