Nanofluids including ceramic and other nanoparticles: synthesis and thermal properties

11 Nanofluids including ceramic and other nanoparticles: synthesis and thermal properties G . P A U L , Indian Institute of Technology, India and I . M A N N A , CSIR-Central Glass and Ceramic Research Institute, India

DOI: 10.1533/9780857093493.2.346 Abstract: This chapter reports the detailed study on the synthesis of nanofluids comprising very low concentrations of nanometric metallic or ceramic particles, rods, tubes, etc. The most common ways of preparing nanofluids are the one-step and two-step methods. While the one-step approach usually yields more stable nanofluids, the two-step method is more versatile as it provides the opportunity to disperse a wide variety of nanoparticles in different types of base fluids. However, the main focus of this chapter is on the thermal conductivity of nanofluids, which is the most researched aspect of nanofluids worldwide. An insight into the different parameters that influence the thermal conductivity of nanofluids is presented. In addition to experimental work, the theories used to try to analyze the cause of the anomalous increase in thermal conductivity are also presented. Key words: nanofluids, thermal conductivity, synthesis, one-step method, two-step method, concentration, temperature, particle size.

11.1

Introduction

Since the pioneering work of Maxwell (1904), there have been several attempts to enhance the thermal properties of fluids by adding solid particles. However, these slurries posed problems of clogging and abrasion of the channels through which the fluid flowed. Recent advances in nanotechnology have made it possible to produce nanometer-sized particles that can overcome these problems. More than a decade ago it was 346 © Woodhead Publishing Limited, 2013

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demonstrated that fluids with suspended nanoparticles, forming a stable colloid and maintaining a quasi-single phase state, exhibit high heat transport properties at a very low amount of dispersion (<1 vol%). The term nanofluid was coined by Choi (1995) to describe this new class of fluids. Nanofluids have now emerged as a promising field in nanotechnology research. Among the properties of nanofluids that have been studied, the largest volume of work has focused on characterization of the thermal properties of nanofluids, particularly the high thermal conductivity they display. There has also been research on modeling the anomalous increase in thermal conductivity.

11.2

Synthesis of nanofluids

Nanofluids can be synthesized by adding nanometer-sized particles to a liquid. However, nanofluids are not simply solid–liquid mixtures. Some special requirements are essential, namely an even and stable suspension, adequate durability, negligible agglomeration of particles, and no chemical change of the dispersed particles or fluid. The synthesis procedure of a nanofluid is a key factor on which the thermal properties depend, and the behavior of a nanofluid is highly dependent on the behavior of the base fluids and the dispersed phases, particle concentration, size and morphology, as well as the presence of dispersants or surfactants. The synthesis of stable nanofluids with minimum agglomeration and controlled properties like thermal conductivity and viscosity for heat transfer applications is the main objective of the community dealing with nanofluid technology. The techniques generally utilized for the synthesis of nanofluids are discussed in the following sections.

11.2.1 One-step process A one-step process is a bottom-up approach that combines the synthesis of nanoparticles with the preparation of nanofluids, as the nanoparticles synthesized (by physical/chemical vapor deposition or chemical methods) are collected in the same fluid/medium. A number of techniques have been reported for the synthesis of nanofluids by a one-step process, including vacuum evaporation onto a running oil substrate (VEROS) (Yatsuya et al., 1978), the direct condensation technique (Eastman et al., 2001), microwave irradiation (Zhu et al., 2004), citrate reduction (Patel et al., 2003; Zhu et al., 2004; Zhang et al., 2006a; Liu et al., 2006; Fuentes et al., 2008; Mishra et al., 2009; Wang and Wei, 2009), submerged arc nanoparticle synthesis system (SANSS) (Lo et al., 2005a, 2005b, 2007), multi-pulse laser ablation (Phuoc et al., 2007), sputtering on running liquid (Tamjid and Guenther, 2010), to name a few. A detailed summary of the different techniques by which

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nanofluids can be synthesized by a one-step process is given in Table 11.1. Most of the materials used as dispersoids have been metals and their oxides as they are easy to produce and chemically stable in solution. The base fluids include water, ethylene glycol, several types of oils, toluene, dielectric fluids, ethanol, etc. Early reports show the possibility of synthesis of silver–silicone oil nanofluid synthesis following a VEROS technique (Yatsuya et al., 1978). In this method, metals were evaporated in vacuum onto the surface of running oil, and thus fine particles in the nanometer range were grown on the surface of the oil. Later, Wagener and Gunther (1999) used a modified VERL technique employing high-pressure magnetron sputtering to develop iron– and silver–silicone oil nanofluids. The particles formed by this technique formed agglomerates. To stabilize the fluids, different dispersants were used. The mean sizes of the particles for Fe nanofluids were 15 nm (without surfactant) and 9 nm (with surfactant). The size for Ag nanofluids varied from 5 to 15 nm by varying the pressure of the system. In another method, Eastman et al. (2001) employed a direct evaporation condensation (DEC) technique to synthesize copper–ethylene glycol nanofluids. This technique involves the vaporization of a source material to be dispersed into the fluid under vacuum conditions in a chamber containing the base fluid, which is rotated, and a thin film of the fluid is constantly being transported over the top of the chamber. Advantages of this technique are that nanoparticles are produced without oxide layers, the size of the nanoparticles is in a narrow range, and nanofluids without particle agglomeration can be produced. On the contrary, the disadvantages are that the liquid must have a very low vapor pressure and this technique can only produce very limited amounts of nanofluids. In another widely used one-step process, nanofluids were prepared by the chemical reduction method (Patel et al., 2003; Zhu et al., 2004; Zhang et al., 2006a; Liu et al., 2006; Fuentes et al., 2008; Mishra et al., 2009; Wang and Wei, 2009). In this technique, a precursor solution is chemically reduced by a reducing agent to produce nanoparticles in suspension under boiling conditions. In SANSS, a solid bar of the particle to be dispersed, submerged in the base fluid, is used as an electrode in a vacuum system which melts and vaporizes in the region of high-temperature electrical arc generated by the system. The base fluid medium also vaporizes and rapidly removes the vapor of the solid and cools it to restrain further particle growth. Nanoparticles are then formed from the evaporated solid and are well dispersed in the cooling medium through three transformation stages, namely nucleation, growth, and condensation (Lo et al., 2005a, 2005b). Alternatively, silver– water nanofluids were prepared by multi-pulse laser ablation technique where a silver bar submerged in de-ionized water was ablated using a double-beam approach (Phuoc et al., 2007).

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Table 11.1 Different available techniques for the synthesis of nanofluids in a one-step process Nanofluid

Preparation method

Ag–silicon oil

Vacuum evaporation onto a running oil substrate (VEROS) (Yatsuya et al., 1978) Vacuum evaporation on running liquids (VERL) process (Wagener and Gunther, 1999) Direct condensation technique (Eastman et al., 2001) Citrate reduction method (Patel et al., 2003) Brust et al. procedure (Patel et al., 2003) Microwave irradiation (Zhu et al., 2004) Microwave dielectric heating (Patel et al., 2005) Submerged arc nanoparticle synthesis system (SANSS) (Lo et al., 2005a) SANSS (Lo et al., 2005b) Magnetic stirring at room temperature (Slistan-Grijalva et al., 2005) Chemical reaction method (Zhang et al., 2006a) Chemical reduction method (Liu et al., 2006) Microwave irradiation (Zhu et al., 2007) Submerged arc nanoparticle synthesis system (SANSS) (Lo et al., 2007) Sol-precipitation method (Tao et al., 2007) Multi-pulse laser ablation technique (Phuoc et al., 2007) Chemical reduction method (Fuentes et al., 2008) Nanoemulsification technique (Han et al., 2008) Plasma arc system (Chang and Chang, 2008) Magnetron sputtering (Hwang et al., 2008) Reduction of copper sulfate using sodium hypophosphite (Kumar et al., 2009) Chemical solution method (Wang and Wei, 2009)

Fe–silicon oil Ag–silicon oil Cu–ethylene glycol Ag–water Au–water Au–toluene Cu–ethylene glycol (EG) Ag–EG Ag–glycerol CuO–water/EG Cu–dielectric fluids Ag–ethylene glycol (PVP) Au–toluene Cu–water CuO–water Ag–water SiO2–water Ag–water Au(core)/Ag(shell)–water Indium–PAO oil Al2O3–water Ag–silicon oil Cu–EG

Spherical Fe3O4 in water/various oils Elliptic Cu nanorods in EG or EG/water mixtures Needlelike CuO nanoparticles in water/ various oils

(Continued)

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Table 11.1 (cont.)

Nanofluid

Preparation method

Octahedral Cu2O nanoparticles in water or water/EG mixtures CePO4 nanofibers in water, ethanol, EG, and their mixtures Hollow Cu or CuS nanoparticles in water, ethanol, EG, and their mixtures Hollow and wrinkled Cu2O nanoparticles in EG or EG/water mixtures Cu2O(core)/CuS(shell) nanoparticles in water Au–water Au (PVP)–water Ag–diethylene glycol

Pulsed laser ablation in liquids technique (Kim et al., 2009) Chemical reaction method (Mishra et al., 2009) Sputtering on running liquid technique (Tamjid and Guenther, 2010)

11.2.2 Two-step process The two-step method is extensively used in the synthesis of nanofluids primarily due to the easy availability of several types of nanopowders and the inherent versatility associated with this approach. In this method, nanoparticles are first synthesized by mechanical alloying, chemical reaction, vapor condensation, or decomposition of organic complex and are then dispersed in the base fluid with mechanical agitation (stirring) applying ultrasonic vibration (Hwang et al., 2008; Li et al., 2008) or any such suitable dispersing technique. Generally, ultrasonic equipment is used to intensively disperse the particles and reduce the agglomeration of particles. The stability factor of the nanofluids is taken care of by using surfactants. The main advantage of this two-step synthesis method is that it produces nanoparticles under clean conditions, without undesirable surface coatings and other contaminants (Lee et al., 1999). The major problem is that agglomeration of nanoparticles may still occur. When finely divided solid nanostructures are immersed in liquids, they often do not form a stable dispersion. Many of the particles acquire surface charge and tend to physically aggregate together in clusters. Though these particles can be easily re-dispersed in liquids by mechanical vibration/agitation, they soon cluster together again to form large aggregates that settle down quickly from the suspension. This can be taken care of by adding dispersants during preparation, which form a coating layer around the nanoparticles that is

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sterically bulky, thus keeping the nanoparticles separated from each other resulting in the formation of stable nanofluids. A detailed summary of nanofluids prepared by a two-step process with and without the use of surfactants is given in the Appendix (Section 11.7).

11.2.3 Other processes While most nanofluid productions to date have used one of the above (single-step or two-step) techniques, other techniques are also available depending on the particular combination of nanoparticle material and fluid. For example, nanoparticles with specific geometries, densities, porosities, charge, and surface chemistries can be synthesized by templating, electrolytic metal deposition, layer-by-layer assembly, microdroplet drying, and other colloid chemistry techniques. Another process is the shape- and size-controlled synthesis of nanoparticles at room temperature (Cao et al., 2006). The structural characteristics of nanoparticles such as mean particle size, particle size distribution, and shape depend on the synthesis method, which can provide an opportunity to ensure good control over such physical characteristics. These characteristics for nanoparticles in suspensions cannot be easily measured. This fact could account for some of the discrepancies in thermal properties reported in the literature among different experimenters (Yu et al., 2007).

11.3

The thermal conductivity of nanofluids

Thermal conductivity is the most important intrinsic parameter to demonstrate the enhancement potential of heat transfer in nanofluids. It has been shown that the thermal conductivity of a nanofluid is influenced by the heat transfer properties of the base fluid and identity/composition, volume fraction, size, shape of the nanoparticles suspended in the liquid (Xuan and Roetzel, 2000). It is also intuitive to anticipate that spatial and temporal distribution and uniformity of dispersed nanoparticles should affect the thermal conductivity. Until now, the development of a comprehensive theory to predict the thermal conductivity of nanofluids has remained elusive, although some attempts and propositions have been made to calculate the apparent conductivity of a two-phase mixture (Xuan and Li, 2000). Most of the data related to the thermal conductivity of nanofluids are consolidated in the Appendix. The volume of experimental research carried out on different particle material and base fluid combinations, the thermal conductivity enhancement of nanofluids for these particle–fluid combinations, and the techniques utilized for the measurement of thermal conductivity by several researchers are provided in the Appendix as extensively as possible.

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11.3.1 Effect of particle concentration As reported in the Appendix (Section 11.7), it is evident that the most extensive research has concerned the effect of nanoparticle loading concentration on the thermal conductivity of nanofluids. Different research groups have reported thermal conductivity ratio data for different nanofluids measured by different techniques at different temperatures and other conditions such as particle size, pH level of the solution, and with/ without addition of surfactants. Most of the thermal conductivity enhancement ratio data for different nanofluids reported by different research groups as a function of particle concentration have been graphically represented in Fig. 11.1. The references in the legends of the graph represent the first few letters of the first author of the article followed by the last two digits of the year of publication. The same convention has been followed for other figures in this chapter. As is evident from Fig. 11.1, the general trend shows a gradual increase in the thermal conductivity ratio of nanofluids with an increase in nanoparticle loading concentration. Most of the studies report the investigation of thermal conductivity of nanofluids for a volume fraction less than 1% (shown as the magnified view of Fig. 11.1) since loading the nanofluids with higher concentrations shows an adverse effect on the stability of nanofluids. Many results show the thermal conductivity of the nanofluid to be almost 2.8 or 3 times that of the base fluid. The thermal conductivity ratio for a particular particle–base fluid combination has been observed to vary as a function of concentration for different reports by different research groups. A comparative study of Al2O3–water nanofluids (shown in Fig. 11.2) validates the fact that although some results show some identical trends (Wen and Ding, 2005; Yoo et al., 2007; Sundar and Sharma, 2008), large variations can be observed in the results. This variation might be attributed to the different conditions and measurement techniques and differences in particle size, purity, surrounding conditions, and nanoparticle synthesis process.

11.3.2 Effect of nature of dispersed particles The effect of different nature/type of particles on the thermal conductivity of nanofluids is shown in Fig. 11.3. It can be observed that the metal particles (Au and Cu) dispersed nanofluids show a higher thermal conductivity ratio than the metal oxide particles (Al2O3, TiO2, SiC, CuO) dispersed nanofluids. This is an expected result since metals possess a higher thermal conductivity than metal oxides. It can also be seen that the thermal conductivity ratio achieved by the metal oxide dispersed nanofluids at a much higher concentration can be achieved by the metal dispersed nanofluids at a much

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11.1 Thermal conductivity as a function of concentration for different nanofluids.

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11.2 Thermal conductivity ratio as a function of particle concentration for Al2O3–water nanofluids reported by various research groups.

11.3 Thermal conductivity ratio of nanofluids with different types of nanoparticle dispersion.

lower concentration. It may be pointed out that in comparison to metal and metal oxide dispersed nanofluids, carbon nanotube dispersed nanofluids give much higher thermal conductivity enhancement at the same concentration. Thus, it is obvious that the thermal conductivity of the dispersed nanoparticles strongly influences the overall conductivity of the nanofluid.

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11.4 Thermal conductivity of nanofluids for different sized particles as a function of concentration (a) and as a function of particle size (b).

11.3.3 Effect of particle size For analyzing the effect of particle size on the thermal conductivity ratio of nanofluids, mainly only spherically shaped particles have been reported. The thermal conductivity ratio as a function of volume concentration for different particle sizes is shown in Fig. 11.4(a). It can be easily seen from the figure that for the same type of particle and base fluid medium, the thermal conductivity ratio for a smaller sized particle is much higher than that for a larger sized particle. This observation is valid for all types of particles and base fluid mediums. This is more clearly demonstrated in Fig. 11.4(b), which graphically represents the thermal conductivity ratio as a function of particle

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size. The general trend is a decrease in thermal conductivity ratio with an increase in particle size (Chopkar et al., 2006; He et al, 2007; Kim et al, 2007). But contradicting the trend, Shima et al. (2009) and Beck et al. (2009) report that for magnetite–water nanofluids, the thermal conductivity ratio increases considerably with an increase in particle size. In most of these cases, particle agglomeration being a key factor that cannot be determined from experiment or theory may contribute largely to the ambiguity of results produced. The accuracy of the particle size reported is also questionable as in many cases the researchers report the data available from the manufacturers.

11.3.4 Effect of particle shape A few articles concerning the effect of particle shape on the thermal conductivity ratio of nanofluids have been published. The thermal conductivity ratio for different particle shapes as a function of volume concentration is shown in Fig. 11.5. From all of the studies in Fig. 11.5 (a)– (c) it is quite clear that cylindrical particles show the maximum thermal conductivity ratio enhancement irrespective of the kind of particle dispersion. It may be assumed that cylinders form a mesh of elongated particles that conducts heat through the base fluid. This fact is further validated by the study by Jiang et al. (2009) which shows that by increasing the aspect ratio of the cylinders, even for the smallest diameter, the thermal conductivity enhancement is the highest. However, cylindrical particles often tend to form agglomerates, are difficult to produce, and are liable to disintegrate into smaller parts during ultrasonic vibration. Hence nanofluids with spherical nanoparticle dispersion are the easiest to prepare and most widely exploited and reported.

11.3.5 Effect of type of base fluid Studies involving the use of several base fluid media, namely water, ethylene glycol, pump oil, ethanol, refrigerant, and toluene (see Fig. 11.1) for the preparation of nanofluids have been reported. Among these fluids, water and ethylene glycol are the most extensively used. The influence of base fluid medium on the thermal conductivity ratio of nanofluids can be seen in Fig. 11.4(a). It may be pointed out that, in general, the ethylene glycol based nanofluids show higher thermal conductivity ratio compared to water based nanofluids, all other parameters being kept constant. Though water has the highest thermal conductivity among fluids, nanofluids with other base fluid media show a higher thermal conductivity ratio. This result is encouraging because heat transfer enhancement is often most desired when fluids with poorer heat transfer properties are utilized. Ethylene glycol alone is a relatively poor heat transfer fluid compared to water, and mixtures of

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11.5 Thermal conductivity ratio of nanofluids for different particle shapes reported by (a) Xie et al. (2002a), (b) Murshed et al. (2005), (c) Timofeeva et al. (2009), and (d) Jiang et al. (2009).

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11.6 Thermal conductivity ratio of different nanofluids as a function of temperature.

ethylene glycol and water fall between the two in heat transfer extremes. Though this is the general trend, it is not the overall trend since some articles report the thermal conductivity ratio of water based nanofluids as higher than that of ethylene glycol based nanofluids (Chopkar et al., 2006, 2007).

11.3.6 Effect of temperature Temperature has a strong influence on the thermal conductivity ratio of nanofluids. The effect of temperature on the thermal conductivity ratio for different nanofluids is graphically represented in Fig. 11.6. Experimental

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data from several research articles measuring the thermal conductivity as a function of temperature are collated and plotted in a single graph to show a general trend for the nature of variation of thermal conductivity ratio. As is evident from the graph, the general trend follows a linearly increasing thermal conductivity ratio with an increase in temperature. Most researchers reporting thermal conductivity ratio as a function of temperature used Ag, Au, Al2O3, and CuO as the dispersoid. The trend of a gradual increase in thermal conductivity ratio with an increase in temperature is encouraging for engine and heat exchanger applications in the transportation industry, where fluids operate at elevated temperatures. However, studies investigating the influence of temperature in the reverse trend of variation (with a decrease in temperature at sub-zero regime) have not been attempted or reported. Such investigations are warranted to probe the possible role of Brownian motion on the enhancement of thermal conductivity of nanofluid.

11.4

Modeling of thermal conductivity

Experimental investigations of the thermal conductivity of nanofluids indicate a substantial increment that needs to be understood and explained through suitable theoretical models. It has also been pointed out that the thermal conductivity of nanofluids depends on the concentration, size, shape/morphology of particles, the base fluid medium, and operating temperature. Originally, the thermal conductivity of nanofluids was attributed to formulations involving the effective thermal conductivity of mixtures from continuum formulations that typically involve only the particle size/shape and volume fraction and assume diffusive heat transfer in both fluid and solid phases. This approach can give a good prediction for micrometer or larger-size solid/fluid systems, but it fails to explain the anomalously high thermal conductivity of nanofluids. Details of previously developed models are given in Table 11.2. Since most classical models fail to explain the thermal conductivity enhancement of nanofluids, the nanofluid community has intensively investigated this phenomenon and a number of mechanisms have been proposed. Keblinski et al. (2002) investigated the possible factors that may enhance the thermal conductivity in nanofluids such as particle size, Brownian motion, the clustering of particles, and the existence of a nanolayer between the nanoparticles and the base fluid. The possible mechanisms are schematically shown in Fig. 11.7. Based on this study several other models have been developed by researchers to validate the thermal conductivity enhancement of nanofluids.

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Table 11.2 Conventional models predicting the thermal conductivity of solid– liquid mixtures Researchers Classical models keff kf

¼1þ

Remarks

kp kf 3j 2kf þkp jðk p kf Þ

Depends on the thermal conductivities of both phases and volume fraction of solid k þðn1Þk ðn1Þjðk k Þ keff p p f f Hamilton Valid for both the spherical and kf ¼ kp þðn1Þkf þjðkf kp Þ and Crosser cylindrical particles and n = 3/y where (1962) y is the particle sphericity k k kf keff Bruggeman ð1  jÞ 2k þ j 2kpeff þkeffp ¼ 0 Valid for spherical particles and eff þkf (1935) considered interaction between particles k þ2k 2jðk k Þ Wasp et al. kkefff ¼ kpp þ2kff þjðkffkppÞ Special case of Hamilton and Crosser’s (1977) model with sphericity y = 1 Maxwell (1904)

11.7 Possible mechanisms of k enhancement (after Keblinski et al. (2002)): (a) enhancement of k due to the formation of highly conductive layer–liquid structure at liquid/particle interface; (b) ballistic and diffusive phonon transport in a solid particle; (c) enhancement of k due to increased effective f of highly conducting clusters.

11.4.1 Effect of liquid layering at the liquid/particle interface Many researchers have used the concept of a liquid/solid interfacial layer to explain the anomalous improvement of the thermal conductivity in nanofluids. Yu and Choi (2003, 2004) suggested models, based on conventional theory, which consider a liquid molecular layer around the nanoparticles. The theory proposed the existence of a solid-like nanolayer at the interface of the solid particle and bulk liquid which acts as a thermal bridge, thus enhancing thermal conductivity (Fig. 11.8). In these models, the thermal conductivity and volume fraction of the nanoparticles were replaced by those of the equivalent particles, i.e. particles with nanolayers. The models were formulated as follows.

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11.8 Schematic cross-section of nanofluid structure consisting of nanoparticles, bulk liquid, and nanolayers at the solid/liquid interface (Yu and Choi, 2003).

Modified Hamilton and Crosser: keff kpe þ 2kf  2fðkpe  kf Þð1 þ bÞ3 ¼ kf kpe þ 2kf  fðkpe  kf Þð1 þ bÞ3

½11:1

where kpe ¼

½2ð1  gÞ þ ð1 þ bÞ3 ð1 þ 2gÞg ð1  gÞ þ ð1 þ bÞ3 ð1 þ 2gÞ

kp

b ¼ h=r and g ¼ klayer =kp for layer thickness of h and particle radius r. Modified Maxwell: keff nfeff A ¼1þ kf 1  feff A

½11:2

where A¼

1X kpj  kf j¼a;b;c k þ ðn  1Þk 3 pj f

Xue and Xu (2005) derived an expression for the effective thermal conductivity of nanofluids taking into consideration the thermal conductivity of the solid and liquid, their relative volume fraction, particle size, and interfacial properties. They calculated the thermal conductivity of the complex particle–liquid structure and adopted the model of Bruggeman

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(1935) to predict the thermal conductivity of nanofluids as   f keff  kf f ðkeff  k2 Þð2k2 þ kp Þ  aðkp  k2 Þð2k2 þ keff Þ ½11:3 1 þ a 2keff þ kf a ð2keff þ k2 Þð2k2 þ kp Þ þ 2aðkp  k2 Þðk2  keff Þ where a ¼ ½r=ðr þ hÞ3 and k2 is the thermal conductivity of the layer. Murshed et al. (2008) modeled the thermal conductivity of nanofluids considering the interfacial liquid layering concept with the assumption that temperature fields are continuous in the particle, interfacial layer and liquid, and at the interfacial boundaries and the heat fluxes across the interfaces (particle/layer and layer/fluid) are also continuous: keff ¼

ðkp  klr Þfklr ð2g31  g3 þ 1Þ þ ðkp þ 2klr Þg31 ½fg3 ðklr  kf Þ þ kf  g31 ðkp þ 2klr Þ  ðkp  klr Þfðg31 þ g3  1Þ

½11:4

keff ¼

ðkp  klr Þfklr ðg21  g2 þ 1Þ þ ðkp þ klr Þg21 ½fg2 ðklr  kf Þ þ kf  g21 ðkp þ 2klr Þ  ðkp  klr Þfðg21 þ g2  1Þ

½11:5

Equation 11.4 is applicable for spherical particles while equation 11.5 is applicable for cylindrical particles and g ¼ 1 þ h=a and g1 ¼ 1 þ h=2a. In another study, Tillman and Hill (2006) estimated the interfacial layer thickness by considering a steady-state heat conduction condition and assuming the thermal conductivity of the layer as a function of the distance from the particle center. Since most of the earlier reports assumed the layer thickness, this brought a new dimension to the study. The effective thermal conductivity was calculated as: keff  kf ðka  kf Þð2ka þ kp Þd þ ð2ka þ kf Þðkp  ka Þ f ¼ keff þ 2kf ðka þ 2kf Þð2ka þ kp Þd þ ð2ka  kf Þðkp  ka Þ

½11:6

where ka is the nanolayer thermal conductivity and δ is the ratio of the outer and inner interface of the nanolayer.

11.4.2 Effect of Brownian motion When Keblinski et al. (2002) proposed a heat transfer mechanism in nanofluids, it was summarized that the movement of particles due to Brownian motion was too slow to transport a significant amount of heat through the nanofluid. Later, Jang and Choi (2004) developed a model based on kinetics, thermal diffusion, and Brownian motion of the particles.

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Nanofluids: synthesis and thermal properties The effective thermal conductivity was modeled as:  2 keff kp rf kB T ¼ ð1  fÞ þ f þ 3c f Pr kf kf rp 3pmf ff lf

363

½11:7

The empirical parameter c is a limiting factor for predicting the thermal conductivity of nanofluids. This model was modified by Jang and Choi (2007) considering nano-convection induced by Brownian motion. Koo and Kleinstreuer (2004) predicted the thermal conductivity of CuO nanofluids modifying the equation of Maxwell (1904) and using the Brownian motion effect. The thermal conductivity was predicted as: kp  kf kf 2kf þ kp  fðkp  kf Þ !1=2 kB T 4 þ 5610 brf Cpf f ½ð134:63 þ 1722:3fÞ 2rp rp

keff ¼ kf þ 3f

þ ð0:4705  6:04fÞT

½11:8

where β is related to the Brownian motion of the nanoparticle and empirically determined as: b ¼ 0:0137ð100fÞ0:8229

f < 0:01

0:7272

f > 0:01

b ¼ 0:0011ð100fÞ

½11:9

On the other hand, Evans et al. (2006) used kinetic theory to demonstrate that the hydrodynamic effects associated with Brownian motion of the particles have only a minor effect on the thermal conductivity of nanofluids. This was supported by molecular dynamic simulation studies considering suitable parameters required for the simulation. Prasher et al. (2006b) argued that Keblinski et al. (2002) and Evans et al. (2006) had not considered the energy transport due to convection caused by the Brownian motion of the particles. Accordingly, they analyzed the problem considering convection and the effective thermal conductivity was expressed on the lines of the Maxwell–Garnet model as:    keff Re: Pr ½kp ð1 þ 2aÞ þ 2km  þ 2f½kp ð1  aÞ  km Þ ½11:10 ¼ 1þ 4 ½kp ð1 þ 2aÞ þ 2km   f½kp ð1  aÞ  km Þ kf where α = 2Rbkm/d is the nanoparticle Biot number, d is the particle diameter, and Rb is the interfacial thermal resistance. Murshed et al. (2009) predicted the thermal conductivity of nanofluids considering static and dynamic effects. In considering the dynamic effect on the thermal conductivity of nanofluids, a modified effective diffusion

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11.9 Dynamic mechanisms of nanoparticles in base fluid (Murshed et al., 2009).

coefficient was used. Also, both Brownian and potential forces were considered on the complex nanoparticles (Fig. 11.9). This led to a very complicated model for determining the thermal conductivity of nanofluids, the details of which have been given elsewhere (Murshed et al., 2009).

11.4.3 Effect of clustering and aggregate formation of nanoparticles After the proposal of Keblinski et al. (2002) regarding the effect of clustering being a mechanism for enhanced thermal heat conduction in nanofluids, Wang et al. (2003) used a fractal model to predict the thermal conductivity of nanofluids taking into account the effect of clustered nanoparticles in suspension. The researchers made use of the effective medium theory and the concept of fractal dimensions for nanoparticle clusters to predict the effective thermal conductivity of nanofluids utilizing the model of Bruggeman (1935) (equation 11.3). In the proposed model, the volume concentration f was replaced by the fractal volume fraction f(r):  r Df1 3 ½11:11 fðrÞ ¼ a to get the effective thermal conductivity of the cluster. The effective medium/Maxwell–Garnett theory was modified to predict the thermal conductivity of nanofluids as: 0 R ? kcl ðrÞnðrÞ 1 keff @ð1  fÞ þ 3f 0 kcl ðrÞþ2kf drA ½11:12 ¼ R ? f nðrÞ kf dr ð1  fÞ þ 3f 0 kclkðrÞþ2k f

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11.10 Schematic illustration of a single aggregate consisting of the backbone (black circles) and dead ends (gray circles). The aggregate is decomposed into dead ends with the fluid and the backbone (Prasher et al., 2006c).

where n(r) is the radius distribution function, which represents the fractal characteristics of the space distribution of clusters. Prasher et al. (2006c) analyzed the effects of aggregation and its kinetics to predict the effective thermal conductivity of nanofluids. They suggested that a fractal cluster is embedded within a sphere of radius equal to r and is composed of a few approximately linear chains called the backbone of the cluster, with other particles called dead ends (Fig. 11.10). The volume fraction of particles belonging to dead ends was calculated as fnc = f (r)  fc in which fc ¼ ðr=aÞdl 3 is the volume fraction backbone particles. The thermal conductivity of the aggregate due to dead end particles is calculated from the Bruggeman (1935) equation as: ð1  fnc Þ

kf  knc kp  knc þ fnc ¼0 kf þ 2knc kp  2knc

½11:13

Assuming the backbone particles to form randomly oriented cylindrical chains, the model of Nan et al. (2004) can be used to predict the thermal conductivity of an aggregate sphere with both chains and dead ends as: ka ¼ knc

3 þ fc ½2b11 ð1  L11 Þ þ b33 ð1  L33 Þ 3  fc ð2b11 L11 þ b33 L33 Þ

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where L11 ¼ 0:5p2 =ðp2  1Þ  0:5pcosh1 p=ðp2  1Þ1:5 L33 ¼ 1  2L11 knc  kf b11 ¼ kf þ L11 ðknc  kf Þ p ¼ r=a The thermal conductivity of the whole nanofluid system has been calculated from the Maxwell–Garnet model as: keff ka þ 2kf þ 2fa ðka  kf Þ ¼ kf ka þ 2kf  fa ðka  kf Þ

½11:15

Evans et al. (2008) used the theory of Prasher et al. (2006c) and compared the effective thermal conductivity predicted from the fractal model using a random walker Monte Carlo algorithm. It was predicted that thermal conductivity enhancement due to aggregation was also strongly dependent on the chemical dimension of the aggregates and the radius of gyration of the aggregate. Wang et al. (2009c) proposed a statistical clustering model to determine the macroscopic characteristics of clusters. It has been suggested that the thermal conductivity of a nanofluid can be estimated from the existing effective medium theory without considering the fractal model reported earlier.

11.4.4 Other models In several other models, molecular dynamic simulation has been utilized to predict the thermal conductivity of nanofluids. For instance, Sarkar and Selvam (2007) used the equilibrium molecular dynamic simulation method utilizing the Green–Kubo formulation to predict the thermal conductivity of copper and argon nanofluids as a function of particle concentration. In another study, Sankar et al. (2008) predicted the thermal conductivity enhancement of platinum–water nanofluids using equilibrium molecular dynamic simulation as a function of nanoparticle concentration and showed significant enhancement from a particle concentration of 1 to 7%.

11.5

Summary and future trends

The enhancement in thermal conductivity of nanofluids has been observed to be dependent on a combination of factors such as concentration of nanoparticles dispersed in the base fluid, operating temperature, size of the nanoparticles, and type of surfactant used for preparation of the nanofluid.

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The level of enhancement observed for many different kinds of nanofluids can hardly be explained by existing theoretical models in the literature. A single unified theory that may explain the many-fold increase in thermal conductivity still remains elusive. For wide-scale application, the effects of erosion, particle settling, and agglomeration need to be studied in detail. The agglomeration of particles in a nanofluid is aggravated by the two-step process of producing nanofluids where powders are added to liquids. The dispersion and suspension of nanoparticles in a fluid pose a difficult colloidal chemistry problem, and considerable work remains to be done if the two-step process is ever to develop into large-scale production. (The two-step process is currently the most economical way to produce nanofluids and has good potential for scale-up to commercial production levels.) Better characterization of nanofluids is also important for developing engineering designs based on the work of multiple research groups, and fundamental theories to guide this effort should be improved. Important features for commercialization must be addressed, including particle settling, particle agglomeration, surface erosion, and large-scale nanofluid production at acceptable cost.

11.6

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Appendix: thermal conductivity details of nanofluids prepared by two-step process

Effect studied/notes

© Woodhead Publishing Limited, 2013

Reference

Nanofluid

Concentration (vol %)

Enhancement ratio

Synthesis method

Masuda et al. (1993)

Al2O3–water (31.858C) Al2O3–water (46.858C) Al2O3–water 66.858C) SiO2–water (31.858C) SiO2–water (46.858C) SiO2–water (66.858C) TiO2–water (31.858C) TiO2–water (66.858C) TiO2–water (88.858C)

1.30–4.3 1.30–4.3 1.30–4.3 1.1–2.30 1.1–2.30 1.1–2.40 3.25–4.30 3.25–4.30 3.10–4.30

1.11–1.32 1.10–1.23 1.1–1.26 1.01–1.10 1.009–1.01 1.005–1.007 1.080–1.11 1.08–1.11 1.075–1.099

Two-step method

Lee et al. (1999)

Al2O3–water CuO–water Al2O3–ethylene glycol CuO–ethylene glycol

1.00–4.30 1.00–3.41 1.00–5.00 1.00–4.00

1.03–1.10 1.03–1.12 1.03–1.18 1.05–1.23

Two-step method

Transient hotwire method

—

Wang et al. (1999)

Al2O3–water CuO–water Al2O3–ethylene glycol CuO–ethylene glycol Al2O3–engine oil Al2O3–pump oil

3.00–5.50 4.50–9.70 5.00–8.00 6.20–14.80 2.25–7.40 5.00–7.10

1.11–1.16 1.17–1.34 1.25–1.41 1.24–1.54 1.05–1.30 1.13–1.20

Two-step method

Steady-state parallel-plate technique

—

Xuan and Li (2000)

Cu(+ laurate salt)–water 2.50–7.50 Cu (+ oleic acid)– 2.50–7.50 transformer oil

1.22–1.75 1.12–1.43

Two-step method

Transient hotwire method

—

Temperature effect

Ceramic nanocomposites

Thermal conductivity measurement technique

376

11.7

Choi et al. (2001)

MWCNT (+ dispersant)– 0.04–1.02 polyalphaolefin

Transient hotwire method

—

Eastman et al. Cu (old)–ethylene 0.10–0.56 (2001) glycol Cu (fresh)–ethylene 0.11–0.56 glycol Cu (+ thioglycolic acid)– 0.01–0.28 ethylene glycol

1.016–1.10

One-step physical method

Transient hotwire method

—

1.002–1.41

One-step physical method

Xie et al. (2002a)

SiC–water SiC–water SiC–ethylene glycol SiC–ethylene glycol

0.78–4.18 1.00–4.00 0.89–3.50 1.00–4.00

1.03–1.17 1.06–1.24 1.04–1.13 1.06–1.23

Two-step method

Transient hotwire method

—

Xie et al. (2002b)

Al2O3–water Al2O3–ethylene glycol Al2O3–ethylene glycol Al2O3–ethylene glycol Al2O3–ethylene glycol Al2O3–pump oil

1.80–5.00 1.80–5.00 1.80–5.00 1.80–5.00 1.80–5.00 5.00

1.07–1.21 1.06–1.17 1.06–1.18 1.10–1.30 1.08–1.25 1.39

Two-step method

Transient hotwire method

Solid crystalline phase effect, morphology effect, pH value effect, base fluid effect

Xie et al. (2002c)

Al2O3–water Al2O3–ethylene glycol Al2O3–pump oil Al2O3–glycerol

5.00 5.00 5.00 5.00

1.23 1.29 1.38 1.27

Two-step method

Hot-wire method

Base fluid effect

Das et al. (2003)

Al2O3–water (218C) Al2O3–water (368C) Al2O3–water (518C) CuO–water (218C) CuO–water (368C) CuO–water (518C)

1.00–4.00 1.00–4.00 1.00–4.00 1.00–4.00 1.00–4.00 1.00–4.00

1.02–1.09 1.07–1.16 1.10–1.24 1.07–1.14 1.22–1.26 1.29–1.36

Two-step method

Temperature oscillation technique

Temperature effect

1.031–1.14

One-step physical method

377

Two-step method

Nanofluids: synthesis and thermal properties

© Woodhead Publishing Limited, 2013

1.02–2.57

Patel et al. (2003)

Citrate-reduced Ag– water (308C) Citrate-reduced Ag– water (608C) Citrate-reduced Au– water (308C) Citrate-reduced Au– water (608C) Citrate-reduced Au– water (308C) Citrate-reduced Au– water (608C) Thiolate-covered Au– toluene (308C) Thiolate-covered Au– toluene (608C) Thiolate-covered Au– toluene (308C) Thiolate-covered Au– toluene (608C) Thiolate-covered Au– toluene (308C) Thiolate-covered Au– toluene (608C)

Concentration (vol %)

Enhancement ratio

Synthesis method

0.001

1.03

Two-step method

Transient hotwire method

0.001

1.04

0.00013

1.03

0.00013

1.05

0.00026

1.05

0.00026

1.08

0.005

1.03

0.005

1.05

0.008

1.06

0.008

1.07

0.01 1

1.06

0.01 1

1.09

Effect studied/notes Temperature effect

Ceramic nanocomposites

Nanofluid

378

© Woodhead Publishing Limited, 2013

Reference

Thermal conductivity measurement technique

Xie et al. (2003)

0.40–1.00 0.23–1.00

1.03–1.07 1.02–1.13

0.25–1.00

1.04–1.20

MWCNT (+ sodium dodecyl sulfate)–water

0.60

Wen and Ding Al2O3 (+ sodium (2004a) dodecylbenzene sulfonate)–water Wen and Ding MWCNT (+ sodium (2004b) dodecyl benzene)– water (208C) MWCNT (+ sodium dodecyl benzene)– water (458C)

Assael et al. (2004) © Woodhead Publishing Limited, 2013

Two-step method

Transient hotwire method

Nitric acid treatment

1.07–1.38

Two-step method

Transient hotwire method

Treatment effect, dispersant concentration effect, sonication time effect

0.19–1.59

1.01–1.10

Two-step method

Transient hotwire method

—

0.04–0.84

1.04–1.24

Two-step method

Not available

Temperature effect

0.04–0.84

1.05–1.31

Nanofluids: synthesis and thermal properties

MWCNT–water MWCNT–ethylene glycol MWCNT (+ 01eylamine)–decene

379

380

Enhancement ratio

Synthesis method

DWCNT (+ hexadecyltrimethyl ammonium bromide)– water DWCNT (+ hexadecyltrimethyl ammonium bromide)– water MWCNT (+ hexadecyltrimethyl ammonium bromide)– water MWCNT (+ nanosperse AQ)–water

0.75

1.03

Two-step method

Transient hotwire method

Dispersant effect, sonication time effect

1.00

1.08

0.60

1.34

0.60

1.28

Chon et al. (2005)

Al2O3–water Al2O3–water Al2O3–water Al2O3–water Al2O3–water Al2O3–water Al2O3–water Al2O3–water

1.00 1.00 1.00 1.00 1.00 1.00 1.00 1.00

1.09 1.15 1.03 1.10 1.004 1.09 1.08 1.29

Two-step method

Transient hotwire method

Temperature effect

Hong et al. (2005)

Fe–ethylene glycol

0.20–0.55

1.13–1.18

Two-step method

Transient hotwire method

Sonication time effect

Reference

Nanofluid

Assael et al. (2005)

(218C) (718C) (218C) (718C)) (218C) (718C)) (218C) (718C))

Effect studied/notes

Ceramic nanocomposites

© Woodhead Publishing Limited, 2013

Concentration (vol %)

Thermal conductivity measurement technique

Marquis and Chibante (2005)

1.10–1.46

Two-step method

Thermal constants analyzer technique

Treatment effect

Two-step method

Transient hotwire method

—

1.30–2.17

2.83

Murshed et al. TiO2 (+ cetyl trimethyl (2005) ammonium bromide)– water TiO2 (+ cetyl trimethyl ammonium bromide)– water

0.50–5.00

1.05–1.30

0.50–5.00

1.08–1.33

Wen and Ding Al2O3–water (2005)

0.31–0.72

1.02–1.06

Two-step method

Not specified

—

Ding et al. (2006)

0.05–0.49

1.00–1.10

Two-step method

Transient hotwire method

Temperature effect

0.05–0.49

1.07–1.27

0.05–0.49

1.18–1.79

0.10–0.55

1.05–1.18

Two-step method

Transient hotwire method

Cluster size effect

Hong et al. (2006)

MWCNT (+ gum arabic)–water (208C) MWCNT (+ gum arabic)–water (258C) MWCNT (+ gum arabic)–water (308C) Fe–ethylene glycol

Nanofluids: synthesis and thermal properties

© Woodhead Publishing Limited, 2013

SWCNT (+ dispersant)– 0.25–1.00 diesel oil (Shell Rotella 15W-40) MWCNT (I) 0.25–1.00 (+sucinimide–poly alpha olefin) (BP Amoco DS-166) MWCNT (II) 1.00 (+sucinimide-poly alpha olefin) (BP Amoco DS-166)

381

382

Nanofluid

Concentration (vol %)

Enhancement ratio

Synthesis method

Hwang et al. (2006)

CuO–water SiO2–water MWCNT–water CuO–ethylene glycol MWCNT–mineral oil

1.00 1.00 1.00 1.00 5.00

1.05 1.03 1.07 1.09 1.09

Two-step method

Transient hotwire method

—

Kang et al. (2006)

Ag–water SiO2–water Diamond–ethylene glycol

0.10–0.39 1.00–4.00 0.13–1.33

1.03–1.11 1.02–1.05 1.03–1.75

Two-step method

Transient hotwire method

—

Li and Peterson (2006)

Al2O3–water (27.58C) Al2O3–water (32.58C) Al2O3–water (37.78C) CuO–water (28.98C) CuO–water (31.38C) CuO–water (33.48C)

2.00–10.00 2.00–10.00 2.00–10.00 2.00–6.00 2.00–6.00 2.00–6.00

1.08–1.11 1.15–1.22 1.18–1.29 1.35–1.36 1.35–1.50 1.38–1.51

Two-step method

Steady-state cutbar method

Temperature effect

0.05 0.10 0.10 0.05 0.10 0.05 0.20 0.20 0.20

1.04 1.24 1.24 1.12 1.11 1.09 1.10 1.04 1.13

One-step chemical method

Transient hotwire method

Settlement time effect

Liu et al.(2006) Cu–water Cu–water Cu–water Cu–water Cu–water Cu–water Cu–water Cu–water Cu–water

Effect studied/notes

Ceramic nanocomposites

© Woodhead Publishing Limited, 2013

Reference

Thermal conductivity measurement technique

Putnam et al. (2006)

1.003–1.013

Two-step method

Micron-scale beam deflection technique

—

1.000–1.015

1.002–1.009

© Woodhead Publishing Limited, 2013

Wen and Ding TiO2–water (pH=3) (2006)

0.29–0.68

1.02–1.06

Two-step method

Hot-wire method

Dispersant HNO3 and NaOH

Yang and Han Bi2Te3–hexadecane oil (2006) (208C) Bi2Te3–hexadecane oil (508C) Bi2Te3–perfluoro–nhexane (38C) Bi2Te3–perfluoro-nhexane (508C)

0.80

1.06

Two-step method

3ω technique

Surfactant used

0.80

1.04

0.80

1.08

0.80

1.06

Yang et al. (2006a)

0.04–0.34

486

Two-step method

Transient hotwire method

Dispersing energy effect, aspect ratio effect, dispersant concentration effect

MWCNT (+ poly isobutene succinimide)–poly alpha olefin

Nanofluids: synthesis and thermal properties

11-mercapto-10.01–0.07 undecanol functionalized Au (+ alkenethiolate )–ethanol Dodecanethiol 0.1 1–0.36 functionalized Au (+ alkenethiolate )–toluene C60–C70 fullerenes– 0.15–0.60 toluene

383

384

© Woodhead Publishing Limited, 2013

Concentration (vol %)

Enhancement ratio

Synthesis method

Al2O3–water(radius 7.5 nm) Al2O3–water(radius 10 nm) Al2O3–water(radius 13.5 nm)

0.5

Two-step process

Temperature oscillation technique

—

Al2O3–water(radius 20 nm)

0.5

1.24 (708C) 1.20 (858C) 1.39 (708C) 2.01 (858C) 1.29 (708C) 1.6 (858C) 1.26 (708C) 1.34 (858C) 1.26 (708C) 1.34 (858C)

Chopkar et al. (2006)

Al70Cu30–EG Al70Ag30–EG Al70Cu30–EG(crystallite size 83–90 nm)

0.2–2.5 0.2–2.5 0.5

1.06–2.26 1.04–2.47 1.03–1.38

Two-step process

Thermal comparator technique

—

Zhang et al. (2006a)

Au–toluene Al2O3–water (108C) Al2O3–water (308C) Al2O3–water (508C) CNT–water

0.003 1.3–15.0 1.3–15.0 1.3–15.0 0.1–0.9

1.08 1.04–1.20 1.05–1.23 1.04–1.24 1.03–1.41

One- and two- Transient short step methods hot-wire method

Surfactant used for CNFs and temperature effect

Zhu et al. (2006)

Fe3O4–water

0.5–5.0

1.17–1.41

One-step method

—

Reference

Nanofluid

Prasher et al. (2006a)

0.5 0.5

Transient hotwire method

Effect studied/notes

Ceramic nanocomposites

Thermal conductivity measurement technique

Assael et al. (2006)

Up to 0.48 Up to 0.25 0.6

1.03 1.09 1.14–1.21

Two-step process

Transient hotwire method

Dispersant used for preparation

0.6

1.07–1.39

0.6

1.09

0.6-1.0

1.02–1.34

0.6

1.11–1.13

0.6

1.28

Zhang et al. (2006b)

Al2O3–water (108C) Al2O3–water (308C) Al2O3–water (508C) CuO–water (108C) CuO–water (238C) CuO–water (308C)

1.3–15.0 1.3–15.0 1.3–15.0 2.55–2.65 2.6–5.2 5.2

1.04–1.20 1.05–1.23 1.04–1.24 1.06–1.09 1.06–1.16 1.17

Two-step process

Transient short hot-wire method

Temperature effect

Lee et al. (2006)

CuO–water (pH=6) CuO–water (pH=3)

0.03–0.30 0.03–0.30

1.02–1.07 1.04–1.12

Two-step process

Transient hotwire method

pH effect

Hwang et al. (2007)

Fullerene–oil MWCNT–oil MWCNT–water Mixed fullerene–water

1.5–5.0 0.2–0.5 0.25–1.00 0.5–1.5

1.02–1.06 1.05–1.09 1.01–1.06 0.99–0.97

Two-step method

Transient hotwire method

Surfactant used for water nanofluids

Nanofluids: synthesis and thermal properties

© Woodhead Publishing Limited, 2013

Cu–EG MWCNT–EG MWCNT (+ sodium dodecyl sulfate)–EG MWCNT (+ sodium dodecyl sulfate)–water MWCNT (+ sodium dodecyl sulfate)– vacuum oil MWCNT (+ cetyl trimethyl ammonium bromide)–water MWCNT (+ Triton X100)–water MWCNT (+ nanosphere)–water

385

386

Nanofluid

Concentration (vol %)

Enhancement ratio

Synthesis method

Yoo et al. (2007)

Fe–ethylene glycol WO3–ethylene glycol TiO2–deionized water Al2O3–deionized water

0.20–0.55 0.05–0.30 0.1–1.0 0.3–1.0

1.15–1.18 1.05–1.13 1.10–1.15 1.01–1.05

Two-step process

Transient hotwire method

Beck et al. (2007)

Al2O3–ethylene glycol

0.01–0.04

1.03–1.14

Two-step process

— Liquid metal transient hot-wire method

Chen et al. (2007)

TiO2-ethylene glycol (208C) TiO2-ethylene glycol (408C)

0.1–1.8

1.01–1.13

Two-step process

Transient hotwire method

Temperature effect

0.1–1.8

1.02–1.14

Jwo et al (2007)

CuO–DI water

0.1–0.4

1.02–1.10

SANSS

Transient hotwire method

—

Jana et al. (2007)

CNT–water Cu–water

0.3–0.8 0.05–0.30

1.24–1.34 1.17–1.74

Two-step process

Modified hot-wire — technique

Wang et al. (2007)

TiO2–water (188C) TiO2–water (368C) TiO2–water (438C) TiO2–water (528C) TiO2–water (658C) SiO2–water (208C) SiO2–EG (208C) SiO2–ethanol (208C)

1.0–4.0 1.0–4.0 1.0–4.0 1.0–4.0 1.0–4.0 1.0 1.0 1.0

1.03–1.11 1.05–1.14 1.06–1.15 1.08–1.17 1.10–1.20 1.03 1.04 1.05

Two-step process

3ω technique

Effect studied/notes —

Temperature effect

Ceramic nanocomposites

© Woodhead Publishing Limited, 2013

Reference

Thermal conductivity measurement technique

Al2O3–water (38 nm) Al2O3–EG (38 nm) ZnO–water (10 nm) ZnO–water (30 nm) ZnO–water (60 nm) ZnO–EG (30 nm) ZnO–EG (60 nm) TiO2–water (10 nm) TiO2–water (34 nm) TiO2–water (70 nm) TiO2–EG (10 nm) TiO2–water (34 nm) TiO2–water (70 nm)

0.3–3.0 1.0–3.0 1.0–3.0 1.0–3.0 1.0–3.0 1.0–3.0 1.0–3.0 1.0–3.0 1.0–3.0 1.0–3.0 1.0–3.0 1.0–3.0 1.0–3.0

1.01–1.08 1.03–1.11 1.05–1.15 1.03–1.11 1.02–1.07 1.06–1.21 1.03–1.11 1.03–1.11 1.028–1.09 1.02–1.06 1.05–1.15 1.04–1.12 1.02–1.08

Two-step process

Transient hotwire method

Surfactant used for nanofluid preparation

Shaikh et al. (2007)

CNT–PAO oil EXG–PAO oil HTT–PAO oil

0.1–1.0 0.1–1.0 0.1–1.0

1.34–2.61 1.18–2.31 1.11–2.03

Two-step process

Indirect method

Surfactant used

Kao et al. (2007)

CuO–brake fluid

2.0

1.6

One-step process

Transient hotwire method

—

Philip et al. (2007)

Fe3O4–kerosene

0.02–7.80

1.01–1.23

Two-step process

Transient hotwire method

Surfactant used

He et al. (2007) TiO2–water (95–210 nm) 0.6 TiO2–water 0.18–1.92

1.03–1.01 1.01–1.06

Two-step process

Transient hotwire method

Particle size effect

Nanofluids: synthesis and thermal properties

© Woodhead Publishing Limited, 2013

Kim et al. (2007)

387

388

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Concentration (vol %)

Enhancement ratio

Synthesis method

Al70Cu30–EG Al70Cu30–water Al70Ag30–EG Al70Ag30–water Al70Cu30–EG(crystallite size 83–9 nm) Al70Cu30-water (crystallite size 83– 9 nm)

0.2–2.5 0.2–2.5 0.2–2.5 0.2–2.5 0.5

1.06–2.26 1.16–2.62 1.04–2.47 1.19–2.77 1.03–1.38

Two-step process

Thermal comparator method

Particle size effect

0.5

1.10–1.45

Zhu et al. (2007)

CuO–water

1.0–5.0

1.18–1.31

One-step process

Transient hotwire method

—

Phuoc et al. (2007)

Ag–water

0.01

1.03–1.05

One-step process

Transient hotwire method

—

Karthikeyan et al. (2008)

CuO–ethylene glycol CuO–water

0.005–1.005 0.03–1.00

1.13–1.54 1.01–1.32

One-step process

Transient hotwire method

—

Chen et al. (2008a)

Titanate nanotubes– water(208C) Titanate nanotubes– water(408C)

0.1–0.6

1.001–1.040

Two-step process

Transient hotwire method

Temperature effect

0.1–0.6

1.02–1.05

0.01–0.05 0.01–0.05 0.001–0.01

1.11–1.45 1.04–1.18 1.02–2.57

Two-step process

Transient hotwire method

—

Reference

Nanofluid

Chopkar et al. (2007)

Murshed et al. Al–ethylene glycol (2008) TiO2–ethylene glycol CNT–engine oil

Effect studied/notes

Ceramic nanocomposites

Thermal conductivity measurement technique

0.1–1.0 0.2–1.0

1.001–1.12 1.04–1.18

Two-step process

Transient short hot-wire method

—

Choi et al. (2008)

Al2O3–transformer oil (sphere) Al2O3–transformer oil (fibre) AlN–transformer oil (sphere)

0.5–4.0

1.05–1.21

Two-step process

Transient hotwire method

—

0.5

1.05

0.5

1.08

Li et al. (2008)

Cu–water

0.02–0.80

1.08–1.18

Two-step method

Thermal constants analyzer technique

—

Glory et al. (2008)

MWCNT (+ gum Arabic 0.5 1 wt%)–water(15–758C) 1.0 1.5 MWCNT (+ gum Arabic 0.01 2 wt%)–water(15–758C) 0.10 0.20 1.00 2.00 3.00

1.14–1.09 1.26–1.15 1.34–1.27 1.019–1.002 1.04–1.02 1.08–1.06 1.24–1.17 1.36–1.38 1.47–1.63

Two-step process

Cylindrical cell method

Gum arabic stabilizer used, temperature effect

Sundar and Sharma (2008)

Al2O3–water CuO–water Al2O3–water (30–608C)

1.02–1.07 1.07–1.24 1.02–1.09 1.04–1.13 1.05–1.23 1.19–1.36 1.24–1.42 1.33–1.50

Two-step process

Transient hotwire method

Temperature effect

CuO–water (30–608C)

0.1–0.8 0.1–0.8 0.2 0.4 0.8 0.2 0.4 0.8

389

CNT–DI water CNT–ethylene glycol

Nanofluids: synthesis and thermal properties

© Woodhead Publishing Limited, 2013

Chen et al. (2008b)

390

Effect studied/notes

© Woodhead Publishing Limited, 2013

Reference

Nanofluid

Concentration (vol %)

Enhancement ratio

Synthesis method

Oh et al. (2008)

Al2O3–water Al2O3–EG

1.0–5.5 1.0–4.0

1.04–1.13 1.02–1.10

Two-step process

3ω technique

—

Han et al. (2008)

Indium–PAO oil (30– 908C)

8.0

1.11–1.13

One-step process

3ω-wire technique

Temperature effect

Jha and Ramaprabhu (2008)

MWCNT–water MWCNT–EG Cu/MWCNT–water Cu/MWCNT–EG

0.04 0.04 0.03 0.03

1.15 1.07 1.35 1.10

Two-step procedure

Transient hotwire method

—

Chen et al. (2008a)

TiO2–water TiO2–EG TNT–water TNT–EG

0.22–1.18 0.08–1.80 0.12–0.60 0.1–1.8

1.02–1.06 1.02–1.15 1.003–1.036 1.01–1.14

Two-step procedure

Transient hotwire method

—

Yu et al. (2008a)

Diamond (plasma treated)-water (20– 508C) Diamond (untreated)water (20–508C)

0.15

1.17–1.25

Two-step process

Transient hotwire method

Stabilizer used, temperature effect

0.15

1.06–1.10

2.2 1.2 2.5 0.4 0.8 0.4 0.8

1.03–1.01 1.007–1.015 1.036–1.044 1.004–1.030 1.016–1.047 1.025–1.032 1.059–1.062

Two-step process

Transient multicurrent hot-wire method

Temperature effect

Pen˜as et al. (2008)

SiO2–water (20–608C) SiO2–EG (20–608C) CuO–water (20–608C) CuO–EG (20–608C)

Ceramic nanocomposites

Thermal conductivity measurement technique

Ag–water (10–508C) Ag–water (10–508C)

1.12 1.14

1.10–1.47 1.16–1.60

One-step process

Transient hotwire method

Temperature effect

Garg et al. (2008)

Cu–EG

0.4–2.0

1.02–1.13

Two-step process

Transient hotwire method

—

Tsai et al. (2008)

Fe3O4–diesel Fe3O4–diesel (75/25) Fe3O4–diesel (50/50) Fe3O4–diesel (25/75)

oil oil/PDMS

1.1–4.5 1.0–2.0

1.04–1.19 1.04–1.08

Two-step process

Transient hotwire method

Surfactant used to stabilize

oil/PDMS

1.0–2.3

1.03–1.06

oil/PDMS

1.0–2.3

1.03–1.07

0.01–0.30

1.002–1.014

Two-step process

Transient hotwire method

—

1.001–1.29

Two step process

Transient hotwire method

Particle size effect

Two step process

Transient hotwire method

—

Lee et al. (2008)

Al2O3–water

Mintsa et al. (2009)

Al2O3–water(particle 0.08–17.50 size = 36 nm) 2.0–18.0 Al2O3–water(particle size = 47 nm) CuO–water(particle size 1.0–13.5 = 29 nm)

Murshed et al. Al2O3–DI water TiO2–DI water (2009)

0.01–0.04 0.01–0.04

1.04–1.28 1.01–1.22 1.03–1.42 1.18–1.27

Nanofluids: synthesis and thermal properties

© Woodhead Publishing Limited, 2013

Singh and Raykar (2008)

391

392

Nanofluid

Concentration (vol %)

Enhancement ratio

Synthesis method

Jha and Ramaprabhu (2009)

MWNT–DI water Pd/MWNT–DI water Au/MWNT–DI water Ag/MWNT–DI water MWNT–EG Pd/MWNT–EG Au/MWNT–EG Ag/MWNT–EG

0.04 0.03 0.03 0.03 0.04 0.03 0.03 0.03

1.15 1.16 1.28 1.37 1.07 1.09 1.10 1.11

Two-step process

Transient hotwire method

—

Jung and Yoo (2009)

Al2O3(150 nm)–water 1.0 (20–708C) Al2O3(47 nm)–water(20– 1.0 708C) 4.0

1.07–1.10

Two-step process

Not specified

Temperature effect

Shima et al. (2009)

Magnetite–water(2.8– 9.5 nm)

1.0 5.5

1.007 1.05–1.25

One-step procedure

Thermal property Surfactant used, analyzer particle size effect

Timofeeva et al. (2009)

AlOOH–water/EG (50/ 50)(shape platelets) AlOOH–water/EG (50/ 50)(shape blades) AlOOH–water/EG (50/ 50)(shape bricks) AlOOH–water/EG (50/ 50)(shape cylinders)

1.0–7.0

1.02–1.18

Two-step process

Transient hotwire method

1.0–7.0

1.03–1.18

1.0–7.0

1.04–1.24

1.0–7.0

1.05–1.34

Effect studied/notes

1.08–1.12 1.14–1.24

Particle shape effect

Ceramic nanocomposites

© Woodhead Publishing Limited, 2013

Reference

Thermal conductivity measurement technique

Cu–water Al2O3–water

0.02–0.80 0.02–0.08

1.08–1.18 1.07–1.17

Two-step process

Thermal constants analyzer technique

Surfactant used

Jiang et al. (2009)

CNT–R113(d=80 nm AR=18.8) CNT–R113(d=80 nm AR=125.0) CNT–R113(d=15 nm AR=100.0) CNT–R113(d=15 nm AR=666.7)

1.0

1.43

Two-step process

Size and shape effect

1.0

1.50

Thermal constants analyzer technique

1.0

1.82

1.0

2.04

Singh et al. (2009)

SiC–water SiC–water (23–708C)

0.01–0.08 1.0 2.0 4.0

1.04–1.30 1.04 1.09–1.12 1.22–1.23

Two-step process

Transient hotwire method

—

Hwang et al. (2009)

Al2O3–water

0.30

1.0144

Two-step procedure

Transient hotwire method

—

Kim et al. (2009)

Au–water

0.018

1.10

One-step process

Transient hotwire method

—

Sinha et al. (2009)

Cu–EG

0.55–1.00

1.30–1.62

Two-step process

—

Ali et al. (2010)

Al–water Al–EG Al–ethanol

0.42

1.18 1.21 1.24

One-step process

Guarded hot parallel-plate method Hot-wire laser probe beam deflection technique

Surfactant used, particle clustering observed

Nanofluids: synthesis and thermal properties

© Woodhead Publishing Limited, 2013

Wang et al. (2009a)

393

Zhang et al. (2010)

Carbon-coated Al– polyethylene glycol

© Woodhead Publishing Limited, 2013

Carbon-coated Cu– polyethylene glycol

Carbon coated Fe– polyethylene glycol

Xie et al. (2010)

MgO–EG

Paul et al. Au–water (2010) Abareshi et al. Fe3O4–DI water (2010)

Concentration (vol %)

Enhancement ratio

Synthesis method

0.1–1.5 (without dispersant) 0.1–1.5 (with glycerin dispersant) 0.1–1.5 (without dispersant) 0.1–1.5 (with glycerin dispersant) 0.1–1.5 (without dispersant) 0.1–1.5 (with glycerin dispersant) 0.45–5.00

1.15–1.37

Two-step process

Transient plane source method

Nanofluid dispersed by ball milling exhibits the best stability, followed by nanofluids dispersed by ultrasonic dispersion way and magnetic stirring way

1.06–1.40

Two-step process

Transient short hot-wire method

—

0.00006– 0.00025 0.25–3.00

1.12–1.48

One-step process One-step process

Transient hotwire method Transient hotwire method

Nanoparticle size varied Surfactant used

1.16–1.40

1.15–1.45

1.16–1.49

Effect studied/notes

1.15–1.27

1.16–1.30

1.05–1.11

Ceramic nanocomposites

Nanofluid

394

Reference

Thermal conductivity measurement technique

Chang et al. (2011)

CuO–water

1.07–1.11

Two-step process

Transient hotwire method

0.24–1.40

1.02–1.13

Two-step process

Transient hotwire method

0.24–1.40

1.02–1.13

0.24–1.40

1.03–1.04

0.24–2.4 0.26–2.52

1.01–1.09 1.03–1.11

Two-step process

Transient hotwire method

Venerus and SiO2–water Jiang (2011)

0.025–0.158

1.04–1.15

Two-step process

Optical technique Data consistent with EMT

Kim et al. (2011)

0.01–0.05 0.01–0.05 (pH 4) 0.01–0.05(pH 7) 0.01–0.05(pH 11)

1.02–1.11 1.01–1.11

Two-step process

Transient hotwire method

Rod type nanoparticles used, NP bead milled

0.01–0.10

1.10–1.16

Two-step process

Transient hotwire method

Nanoparticle size varied, variation of time studied

Phuoc et al. (2011)

© Woodhead Publishing Limited, 2013

MWCNT–water (dispersing agent chitosan 0.1wt%) MWCNT–water (dispersing agent chitosan 0.2wt%) MWCNT–water (dispersing agent chitosan 0.5wt%) Palabiyik et al. TiO2–PG (2011) Al2O3–PG

Paul et al. (2011)

Al2O3–water

Al95Zn05–EG

1.0–1.04

Surfactant used, NP prepared by the HiGee system Surfactant stabilized

Clustering theory

1.01–1.05

Nanofluids: synthesis and thermal properties

0.01–0.4

395

Gupta et al. (2011)

Graphene–water Graphene–water Graphene–water Graphene–water Graphene–water Al2O3–water CuO–water Cu–water CNT–water Graphene–water

(308C) (358C) (408C) (458C) (508C)

Concentration (vol %)

Enhancement ratio

Synthesis method

0.01–0.20 0.01–0.20 0.01–0.20 0.01–0.20 0.01–0.20 0.5–1.0 0.5–1.0 0.1–1.0 0.1–1.0 0.02–0.20

1.02–1.11 1.02–1.14 1.02–1.17 1.02–1.21 1.05–1.27 1.02–1.04 1.03–1.06 1.04–1.12 1.06–1.40 1.04–1.18

One-step process

Transient hotwire method

Effect studied/notes Enhancement due to Brownian motion and micro-convection effects

Ceramic nanocomposites

Nanofluid

396

© Woodhead Publishing Limited, 2013

Reference

Thermal conductivity measurement technique