Ramesh and Prabhu Nanoscale Research Letters 2011, 6:334 http://www.nanoscalereslett.com/content/6/1/334 macroscopic characteristics of clusters, and then, the thermal conductivity of a nanofluid [73]. Sitprasert et al. modified the Leong model inorder to predict both the temperature and the volume fraction dependence of the thermal conductivity of nanofluids for both non-flowing and flowing fluids [57]. Murugesan and Sivan developed lower and upper limits for thermal conductivity of nano- fluids. The upper limit is estimated by coupling heat transfer mechanisms like particle shape, Brownian motion and nanolayer while the lower limit is based on Maxwell’s equation [74]. Teng et al. proposed an empiri- cal equation incorporating the nanoparticle size, tem- perature and lower weight fraction of Al2O3/water nanofluid [75]. By considering nanoparticles as liquid- like particles, Meibodi et al. expressed a model for esti- mation of upper and lower limits of nanofluid thermal conductivity [76]. Viscosity Viscosity is an intrinsic property of a fluid that influ- ences flow and heat transfer phenomena. The addition of nanoparticles to the base fluid shows Newtonian and/ or Non-Newtonian behaviour depending on the volume percentage of particles, temperature and methods used to disperse and stabilize the nanoparticle suspension [41,77-79]. The effective viscosity of nanofluid increases by increasing concentration of particles and decreases with increase in temperature [14,41,78,80-82]. The effec- tive viscosity of fluid containing a dilute suspension of small particles is given by Einstein’s equation. Mooney extended Einstein equation to apply to a suspension of finite concentration [83]. Later Brinkman modified the Einstein equation to more generalized form [84]. How- ever, the experimentally measured nanofluids viscosities deviate from the classical model because these models relate viscosity as a function of volume concentration only and there is no consideration of temperature dependence and particle aggregation [77]. Pak and Cho measured viscosities of the dispersed fluids with g-Al2O3 and TiO2 particles at a 10% volume concentration and were approximately 200 and 3 times greater than that of water [81]. Wang et al. observed 20 to 30% increase in viscosity of water when 3 vol.% Al2O3 nanoparticles is added to water [14]. Das et al. measured the viscosity of water-based Al2O3 nanofluids at 1 and 4 vol.%. They found that the increase of viscosity with particles con- centration but the fluid remains Newtonian in nature [78]. Experimental studies on CNT nanofluid by Ding et al. [41] found the shear thinning behaviour at low shear rates but slight shear thickening at shear rates greater than 200s-1. Kulkarni et al. investigated the rheological behaviour of copper oxide (CuO) nanoparticles of 29 nm average diameter dispersed in deionized (DI) water over a range of volumetric solids concentrations of 5 to 15% and temperatures varying from 278 to 323 K. (ρCp)nf = (1 − ϕ)(ρCp)f + ϕ(ρCp)s. Nelson and Banerjee used differential scanning calori- meter for measurement of specific heat capacity of exfo- liated graphite nanoparticle fibers suspended in polyalphaolefin at mass concentrations of 0.6 and 0.3%. They found an increase in the specific heat of the nano- fluid with increase in the temperature. The specific heat capacity of the nanofluid was found to be enhanced by These experiments showed that nanofluids exhibited time-independent pseudoplastic and shear-thinning behaviour. The suspension viscosities of nanofluids decrease exponentially with respect to the shear rate [79]. Similarly Namburu et al. showed the non-Newto- nian behaviour at sub-zero temperatures below -10°C and Newtonian behaviour above -10°C in SiO2 nanofluid [77]. Chen et al. categorized the rheological behaviour of nanofluids into four groups as dilute nanofluids, semi- dilute nanofluids, semi-concentrated nanofluids, concen- trated nanofluids [85]. Xinfang et al. measured the visc- osity of Cu-H2O nanofluid by using capillary viscometers and results showed that the temperature and sodium dodecylbenzenesulfonate (SDBS) concentra- tion are the major factors affecting the viscosity of the nano-copper suspensions, while the effect of the mass fraction of Cu on the viscosity is not as obvious as that of the temperature and SDBS dispersant for the mass fraction chosen in the experiment [86]. Recently Masoumi et al. introduces a new theoretical model for the prediction of the effective viscosity of nanofluids based on Brownian motion. This model could calculate the effective viscosity as a function of the temperature, the mean particle diameter, the nanoparticle volume fraction, the nanoparticle density and the base fluid phy- sical properties [87]. Specific heat Research work on the specific heat of nanofluids is lim- ited compared to that on thermal conductivity and visc- osity. The specific heat of nanofluid depends on the specific heat of base fluid and nanoparticle, volume con- centration of nanoparticles, temperature of the fluids and the literature suggests that the specific heat of nanofluid decreases with an increase in the volume con- centration and increases with temperature [88-90]. According to Pak and Cho, the specific heat of nano- fluids can be calculated using the following equation [81]: Cρnf = ϕCps + (1 − ϕ)Cpbf. Under the assumptions of local thermal equilibrium between the nanoparticles and the base fluids, Xuan and Roetzel expressed specific heat equation for nanofluid as [91] Page 7 of 15 (1) (2)