Elsevier

Thermochimica Acta

Volume 637, 10 August 2016, Pages 11-16
Thermochimica Acta

Experimental studies on viscosity, thermal and electrical conductivity of aluminum nitride–ethylene glycol (AlN–EG) nanofluids

https://doi.org/10.1016/j.tca.2016.05.006Get rights and content

Highlights

Abstract

The paper presents the results of experimental research studies carried out on basic physical properties of aluminum nitride–ethylene glycol nanofluids. Nanofluids were prepared with two step method with use of the commercial available AlN nanoparticles. Dynamic viscosity of the material in the range of shear rates from 0.01 to 1000 s−1 at pressure 0.1 MPa, and constant temperature of 298.15 K was measured. Thermal conductivity was measured using the transient line heat source method at a constant temperature of 298.15 K. Finally the electrical conductivity at constant temperature of 298.15 K was measured. It was presented that this material exhibits non-Newtonian nature and thermal conductivity increases linearly with the concentration of nanoparticles in suspension. AlN–EG nanofluid presents also a significant increase in electrical conductivity with the concentration of nanoparticles.

Introduction

Nanofluids is a very fast developing group of nanomaterials, which has a lot of potential applications in industry [1], [2]. The possibilities for using these materials in technological processes come mainly from widely described thermal conductivity enhancement of suspensions as compared to the base fluid first time reported in 1995 by Choi [3]. Due to the fact that the scientific and industrial community is actively seeking the possibilities of energy saving the potentiality of increase the efficiency of heat transfer systems cannot be overestimated. In view of these facts, many scientists proceed intensive research on thermal conductivity [4], [5], [6], [7], [8], [9], [10] and electrical conductivity [11], [12], [13], [14] of nanofluids. Note, however, that addition of nanoparticles to the base fluid also changes the viscosity of the suspension [15], [16], [17], [18], [19], [20], [21]. In some cases, these changes might be very significant [22], [23], [24], [25]. Therefore, considering the possible application of the specific nanofluid it should be taken into account not only increase the thermal conductivity, but also an increase in viscosity. Unfortunately, at the moment there are no coherent theoretical models of thermal conductivity and rheological properties that could be applied to any of nanofluid. There is a need to provide high-quality experimental data, which will allow to construct a theoretical model in the future. This paper brings a contribution in this field.

One of the most common uses of AlN nanoparticles is the advanced ceramics industry. However, it turns out that aluminum nitrides can also find many applications in the heat exchange process. Wozniak et al. [26] measured thermal conductivity of highly loaded AlN–PPG (polypropylene glycol) dispersions, and its’ rheological properties [27]. They also presented paper [28], where they described possibility of use aluminum nitride dispersion as heat-transferring systems. They concluded that in some conditions AlN–PPG systems could be applied as heat transferring media. Thermal conductivity of AlN–ethanol nanofluid was measured by Hu et al. [29], and described that thermal conductivity enhance with concentration of nanoparticles in suspension. They also reported that thermal conductivity of AlN–ethanol nanofluids depend on temperature. Yu et al. [30] presented results of experimental measurements of viscosity and thermal conductivity of AlN–EG and AlN–PG nanofluids. They measured dynamic viscosity in small shear rate range (from 1 to 100 s−1), and show that both of this nanosuspensions present non-Newtonian nature. Dependence of thermal conductivity on temperature was also measured, and they found that temperature has little effect on thermal conductivity enhancement ratios.

Not only rheological and thermal properties, but also a dielectric properties of AlN suspensions are interesting. Dong at al. [31] investigated suspensions of AlN nanoparticles in transformer oil. The average size of nanoparticles was 50 nm. They studied electrical conductivity as function of temperature and volume fraction. They revealed over 800 times increase in electrical conductivity for 0.5% volume fraction in comparison to the base fluid. Inconsistency Maxwell model with experimental data was presented, and authors introduced new model which is in good correlation in their experiment.

Section snippets

AlN nanoparticles

Aluminum nitride nanoparticles are commercially available and were purchased from PlasmaChem GmbH (Berlin, Germany), catalog number PL-HK-AlN with average particle size 20 nm, and specific surface 80 ± 7 m2/g as provided by supplier. To get scanning electron microscope (SEM) pictures of dry AlN nanoparticles a VEGA3 (TESCAN Brno, s.r.o., Brno, Czech Republic) microscope was used. Fig. 1 presents SEM image of nanoparticles used to prepare nanofluids, and it might be seen that particle size

Dynamic viscosity

Determine of dynamic viscosity curves is one of the fundamental rheological measurements. This examination can determine whether the liquid nature is non-Newtonian, and if so, whether it shear thinned or thickened. Dynamic viscosity curves of AlN–EG nanofluids were measured at constant temperature of 298.15 K. Measurements were planned on a logarithmic scale, and each data point was collected after 100 s of constant shear rate. Results of this measurements are presented in Fig. 2, and summarized

Conclusions

Paper presents results of experimental investigation on viscosity, thermal and electrical conductivity of alumina nitride nanoparticles suspended in ethylene glycol. Results clearly show that AlN–EG is a shear-thinning non-Newtonian fluid.

The measurement of the thermal conductivity showed that it increases with the volume fraction of nanoparticles in suspension. Classical theoretical models are not applicable to describe the thermal conductivity enhancement. However, a linear function (4) might

Acknowledgement

The authors wish to thank Piotr Sagan (University of Rzeszow) for the SEM picture of nanoparticles.

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