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Determining the Thermal Conductivity of Nanofluids using the Hot Disk TPS

Nanofluids are an exciting field of scientific discovery and research. A nanofluid is composed of a base fluid (water, oil, glycol etc.) that has nanoparticles added to it (Figure 1). The nanoparticles consist of a material with a much higher thermal conductivity, and therefore their addition considerably increases the heat transfer capabilities of the fluid. However, work is still being performed to quantify the ideal conditions and production mechanisms for producing a high quality nanofluid, as coagulation and poor dispersion of the nanoparticles has lead to issues of substandard performance. Wang and Li (2009) performed research to determine the effects of pH on the viscosity and thermal conductivity of nanofluids to enhance the production of a high performance product. Low viscosity and high thermal conductivity are the ideal parameters for a good quality nanofluid.

Thermal Conductivity Applications Nanofluid Series (1)

Figure 1. A series of nanofluids with differing concentrations of nanoparticles1.

Wang and Li (2009) created two different nanofluids to begin their research, one was a mixture of Al2O3 nanoparticles and water, while the other contained Cu nanoparticles and water. The Al2O3 particles were between 15 and 50 nanometers in size with a surface area of 100 m2/g, while the Cu particles were 25-60 nm with a surface area of 30-50 m2/g. HCl and NaOH were used to alter the pH. Nanofluids of each type with a variety of weight fractions (0.01-0.9%) of nanoparticles were tested for viscosity and thermal conductivity at different pH levels. Viscosity was measured using a capillary viscometer and thermal conductivity measurements were collected with the Hot Disk TPS Thermal Conductivity System.

The fast test time, lack of sample preparation, and flexible sample size makes the Hot Disk TPS an attractive choice for thermal conductivity testing. Measurements of thermal conductivity were performed using a 2.001 mm radius sensor composed of nickel foil encased in Kapton (Figure 2), which was dipped into the different nano-suspensions. Wang and Li used a voltage of 0.02W with a test time of 5s, each sample was tested 10 times and the average was taken.

Thermal Conductivity Applications 7577sensor (1)

Figure 2. Illustration of the Hot Disk 7577 sensor (2.001 mm radius) used by Wang and Li (2009) to test the thermal conductivity of a series of aluminum oxide and copper nanofluids.

Conversely, there a liquid cell available that is ideal for measuring the thermal conductivity of liquids using the TPS system (Figure 3). This cell holds a small volume of the fluid to be tested (between 2-3 mL) and places the sensor in the middle of the fluid space to ensure maximum probing depth during the test. This cell is an excellent choice for thermal conductivity testing of expensive nanofluids, as it does not require a large volume of sample to produce accurate results.

Thermal Conductivity Applications Liquid Cell Internal And Loading Comp (1)

Figure 3. Diagram of the liquid cell used by the Hot Disk TPS system to test the thermal conductivity of liquids. The image on the right shows how the cell is loaded and set up in relation to the sensor.

Wang and Li (2009) determined that the lowest viscosity occurred at a pH of 8.0 for the Al203-H2O nanofluid and 9.5 for the Cu-H2O nanofluid (Figure 4). Conclusions drawn from the viscosity data indicated that pH affects the electro kinetic forces between the nanoparticles; the ideal pH level created strong repulsion between the particles, which resulted in excellent dispersion, fluidity and stability of the fluid, and prevented coagulation.

Thermal Conductivity Applications PH Vs

Figure 4. Graph from Wang and Li (2009) illustrating the change in nanofluid viscosity relative to pH.

Thermal conductivity measurement results showed a remarkably similar trend to that of viscosity, as the highest thermal conductivity coincided with the lowest viscosity. The ideal pH levels were again 8.0 and 9.5 for the aluminum oxide and copper fluids respectively (Figure 5a). Additionally, the ideal weight fraction of nanoparticles was determined to be 0.4% (Figure 5b). Wang and Li (2009) concluded that an increase in surface charge accompanied the increase in pH, thereby enhancing mobility and heat transfer due to increased repulsion between particles. A maximum increase in thermal conductivity of 13% was observed for the Al2O3-H2O nanofluid, and 15% for the Cu-H2O nanofluid compared to that of the distilled water base.

Thermal Conductivity Applications TC Vs PH

Figure 5. Graphs taken from Wang and Li (2009) illustrating the thermal conductivity ratio between the two nanofluids and water against pH and weight fraction (% of particles) of the solution.

This study successfully demonstrated that pH is an effective tool for maximizing the heat transfer potential of nanofluids as it can aid in reducing viscosity and increasing thermal conductivity. Knowledge of how surface charge and repulsion affect the coagulation or dispersion of the nanoparticles can help researchers produce high performance nanofluids in the future. The ability of the Hot Disk TPS thermal conductivity testing system to effectively measure the thermal conductivity of liquids makes it an ideal tool for nanofluid research and development.


NanoEngineering Group. Nanofluids. The Massachusetts Institute of Technology (Online). Available at: 

Sivashanmugam, P. 2012. ‘Application of Nanofluids in Heat Transfer’ in Salim N Kazi (ed), An Overview of Heat Transfer Phenomena. Published online, available at: 

Wang, XJ and Li, XF. 2009. Influence of pH on Nanofluids’ Viscosity and Thermal Conductivity. Chinese Physics Letters. 26(5): 056601. 
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