RESUMO
In this paper, the thermal conductivity (knf) of cerium oxide/ethylene glycol nanofluid is extracted for different temperatures (T = 25, 30, 35, 40, 45, and 50 °C) and the volume fraction of nanoparticles ( φ = 0, 0.25, 0.5, 0.75, 1, 1.5, 2 and 2.5%) and then knf is predicted by two methods including Artificial Neural Network (ANN) and fitting method. For both methods, the results have been presented and compared. The experiments showed that with increasing φ and temperature, the thermal conductivity ratio (TCR) of nanofluid increases. It was also observed that when the experiments are performed at high temperatures, the rate of increase in knf is much higher than the change in the same amount of φ change at low temperatures. An ANN with 7 neurons has a correlation coefficient very close to 1 and this proves that the outputs are compatible with experimental results. Also, it can be seen that the ANN could predict the thermal behavior of cerium oxide/ethylene glycol nanofluid more accurately.
RESUMO
Adhesion is a critical factor in microelectromechanical systems (MEMSs) and is influenced by many parameters. In important fields, such as microassembly, an improved understanding of adhesion can result in higher precision. This study examines the influence of deposition of gold and titanium onto the atomic force microscope (AFM) tips in adhesion forces and Young's modulus, between a few MEMS substrates (silicon, gold, and silver) and the AFM tips. It was found that, except for gold substrate, an AFM tip coated with gold has the highest adhesion force of 42.67 nN for silicon substrates, whereas the titanium-coated AFM tip decreases the force for all the samples. This study suggests that such changes must be taken into account while studying the adhesion force. The final results indicate that utilizing gold substrate with titanium AFM tip led to the lowest adhesion force, which could be useful in adhesion force measurement during microassembly.