Development and modeling of thermally conductive polymer/carbon composites

Thermally conductive polymer composites can replace metals in many applications. This technology is a substantial improvement since polymers are commonly used due to their thermal isolating properties. The advantages of thermally conductive polymers over metals are reduced density; increased corrosion, oxidation, and chemical resistance; increased processibility; and properties are adjustable to fit the application. However, polymers have many disadvantages; for example, creep, thermal instability, and a limited number of processing techniques. The main application for thermally conductive polymers is heat sinks. Other possible benefits are faster injection molding cycle times and improved thermal stability.; The main objectives of this project were to develop a model to predict thermal conductivity of the carbon filled polymer composites and to determine if a synergism between fillers exists. Carbon black, synthetic graphite, and carbon fiber were added to nylon 6,6 or polycarbonate matrixes. For this work, the transverse and longitudinal thermal conductivities were measured. In addition, optical microscopy and image analysis were also performed to characterize the structure of the composites. From these studies, it was found that all three fillers positively effect the transverse thermal conductivity. Synergisms in transverse thermal conductivity were found between synthetic graphite and carbon fiber, between synthetic graphite and carbon black in both polymer matrixes and between carbon black and carbon fiber in polycarbonate only.; The results were used to improve the predictions of a transverse thermal conductivity model. The improved model was based on the Nielsen Model. This model includes the aspect ratio, thermal conductivity, and packing of the filler(s), and the thermal conductivity of the polymer. The model was revised by updating a parameter, which was originally determined for each filler and now the value is the same for each filler used in this project. The updates to the model significantly improved the predictive nature of the model for the systems studied.