College - Author 1
College of Science and Mathematics
Department - Author 1
Degree Name - Author 1
BS in Physics
Colleen Marlow, College of Science and Mathematics, Physics Department
Carbon nanotube (CNT) random networks have shown great promise in electronic applications. For example, they have been used as the active layer in thin film transistor biosensors and as electrodes in supercapacitors (Hu, 2010). Although CNT networks applications are numerous, some of the key details of their electrical behavior are not fully understood. In particular, it is known that the junctions between tubes in CNT networks play a key role in determining the sensing properties of the network (Thanihaichelvana, et al., 2018), however, the mechanism by which metallic-semiconducting (m-s) tube junctions affect the electrical sensing properties of the network is not known. Experimental studies of individual single tube junctions have shown that Schottky barriers form at m-s junctions and that current-voltage (I-V) characteristics can be used to estimate the Schottky barrier height of the junction (Fuhrer, et al., 2000). While this simple method works well for characterizing individual m-s junction devices, a model sufficient to describe transport across a network of multiple junctions is lacking. Svensson et al. have modeled the transport across an m-s junction as an ideal diode, treating network junctions in the same way as bulk materials (Svensson, et al., 2009). In this study we use these two data analysis methods and computational simulations to determine if a transport model based on thermionic emission can be used to describe our CNT devices. We also determine if either of the two data analysis methods sufficiently predict a feature of the network key to transport behavior, the number of m-s junctions in the network path.