Topological and Electrical Properites of Carbon Nanotube Networks

A major challenge to the study of the structure-property relationship of carbon nanotube (CNT) networks is to characterize the complex nanostructure with complicated nanoscale contacts and pore structures. An image-based characterization methodology was proposed to extract CNT network information directly from scanning electron microscope (SEM) images of various CNT thin films to characterize critical topological factors including bundle size, diameter, and orientation from the CNT networks. This approach provided high-fidelity and fast analysis of CNT network structures with low false positive rate (FPR) of ~3% and ~90% accuracy in most of our case studies. We applied the new approach to study different networks of multi-walled carbon nanotube (MWNT), single-walled carbon nanotube (SWNT), MWNT-SWNT mixed, and stretched MWNTs with different CNT alignments, which revealed the electrical conductivity-structure relationships of MWNT networks. On the other hand, controlling the transfer of electrical and mechanical properties of nanotubes into nanocomposites remains one of the major challenges due to the lack of adequate measurement systems to quantify the variations in bulk properties while the nanotubes were used as the reinforcement material. One-way analysis of variance (ANOVA) on thickness and conductivity measurements were conducted. By analyzing the data collected from both experienced and inexperienced operators, we found some operation details users might overlook that resulted in variations, since conductivity measurements of CNT thin films are very sensitive to thickness measurements. In addition, we demonstrated how issues in measurements damaged samples and limited the number of replications resulting in large variations in the electrical conductivity measurement results. Based on this study, we proposed a faster, more reliable approach to measure the thickness of CNT thin films that operators can follow to make these measurement processes less dependent on operator skills.