Computational Analysis of Carbon Nanotube Networks in Multifunctional Polymer Nanocomposites



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Carbon nanotubes (CNTs) have attracted much attention as reinforcements in polymer composite materials because of their unique mechanical, electrical, and thermal properties. The high electrical conductivity of CNTs is especially promising for use in multifunctional materials. Dispersing a small amount of CNTs in electrically insulating polymers has been shown to increase the conductivity of the material by many orders of magnitude because the high aspect ratio CNTs form percolating networks at very low volume fractions. Additionally, it has been shown that the application of mechanical strain to these nanocomposites results in a change in material resistivity, or piezoresistivity. Many experimental research e?orts have focused on optimizing this e?ect for strain and damage sensing applications, but much is still unknown about the dominant mechanisms a?ecting piezoresistivity. The objective of this work was to develop a computational model that can predict and investigate the electrical and piezoresistive properties of CNT/polymer composites. The nanocomposites were modeled as random networks of resistors in 2D and 3D in order to understand the mechanisms that a?ect the percolative, electrical, and piezoresistive performance of di?erent material systems. The model was used extensively to analyze and predict the electrical conductivity of 2D single-walled car- bon nanotube thin ?lms and 3D multi-walled carbon nanotube (MWCNT)/polymer nanocomposites. It was found that the contact resistance between individual nanotubes greatly a?ects the conductivity of 2D ?lms as well as 3D MWCNT/polymer materials. Additionally, it was shown that the electrical conductivity model could be calibrated to experimental results by adjusting the contact resistance alone. The 3D random resistor network model was also used to predict the piezoresis-tive properties for MWCNT/polymer Nano composites. The dominant mechanisms that cause the piezoresistive e?ect in these material systems were investigated, and the Poisson?s ratio of the composite was found to greatly impact the piezoresistive performance. The predictions indicated that decreasing the Poisson?s ratio of the composite leads to higher strain sensitivity, which could have implications for choosing material systems for strain sensor applications.