LAUSR

Abstract An effective and flexible method for the generation of computational samples for mesoscopic modeling of anisotropic networks of carbon nanotube (CNT) bundles with various degrees of CNT alignment is developed and applied for investigation of structural self-organization of nanotubes into vertically aligned CNT forests and fibers. Structural characteristics of the computational samples, such as bundle size distribution, average and maximum bundle sizes, magnitude of the Herman orientation factor, average tilt of CNT segments with respect to the direction of alignment, and average tortuosity of the nanotubes, are calculated and related to parameters of the sample preparation procedure. Good agreement between the computer-generated and experimentally-grown network structures is demonstrated, and several examples of the applications of the mesoscopic modeling for investigation of mechanical and thermal transport properties of the CNT materials are provided. The high degree of control over the structure of computational samples, provided by the sample generation procedure, enables fine-tuning of the structural characteristics of in silico generated samples to match those to particular experimental materials, as well as an efficient exploration of the multidimensional space of structural parameters aimed at optimization of mechanical and transport properties and establishing structure – property relationships for this important class of network materials.