From Collisions to Bundles: An Adaptive Coarse-Grained Model for the Aggregation of High-Aspect-Ratio Carbon Nanotubes

Abstract

We present an adaptive mesoscale model for carbon nanotube (CNT) systems. In our model, CNTs are represented as a chain of nodes connected by tensile and torsion springs to describe stretching and bending of the chain, with intermolecular interactions being calculated by using a mesoscopic Lennard-Jones potential. Computational adaptivity was achieved by dynamically adjusting node spacing and number during the simulation to optimize the number of simulated particles and lower computational effort. Adaptive simulations were up to 5 times faster than nonadaptive ones while quantitatively preserving all system dynamics. In particular, the model enables the study of the time scale of CNT bundling that leads to the formation of dilute CNT networks, so-called aerogels. These aerogels constitute the first step in the direct spinning of CNT fibers from chemical vapor deposition synthesis. Understanding the factors governing CNT bundling and network formation is key to controlling CNT fiber microstructure and therefore optimizing their properties. Using the model, we simulated the bundling dynamics of two CNTs with an initial point contact at varying angles for CNT lengths of up to 10 μm. We find that bundling times are an increasing function of initial collision angle and follow a power law with increasing CNT length, ranging from 10–1 to 103 ns. We postulate that when the bundling time becomes of the same order as the CNT bundle collision time, the aerogel will form.