Particle In Cell Simulation of Nanotube Growth

Nano materials have a wide range of applications, ranging from drug delivery in medical applications to new composite materials in aerospace applications. Experimental data on nanoparticle growth exists, but numerical simulations are needed to understand the underlying physical mechanisms and to enable better prediction of nanomaterial production. The numerical models of plasma assisted production span from detailed atomistic models including interaction of individual atoms to fluid models coupled with Maxwell equations that determine plasma composition and kinetic models describing the nanoparticle growth. How- ever, existing models are either computationally extremely expensive as in the case of atomistic models or don’t include kinetic effects that are important for the nanoparticles production. We propose to use Particle-In-Cell (PIC) methods to simulate growth rates of nanotubes. Because PIC is less computationally expensive than atomistic simulations and can still capture kinetic effects that fluid simulations cannot, it is able to address this important issue in a unique way. In addition, PIC predictions can be fed to existing plasma fluid codes for more accurate production modeling. We will extend an existing commercially available Particle-In-Cell software package for plasma physics sim- ulations to include models that can be used to predict the growth rates of nanoparticles. The predicted growth rates can be used to better understand the physics of nanoparticle production and its optimization. We developed and implemented prototype surface kinetic chemistry models for non-uniform particle fluxes in Vorpal, the computational engine of commercial Particle-In-Cell software VSim. We also extended the geometric and meshing capabilities of Vorpal, by developing new prototype algorithms that take into account changes in nanotube geometry due to its growth. During Phase I we also examined the importance of stream boundary conditions for nanoparticles growth simulation in plasma environment. In Phase II we will extend meshing capabilities of Vorpal computational engine by extending a cut-cell algorithm to non-uniform orthogonal meshes. We will also improve and speed up electrostatic solvers in the Vorpal computational engine. In addition, we will expand surface process models used by the PIC method in the Vorpal computational engine. Commercial applications and other benefits: Simulation of advanced materials for organic photovoltaics, batteries, thermoelectrics, advanced coatings, and pharmaceuticals will speed development of these and other novel products and will help US indus- tries compete and be more efficient. The usage of the PIC method will enhance numerical simulations of nanoparticle production in non-equilibrium conditions beyond what is possible with existing approaches.