(133d) Coupled Computational Fluid Dynamics – Aerosol Dynamics Model for Prediction of Nanostructured Films in an Aerosol Chemical Vapor Deposition Reactor

Nanomaterials have been explored for use in countless applications over the past decade due to the enhanced properties exhibited at the nanoscale. Controlling the morphology of nanomaterials allows for optimization of these properties, such as specific surface area, conductivity, fluorescence, and chemical reactivity. Consequently, nanomaterials have been investigated in a myriad of applications, including solar energy harvesting, battery electrodes, photocatalysis, water splitting, and drug delivery. However, synthesis of high-performance nanomaterials frequently involves complex batch processes with low yields, making scale-up difficult.

Aerosol chemical vapor deposition (ACVD) is a single-step, ambient pressure technique used to deposit highly crystalline thin films consisting of oriented nanostructures. Nanostructured films synthesized using ACVD have been utilized as battery electrodes, sensors, and in CO₂ reduction. The morphology of the deposited film can be controlled by modifying easily measured reactor variables such as precursor feed rate, gas flow rate, and reaction chamber temperature. In ACVD, nucleation of particles occurs via thermal decomposition or reaction of a vapor precursor in the heated reaction chamber, followed by particle growth via coagulation and condensation reactions. Particles are deposited on a heated substrate, where the particles sinter into structured films. Film morphology may be predicted using three characteristic rates, reaction rate, particle arrival rate, and sintering rate. Due to the easily controllable morphology and atmospheric pressure operation ACVD is a promising technique for the large-scale production of nanostructured thin films.

In this work, a finite element based computational fluid dynamics (CFD) model is coupled with an aerosol dynamics moment model to predict film morphology with experimental confirmation. This model couples the large scale fluid dynamics from CFD with nano range particle nucleation, growth, and deposition to predict film morphology. Additionally, a large area deposition analysis is performed on a multi-feed geometry by calculating a spatially-resolved mass flux to the substrate surface for estimation of film height and uniformity. Reactor conditions such as reactor temperature, precursor feed rate, residence time, and feed-to-feed distance are investigated and optimized to maximize film uniformity while retaining the desired morphology.