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CFD-Aerosol Modeling of the Effects of Wall Composition and Inlet Conditions on Carbon Nanotube Catalyst Particle Activity

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The evolution of iron catalyst particles during aerosol (floating catalyst) Chemical Vapor Deposition (CVD) synthesis of Carbon Nanotubes (CNTs) from CO is computed using a multi species Computational Fluid Dynamics (CFD) model incorporating a lognormal aerosol method of moments (MOM) to describe their dynamics and a combined chemical kinetics and equilibrium model for catalytic production of CO2. The influence of the presence of iron at the reactor walls, the fed particle size, number concentration and polydispersity and the effect of the catalytic production of CO2 at the reactor wall are studied in terms of particle size, concentration and polydispersity and reagent concentration during CNT synthesis. It is found that iron catalyst particle dynamics are essentially insensitive to wall iron concentrations and, for a wide range of particle sizes and concentrations, it is found that the catalyst particles are stable up to a critical CNT window in which CNT nucleation and growth occurs. Concentrations of catalyst particles significantly above 1 × 1014 #/m3, however, lead to poor control over catalyst particle size and polydispersity at the CNT nucleation front which, in turn, leads to poor control over CNT diameter. The location of the growth window is shown to be directly associated with the availability of catalytically produced CO2 diffusing from the reactor walls to the reactor core. These results help to explain the large variations in CNT diameter and chirality and the inefficient use of catalyst material in other floating catalyst CNT processes based on in-situ catalyst particle synthesis.
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Document Type: Research Article

Publication date: 2008-08-01

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  • Journal for Nanoscience and Nanotechnology (JNN) is an international and multidisciplinary peer-reviewed journal with a wide-ranging coverage, consolidating research activities in all areas of nanoscience and nanotechnology into a single and unique reference source. JNN is the first cross-disciplinary journal to publish original full research articles, rapid communications of important new scientific and technological findings, timely state-of-the-art reviews with author's photo and short biography, and current research news encompassing the fundamental and applied research in all disciplines of science, engineering and medicine.
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