We report on optimizing the catalytic chemical vapor deposition synthesis of multiwall carbon nanotubes (MWCNTs) from ethene over supported transition metal SiO2 nanocomposite aerogels using the statistical design of experiments (DOE) approach. DOE allowed us to test 19 different catalysts in a total of 49 reactions instead of testing 27 catalysts in 729 runs as required by a three-level full factorial design. Both catalyst-related and process-related variables were optimized; in particular varied parameters were Fe + Co loading, Fe/Co ratio, Ni loading, C2H4 flow rate, temperature, and duration of the reaction. The results of the optimization indicate that a good catalyst should contain a high overall loading (10 wt %) of iron and cobalt in similar amount, should be free of nickel and should be operated at a relatively low temperature (650-700 °C) at high carbon source space velocity for optimum performance. The uniqueness of this work is that we demonstrated that catalyst-related and process-related variables can be optimized simultaneously in the DOE of MWCNT synthesis.
ASJC Scopus subject areas
- Electronic, Optical and Magnetic Materials
- Physical and Theoretical Chemistry
- Surfaces, Coatings and Films