Data analytics approach to evaluating the impact of acetylene and ethylene precursors on the crystallinity of carbon nanotubes synthesized in a fluidized bed reactor

Abstract

The mass production of highly crystalline carbon nanotubes (CNTs) is highly demanded, yet achieving it remains challenging due to incomplete understanding of how synthetic parameters, except temperature, affect the crystallinity of CNTs. Notably, the choice of carbon precursor significantly influences CNT synthesis, but its impact on crystallinity remains unclear. Here, we employed a data analytics approach to examine the effect of carbon precursors on CNT crystallinity during their synthesis in a fluidized bed reactor. We compared ethylene, acetylene, and a mixture of these. Using Bayesian optimization (BO), we optimized synthesis conditions to maximize I_G/I_D of CNTs for each precursor. Key parameters considered were reaction temperature, precursor concentration, and hydrogen concentration. We conducted three separate BO processes to evaluate the effectiveness of each carbon precursor on CNT crystallinity. The results indicated no significant difference in I_G/I_D of CNTs among the carbon precursors. In addition, multiple linear regression analysis did not support a synergetic effect between acetylene and ethylene. Interestingly, contour plots demonstrated consistent relationships between synthesis parameters and I_G/I_D across different carbon precursors. This data analytics approach allowed us to successfully assess the impact of carbon precursors on the CNT crystallinity and analyze the relationship between synthesis parameters and CNT crystallinity.