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 IG/ID 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 IG/ID 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 IG/ID 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.

One of the key challenges for the commercialization of carbon nanotube fibers (CNTFs) is their large-scale economic production. Among CNTF spinning methods, surfactant-based wet spinning is one of the promising techniques for mass producing CNTFs. Here, we investigated how the coagulation bath composition affects the spinnability and the properties of CNTFs in surfactant-based wet spinning. We used acetone, DMAc, ethanol, and IPA as coagulants and analyzed the relationship between coagulation bath composition and the properties of CNTFs in terms of kinetic and thermodynamic coagulation parameters. From a kinetic perspective, we found that a low mass transfer rate difference (MTRD) is favorable for wet spinning. Based on this finding, we mixed the coagulant bath with solvent in a proper ratio to reduce the MTRD, which generally improved the wet spinning. We also showed that the coagulation strength, a thermodynamic parameter, should be considered. We believe that our research can contribute to establishment of surfactant-based wet spinning of CNTFs.

Mass production of high-quality carbon nanotubes (CNTs) remains a challenge, requiring the development of new wetimpregnated catalyst suitable for the catalytic chemical vapor deposition (CCVD) of CNTs in a fluidized bed reactor. For the successful development of a new catalyst, a highly robust system to synthesize CNTs must be established. Here, we systematically investigated the robustness of CNT synthesis by CCVD using a wet-impregnated catalyst. We statistically tested four factors that could potentially affect the robustness of CNT synthesis system, focusing on carbon yield and IG/ID. First, we tested the effect of vacuum baking before CNT growth. F test and CV equality test concluded that vacuum baking recipe did not significantly reduce the variability of the CNT synthesis. Second, we tested the batch-to-batch variation of catalysts. The results of t test and one-way analysis of variance indicate that there is significant difference in carbon yield and IG/ID among catalysts from different batches. Third, we confirmed that there is spatial non-uniformity of wet-impregnated catalysts within a batch when they are produced in large scale. Finally, we developed a multi-step heating recipe to mitigate the temperature overshooting during the CNT synthesis. The multi-step recipe increased the mean of carbon yield, but did not influence the variability of CNT synthesis. We believe that our research can contribute to the establishment of a robust CNT synthesis system and development of new wet-impregnated catalysts.