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.

Background : Curcuma longa L., in the family Zingiberaceae, is distributed in tropical and/or sub-tropical regions mainly in India and China. This species is commonly called turmeric, powder is used as medicinal herbs and/or flavor enhancer. It has been cultivated in southern region mainly Jindo. However, it might be possible to extend cultivation regions due to rise in average temperature. In order to select superior lines based on agronomic characteristics, we analyzed multivariate and estimated selection effects from C. longa germplasm. Methods and Results : The C. longa germplasm were cultivated in an experimental field located in Eumseong, NIHHS, RDA. The harvested roots were investigated in agronomic characteristics included in yield and then considered its relationship among the 9 germplasm by multivariate analysis method. Results from principal component analysis (PCoA) showed that it represented 70.00% and 80.44% accumulated explanation from four and five principal compounds (PC). PCoA was conducted from 9 agronomic characteristics and then correlation coefficient has been showed by analysis between each main component value and agronomic characteristics. Value of the first PC was 2.25, 24.96% explanation of total dispersion, plant height, number of rootlet and weight of rootlet were correlated with a somewhat higher level as 0.41, 0.43 and 0.52. Value of the fifth PC was 0.94, 10.43% explanation of total dispersion, the number of shoots was correlated with a higher level as 0.87. Selection effects with outstanding candidate lines including higher lines were estimated at 126.13% in yield. Conclusion : These data on multivariate based on agronomic characteristics will be give us invaluable breeding information by selection of superior lines.