[16p-K406-1~16] 12.3 Functional Materials and Novel Devices

4:00 PM - 4:15 PM JST(7:00 AM - 7:15 AM UTC)Presentation by Applicant for JSAP Young Scientists Presentation Award

[16p-K406-11] Machine learning-assisted estimation of factors for self-organized, aligned CNT film formation

〇Miki Ikeda¹, Tomoyuki Miyao², Yoshiyuki Nonoguchi¹ (1.Kyoto Inst. Tech., 2.Nara Inst. Sci. Tech.)

Keywords:

Materials Informatics,Carbon Nanotubes,Evaporative Self-assembly

Controlling the orientation of Carbon Nanotubes (CNTs) is important for making the most of their excellent properties. Recently, we discovered that CNTs evaporate and self-assemble using non-liquid crystalline butyral resin to form long-period structures similar to liquid crystals. However, the study of preparation conditions relied on exhaustive search. In this study, we used machine learning to extract solvent parameters related to self-assembly and reflect the prediction results in experiments, discovering many new combinations of dispersants and solvents.