[10a-E310-1~1] 8.8 Plasma Electronics Award Speech

9:15 AM - 9:45 AM JST(12:15 AM - 12:45 AM UTC)

[10a-E310-1] [The 24th Plasama Electronics Award Speech] Nanoparticle Synthesis Control by Design of Spatiotemporally Modulated Thermal Plasma Fields and Machine-Learning-Based Optimization

〇Yasunori Tanaka¹, Yurina Nagase¹, Rio Okano¹, Yusuke Nakano¹, Tatsuo Ishijima¹, Satoshi Kitayama¹, Shiori Sueyasu², Shu Watanabe², Keitaro Nakamura² (1.Kanazawa Univ., 2.Nisshin Seifun G.)

Keywords:

nanoparticle synthesis,modulated induction thermal plasmas,machine learning optimization

Nanoparticle synthesis in thermal plasmas involves strongly coupled processes such as feedstock evaporation, vapor transport, nucleation, condensation, and particle transport. This study investigates Si nanoparticle synthesis using tandem modulated induction thermal plasmas with time-controlled feedstock feeding. An integrated numerical model was developed to couple electromagnetic thermofluid fields, feedstock motion and evaporation, Si vapor transport, and nanoparticle formation. Sequential approximate multi-objective optimization using a radial basis function network was applied to minimize mean particle diameter and maximize particle number. The optimized condition yielded a mean diameter of about 21 nm and 3.3×10²¹ particles, clarifying the role of synchronized heating, feeding, and quenching in promoting supersaturation and nucleation.