K-4 - Session programs - SCEJ 91st Annual Meeting (Kyoto, 2026)

`SCEJ 91st Annual Meeting (Kyoto, 2026)`

Last modified: 2026-01-20 11:12:38

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Session programs : K-4

The chairs are under negotiation.
Yellow-back on the Technical sessions and session list denotes the on-site/virtual hybrid sessions.
All other sessions will be the on-site only sessions.

K-4 Recent Research and Development of Technology for Physical Properties and Supercritical Fluid 2026

Organizers:

Sato Takafumi (Utsunomiya Univ.), Yamada Hidetaka (Kanazawa Univ.), Shimoyama Yusuke (Science Tokyo), Kawanami Hajime (AIST/Univ. of Tsukuba), Suzuki Shogo (Albion), Makino Takashi (AIST), Tomai Takaaki (Tohoku Univ.), Fujii Tatsuya (AIST), Hoshina Takaaki (Nihon Univ.), Osada Mitsumasa (Shinshu Univ.), Watanabe Masaru (Tohoku Univ.), Matsuda Hiroyuki (Nihon Univ.), Hiraga Yuya (Tohoku Univ.), Murakami Yuya (Shizuoka Univ.), Ushiki Ikuo (Hiroshima Univ.), Kuroki Nahoko (Ochanomizu Univ.)

Supercritical fluids and their properties are becoming increasingly important for sustainable societal development, with a growing range of practical applications. This symposium will highlight recent research and advances in utilising these unique properties in manufacturing. Leading researchers from around the world will present the latest trends and developments in this rapidly evolving field, demonstrating how supercritical fluids' remarkable properties enable innovative manufacturing processes. Presentations at this symposium will be 30 minutes long, including 5-minute question period.

Hall G, Day 1

Time	Paper ID	Title / Authors	Keywords	Topic code	Ack. number
Hall G(2F 28 ), Day 1(Mar. 17)
(13:00–13:35)
13:00– 13:05		Opening Remarks (Utsunomiya U.) (Reg)Sato Takafumi
13:05– 13:35	G114	[Invited lecture] Advancing the prediction of phase separation and thermodynamic properties through the integration of universal molecular descriptors and physics-informed machine learning (Nat. Taipei U. Tech.) *(Reg)Hung Ying-Chieh, Hung Hsiu-Min	Machine learning directed message passing neural network (D-MPNN) COSMO-SAC	K-4	419
(13:35–14:05)
13:35– 14:05	G115	[Invited lecture] Hybrid machine learning models for thermophysical property estimation (Shizuoka U.) *(Reg)Murakami Yuya, (Stu)Hatoyama Atsushi, (Stu)Ikeda Keisuke	Hybrid machine learning Physical property PINNs	K-4	779
(14:05–14:35)
14:05– 14:35	G116	[Invited lecture] Data- and Model-Driven Development of Advanced CO₂ Capture Processes (Nagoya U.) (Reg)Machida Hiroshi	Thermophysical Property Measurement Process Modeling and Simulation CO2 Capture Technologies	K-4	778
14:35– 14:50		Break
(14:50–15:20)
14:50– 15:20	G119	[Invited lecture] Solvent-Mediated Carbamate-to-Alkyl Carbonate Shuttling Enables Fast and High-Capacity CO₂ Capture (Nat. Tsing Hua U.) *Lin Yu-Jeng, Chen Yu-Fan	water-lean solvents non-aqueous solvents CO2 absorption	K-4	806
(15:20–15:50)
15:20– 15:50	G120	[Invited lecture] Liquefied Dimethyl Ether as Viable Green Alternative to Conventional Solvents for Extraction of Natural Products (Chulalongkorn U.) Shotipruk Artiwan	Dimethyl ether Sustainable extraction Bioactive compounds	K-4	807
(15:50–16:20)
15:50– 16:20	G121	[Invited lecture] Solid base catalysis in hot compressed water (U. Tokyo) (Reg)Akizuki Makoto	Hot compressed water Solid base catalysts Solvent effect	K-4	777
16:20– 16:25		Closing Remarks (Kanazawa U.) (Reg)Yamada Hidetaka

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SCEJ 91st Annual Meeting (Kyoto, 2026)

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