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SCEJ 91st Annual Meeting
Mar. 17 (Tue) - 19 (Thu), 2026
Yoshida Campus, Kyoto University
Japanese page
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.
Most recent update: 2025-12-31 20:44:01
 The keywords that frequently used in this topics code. | Keywords | Number | |
|---|---|---|---|
| Sustainable extraction | 1 |  | |
| CO2 Capture Technologies | 1 | | |
| PINNs | 1 | | |
| water-lean solvents | 1 | | |
| Thermophysical Property Measurement | 1 | | |
| Machine learning | 1 | | |
| non-aqueous solvents | 1 | | |
| COSMO-SAC | 1 | | |
| Hot compressed water | 1 | | |
| Process Modeling and Simulation | 1 | | |
| Hybrid machine learning | 1 | | |
| Physical property | 1 | | |
| Solid base catalysts | 1 | | |
| CO2 absorption | 1 | | |
| Dimethyl ether | 1 | | |
| Solvent effect | 1 | | |
| Bioactive compounds | 1 | | |
| directed message passing neural network (D-MPNN) | 1 | | |
| ACKN No. | Title/Author(s) | Keywords | Style |
|---|---|---|---|
| 419 | [Invited lecture] Advancing the prediction of phase separation and thermodynamic properties through the integration of universal molecular descriptors and physics-informed machine learning | Machine learning directed message passing neural network (D-MPNN) COSMO-SAC | O |
| 777 | [Invited lecture] Solid base catalysis in hot compressed water | Hot compressed water Solid base catalysts Solvent effect | O |
| 778 | [Invited lecture] Data- and Model-Driven Development of Advanced CO2 Capture Processes | Thermophysical Property Measurement Process Modeling and Simulation CO2 Capture Technologies | O |
| 779 | [Invited lecture] Hybrid machine learning models for thermophysical property estimation | Hybrid machine learning Physical property PINNs | O |
| 806 | [Invited lecture] Solvent-Mediated Carbamate-to-Alkyl Carbonate Shuttling Enables Fast and High-Capacity CO2 Capture | water-lean solvents non-aqueous solvents CO2 absorption | O |
| 807 | [Invited lecture] Liquefied Dimethyl Ether as Viable Green Alternative to Conventional Solvents for Extraction of Natural Products | Dimethyl ether Sustainable extraction Bioactive compounds | O |
Organizing Committee of SCEJ 91st Annual Meeting (2026)
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