Authors field exact matches “Ono Takumi”; 5 programs are found. (“Poster with Flash” presentations are double-counted.)
The search results are sorted by the start time.
Time | Paper ID | Title / Authors | Keywords | Topic code | Ack. number |
---|---|---|---|---|---|
Day 1 | Y103 | Hydration structure analysis of monohydric- and dihydric-alcohol aqueous solutions via MD simulation | molecular dynamics alcohol aqueous solution hydration structure | SY-51 | 542 |
Day 1 | Chair: | ||||
Y119 | Application of ionic liquids with environmentally benign anion to liquid-liquid extraction | Liquid-Liquid Equilibria Ionic liquid Extraction | SY-51 | 623 | |
Y120 | Prediction of liquid-liquid equilibria using activity coefficient model with molecular information and machine learning | liquid-liquid equilibrium molecular information machine learning | SY-51 | 604 | |
Y121 | Determination and correlation of vapor-liquid equilibria necessary for design and development of carbon dioxide absorption process by chemical absorption method | Vapor-liquid equilibria Carbon dioxide Chemical absorption method | SY-51 | 733 | |
Y122 | Density and Viscosity measurement and modeling for mixture containing amine/water/ether/CO2 | CO2 capture viscosity density | SY-51 | 752 | |
Day 2 | T207 | Development of polymer blends using machine learning | Machine learning Polymer composite | SY-67 | 282 |
Day 2 | X217 | Investigation of nanocellular foaming with refrigerant gas/CO2 binary system | Nanocellular Refrigerant gas CO2 | SY-74 | 526 |
Day 3 | Chair: | ||||
X301 | Selective production of nano sized liposomes using direct ultra sonication in high pressure carbon dioxide and water. | liposome high pressure CO2 ultra sonication | SY-74 | 678 | |
X302 | Correlation of High-Pressure Thermal Conductivities for Binary Systems Using Local Composition Model | High-Pressure Thermal Conductivity Local Composition Model | SY-74 | 168 | |
X303 | Estimation of the solubility of biomass derived organic compounds in high-temperature and high-pressure water | Biomass Solubility Machine learning | SY-74 | 857 |
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SCEJ 51st Autumn Meeting (2020)