1. Fundamental properties

`SCEJ 89th Annual Meeting (Sakai, 2024)`

Last modified: 2024-06-18 12:32:12

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Session programs : 1. Fundamental properties

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Hall I, Day 3

Time	Paper ID	Title / Authors	Keywords	Topic code	Ack. number
Hall I(B3 2F 202), Day 3(Mar. 20)
(9:00–10:00) (Chair: Sugahara Takeshi, Orita Yasuhiko)
9:00– 9:20	I301	Ethylene glycol-based deep eutectic solvents: CO₂ solubility and partial molar volume (Nihon U.) *(Stu)Ainai Y., Yokoyama C., (Reg)Kodama D.	Deep eutectic solvent CO2 solubility Partial molar volume	1-a	168
9:20– 9:40	I302	Viscosity measurement and correlation for dense fluid mixture of carbon dioxide and methanol under pressure (Chuo U.) (Stu)Onodera Norihiro, *(Reg)Funazukuri Toshitaka	viscosity dense mixture correlation	1-a	613
9:40– 10:00	I303	Determination of liquid-liquid equilibria for squalene + ethyl lactate system (Nihon U.) *(Reg)Matsuda Hiroyuki, (Stu)Yamada Risa, (Reg)Kurihara Kiyofumi, (Reg)Tochigi Katsumi	Liquid-liquid equilibria Squalene Ethyl lactate	1-a	455
(10:00–11:00) (Chair: Kodama Daisuke, Hiejima Yusuke)
10:00– 10:20	I304	Dispersion prediction of surface modified nanoparticles using machine learning model with various molecular descriptors (Tokyo Tech) *(Reg)Orita Yasuhiko, (Stu)Shibata Koh, (Reg)Shimoyama Yusuke	machine learning dispersion prediction surface modified nanoparticles	1-b	242
10:20– 10:40	I305	Prediction of Solubilities for Drugs in Aqueous Cyclodextrins Solutions Using ASOG Group Contribution Method (Nihon U.) *(Reg)Tochigi Katsumi, (Reg)Matsuda Hiroyuki, (Reg)Kurihara Kiyofumi	Cyclodextrins Solubilities ASOG group contribution model	1-a	163
10:40– 11:00	I306	Prediction of Phase Equilibria for Systems Containing Ester Lactate Using ASOG Group Contribution Method (Nihon U.) *(Reg)Tochigi Katsumi, (Reg)Matsuda Hiroyuki, (Reg)Kurihara Kiyofumi	Ester Lactate Phase Equilibria ASOG Group Contribution Method	1-a	251
(11:00–12:00) (Chair: Matsuda Hiroyuki, Haruki Masashi)
11:00– 11:20	I307	Prediction of Physical Properties by Atomic Group Contribution Method with Deep Learning (TUS) (Reg)Ohe Shuzo	Prediction Physical properties Deep learning	1-a	702
11:20– 11:40	I308	In-situ observation of microstructural changes in polymers by chemometric analysis of Raman spectra (Kanazawa U.) *(Reg)Hiejima Yusuke, Murakami Kazuki, Ito Asae, Nitta Koh-hei	Raman spectroscopy Chemometrics Polymer	1-b	6
11:40– 12:00	I309	Separation of Propane and Propylene by Urea Clathrate Formation (Osaka U.) Kobatake Ryosuke, *(Reg)Sugahara Takeshi, (Reg)Hirai Takayuki	Organic clathrate Urea C3 separation	1-a	107

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SCEJ 89th Annual Meeting (Sakai, 2024)

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