Machine-learning-assisted prediction of the size of microgels prepared by aqueous precipitation polymerization

被引：1

作者：

Suzuki, Daisuke ^{[1
,2
]}

Minato, Haruka ^{[1
,2
]}

Sato, Yuji ^{[1
,2
]}

Namioka, Ryuji ^{[2
]}

Igarashi, Yasuhiko ^{[3
]}

Shibata, Risako ^{[4
]}

Oaki, Yuya ^{[4
]}

机构：

[1] Okayama Univ, Grad Sch Environm Life Nat Sci & Technol, 3-1-1 Tsushimanaka,Kita Ku, Okayama 7008530, Japan

[2] Shinshu Univ, Grad Sch Text Sci & Technol, 3-15-1 Tokida, Ueda, Nagano 3868567, Japan

[3] Univ Tsukuba, Fac Engn Informat & Syst, 1-1-1 Tennodai, Tsukuba 3058573, Japan

[4] Keio Univ, Fac Sci & Technol, Dept Appl Chem, 3-14-1 Hiyoshi,Kohoku Ku, Yokohama 2238522, Japan

来源：

CHEMICAL COMMUNICATIONS | 2024年 / 60卷 / 93期

基金：

日本学术振兴会; 日本科学技术振兴机构;

关键词：

HYDROGEL MICROSPHERES; PARTICLES;

D O I：

10.1039/d4cc04386c

中图分类号：

O6 [化学];

学科分类号：

0703 ;

摘要：

The size of soft colloids (microgels) is essential; however, control over their size has typically been established empirically. Herein, we report a linear-regression model that can predict microgel size using a machine learning method, sparse modeling for small data, which enables the determination of the synthesis conditions for target-sized microgels. We report a linear-regression model that can predict microgel size using a machine learning method, sparse modeling for small data.

引用

页码：13678 / 13681

页数：5

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