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
基金
日本科学技术振兴机构; 日本学术振兴会;
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
收藏
页码:13678 / 13681
页数:5
相关论文
共 50 条
  • [41] Machine-Learning-Assisted Accurate Band Gap Predictions of Functionalized MXene
    Rajan, Arunkumar Chitteth
    Mishra, Avanish
    Satsangi, Swanti
    Vaish, Rishabh
    Mizuseki, Hiroshi
    Lee, Kwang-Ryeol
    Singh, Abhishek K.
    CHEMISTRY OF MATERIALS, 2018, 30 (12) : 4031 - 4038
  • [42] Machine-learning-assisted Quantitative Analysis in Optical Coherence Tomography Angiography
    Liu, Rongrong
    Mei, Song
    Mao, Zaixing
    Wang, Zhenguo
    Chan, Kinpui
    INVESTIGATIVE OPHTHALMOLOGY & VISUAL SCIENCE, 2020, 61 (07)
  • [43] Machine-learning-assisted orbital angular momentum recognition using nanostructures
    Sharma, Chayanika
    Badavath, Purnesh Singh
    Supraja, P.
    Kumar, R. Rakesh
    Kumar, Vijay
    JOURNAL OF THE OPTICAL SOCIETY OF AMERICA A-OPTICS IMAGE SCIENCE AND VISION, 2024, 41 (07) : 1420 - 1425
  • [44] Machine-Learning-Assisted Descriptors Identification for Indoor Formaldehyde Oxidation Catalysts
    Cao, Xinyuan
    Huang, Jisi
    Du, Kexin
    Tian, Yawen
    Hu, Zhixin
    Luo, Zhu
    Wang, Jinlong
    Guo, Yanbing
    ENVIRONMENTAL SCIENCE & TECHNOLOGY, 2024, 58 (19) : 8372 - 8379
  • [45] Machine-learning-assisted determination of electronic correlations from magnetic resonance
    Rao, Anantha
    Carr, Stephen
    Snider, Charles
    Feldman, D. E.
    Ramanathan, Chandrasekhar
    Mitrovic, V. F.
    PHYSICAL REVIEW RESEARCH, 2023, 5 (04):
  • [46] Unified machine-learning-assisted design of stainless steel bolted connections
    Jiang, Ke
    Zhao, Ou
    JOURNAL OF CONSTRUCTIONAL STEEL RESEARCH, 2023, 211
  • [47] Machine-learning-assisted molecular design of phenylnaphthylamine-type antioxidants
    Du, Shanda
    Wang, Xiujuan
    Wang, Runguo
    Lu, Ling
    Luo, Yanlong
    You, Guohua
    Wu, Sizhu
    PHYSICAL CHEMISTRY CHEMICAL PHYSICS, 2022, 24 (21) : 13399 - 13410
  • [48] Machine-Learning-Assisted Routing in SDN-based Optical Networks
    Troia, Sebastian
    Rodriguez, Alberto
    Martin, Ignacio
    Alberto Hernandez, Jose
    Gonzalez De Dios, Oscar
    Alvizu, Rodolfo
    Musumeci, Francesco
    Maier, Guido
    2018 EUROPEAN CONFERENCE ON OPTICAL COMMUNICATION (ECOC), 2018,
  • [49] Machine-learning-assisted hydrogen adsorption descriptor design for bilayer MXenes
    Tian, Weizhi
    Ren, Gongchang
    Wu, Yuanting
    Lu, Sen
    Huan, Yuan
    Peng, Tiren
    Liu, Peng
    Sun, Jiangong
    Su, Hui
    Cui, Hong
    JOURNAL OF CLEANER PRODUCTION, 2024, 450
  • [50] Machine-Learning-Assisted Signal Detection in Ambient Backscatter Communication Networks
    Toro, Usman Saleh
    ElHalawany, Basem M.
    Wong, Aslan B.
    Wang, Lu
    Wu, Kaishun
    IEEE NETWORK, 2021, 35 (06): : 120 - 125