Machine-Learning-Assisted Prediction of the Size of Microgels Prepared by Aqueous Precipitation Polymerization - Abstract

Aug 4, 2026

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Microgels are soft, water-swollen colloids whose performance depends strongly on their size, yet size control in aqueous precipitation polymerization has often relied on empirical trial-and-error across synthesis parameters. This work presents a linear-regression machine-learning approach tailored for small datasets via sparse modeling for small data (SpM-S). Using controlled variables such as monomer, crosslinker, initiator, surfactant, salt, temperature, stirring speed, and water, the model predicts hydrodynamic diameter from experimental inputs.

LanguageEnglish

CategoryResearch & Report

FormatPDF

Pages4

Reading Time10 min

AccessFree to download

Background and Motivation

Materials and Microgel Preparation

Machine-Learning Approach

Explanatory Variables and Dataset

Structural and Scattering Characterization

Received 00th January 20xx, Accepted 00th January 20xx Machine-Learning-Assisted Prediction of the Size of Microgels Prepared by Aqueous Precipitation Polymerization Daisuke Suzuki,*a, b Haruka Minato,a, b Yuji Sato,a, b Ryuji Namioka,b Yasuhiko Igarashi,c Risako Shibata,d and Yuya Oaki*d DOI: 10. 1039/x0xx00000x 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. Hydrogel nano/microparticles (nanogels/microgels) are hydrophilic or amphiphilic colloids that are highly swollen by water and are dispersed stably in aqueous solution.1 Due to their fascinating properties related to their softness and stimuliresponsiveness, their use in various applications, including controlled uptake/release of functional molecules,2 particulate stabilizers for interfaces,3 and soft colloidal crystals/glasses/gels,4 has been proposed. Among the methods for producing microgels, aqueous free radical precipitation polymerization is an excellent strategy for forming microgels of uniform size under environmentally friendly and cost-effective experimental conditions.1a,5 It is widely accepted that the monomers for these polymers are water soluble, but that upon growing, the polymers become insoluble in water, which results in the formation of nuclei for the growth of microgels; these nuclei then grow until they acquire sufficient colloidal stability.2a,5c To date, tremendous efforts have been devoted to revealing the detailed mechanism of precipitation polymerization,1a,5bc,6 which would allow the size of simple microgels (e.g., a monomer and crosslinker) obtained by precipitation polymerization to be controlled.7 However, copolymerization with various functional monomers to add further functionality to simple microgels is usually required, complicating the reaction and hence the prediction of a. Graduate School of Environmental, Life, Natural Science and Technology, Okayama University, 3-1-1 Tsushimanaka, Kita-ku, Okayama, 700-8530, Japan. [E-mail: d_suzuki@okayama-u.ac.jp](E-mail: d_suzuki@okayama-u.ac.jp) b. Graduate School of Textile Science & Technology, Shinshu University, 3-15-1 Tokida, Ueda, Nagano 386-8567, Japan c. Faculty of Engineering, Information and Systems, University of Tsukuba, 1-1-1 Tennodai, Tsukuba 305-8573, Japan d. Department of Applied Chemistry, Faculty of Science and Technology, Keio University, 3-14-1 Hiyoshi, Kohoku-ku, Yokohama 223-8522, Japan. [E-mail: oakiyuya@applc.keio.ac.jp](E-mail: oakiyuya@applc.keio.ac.jp) Electronic Supplementary Information (ESI) available: [details of any supplementary information available should be included here] . See DOI: 10. 1039/x0xx00000x the microgel size. In addition, various parameters including polymerization temperature and stirring conditions affect the size of the resultant microgel. Thus, in many cases, the microgel size in precipitation polymerizations has been controlled using a trial-and-error approach for each parameter based on experience and intuition of professional researcher(s) . If the size of functional microgels could be predicted, the development of applications that require precise control of the microgel size, such as targeted drug delivery and the formation of colloidal crystals composed of different microgels, would be accelerated. Fig. 1. Schematic illustration of the machine-learning-assisted prediction of the microgel size developed in this study. Against this background, we found that machine learning (ML) is an effective way to predict the size of microgels prepared by aqueous free radical precipitation polymerization (Fig. 1) . ML has been widely applied to the optimization of processes and the exploration of materials, such as contr

FAQs

Why is microgel size control important in soft colloid applications?

Microgels’ functional performance depends on how precisely their size is set, enabling uses such as controlled uptake/release, interface stabilization, and soft colloidal assemblies. Precise size control accelerates application development.

What machine-learning strategy is used to predict microgel size?

The study uses a linear-regression model combined with sparse modeling for small data (SpM-S). This approach builds interpretable, generalizable predictors even with limited datasets.

Which experimental parameters are included as predictors in the model?

The explanatory variables include concentrations of NIPAm, BIS, acrylic acid, monomer, SDS, NaCl, and potassium persulfate, along with water volume, stirring speed, and polymerization temperature.

Aug 4, 2026

Upload by Miles