A “Limited Aggregation Model” to Predict the Size of Acrylamide‐Based Microgels Synthesized with Ionic Surfactants

Abstract: The size of acrylamide‐based microgels can be decreased by addition of ionic surfactants during the classical dispersion polymerization. Nevertheless, the mechanism of such syntheses is not well understood yet. Here, a “Limited Aggregation Model” is proposed by analogy with the limited coalescence mechanism occurring for Pickering emulsion stabilization. In such a model, nuclei aggregate until a constant, high enough surfactant surface coverage is reached, which ensures colloidal stability. Consequently, the total surface of the growing particles, linked to the inverse of their size, is linearly dependent on the surfactant concentration. This law is verified if the surfactant/polymer particle interaction is high enough to guarantee a “total adsorption” of the surfactants onto the particles. This simple model fits very well with all the data extracted from the literature, including very different synthesis conditions. Finally, it not only permits to predict the microgels size, but it is also an interesting tool to investigate the role of each synthesis parameter like initiator, solvent or polymer. For instance, it shows that the surfactant role is not linked to its charge, proving that a phenomenon complementary to the electrostatic repulsion, related to the surfactant tail, ensures the colloidal stability of the growing collapsed microgels.

Object Details

• Location: Deutsche Nationalbibliothek Frankfurt am Main
• Extent: Online-Ressource
• Language: Englisch

References and Relationships

• Bibliographic citation: A “Limited Aggregation Model” to Predict the Size of Acrylamide‐Based Microgels Synthesized with Ionic Surfactants ; day:10 ; month:12 ; year:2023 ; extent:11 ; Macromolecular chemistry and physics ; (10.12.2023) (gesamt 11)

Contributors, Places and Time

• Creator: Brézault, Antoine ; Schmitt, Véronique ; Ravaine, Valérie ; Perrin, Patrick ; Sanson, Nicolas

Further information

• DOI: 10.1002/macp.202300372
• URN: urn:nbn:de:101:1-2023121114165182268878
• Rights: Open Access; Der Zugriff auf das Objekt ist unbeschränkt möglich.
• Last update: 15.08.2025, 7:36 AM CEST