Polymer Science

Degradable Vinyl Polymer Nanoparticles/Latexes by Aqueous Nitroxide-Mediated Polymerization-Induced Self-Assembly



The synthesis of degradable vinyl polymer nanoparticles/latexes in aqueous dispersed media is receiving much attention, particularly for biomedical applications and plastic pollution control, as it can circumvent the severe limitations associated with emulsification of preformed degradable polymers. Polymerisation-induced self-assembly (PISA), which enables the in-situ formation of aqueous suspensions of diblock copolymer nano-objects of high solids content, has become a very popular polymerization process due to its many advantages in terms of simplicity, robustness, scalability and versatility. However, the preparation of degradable vinyl polymer nanoparticles by direct aqueous PISA has never been reported. This severely limits the use of PISA in biomedical and environmental applications. Herein, we report the first aqueous emulsion PISA able to generate degradable vinyl polymer nanoparticles. It relies on radical ring-opening polymerization-induced self-assembly (rROPISA) of traditional vinyl monomers (n-butyl acrylate or styrene) with dibenzo[c,e]oxepane-5-thione (DOT), a thionolactone that features high stability in protic solvents and favourable reactivity with many vinyl monomers, and is a precursor of labile thioester groups in the main chain. Stable aqueous suspensions of thioester-containing diblock copolymer nanoparticles were obtained with both vinyl monomers. Extensive degradation of the copolymers and the nanoparticles was successfully demonstrated under aminolytic or basic conditions. Given the success of the PISA process within the polymer community, this work has the potential to greatly expand its use in many areas, from nanomedicine (providing an extension to biocompatible vinyl polymers) to sustained materials in the context of the polymer circular economy.


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Supplementary material

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Supporting Information
Supporting Information containing additional figures and data