Framboidal ABC triblock copolymer vesicles: a new class of efficient Pickering emulsifier

Pickering emulsions offer important advantages over conventional surfactant-stabilized emulsions, including enhanced long-term stability, more reproducible formulations and reduced foaming problems. The recent development of polymerization-induced self-assembly (PISA) offers considerable scope for t...

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Bibliographic Details
Published inChemical science (Cambridge) Vol. 6; no. 11; pp. 6179 - 6188
Main Authors Mable, C J, Warren, N J, Thompson, K L, Mykhaylyk, O O, Armes, S P
Format Journal Article
LanguageEnglish
Published England 01.01.2015
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Summary:Pickering emulsions offer important advantages over conventional surfactant-stabilized emulsions, including enhanced long-term stability, more reproducible formulations and reduced foaming problems. The recent development of polymerization-induced self-assembly (PISA) offers considerable scope for the design of a wide range of block copolymer nanoparticles with tunable surface wettability that may serve as bespoke Pickering emulsifiers. In the present study, we exploit PISA to design a series of model framboidal ABC triblock copolymer vesicles with exquisite control over surface roughness. Transmission electron microscopy (TEM) and small-angle X-ray scattering (SAXS) were utilized to characterize these nanoparticles, which were subsequently used to stabilize -dodecane emulsion droplets in water. The adsorption efficiency, , of the nanoparticles at the -dodecane/water interface was determined as a function of increasing vesicle surface roughness using a turbidimetry assay. A strong correlation between surface roughness and was observed, with increasing from 36% up to 94%. This is a significant improvement in Pickering emulsifier efficiency compared to that reported previously for similar vesicles with smooth surfaces. In summary, nanoparticles with appreciable surface roughness are much more effective Pickering emulsifiers and this parameter can be readily fine-tuned using a highly efficient PISA formulation.
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ISSN:2041-6520
2041-6539
DOI:10.1039/c5sc02346g