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Issue 46, 2017
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Tunable synthetic control of soft polymeric nanoparticle morphology

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With a growing variety of nanoparticles available, research probing the influence of particle deformability, morphology, and topology on the behavior of all polymer nanocomposites is also increasing. In particular, the behavior of soft polymeric nanoparticles in polymer nanocomposites has displayed unique behavior, but their precise performance depends intimately on the internal structure and morphology of the nanoparticle. With the goal of providing guidelines to control the structure and morphology of soft polymeric nanoparticles, we have examined monomer starved semi-batch nano-emulsion polymerizations that form organic, soft nanoparticles, to correlate the precise structure of the nanoparticle to the rate of monomer addition and crosslinking density. The synthesis method produces 5–20 nm radii polystyrene nanoparticles with tunable morphologies. We report small angle neutron scattering (SANS) results that correlate synthetic conditions to the structural characteristics of soft polystyrene nanoparticles. These results show that the measured molecular weight of the nanoparticles is controlled by the monomer addition rate, the total nanoparticle radius is controlled by the excess surfactant concentration, and the crosslinking density has a direct effect on the topology of each nanoparticle. These studies thus provide pathways to control these 3 structural characteristics of the nanoparticle. This research, therefore provides a conduit to thoroughly investigate the effect of structural features of soft nanoparticles on their individual properties and those of their polymer nanocomposites.

Graphical abstract: Tunable synthetic control of soft polymeric nanoparticle morphology

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The article was received on 01 Aug 2017, accepted on 30 Oct 2017 and first published on 31 Oct 2017

Article type: Paper
DOI: 10.1039/C7SM01533J
Citation: Soft Matter, 2017,13, 8849-8857
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    Tunable synthetic control of soft polymeric nanoparticle morphology

    H. J. Martin, B. T. White, C. J. Scanlon, T. Saito and M. D. Dadmun, Soft Matter, 2017, 13, 8849
    DOI: 10.1039/C7SM01533J

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