journal article·2020-07

Pluronic-coated nanoparticles for enhanced spatial distribution and increased softness of nanocomposite hydrogels

Leandro Toledo, Daniel Palacio, Susana Sánchez, Bruno F. Urbano

Journal of Materials Science· Volume 55· Issue 21
Publisher page

Abstract

Several factors govern the improvement in the mechanical, thermal, and conductive properties, when a nanoscale filler is incorporated into a polymer matrix. These factors include the concentration, size of the filling, distribution, etc. In this research, we present how to improve the distribution of nanoparticles using nanoparticles stabilized with block copolymers of different molar masses. For this purpose, Pluronic L35 and Pluronic F68 copolymers were used to stabilize nanoparticles in order to improve their distribution in a hydrogel of poly(2-hydroxyethyl methacrylate) through an entropic mechanism of interfacial control. The nanoparticles were labelled with fluorescein, and using confocal fluorescence microscopy, it was quantitatively demonstrated that both stabilizers led to a more homogeneous distribution. The nanocomposite hydrogels presented interesting viscoelastic properties. While the hydrogels stabilized with Pluronic L35 did not present significant changes in the rheological properties (e.g. stiffness and shear thinning), the most significant rheological effects were observed for the hydrogels prepared with Pluronic F68-stabilized nanoparticles. These hydrogels exhibited a diminution in the shear complex modulus, viscosity, and critical shear rate to give rise to the shear-thinning region, which was attributed to the differences in the molar masses of the stabilizers and favoured slipping of polymer chains because of unfavourable wetting with the stabilizer coating.
Source: openaire · Quality: candidate unverified · Source record ↗

Research context

OpenAlex topics: Polymer Nanocomposites and Properties · Hydrogels: synthesis, properties, applications · Dielectric materials and actuators

SciVX discovery tags are terminology-based. OpenAlex topics are source-provided classifications.

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