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Quantifying structure and dynamics of bound and bulk polymer in tailor-made rubber-silica nanocomposites
The dynamics of long polymer chains in the presence of nanoparticles have been investigated. The nanocomposites of interest were inspired by tire industry-like rubber materials and consisted of entangled polyisoprene linear chains mixed with chemically pre-treated silica nanoparticles. Combining rhe...
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Published in: | Frontiers in physics 2022-11, Vol.10 |
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Main Authors: | , , , , , , , , , , , , |
Format: | Article |
Language: | English |
Subjects: | |
Citations: | Items that this one cites |
Online Access: | Get full text |
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Summary: | The dynamics of long polymer chains in the presence of nanoparticles have been investigated. The nanocomposites of interest were inspired by tire industry-like rubber materials and consisted of entangled polyisoprene linear chains mixed with chemically pre-treated silica nanoparticles. Combining rheology, dielectric spectroscopy, and neutron spin echo measurements, we measured the modification of the polymer chain dynamics from bulk state to high filler concentration over a broad range of time and length scales. We show that the end-to-end relaxation does not seem to be impacted, whereas the polymer dynamics is significantly slowed down at a very local scale in the presence of nano-fillers. In addition to this length scale dependent different dynamics, additional Neutron Spin Echo spectroscopy experiments and Small Angle Scattering on labelled polymer chains, irreversibly bound to the filler surface and re-dispersed in a fully deuterated matrix, revealed a negligible dynamical behavior of this particular population of localized chains in the rubber layer. The conformational statistics of these chains is that of self-avoiding walk train within a shell that is thinner than the size of the chain. To the best of our knowledge, this work is the first of its kind which measures the single chain form factor in the bound layer of chemically multi-linked chains to the filler surface. |
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ISSN: | 2296-424X 2296-424X |
DOI: | 10.3389/fphy.2022.1023234 |