Structure Determination of Polymer Nanocomposites by Small Angle Scattering

نویسندگان

  • Julian Oberdisse
  • Wim Pyckhout-Hintzen
  • Ekkehard Straube
  • Eckehard Straube
چکیده

The characterization of the structure of polymer nanocomposites by the techniques of small-angle scattering of neutrons and X-rays is reviewed. Within a concise introduction to elastic small-angle scattering, the basics of shape, structure, and interactions of particles are presented, as well as the scattering from polymer chains. The scattering properties of polymer chains can be described within models that can also account for stress-strain properties. In neutron scattering, stretched polymer chains and their fluctuation width can be directly accessed by taking advantage of the difference in the scattering length between H and D atoms. Further, SANS can equally cope with the mounting interest in the characterization of polymers in nowadays popular multiphase materials. Using contrast matching or a combination of two probes, i.e. neutrons and x-rays, the solid phase can be made invisible or visible. This allows to extract new structural information about the rubbery matrix that could not be obtained separately by other methods. Current literature on filler scattering in polymer nanocomposites will be reviewed. The most common fillers where scattering has been shown to be decisive are carbon black, silica, polymeric fillers, and, to a lesser extent, clay. Scattering is a powerful tool to analyse the filler structure in a non destructive way, in the dry (pure filler) state, in solution, or inside macroscopically thick nanocomposite samples. The scattered intensity is obtained as an average over the complete sample, thereby decreasing the impact of possible artefacts as it might be the case in direct-space imaging. In this review part, it is attempted to give an overview of the morphology of the hard component typically encountered in filled polymer systems, its modes of deformation as the sample is deformed, as well as a summary of possible routes of data interpretation and modelling.

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تاریخ انتشار 2017