The dynamics of the polymer matrix in filled rubbers is modified by the presence of solid particles. We used low-field proton NMR to investigate model filled samples consisting of a dispersion of grafted silica particles into an elastomeric matrix. Exploiting magic-sandwich echo experiments, we were able to determine the fraction of polymer with slower dynamics and to correlate it to the silica specific surface. The presence of immobilized polymer—most probably due to a gradient of glass transition temperature around the solid particles—is detected whether there is a covalent bond between the filler and the matrix or not. Moreover, the fraction of immobilized polymer decreases in similar ways with either an increase of the temperature or the addition of solvent. In the case of covalent bonds between the silica and the polymer, multiple-quantum experiments reveal that the cross-link density of the elastomer matrix is locally increased in the vicinity of the particles. This is an observation that was not made in any conventional filled elastomer system and it can be attributed to the good particle dispersion and the covalent links in our model samples.

We present proton–proton multiple-quantum investigations on a series of monomodal and strongly bimodal end-linked poly(dimethylsiloxane) model networks. A robust pulse sequence characterized by a well-defined double-quantum Hamiltonian along with a specific normalization approach is used to obtain double-quantum build-up curves. These curves are analyzed in terms of the spin dynamics of a local subsystem of monomer-fixed spins, where analytical fitting functions yielding residual dipole–dipole coupling constants are derived on the basis of exact solutions provided by simulations. Further employing the novel experimental strategy of double-quantum preselection of elastically active network chains, it is shown that the network response is purely heterogeneous, and that the data can be analyzed in terms of distributions of local dynamic order parameters using different models. The results yield consistent proof that local chain order in bimodal networks obeys a linear mixing law of short- and long-chain components. The order parameter distribution in a long-chain monomodal network is found to be surprisingly narrow, with a rather high average order parameter. Implications on the validity of present theories used to explain order and dynamics in networks are discussed.