Collective Features in Polyisobutylene. A Study of the Static and Dynamic Structure Factor by Molecular Dynamics Simulations

We present a study of the static and dynamic structure factor of polyisobutylene (PIB) by fully atomistic molecular dynamics simulations. The reliability of the simulated cell is first assured by computing the magnitudes measured by diffraction and neutron spin echo techniques on a fully deuterated sample and directly comparing the results with those previously obtained from experiments [ Farago B. ; Phys. Rev. E 2002, 65, 051803 ]. Taking advantage of the validated simulations, we have disentangled the contributions to the static and dynamic structure factor by using a suitable grouping of the partial correlation functions based on specific molecular groups in PIB: main-chain (MC) atoms and methyl group (MG) atoms. Regarding the structural features, we can attribute the temperature dependence of the first structure factor peakwhich is dominated by interchain correlations mainly from backbone atomspredominantly to the evolution of the MC/MG cross-correlations. Paradoxically, in the momentum transfer region where the MG/MG correlations present their main peak, the total structure factor displays a minimum due to a strong negative feature of the MC/MG cross-correlations. Concerning the dynamics, the decay of the intramolecular correlations takes place through highly correlated motions relating pairs of MGs and MG and MC atoms. At intermolecular level, the difference between pair and self-correlations for MC atoms is enhanced as the system approaches the glass-transition, indicating a gradual increase of collectivity. This collectivity of the backbones is ultimately the responsible for the modulation of the activation energy with the structure factor found in the experiments and reproduced by the simulations. Finally, we analyze the contributions of the analytical ansatz recently proposed to describe the collective relaxation time [ Colmenero J. ; J. Chem. Phys. 2013, 139, 044906 ] in order to identify the key ingredient leading to the above-mentioned modulation of the activation energy, which is successfully accounted for by the model.