Literature DB >> 30195298

Looking at the dynamical heterogeneity in a supercooled polymer system through isoconfigurational ensemble.

Cristian Balbuena1, Melisa M Gianetti1, Ezequiel R Soulé1.   

Abstract

The dynamic correlations that emerge in a polymer system in supercooling conditions have been studied using molecular dynamic simulations. It is known that when a glass former approaches the glass transition temperature, the dynamics of the system (in terms of the mobilities of the particles) not only significantly slows down but also becomes more heterogeneous. Several theories relate this slowing down to increasing spatial (structural) correlations, for example, through the onset of cooperative relaxation regions in the Adam-Gibbs theory. In this work, we employ Pearson's coefficient in the isoconfigurational ensemble (ICE) which allows us to study the dynamic correlations of the monomers in the ICE and establish the relation between the structure of the monomers and its dynamic behavior. Similar to what happens with mobility, monomers with highest correlation are clustered, and the clustering increases with decreasing temperature. An interesting result is that regions with high ICE dynamic correlation are not coincident with highly mobile or immobile regions. These results represent a new approach to the study of dynamic heterogeneity that emerges in glass forming liquids, complementing the more traditional characterization in terms of mobility. The methodology proposed in this work that characterize the connected dynamic regions to structural causes can represent an alternative way to observe the cooperative relaxation regions.

Entities:  

Year:  2018        PMID: 30195298     DOI: 10.1063/1.5039644

Source DB:  PubMed          Journal:  J Chem Phys        ISSN: 0021-9606            Impact factor:   3.488


  1 in total

1.  Neural Networks Reveal the Impact of the Vibrational Dynamics in the Prediction of the Long-Time Mobility of Molecular Glassformers.

Authors:  Antonio Tripodo; Gianfranco Cordella; Francesco Puosi; Marco Malvaldi; Dino Leporini
Journal:  Int J Mol Sci       Date:  2022-08-18       Impact factor: 6.208

  1 in total

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