A methodology of hydrodynamic complexity in topologically hyper-branched polymers undergoing hierarchical multiple relaxations

Lu, Haibao, Wang, Xiaodong, Hossain, Mokarram and Fu, Richard (2020) A methodology of hydrodynamic complexity in topologically hyper-branched polymers undergoing hierarchical multiple relaxations. Macromolecular Chemistry and Physics. ISSN 1022-1352 (In Press)

[img] Text
manuscript_macp.202000052.pdf - Accepted Version
Restricted to Repository staff only until 15 May 2021.

Download (1MB) | Request a copy

Abstract

A hydrodynamic model was proposed to describe conformational relaxation of molecules, viscoelasticity of arms and hierarchical multiple-shape memory effect (multi-SME) of hyper-branched polymer. Fox-Flory and Boltzmann’s principles were employed to characterize and predict the hierarchical relaxations and their multi-SMEs in hyper-branched polymers. A constitutive relationship among relaxation time, molecular weight, glass transition temperature and viscoelastic modulus was then formulated. Results revealed that molecular weight and number of arms of the topologically hyper-branched polymers significantly influence their hydrodynamic relaxations and shape memory behaviors. The effectiveness of model has been demonstrated by applying it to predict mechanical and shape recovery behaviors of hyper-branched polymers, and the theoretical results show good agreements with the experimental ones. We expect this study provides an effective guidance on designing multi-SME in topologically hyper-branched polymers.

Item Type: Article
Uncontrolled Keywords: thermodynamics, relaxation, hyperbranched
Subjects: F200 Materials Science
F300 Physics
H900 Others in Engineering
Department: Faculties > Engineering and Environment > Mathematics, Physics and Electrical Engineering
Depositing User: Rachel Branson
Date Deposited: 18 May 2020 12:39
Last Modified: 18 May 2020 12:45
URI: http://nrl.northumbria.ac.uk/id/eprint/43161

Actions (login required)

View Item View Item

Downloads

Downloads per month over past year

View more statistics