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Computational and theoretical modelling of self-healable polymer materials

Amin, D. (2016) Computational and theoretical modelling of self-healable polymer materials. PhD thesis, University of Reading

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In this thesis we study self-healing polymeric materials, these are materials which can autonomously heal upon fracture (showing a partial or full recovery of mechanical strength). While there are a number of approaches to self-healing we focus on modelling supramolecular polymer networks. These are formed by physical association of linear or branched polymers via reversible and highly directional non-covalent bonds. We carry out hybrid molecular dynamics/Monte Carlo simulations of supramolecular networks formed by unentangled telechelic chains. The association of stickers leads to the formation of a transient network. At high bonding energies, the majority of stickers are fully reacted and the fraction of open stickers is less than 1%. We find the dynamical behaviour of such systems is dominated by a partner exchange mechanism in which stickers exchange their associated partners by the association and disassociation of sticker clusters. We propose a phantom chain hopping model to describe chain relaxation dynamics in supramolecular networks, which provides numerical predictions in reasonably good agreement with our simulation results. These systems are then studied under both shear and planar extensional flows. The presence of transient networks leads to a huge increase in viscosity. We find strain hardening behaviour in start-up flow for shear rates higher than the reciprocal of the average bond lifetime which we conclude results from the non-Gaussian stretching of polymer chains. An overall reduction in the number of network strands is also seen which ultimately leads to shear thinning behaviour in steady-state. We also carry out simulations of mildly entangled monodisperse polymer chains under planar extensional flow by taking advantage of the computational benefits afforded by using GPUs in scientific computing. The method developed is found to be 10 times faster than a CPU approach while providing similar accuracy. These simulations are shown alongside experiments of uniaxial extension and provide qualitatively similar behaviour (both showing extensional thickening at intermediate rates).

Item Type:Thesis (PhD)
Thesis Supervisor:Wang, Z. and Likhtman, A.
Thesis/Report Department:Department of Mathematics and Statistics
Identification Number/DOI:
Divisions:Science > School of Mathematical, Physical and Computational Sciences > Department of Mathematics and Statistics
ID Code:68437


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