A deconvolution protocol of the mechanical relaxation spectrum to identify and quantify individual polymer feature contributions to self-healing

Journal Article (2019)
Author(s)

Vincenzo Montano (TU Delft - Novel Aerospace Materials)

Stephen J. Picken (TU Delft - ChemE/Advanced Soft Matter)

S. Van der ZWAAG (TU Delft - Novel Aerospace Materials)

SJ García (TU Delft - Novel Aerospace Materials)

Research Group
Novel Aerospace Materials
Copyright
© 2019 V. Montano, S.J. Picken, S. van der Zwaag, Santiago J. Garcia
DOI related publication
https://doi.org/10.1039/c9cp00417c
More Info
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Publication Year
2019
Language
English
Copyright
© 2019 V. Montano, S.J. Picken, S. van der Zwaag, Santiago J. Garcia
Research Group
Novel Aerospace Materials
Issue number
19
Volume number
21
Pages (from-to)
10171-10184
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Abstract

Starting from experimental macro-rheological data, we develop a fitting protocol that succeeded in the separation of the overlapping relaxation phenomena in the dissipative regime for a set of intrinsic healing polymers healing most effectively near their glass transition temperature T g . To allow for a proper deconvolution, the rheological master curves are converted to a relaxation spectrum (H(τ)) and this is fitted using an optimized mechanical model, e.g. the Maxwell-Weichert model. The deconvolution of overlapping segmental mobility and reversible interactions is successfully demonstrated for a set of polyimide and polyamide polymers containing none, one and two reversible dynamic features near-T g . Through the fitting parameters, the relaxation timescale of each feature and their apparent process enthalpies are obtained. The quantitative data obtained using the fitting protocol are then compared to macroscopic healing results. As a result, a clear correspondence between the energy stored by the system to accomplish reversible (e.g. H-bonds, π-π) and chain interdiffusion relaxation transitions and the healing efficiency of such polymers are obtained. The implementation of this protocol allows for a clearer identification of the relevant mechanisms in self-healing polymers and paves the way for the development of more efficiently healable polymeric systems.

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