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A deconvolution protocol of the mechanical relaxation spectrum to identify and quantify individual polymer feature contributions to self-healing
Author(s) -
Vincenzo Montano,
Stephen J. Picken,
Sybrand van der Zwaag,
Santiago J. García
Publication year - 2019
Publication title -
physical chemistry chemical physics
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.053
H-Index - 239
eISSN - 1463-9084
pISSN - 1463-9076
DOI - 10.1039/c9cp00417c
Subject(s) - deconvolution , relaxation (psychology) , self healing , self healing material , polymer , feature (linguistics) , blind deconvolution , statistical physics , physics , materials science , polymer science , nuclear magnetic resonance , optics , biology , medicine , philosophy , neuroscience , linguistics , alternative medicine , pathology
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 Tg. 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-Tg. 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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