Impact of Crystallization on the Development of Statistical Self-Bonding Strength at Initially Amorphous Polymer–Polymer Interfaces

Abstract

To investigate the mechanisms of the adhesion (self-bonding) strength (s) development during the early stages of self-healing of polymer–polymer interfaces and fracture thereof, it is useful to operate not only with the average s value but with the s distribution as well. The latter has been shown to obey Weibull’s statistics for such interfaces. However, whether it can also follow the most widely used normal (Gaussian) distribution is currently unclear. Moreover, a more complicated self-healing case, when the s development at an initially amorphous interface is accompanied by its crystallization, has not been investigated yet in this respect. In order to address these two important issues, 10 pairs of amorphous poly(ethylene terephthalate) (PET) samples were kept in contact for various periods of time from 5 min to 15 h at a temperature (T) of 94 °C (preserving the amorphous state) or T = 150 °C (giving rise to cold crystallization), or both Ts. Thereafter, the as-formed amorphous and semi-crystalline PET–PET auto-adhesive joints were shear fractured in tension at ambient temperature. For the first time, the statistical distributions of a number of the measured s data sets were analyzed and discussed using both Weibull’s and the Gaussian model, including several normality tests.

Tensile Strength Statistics and Fracture Mechanism of Ultrahigh Molecular Weight Polyethylene Fibers: On the Weibull Distribution

“…The studies involved materials such as UHMWPE fiber, polyamide6, and polypropylene. In other papers, the researcher extended the Weibull distribution to the self-bonding strength analysis of amorphous poly(ethylene terephthalate) (PET) 33 and amorphous polystyrene (PS). 34 To help understand the mechanisms of the self-healing interface.…”

Evolution of Statistical Strength during the Contact of Amorphous Polymer Specimens below the Glass Transition Temperature: Influence of Chain Length

“…In turn, learning the most correct form of this distribution can give complementary useful information aimed at in-depth analysis of the deformation and fracture mechanisms in materials of various chemical origins. Indeed, on one hand, for the materials characterized by both extremely high ( σ > 1 GPa [ 5 , 6 , 7 ], both organic and inorganic fibers) and extremely low strengths ( σ < 1 MPa [ 8 , 9 , 10 , 11 ], weak polymer–polymer interfaces), i.e., for the materials drastically differing in the σ value, by 3 orders of magnitude, the σ statistical distributions have been found to follow the same distribution form: the Weibull distribution [ 12 , 13 , 14 , 15 , 16 , 17 , 18 , 19 ]. In this case, one observes a linear plot in the specific coordinates lnln [1/(1 − P j )] = f(ln σ ), where P j is the cumulative probability of failure.…”

“…It should be noted that the impact of some molecular and structural factors such as the chain architecture, and the presence or the absence of crystallites on the statistical adhesion strength of weak polymer–polymer interfaces, has already been investigated [ 8 , 9 , 10 , 11 ]. However, little is known about the influence, if any, of such an important molecular factor as the chain length on the adhesion strength statistics.…”

Tensile Strength Statistics of High-Performance Mono- and Multifilament Polymeric Materials: On the Validity of Normality

“…This additional information is useful for a better understanding of the deformation and fracture mechanisms of high-performance materials. For instance, if the strength distribution conforms to the standard Weibull's distribution function [2,[4][5][6][7][8][9][10][11][12][13][14][15][16][17], it means that the fracture mechanism is controlled by surface or interface cracks [2,6,22,23]. In contrast, if the Gaussian model is valid while the Weibull's one is not, it implies that this process is controlled by the sum of many independent and equally-weighed factors [8,24], i.e., it is a random process.…”

“…For instance, it is expected that the material's brittleness should have an impact on the type of statistical distribution of a mechanical property. In particular, this factor becomes critical for brittle and quasi-brittle materials to which the high-performance polymer materials belong, and for which the data scatter is rather broad [2,[4][5][6][7][8][9][10][11][12][13][14][15][16][17]22,23].…”