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

Abstract

A comprehensive study of the statistical distribution of the auto-adhesion lap-shear strength (σ) of amorphous polymer–polymer interfaces using various types of statistical tests and models is a useful approach aimed at a better understanding of the mechanisms of the self-healing interface. In the present work, this approach has been applied, for the first time, to a temperature (T) range below the bulk glass transition temperature (Tgbulk). The interest of this T range consists in a very limited or even frozen translational segmental motion giving little or no chance for adhesion to occur. To clarify this issue, the two identical samples of entangled amorphous polystyrene (PS) with a molecular weight (M) of 105 g/mol or 106 g/mol were kept in contact at T = Tgbulk − 33 °C for one day. The as-self-bonded PS–PS auto-adhesive joints (AJ) of PSs differing in M by an order of magnitude were fractured at ambient temperature, and their σ distributions were analyzed using the Weibull model, the quantile-quantile plots, the normality tests, and the Gaussian distribution. It has been shown that the Weibull model most correctly describes the σ statistical distributions of the two self-bonded PS–PS AJs with different M due to the joints’ brittleness. The values of the Weibull modulus (a statistical parameter) m = 2.40 and 1.89 calculated for PSs with M = 105 and 106 g/mol, respectively, were rather close, indicating that the chain length has a minor effect on the σ data scatter. The Gaussian distribution has been found to be less appropriate for this purpose, though all the normality tests performed have predicted the correctness of the normal distribution for these PS–PS interfaces.

Inserting urethane into 1,2,3‐triazole click‐cured solid propellant binder to form microphase separation and enhance mechanical and combustible performances

“…-82.0 C. The PUPET presents higher T g than the PPET, simultaneously, the resulting PUTA composites have higher T g than the PTA composites, which means the molecular motion of polyether is more restricted by additional intermolecular forces. 33,34 Since hydrogen bonding can act as additional intermolecular forces, 35,36 the increase in T g means the successful insertion of the urethane structure.…”

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

“…The Weibull distribution is based on the “weakest link” theory . If the strength distribution of the fiber conforms to the Weibull function, it means that its fracture is affected by random defects, which may be surface cracks, interface defects, , chain entanglement, and end points . In addition, for brittle materials, on the one hand, fracture is difficult to predict, and crack propagation rarely occurs; on the other hand, the strength of fiber materials is very discrete, and unexpected fractures are easy to occur when the average value is used to describe the strength properties of the fiber.…”

“…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) and amorphous polystyrene (PS) . To help understand the mechanisms of the self-healing interface.…”

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].…”