Abstract

Glass fibers slowly dissolve and age when exposed to water molecules. This phenomenon also occurs when glass fibers are inside fiber-reinforced composites protected by the matrix. This environmental aging results in the deterioration of the mechanical properties of the composite. In structural applications, GFRPs are continuously exposed to water environments for decades (typically, the design lifetime is around 25 years or even more). During their lifetime, these materials are affected by various temperatures, pH (acidity) levels, mechanical loads, and the synergy of these factors. The rate of the degradation process depends on the nature of the glass, sizing, fiber orientation, and environmental factors such as acidity, temperature, and mechanical stress. In this work, the degradation of typical industrial-grade R-glass fibers inside an epoxy fiber-reinforced composite was studied experimentally and computationally. A Dissolving Cylinder Zero-Order Kinetic (DCZOK) model was applied and could describe the long-term dissolution of glass composites, considering the influence of fiber orientation (hoop vs. transverse), pH (1.7, 4.0, 5.7, 7.0, and 10.0), and temperature (20, 40, 60, and 80 °C). The limitations of the DCZOK model and the effects of sizing protection, the accumulation of degradation products inside the composite, and water availability were investigated. Dissolution was experimentally measured using ICP-MS. As in the case of the fibers, for GFRPs, the temperature showed an Arrhenius-type influence on the kinetics, increasing the rate of dissolution exponentially with increasing temperature. Similar to fibers, GFRPs showed a hyperbolic dependence on pH. The model was able to capture all of these effects, and the limitations were addressed. The significance of the study is the contribution to a better understanding of mass loss and dissolution modeling in GFRPs, which is linked to the deterioration of the mechanical properties of GFRPs. This link should be further investigated experimentally and computationally.

Full Text
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