Correlating the adhesion of an acrylic coating to the physico-mechanical behavior of a polypropylene substrate
Source abstract
Abstract
Abstract In this study, the adhesion strength of an acrylic coating onto a polypropylene (PP) based substrate was studied. The adhesion strength of the PP containing various wt% of oxidized wax was found to be dependent on the surface free energy of the substrate. To this end, the geometric mean and the acid–base approaches were used to estimate the surface free energy, both of which shown to exist a direct relation between surface hydrophilicity and the content of the oxidized wax. The viscoelastic behavior of the adherend also contributed to the coating adhesion. The viscoelastic response of the blends was investigated using dynamic mechanical thermal analysis (DMTA) and universal testing machine (UTM) for the bulk properties of the substrate and a depth sensing indentation technique for its near-surface, respectively. Bulk and near-surface moduli were decreased as a result of incorporation of wax additive into PP matrix. These were attributed to the homogeneous distribution of low molecular weight wax chains in the polymeric matrix and hence their impact on substrate integrity. The DMTA analysis showed a single phase matrix for all blends. Using a linear trend line, the adhesion test results revealed a good correlation to surface free energy calculations compared with the substrate modulus. The weaker effect of substrate modulus on surface adhesion was ascribed to the substrate plasticity and/or development of internal stresses in the coating layer. However, the glass transition temperature of the coating suggested that the internal stresses in coating layer were responsible to relax prior to conducting adhesion measurement.
Source: openaire · Quality: candidate unverified · Source record ↗
Research context
OpenAlex topics: Corrosion Behavior and Inhibition · Advanced Sensor and Energy Harvesting Materials · Mechanical Behavior of Composites
SciVX discovery tags are terminology-based. OpenAlex topics are source-provided classifications.
References (22)
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