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Multi-layer in-situ for evaluation of dynamic mechanical properties of pressure sensitive adhesives

International Journal of Adhesion and Adhesives
Publication Date
DOI: 10.1016/j.ijadhadh.2006.09.014
  • Viscoelasticity
  • Pressure-Sensitive Adhesive
  • Postage Stamp
  • Multilayer Lap-Shear
  • Time–Temperature Superposition
  • Mathematics


Abstract A multi-layer, lap-shear geometry has been developed as a reliable testing method for evaluation of the dynamic mechanical properties of polyacrylic pressure-sensitive adhesives (PSA) for postage stamp applications. In-situ tests of four different PSA samples constructed by laminating, water-based polyacrylic PSAs to the face paper used for stamps were carried out with a dynamic mechanical analyzer (DMA) over a temperature range from −50 to 60 °C at frequencies of 0.1, 1, 10, and 100 Hz. This geometry utilizes the tension mode on the DMA, but the results were converted to shear properties of the PSA layers in the laminate. The effect of the specimen thickness (2–10 layers) on the dynamic mechanical properties was studied and the results suggest that a multi-layer geometry with 5–10 layers can be an appropriate structure to produce enhanced responses. Therefore, a geometry with 8-layer laminates was used for frequency sweep/isothermal temperature and for frequency sweep/temperature step tests. The results showed three relaxation responses: glassy, transition, and flow regions with respect to the frequency and temperature. The tensile properties of the face papers were also tested under the same conditions as those of the multi-layer geometry. Significant differences were found between the shear behaviors of the multi-layer geometry and the tensile behaviors of the face paper alone. This suggests that the tensile deformation of the face paper in the multi-layer geometry can be ignored as it did not contribute to the shear behaviors of the PSA layers. Master curves were produced by time–temperature superposition with a reference temperature of 23 °C. They can be used to predict the long- and short-term performance of these samples.

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