%0 Journal Article
%T Revealing the Impact of Viscoelastic Characteristics on Performance Parameters of UV-Crosslinked Hotmelt Pressure-Sensitive Adhesives: Insights from Time-Temperature Superposition Analysis.
%A Guder M
%A Günther R
%A Bremgartner K
%A Senn N
%A Brändli C
%J Polymers (Basel)
%V 16
%N 15
%D 2024 Jul 25
%M 39125149
%F 4.967
%R 10.3390/polym16152123
%X This study emphasizes the influential role of rheology in decoding the viscoelastic properties of pressure-sensitive adhesives (PSAs) vital to predicting key application features such as shear, tack, and peel, depending on the flow characteristics of PSAs during bonding and debonding processes. By applying the principle of time-temperature superposition (TTS), we extend the scope of our frequency analysis, surpassing the technical constraints of the available apparatus. Our exploration aims to uncover the general correlations between PSAs' viscoelastic properties and their performance in end-use applications. Initially, the adhesive performance and viscoelastic properties of a UV-crosslinkable styrene-butadiene-styrene (SBS) model adhesive prior and subsequent to UV irradiation were examined. The subsequent crosslinking reaction increased cohesive strength and heat resistance, although tack and peel strength observed a substantial decline. We successfully demonstrated these effects by logging the viscoelastic properties, specifically the storage modulus G' at lower frequencies, which mirrors the shear strength at higher temperatures and the shift in the tan δ peak to represent each PSA's tack. These correlations were partially reflected in three commercial UV crosslinkable acrylic PSA products, although the effect of UV irradiation was less distinctive. This study also revealed the challenges in predicting tack and peel strength, which result from a complex interplay of bonding and debonding processes. Our findings reinforce the necessity for more sophisticated analysis techniques and models that can accurately predict the end-use performance of PSAs across different physical structures and chemical compositions. Further research is needed to develop these predictive models, which may reduce the need for labor-intensive testing under real-life conditions.