Towards the Multiscale Design of Pressure Sensitive Adhesives

TL;DR

This paper introduces a multiscale computational framework that links microstructural features of pressure-sensitive adhesives to their macroscopic rheological and mechanical properties, aiding in the design of improved formulations.

Contribution

It develops a coupled multiscale modeling approach combining continuum and mesoscale models, calibrated with experimental data, to predict PSA behavior based on microstructure.

Findings

Successfully reproduces experimental rheological trends.

Differentiates mechanical signatures based on crosslink density.

Provides insights into how microstructure influences failure modes.

Abstract

Pressure-sensitive adhesives (PSAs) are soft polymeric materials that exhibit complex rheological and mechanical behavior gov- erned by the interplay between polymer architecture, crosslink density, and entanglement constraints. Predicting their rheological properties from underlying microstructure remains a central challenge in adhesive design. In this work, we adopt a multiscale com- putational framework based on the Lagrangian Heterogeneous Multiscale Method (LHMM), coupling a macroscopic continuum description with a mesoscale polymer network model featuring breakable bonds embedded in a viscous medium. The approach enables consistent information transfer across scales and captures both elastic network response and viscous dissipation. The framework is calibrated using experimental rheological data and tensile measurements for four PSA formulations with varying gel fractions and crosslink densities. The simulations reproduce key experimental trends in storage modulus (G'), loss modulus (G"), and tensile stress-strain behavior under planar extension, while differentiating the distinct mechanical signatures of each formula- tion. The results elucidate how crosslink density and effective network connectivity control stiffness, stress localization, and failure characteristics. Overall, the proposed multiscale methodology provides a predictive platform for linking microstructural design pa- rameters to macroscopic mechanical properties and offers a rational basis for the formulation and optimization of next-generation PSAs.