Multiscale descriptor-based framework for predicting on-demand debonding in silane-modified polyether interfaces
Silane-modified polyethers (SMPs) are widely used as adhesives and sealants; however, their permanent adhesion complicates repair, reuse, and recycling of adherend materials at the end of service life. In this research, on-demand debonding in SMP systems incorporating thermally expandable particles is demonstrated, and the physical mechanisms governing debonding efficiency are elucidated. Systematic thermoanalytical and mechanical characterization identifies key thermal and mechanical material descriptors that act as competing driving and resisting contributions to on-demand debonding across filler, polymer, and joint scales. The correlation between these multiscale descriptors establishes a quantitative relationship between material properties and joint-level debonding performance, expressed through a debonding index. This relationship exhibits a threshold-controlled response, enabling a logistic model that captures the bounded and nonlinear evolution of debonding efficiency across different SMP formulations and adherend combinations. The resulting framework accurately predicts debonding efficiency without refitting the model parameters. Overall, this descriptor-based framework provides a rational foundation for predicting and designing on-demand debondable SMP adhesives and sealants, supporting circularity in bonded assemblies.
Authors
- Anja Huch
- Roman Elsener (ORCID: https://orcid.org/0000-0001-7832-8880)
- Mark Schubert
- Rahel Graf
- Dharun Vadugappatty Srinivasan
Publication Details
- Journal
- Materials & Design
- Published
- 2026-02-14
- DOI
- https://doi.org/10.1016/j.matdes.2026.115664
- Primary Topic
- Machine Learning in Materials Science
- Type
- article
- Field-Weighted Citation Impact
- 0.00