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Updated: Sep 16, 2026

Fabrication of a Bioactive, PCL-based "Self-fitting" Shape Memory Polymer Scaffold
Published on: October 23, 2015
Soft, Reactive, and Alive: A Dynamic Framework for Degradation and Functional Stability of Polymeric Biomaterials
Alfredo Rondinella1, Elia Marin1,2,3
1Department Polytechnic of Engineering and Architecture, University of Udine, 33100 Udine, Italy.
Abstract:
Polymeric biomaterials do not degrade in vivo through isolated chemical or mechanical events. Hydrolysis, oxidation, enzymatic cleavage, fatigue, wear, protein adsorption, and lipid uptake often interact across the material surface, bulk, and surrounding biological environment, producing time-dependent changes in both structure and function. Here, we propose a conceptual framework for organizing polymer degradation under biomedical conditions as a directional network of coupled mechanisms, emphasizing how established degradation pathways can influence one another and collectively contribute to time-dependent functional loss. The framework distinguishes degradation reactions from interfacial modulators and links molecular damage to functionality retention, defined according to application-specific endpoints such as mechanical strength, mass retention, dimensional stability, or interfacial performance. We introduce a qualitative interaction matrix to describe how individual mechanisms can amplify or modulate downstream pathways, and we use this matrix to identify recurrent degradation archetypes across major biomedical polymer classes, including polyesters, polyolefins, polyamides, polyurethanes, silicones, polyacrylates, polyvinyl polymers, polyimides, and natural polymers. This perspective emphasizes that material optimization strategies rarely eliminate degradation; instead, they shift the hierarchy of active pathways. By reframing biocompatibility as a dynamic, functionality-dependent property, the proposed framework provides a structured basis for comparing polymeric biomaterials, designing more realistic in vitro tests, and developing future data-driven models of long-term implant performance.
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