An experimental and theoretical study of the erosion of semi-crystalline polymers and the subsequent generation of microparticles

• Published in: Soft matter 2019-10, Vol.15 (41), p.832-8312
• Main Authors: Gaillard, Thibaut, George, Matthieu, Gastaldi, Emmanuelle, Nallet, Frdric, Fabre, Pascale
• Format: Article
• Language: English
• Subjects: Accelerated erosion; Condensed Matter; Crack propagation; Crystal structure; Crystallinity; Degradation; Erosion rates; Fracture mechanics; Heterogeneity; Kinetics; Materials Science; Mechanical properties; Microparticles; Microplastics; Organic chemistry; Pattern formation; Physics; Plastic debris; Polymers; Soft Condensed Matter; Spherulites
• ISSN: 1744-683X; 1744-6848
• DOI: 10.1039/c9sm01482a

The increase of plastics and microplastics in the environment is a major environmental challenge. Still, little is known about the degradation kinetics of macroplastics into smaller particles, under the joint actions of micro-organisms and physico-chemical factors, like UV or mechanical constraints. In order to gain insight into (bio)-degradation in various media, we perform accelerated erosion experiments by using a well-known enzymatic system. We show that the microstructure of semi-crystalline polymers plays a crucial role in the pattern formation at their surface. For the first time, the release of fragments of micrometric size is evidenced, through a mechanism that does not involve fracture propagation. A geometric erosion model allows a quantitative understanding of erosion rates and surface patterns, and provides a critical heterogeneity size, parting two types of behavior: spherulites either released, or eroded in situ . This new geometric approach could constitute a useful tool to predict the erosion kinetics and micro-particle generation in various media. Degradation of a semi-crystalline polymer, via an enzymatic erosion experiment, evidences microstructure dependent surface patterns and microparticles release. A generic geometric model accounts for the mass loss and predicts a critical release size.