Issue 6, 2022

Supercritical CO2 blown poly(ε-caprolactone) covalent adaptable networks towards unprecedented low density shape memory foams

Abstract

Recent studies have highlighted the efficacy and benefit of shape-memory polymer foams over bulk materials, especially for self-deploying medical devices. In that field, poly(ε-caprolactone) covalent adaptable networks (PCL-CAN) are materials of choice since they combine biocompatibility, excellent shape memory properties with reconfiguration ability of the network allowing the design of biomedical devices of complex shapes. The preparation of PCL-CAN foams was here investigated by using a solvent-free supercritical CO2 foaming process. Starting from a mixture of low molar mass PCL stars bearing furan or maleimide as end-groups, a two-step foaming process was developed that leads to highly porous foams of unprecedented low density (0.02 g cm−3). We took advantage of the thermo-reversible Diels–Alder addition to control the molar mass and the network crosslinking density of the PCL throughout the foaming process. Adjusting the amount of Diels–Alder adducts at each foaming step is key to allow foaming of the mixture and reach closed-cell foams of low density that exhibit excellent shape memory properties. Thanks to the thermoreversibility of the Diels–Alder reaction, these foams are also recyclable at high temperature. These innovative shape memory PCL-CAN foams are attractive candidates as self-deploying implants for vessels occlusion, as shown by dynamic mechanical analysis and illustrated by mechanical occlusion of a large simulated vessel.

Graphical abstract: Supercritical CO2 blown poly(ε-caprolactone) covalent adaptable networks towards unprecedented low density shape memory foams

Supplementary files

Article information

Article type
Paper
Submitted
13 Jan 2022
Accepted
20 Feb 2022
First published
22 Feb 2022
This article is Open Access
Creative Commons BY-NC license

Mater. Adv., 2022,3, 2918-2926

Supercritical CO2 blown poly(ε-caprolactone) covalent adaptable networks towards unprecedented low density shape memory foams

M. Houbben, J. Thomassin and C. Jérôme, Mater. Adv., 2022, 3, 2918 DOI: 10.1039/D2MA00040G

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