Issue 26, 2023

3D printable adhesive elastomers with dynamic covalent bond rearrangement

Abstract

Repairable adhesive elastomers are emerging materials employed in compelling applications such as soft robotics, biosensing, tissue regeneration, and wearable electronics. Facilitating adhesion requires strong interactions, while self-healing requires bond dynamicity. This contrast in desired bond characteristics presents a challenge in the design of healable adhesive elastomers. Furthermore, 3D printability of this novel class of materials has received limited attention, restricting the potential design space of as-built geometries. Here, we report a series of 3D-printable elastomeric materials with self-healing ability and adhesive properties. Repairability is obtained using Thiol–Michael dynamic crosslinkers incorporated into the polymer backbone, while adhesion is facilitated with acrylate monomers. Elastomeric materials with excellent elongation up to 2000%, self-healing stress recovery >95%, and strong adhesion with metallic and polymeric surfaces are demonstrated. Complex functional structures are successfully 3D printed using a commercial digital light processing (DLP) printer. Shape-selective lifting of low surface energy poly(tetrafluoroethylene) objects is achieved using soft robotic actuators with interchangeable 3D-printed adhesive end effectors, wherein tailored contour matching leads to increased adhesion and successful lifting capacity. The demonstrated utility of these adhesive elastomers provides unique capabilities to easily program soft robot functionality.

Graphical abstract: 3D printable adhesive elastomers with dynamic covalent bond rearrangement

Supplementary files

Article information

Article type
Paper
Submitted
24 mar 2023
Accepted
12 jun 2023
First published
12 jun 2023

Soft Matter, 2023,19, 4964-4971

Author version available

3D printable adhesive elastomers with dynamic covalent bond rearrangement

S. V. Wanasinghe, B. Johnson, R. Revadelo, G. Eifert, A. Cox, J. Beckett, T. Osborn, C. Thrasher, R. Lowe and D. Konkolewicz, Soft Matter, 2023, 19, 4964 DOI: 10.1039/D3SM00394A

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