3D-Printable Intelligent Polymers Based on Dynamic Reversible Urea Bonds

The limited availability of oil and therefore oil-based polymers leads to an investigation of more sustainable materials. For this purpose, longer polymer lifetimes are crucial. One approach to solve this challenge are self-healing materials, which are able to recover mechanical damage or certain properties. Self-healing polymers can be divided into extrinsic and intrinsic healing polymers. Extrinsic healing polymers are based on healing agents inside of, e.g., microcapsules in order to heal damages. The downside is the decreasing amount of microcapsules over multiple healing cycles. Intrinsic healing, on the other hand, is usually based on reversible bonds with the benefit of multiple healing cycles at the same location. This study represents the synthesis and investigation of intelligent abilities of 3D-printable polymers based on dynamic reversible urea bonds. Particular noteworthy is the intrinsic self-healing ability in complex 3D-printed structures.<br/>The synthesized polymers contain sterically hindered urea molecules with the aim to weaken the amide bond by disrupting the co-planarity due to the bulky substituent with the ultimate goal of bond reversibility. Commercially available monomers undergo light-induced polymerization, resulting in networks that are crosslinked by hindered urea bonds. For this purpose, the polymers were either casted in a PTFE-mold or DLP-3D-printed and afterwards thermally annealed for 24 h at 100 °C. These moieties are reversible and can therefore be converted back into the starting compounds (i.e. isocyanate and amine) through thermal treatment or undergo exchange reactions.<br/>The resulting polymers were analyzed by various techniques such as mechanical testing (tensile strength, 3-point-flexure, (dynamic) thermo-mechanical analysis) or the self-healing quantification by indentation. Furthermore, a comparison between casted and 3D-printed polymers was performed.<br/>Besides, all polymers reveal a self-healing behavior even in complex, hollow structured printed specimens with healing efficiencies up to 100%. However, 3D-printed polymers showed a worse self-healing-effect compared to the casted equivalent. This may be caused by the layered structure of 3D-printed polymers which would lead to a less dense material.<br/>Furthermore, dynamic thermal-mechanical analysis was performed to investigate the shape-memory behavior of the polymers, since these polymers should be able to possess this intelligent ability due to the highly dynamic bond at elevated temperatures. The specimens were twisted at 80 °C and cooled down to room temperature in order to fix the temporary shape. The following heating process recovers the specimen to its original shape. The fixity rate of the temporary shape and the recovery rate of the original shape were calculated. The fixity rate revealed, that casted polymers showed an overall better performance. The recovery rate on the other hand was better with shape recoveries near 100%. The resulting polymers showed excellent mechanical properties with tensile E-moduli between 0.34 GPa up to 3.43 GPa and flexural moduli between 0.44 GPa up to 0.76 GPa. Additionally, during these tests, the casted polymers performed better (2.5 GPa <i>vs.</i> 1.0 GPa for tensile- and 0.71 GPa <i>vs.</i> 0.52 GPa for flexure E-moduli in general).<br/>In summary, the materials are able to be DLP-3D-printed in complex structures and still reveal good thermal, intelligent and mechanical properties. Although 3D-printed specimens show slightly worse properties compared to their casted equivalents, the ability to print complex structures with intelligent properties is a great addition to the current state of research on more sustainable polymers. In the future, this system can be further investigated in terms of faster printing time, better specimen resolution and faster self-healing.