Reversible Deactivation Radical Polymerization: From Polymer Network Synthesis to 3D Printing

3D printing has changed the fabrication of advanced materials as it can provide customized and on-demand 3D networks. However, 3D printing of polymer materials with the capacity to be transformed after printing remains a great challenge for engineers, material, and polymer scientists. Radical polymerization has been conventionally used in photopolymerization-based 3D printing, as in the broader context of crosslinked polymer networks. Although this reaction pathway has shown great promise, it offers limited control over chain growth, chain architecture, and thus the final properties of the polymer networks. More fundamentally, radical polymerization produces dead polymer chains incapable of postpolymerization transformations. Alternatively, the application of reversible deactivation radical polymerization (RDRP) to polymer networks allows the tuning of network homogeneity and more importantly, enables the production of advanced materials containing dormant reactivatable species that can be used for subsequent processes in a postsynthetic stage. Consequently, the opportunities that (photoactivated) RDRP-based networks offer have been leveraged through the novel concepts of structurally tailored and engineered macromolecular gels, living additive manufacturing and photoexpandable/transformable-polymer networks. Herein, the advantages of RDRP-based networks over irreversibly formed conventional networks are discussed.

Automated summary

3D printing technology has revolutionized the fabrication of advanced materials, but the challenge remains in printing polymer materials with post-printing transformability. Traditional radical polymerization used in photopolymerization-based 3D printing offers limited control over chain growth and architecture, unlike the reversible deactivation radical polymerization which allows for network homogeneity tuning and the production of reactivatable species for subsequent processes. RDRP-based networks have advantages over conventionally formed networks in terms of controllability and post-synthetic transformations.