Molecular engineering of catalyst-free transesterification for 3D printing gradient-programmable covalent adaptive networks

Covalent adaptive networks (CANs) based on transesterification have shown significant promise in enabling the recycling of chemically cross-linked thermosetting photopolymers. However, the conventional ester bond is inherently stable, necessitating the use of catalysts to drive transesterification. To circumvent the requirement for catalysts in the transesterification reactions, we introduced electron-withdrawing groups (EWGs) adjacent to the ester bond, leveraging their inductive effects to facilitate direct, catalyst-free transesterification and achieving an activation energy (E_a) of 55.72 ± 3.13 kJ·mol⁻¹. Both model reactions and density functional theory (DFT) calculations confirmed the strong activation effect of these EWGs (i.e., -COOEt, -CN), with higher electronegativity corresponding to increased conversion rate. The CANs produced via this catalyst-free approach exhibit excellent ultraviolet (UV) curable properties, tunable mechanical characteristics and superior recyclability. Furthermore, gradient programming of 3D-printed structures can be elegantly realized and prepared using UV curable gradient CANs. Overall, this report details the influence of EWGs on transesterification, providing a viable framework for designing and preparing CANs applicable to photocuring-based 3D printing.