Fast‐Reprocessing, Postadjustable, Self‐Healing Covalent Adaptable Networks with Schiff Base and Diels–Alder Adduct
Covalent adaptable networks (CANs) are a new type of polymers, which possess excellent performance of thermosets and reprocessability of thermoplastics. Nevertheless, it is still a challenge to realize rapid reprocessing and postadjusting (adjust properties after preparation). Herein, for the first...
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Veröffentlicht in: | Macromolecular rapid communications. 2022-07, Vol.43 (13), p.e2100777-n/a |
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Sprache: | eng |
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Zusammenfassung: | Covalent adaptable networks (CANs) are a new type of polymers, which possess excellent performance of thermosets and reprocessability of thermoplastics. Nevertheless, it is still a challenge to realize rapid reprocessing and postadjusting (adjust properties after preparation). Herein, for the first time, a method of combining Schiff base and Diels–Alder adduct in one network is developed to achieve rapid reprocessing and postadjusting. Through the dissociation of the Diels–Alder adduct at high temperatures, the cross‐link densities of the networks are reduced, thereby accelerating the rearrangement of the networks and realizing the rapid reprocessing and self‐healing. Moreover, the reconnecting degree of network after dissociation of Diels–Alder adduct can be easily controlled by annealing; as a result, the properties of the obtained CANs are postadjustable. This work provides a simple and promising approach of achieving excellent reprocessing and postadjusting for CANs via the synergism of an associative dynamic chemistry with a dissociative dynamic chemistry.
Schiff base and Diels–Alder adduct concurrent covalent adaptable networks (CANs) are reported. At high temperatures, the dissociation of Diels–Alder adduct imparts fast reprocessing to CANs due to the reduced cross‐link density of networks. Meanwhile, after reprocessing, the post‐adjusting ability of the CANs’ properties can be achieved by controlling the reconnecting degree of Diels–Alder adduct via annealing. |
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ISSN: | 1022-1336 1521-3927 |
DOI: | 10.1002/marc.202100777 |