Dual-Network Shape-Memory Polymer Fibers for 4D-Printed Self-Healing and Adaptive Infrastructure Materials

Abstract

We present a novel class of dual-network shape-memory polymer fibers designed for 4D-printed adaptive infrastructure materials that simultaneously achieve programmable shape morphing and autonomous self-healing. The proposed fiber architecture integrates a permanent acrylate network, formed via free-radical polymerization of a difunctional monomer, with a dynamic boronic-ester network that undergoes reversible transesterification under moisture or mild heat. This orthogonal crosslinking strategy decouples mechanical robustness from reconfigurability, allowing the fiber to generate recovery stresses sufficient to close cracks in cementitious matrices while autonomously repairing micro-damage within the fiber itself. The fibers are fabricated through a continuous dry-spinning process with in-line UV and thermal curing, yielding diameters of approximately 150 micrometers. They are subsequently co-extruded with a thermoplastic polyurethane carrier matrix to form composite filaments for fused deposition modeling. A stimulus-responsive poly(N-isopropylacrylamide) hydrogel coating on the fiber surface transduces moisture ingress from matrix cracks into a swelling pressure, which triggers Joule heating via an embedded nichrome wire. This thermal stimulus activates both the shape-memory contraction and the dynamic bond exchange, enabling crack closure and healing without external intervention. The crosslink density ratio between permanent and dynamic networks is systematically tuned between 0.5 and 5.0 to balance stiffness, healing efficiency, and actuation force. For a ratio of 2.0, the fiber maintains a Young’s modulus above 1.5 gigapascals and achieves a healing efficiency exceeding 85% after five damage-heal cycles. The constitutive model we derive relates programming strain to recovery stress, accounting for the relaxation behavior of the dynamic network. This work translates the dual-network concept into a scalable, durable reinforcement for 4D-printed infrastructure components such as self-healing bridge beams and adaptive façade panels. The system therefore addresses a critical gap in autonomous damage management for civil infrastructure.