Performance and 4D Printing of Thermoplastic Polyurethane/Carbon Nanotube Composites

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Abstract
Thermoplastic polyurethane (TPU) possesses excellent mechanical properties, shape memory effects, and good processability due to its unique microphase‐separated structure. Carbon nanotubes (CNTs) have shown significant advantages in enhancing the mechanical properties of polymer matrices and endowing them with intelligent responsiveness due to their unique three‐dimensional tubular structure, high interfacial activity, and photothermal conversion ability. However, achieving uniform dispersion of CNTs within the TPU matrix and fully realizing their functional potential remains a key challenge in preparing high‐performance composites via melt blending. This study successfully prepared TPU/CNTs composites with varying CNTs contents (0–0.6 wt%) using a Haake torque rheometer. The results indicate that when the CNTs content reaches 0.6 wt%, an agglomeration phenomenon occurs, adversely impacting the mechanical properties and shape memory capability of the composites. At a CNTs content of 0.4 wt%, the composite exhibited the best overall performance: melt strength and structural stability were significantly enhanced, which facilitated fused deposition modeling (FDM) printing; tensile strength reached 26.7 MPa. The composites exhibit exceptional shape memory properties with a shape fixation ratio ( R f ) > 99% and shape recovery ratio ( R r ) reaching 93.27%, accompanied by rapid response times (< 30 s). This performance enhancement originates from CNTs' near‐full‐spectrum absorption (> 95%) across the ultraviolet–visible‐near‐infrared range (200–2500 nm), particularly in the visible and NIR regions that constitute ~80% of solar energy. The absorbed photons are efficiently converted into lattice vibrational energy (thermal energy). Furthermore, TPU's inherently low thermal conductivity confines the heat generated by CNTs within the polymer matrix, minimizing thermal dissipation losses. Concurrently, 3D printing induces alignment of CNTs, which optimizes light capture efficiency and photothermal conversion. These synergistic effects collectively elevate the shape memory capabilities of the composites. Building on this, successful four‐dimensional (4D) printing of complex models and high‐precision negative Poisson's ratio (NPR) structures was achieved; these structures undergo programmable shape changes upon thermal and photonic stimulation. This work provides a novel strategy for developing high‐performance 4D printing smart materials and structures through straightforward melt blending.

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