DOI: 10.1002/pls2.70054 ISSN: 2690-3857

A Facile Preparation of Segmented Thermoplastic Polyurethane With Octamethyl Polyhedral Oligomeric Silsesquioxane‐Based Thermoresponsive Programmable Shape Memory Nanocomposites With Enhanced Mechanical Performance

Ramya Devi, Piyush Gupta, Kinsuk Naskar, Santanu Chattopadhyay

ABSTRACT

Thermoresponsive shape memory polymers derived from segmented thermoplastic polyurethanes (TPUs) present a compelling integration of processability, elasticity, and programmable deformation. This study involves the incorporation of octamethyl polyhedral oligomeric silsesquioxane (OMP) into an aliphatic polyether‐based TPU through solution casting followed by melt blending to create mechanically robust shape‐memory nanocomposites. This study systematically examined the effect of OMP loading (1–4 wt.%) on microphase morphology, short‐range ordering, thermomechanical behavior, and shape‐memory performance. Microscopic studies and X‐ray diffraction studies indicate that low to intermediate OMP contents facilitate homogeneous dispersion and inhibit hard‐segment aggregation, whereas higher loadings lead to OMP–OMP self‐association and the formation of rigid OMP‐rich domains. DSC and DMA indicate that OMP influences segmental dynamics through modifications in hydrogen bonding, heat‐capacity increments, and elevation of storage modulus, thus adjusting elastic energy storage and dissipation. The nanocomposite with 2 wt.% OMP demonstrated the optimal balance of tensile strength, extensibility, and viscoelastic contrast, achieving a peak shape recovery of 97.0% with values decreasing to 80.2% over five repeated cycles alongside significant shape fixity of 94.0%–84.0%. Cyclic thermomechanical tests demonstrated enhanced durability and recovery retention for this composition, while thermally triggered recovery experiments revealed rapid and complete recovery, underscoring its responsiveness to practical stimuli. This study demonstrates that melt‐blended TPU/OMP nanocomposites function as effective thermally programmable shape‐memory materials and clarifies the structure–property relationships that influence their performance.

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