4D printing of PETG/TPU shape memory nanocomposites reinforced with carbon nanotubes (CNTs) and carbon black (CB)
Source abstract
Abstract
4D printing of shape memory polymers (SMPs) enables time-programmed function, and conductive fillers are promising additives for improving thermomechanical performance and potentially enabling future electrothermal activation. In this study, the effect of carbon-based nanoparticles, including carbon black (CB) and carbon nanotubes (CNTs), on a PETG/TPU (70/30) blend was investigated using formulations reinforced with CB or CNT at 6 wt% and a hybrid CB3%/CNT3%. The mechanical properties, dynamic mechanical thermal analysis (DMTA), printability, morphology, and shape memory behavior of the nanocomposites were investigated. The neat PETG/TPU showed UTS 26.22 MPa and elongation 7.9%; the elastic modulus was essentially unchanged by adding nanoparticles. Among the nanocomposites, CB6% provided the best overall balance, with UTS 26.63 MPa, elongation 7.67%, and 92% recovery in 19 s (fixity 100%). CNT6% exhibited lower strength but higher ductility (elongation roughly doubled versus the neat blend) with recovery close to CB6%. The CB3%/CNT3% hybrid showed the weakest mechanical response and the lowest recovery. microstructural properties supported these trends: CB showed more uniform dispersion and fewer interlayer voids; CNT was broadly similar but slightly rougher; the hybrid displayed more defects. Overall, 6 wt% CB is identified as the most balanced composition for PETG/TPU when mechanical integrity, printability, and rapid thermally induced shape recovery are required. 6 wt% CNT is suitable where additional deformability is desired, while the equal-ratio hybrid should be avoided unless dispersion is improved.
Source: openalex_inverted_index · Quality: baseline
Research context
OpenAlex topics: Polymer composites and self-healing · Advanced Materials and Mechanics · Additive Manufacturing and 3D Printing Technologies
SciVX discovery tags are terminology-based. OpenAlex topics are source-provided classifications.
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