4D Printing of Electroactive Triple-Shape Composites

Triple-shape polymers can memorize two independent shapes during a controlled recovery process. This work reports the 4D printing of electro-active triple-shape composites based on thermoplastic blends. Composite blends comprising polyester urethane (PEU), polylactic acid (PLA), and multiwall carbon nanotubes (MWCNTs) as conductive fillers were prepared by conventional melt processing methods. Morphological analysis of the composites revealed a phase separated morphology with aggregates of MWCNTs uniformly dispersed in the blend. Thermal analysis showed two different transition temperatures based on the melting point of the crystallizable switching domain of the PEU (T-m similar to 50 +/- 1 degrees C) and the glass transition temperature of amorphous PLA (T-g similar to 61 +/- 1 degrees C). The composites were suitable for 3D printing by fused filament fabrication (FFF). 3D models based on single or multiple materials were printed to demonstrate and quantify the triple-shape effect. The resulting parts were subjected to resistive heating by passing electric current at different voltages. The printed demonstrators were programmed by a thermo-mechanical programming procedure and the triple-shape effect was realized by increasing the voltage in a stepwise fashion. The 3D printing of such electroactive composites paves the way for more complex shapes with defined geometries and novel methods for triggering shape memory, with potential applications in space, robotics, and actuation technologies.

Automated summary

This research presents the 4D printing of electro-active triple-shape composites based on thermoplastic blends. The composites were suitable for 3D printing by fused filament fabrication (FFF) and the resulting parts demonstrated the triple-shape effect. By increasing voltages in a stepwise fashion, the printed demonstrators realized the triple-shape effect through a thermo-mechanical programming procedure. The 3D printing of such electroactive composites opens up possibilities for complex shapes with defined geometries and novel methods for triggering shape memory, which could have potential applications in space, robotics, and actuation technologies.