Search

3D printed silicones have demonstrated great potential in diverse areas by combining the advantageous physiochemical properties of silicones with the unparalleled design freedom of additive manufacturing. However, their low-temperature performance, which is of particular importance for polar and space applications, has not been addressed. Herein, a 3D printed silicone foam with unprecedented low-temperature elasticity is presented, which is featured with extraordinary fatigue resistance, excellent shape recovery, and energy-absorbing capability down to a low temperature of −60 °C after extreme compression (an intensive load of over 66000 times its own weight). The foam is achieved by direct writing of a phenyl silicone-based pseudoplastic ink embedded with sodium chloride as sacrificial template. During the water immersion process to create pores in the printed filaments, a unique osmotic pressure-driven shape morphing strategy is also reported, which offers an attractive alternative to traditional 4D printed hydrogels in virtue of the favorable mechanical robustness of the silicone material. The underlying mechanisms for shape morphing and low-temperature elasticity are discussed in detail.

3D printed silicone elastomers, combining the favorable physiochemical performances of silicones with the unparalleled advantages of additive manufacturing, have gained significant attention from academia and industry owing to their attractive applications. However, there were several limitations in their existing printing techniques, such as the poor mechanical robustness of the products by vat polymerization and the conflicting rheological requirements for material extrusion. Herein, we report 3D printing of an ultraviolet (UV)-induced, platinum-catalyzed hydrosilylation silicone composite via UV-assisted material extrusion. It was found that UV assistance could enhance the shape-retaining capability during printing, and the achieved materials presented remarkably enhanced mechanical properties by almost an order of magnitude compared to other UV-cured silicones for 3D printing. The material was UV-curable and heat-curable, which allowed for manipulating the tensile, tearing, and relaxation properties by adjusting the curing technique. Moreover, the UV-cured silicone composites exhibited much enhanced mechanical isotropy compared to their heat-cured counterparts, indicating the potential to minimize the intrinsic hurdle of anisotropy for 3D printed products. Structural origins for the excellent properties were discussed in detail. This work will open new opportunities for developing high-performance 3D-printed silicones for engineering applications.

It is still a challenge to fabricate polymer-based composites with excellent thermal conductive property because of the well-known difficulties such as insufficient conductive pathways and inefficient filler–filler contact. To address this issue, a synergistic segregated double network by using two fillers with different dimensions has been designed and prepared by taking graphene nanoplates (GNPs) and multiwalled carbon nanotubes (MWCNT) in polystyrene for example. In this structure, GNPs form the segregated network to largely increase the filler–filler contact areas while MWCNT are embedded within the network to improve the network-density. The segregated network and the randomly dispersed hybrid network by using GNPs and MWCNT together were also prepared for comparison. It was found that the thermal conductivity of segregated double network can achieve almost 1.8-fold as high as that of the randomly dispersed hybrid network, and 2.2-fold as that of the segregated network. Meanwhile, much higher synergistic efficiency (f) of 2 can be obtained, even greater than that of other synergistic systems reported previously. The excellent thermal conductive property and higher fare ascribed to the unique effect of segregated double network: (1) extensive GNPs–GNPs contact areas via overlapped interconnections within segregated GNPs network; (2) efficient synergistic effect between MWCNT network and GNPs network based on bridge effect as well as increasing the network-density.