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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.

Deep-learning (DL) methods, in consideration of their excellence in dealing with highly complex structure-performance relationships for materials, are expected to become a new design paradigm for breakthroughs in material performance. However, in most cases, it is impractical to collect massive-scale experimental data or open-source theoretical databases to support training DL models with sufficient prediction accuracy. In a dataset consisting of 483 porous silicone rubber observations generated via ink-writing additive manufacturing, this work demonstrates that constructing low-dimensional, accurate descriptors is the prerequisite for obtaining high-precision DL models based on small experimental datasets. On this basis, a unique convolutional bidirectional long short-term memory model with spatiotemporal features extraction capability is designed, whose hierarchical learning mechanism further reduces the requirement for the amount of data by taking full advantage of data information. The proposed approach can be expected as a powerful tool for innovative material design on small experimental datasets, which can also be used to explore the evolutionary mechanisms of the structures and properties of materials under complex working conditions., (© 2022 Wiley-VCH GmbH.)

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 f are 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.