Reconfigurable Shape Memory and Self-Welding Properties of Epoxy Phenolic Novolac/Cashew Nut Shell Liquid Composites Reinforced with Carbon Nanotubes

Polymers (Basel). 2018 Apr 28;10(5):482. doi: 10.3390/polym10050482.

Abstract

Conventional shape memory polymers (SMPs) can memorize their permanent shapes. However, these SMPs cannot reconfigure their original shape to obtain a desirable geometry owing to permanent chemically or physically crosslinked networks. To overcome this limitation, novel SMPs that can be reconfigured via bond exchange reactions (BERs) have been developed. In this study, polymer composites consisting of epoxy phenolic novolac (EPN) and bio-based cashew nut shell liquid (CNSL) reinforced by multi-walled carbon nanotubes (CNTs) were prepared. The obtained composites exhibited shape memory and self-welding properties, and their shapes could be reconfigured via BERs. Their shape memory mechanisms were investigated using variable-temperature Fourier transform infrared spectroscopy and dynamic mechanical analysis. The EPN/CNSL composite containing 0.3 wt % CNTs showed the highest shape fixity and shape recovery ratio. Furthermore, shape memory behavior induced by irradiation of near-infrared (NIR) light was also observed. All samples showed high shape recovery ratios of nearly 100% over five cycles, and increasing the CNT content shortened the recovery time remarkably. The ability of shape reconfiguration and stress relaxation affected the photo-induced shape memory properties of reshaped samples. Additionally, the self-welding properties were also influenced by stress relaxation. The hindrance of stress relaxation caused by the CNTs resulted in a decrease in adhesive fracture energy (Gc). However, the Gc values of EPN/CNSL composites were comparable to those of epoxy vitrimers. These results revealed that the material design concepts of thermal- and photo-induced shape memory, shape reconfiguration, and self-welding were combined in the EPN/CNSL composites, which could be feasible method for advanced smart material applications.

Keywords: shape memory; stress relaxation; thermal properties; welding/joining.