A group of Madrid-based researchers has developed a method for producing shape-memory composites that could help develop shape-memory materials suitable for use in a wide range of industrial sectors and application areas. The research is published in the journal Polymers.
Study: Shape memory composites based on ionic elastomers. Image Credit: Gorodenkoff/Shutterstock.com
Shape-memory polymers have relatively good application potential in the biomedical and construction industries, as well as a growing market for shape-memory polymers. Meeting the demand in the automotive and aerospace sectors will advance the use of the products in the near future.
Although it has been predicted that the biomedical end-use area might be the most promising area for shape memory polymers, they are limited in terms of mechanical properties for use in other applications, such as in this regarding flexibility, compared to shape memory polymers. memory alloys.
Evolution of the vulcanization curve (by the elastic component of the torque, S’) of the systems reinforced with different quantities (phr) of (a) CB and (b) CNT. Image credit: González-Jiménez, A. et al., Polymers
Published in the journal Polymers, a led by Dr. Antonio González-Jiménez of Materials Science and Engineering, Rey Juan Carlos University, Madrid, details how improving characteristics such as cross-linking, modifying deformation characteristics and adding elastomers and polymer matrix fillers could produce superior shape memory materials.
Shape memory materials belong to a class of smart materials which can also be called functional materials. They have the ability to react and adapt to external environmental conditions. Such materials are capable of physically changing shape and returning to their original form after exposure to various stimuli.
For some time now, shape memory materials of different compositions have attracted the attention of researchers due to their multifunctional capabilities and increased demand for advanced components such as flexible actuators.
Therefore, researchers have focused their efforts on developing new smart materials that respond to various stimuli such as light, heat, magnetic fields, electricity, as well as exposure to water and air. humidity.
Master curves in which the modulus of elasticity (G’) is plotted against the frequency of the XNBR-4MgO-0.5DCP samples with (a) BC content between 0 and 30 phr and (b) CNT content between 0 and 15 phr. The curves were obtained from the principle of time-temperature superposition with Tref = 40°C. Image credit: González-Jiménez, A. et al., Polymers
With momentum toward the development of soft actuators, Dr. González-Jiménez explains, “In recent years, different approaches have been made to obtain polymers that retain an elastic character in their temporary forms, including a crystallizable thermoplastic network interpenetrating with an elastomeric matrix of liquid crystal elastomers and mixtures of elastomers and additives to small molecules..”
Therefore, the team focused its efforts on three main objectives: (i) to assess the influence of the introduction of conductive fillers (carbon black) and nano-fillers (multi-walled carbon nanotubes) on the network structure and shape memory properties of cross-linked XNBR compounds with covalent and ionic bonds; (ii) comparison of the reinforcement efficiency of different fillers by studying their viscoelastic behavior, their mechanical properties and their electrical properties; and (iii) develop proof of concept for advanced and contemporary applications based on the use of ionic elastomers with improved thermal and electrical conductivities.
Shape memory elastomeric composites
The effect of introducing conductive fillers and nano-fillers into XNBR compounds has been studied to varying degrees in order to establish ways to improve various functions of shape memory materials such as elasticity, conductivity and thermal stability.
Electroactive shape-recovery behavior of the XNBR nanocomposite with 15 pce of CNT as a function of the application time of a constant voltage of 50 V. Image credit: González-Jiménez, A. et al., Polymers
“The most popular approach to creating elastic behavior to deform the material into a different shape is the introduction of a dual lattice, where at least one of them exhibits a thermal transition, such as the transition vitreous or melting temperature”, says Dr. González-Jiménez.
The Madrid researchers introduced carbon black with a high specific surface area as well as multi-walled carbon nanotubes. After preparing samples, they applied a series of tests and methods to observe shape memory behavior, electrical conductivity and thermal conductivity. The team observed increases in elasticity and thermostable behavior that allow samples to return to their original or permanent shape demonstrating shape memory functionality.
“The introduction of carbon nanotubes makes it possible to obtain the best fixing properties (between 4 and 10% improvement compared to the unfilled material for the CNT contents studied)”, explains Dr. González-Jiménez. Reinforcement using the nanotubes was the most effective approach, while reinforcement with carbon black has a comparative influence but more than twice the amount of filler than that of the nanotubes was required.
The team was able to successfully develop shape-memory elastomeric composites through the introduction of carbon black fillers and carbon nanotubes, improving the shape-memory behavior of the materials. Additionally, the researchers also observed improvements in bonding properties as well as thermal conductivity as well as the possibility of developing materials that can be manipulated via electrical currents.
Overall, the developments have opened up the field of selective shape recovery, which will require further research to assess specific application areas. This should broaden the application potential of shape memory composites.
References and further reading:
González-Jiménez, A.; Bernal-Ortega, P.; Salamanca, FM; Valentin, JL Shape memory composites based on ionic elastomers. Polymers 2022, 14, 1230. https://www.mdpi.com/2073-4360/14/6/1230