Dynamic Polyester Networks for Advanced Functional Materials
Polyesters are of particular interest as they are well-established materials of industrial and manufacturing interest; poly(ethylene terephthalate)—the most common polyester—currently makes up nearly 20% of all plastic materials produced globally. Similarly, poly(lactic acid) is an increasingly important polymer as it can be readily sourced from natural and renewable sources (e.g., corn starch). Therefore, advances in polyester technologies, including the development of robust dynamic network systems, is of great interest to the polymer science community. To that end, we investigated 1) the effect of Brønsted acid catalyst strength on dynamic network properties in a rubbery polyester system, 2) the development of a dynamic network system using polyester bottlebrush polymers, and 3) the demonstration of a dynamic polyester bottlebrush polymer composite with CNT-filler particles to generate soft and conductive materials.
In the study of Brønsted-acid catalyzed dynamic networks, we found that with a rubbery polyester (4-methylcaprolactone) stress relaxation occurred even at mild conditions (25 to 75 °C). In comparison to the more conventional Lewis acid-catalyzed systems, Brønsted-acid-catalyzed systems have significantly lower activation energies (49 to 67 kJ mol−1 as compared to 90 to 150 kJ mol−1). The rates of stress relaxation have a clear dependence on acid strength. The benefit of lower temperature compatibility was demonstrated by the reprocessing of a cylindrical sample at 85 °C over two cycles of damaging and then healing. Analysis reveals that with increasing acid strength (pKa), the apparent activation energy increases and the kinetic prefactor decreases.
In the study of bottlebrush polymer dynamic networks, we found that the advantageous mechanical properties of bottlebrush polymer networks could be realized in conjunction with dynamic network properties. The samples generated showed very low moduli, from 8 to 60 kPa, significantly lower than is attainable with conventional linear networks. Furthermore, through formulation design and varying of precursor polymer molecular weight, the modulus of the final material can be targeted and predicted. In terms of their dynamic character, these networks showed activation energies in approximate agreement with reported Lewis acid activation energy systems (89 kJ mol−1) and was invariant over the crosslinking loadings investigated. As a demonstration of the ability to self-heal, tensile samples were generated, strained to break, and re-annealed. Over two cycles of healing, the network retained >85% of its toughness.
Lastly, we report a carbon nanotube composite that uses bottlebrush polymer precursors for the matrix material. Bottlebrush polymer networks have shown promise as super-soft elastomers. While the inclusion of CNT-filler raises the modulus relative to the unfilled samples, at loadings of 0.25wt% and 0.5wt% CNT the generated composites were still characteristically soft, with modulus values of 66 kPa and 140 kPa, respectively. Despite the low loadings, the CNT-filler imbues the materials with significant conductivities. While the unfilled samples behave as insulating material, the reported composites have conductivities of 0.006 S/m2 and 0.054 S/m2. There is ongoing work to fully understand the dynamic nature of these materials and the impact of the CNT filler particles.