(706e) Designed Experimental Study of Vapor-Grown Carbon Nanofiber/Vinyl Ester Nanocomposites with Focus On the Effects of Formulation and Processing Factors On the Nanocomposite Dynamic Mechanical Properties Over a Wide Temperature Range

Toghiani, H., Mississippi State University
Lacy, T. E., Mississippi State University
Pittman, C. U. Jr., Mississippi State University
DuBien, J. L., Mississippi State University

Polymer nanocomposites have been considered for automotive applications, but commercialization has not yet been fully realized. This is primarily due to processing and fabrication issues such as nanoreinforcement dispersion in the polymer matrix and poor nanoreinforcement-matrix interfacial adhesion. A designed experimental study was conducted on vapor-grown carbon nanofiber (VGCNF)/vinyl ester (VE) nanocomposites to better understand the effects of formulation and processing factors on the nanocomposite dynamic mechanical (viscoelastic) properties. VGCNF/VE nanocomposites represent a class of thermoset nanocomposites that are suitable for use in many automotive structural parts. The objective of the study was to identify a statistically reliable and reproducible method to fabricate these materials with a focus on prediction and optimization of their mechanical performance through response surface modeling. For engineering purposes, the thermal behavior of polymers must be characterized as part of component design and during assessments of the material’s in-service performance. The inclusion of nanoreinforcements may significantly affect the thermo-mechanical behavior of the nano-phased matrix, including its glass transition temperature (Tg) and thermal expansion behavior. By including temperature in the design, the influence of its variation on the nanocomposites’ viscoelastic properties (stiffness and energy dissipation characteristics) during service were analyzed and incorporated into the response surface models. The design factors considered were 1) the use of oxidized versus pristine VGCNFs, 2) the use of dispersing agent versus none, 3) the mixing method (ultrasonication, high-shear mixing, and a combination of both), 4) the nanofiber weight fraction (0, 0.25, 0.50, 0.75, and 1.00 parts per hundred parts resin (phr)), and 5) temperature (30, 60, 90, and 120 °C). Viscoelastic properties (storage and loss moduli) were selected as the mechanical responses since these are measures of stiffness and energy dissipation, respectively. These measures are of interest for automotive applications. Statistical analysis was used to construct response surface predictive models and to determine the nanocomposite formulation and mixing method leading to optimal viscoelastic properties. The predicted storage modulus was a function of three variable combinations, i.e., high-shear mixing (or high-shear mixing/ultrasonication), ultrasonication with oxidized VGCNFs in the presence of a dispersing agent, and ultrasonication with other VGCNF type/dispersing agent combinations over the entire temperature range (30-120 °C). A similar but simpler dependence was observed for the loss modulus, where the ultrasonication combinations all had the same effect on the predicted loss modulus. The response surface models were used to identify combinations of formulation and processing factors that would lead to optimal predicted nanocomposite viscoelastic properties over the entire temperature range. The use of high-shear mixing, oxidized VGCNFs without a dispersing agent or pristine VGCNFs with a dispersing agent, and a VGCNF weight fraction of ~0.50 phr are recommended for maximizing the storage modulus at low, moderate, and high temperatures. For a low loss modulus over the same temperature range, the combination of oxidized VGCNFs with no dispersing agent is recommended. However, the combination of pristine VGCNFs with a dispersing agent would yield moderate energy dissipation. To maximize the loss modulus over the entire temperature range, the use of ultrasonication with ~0.25 phr VGCNF is recommended. Nevertheless, this will result in lower predicted storage moduli compared to the high-shear mixing. These predictive models will enable composite manufacturers to tailor nanocomposites for different applications through the manipulation of   design factors.