Abstract
The focus of this study is to investigate the capabilities of 3D RVE models in predicting the tensile modulus of carbon nanotube polypropylene (CNT/PP) composites which differ slightly in the dispersion, agglomeration and orientation states of CNT within the PP matrix. The composites are made using melt mixing followed by either injection molding or melt spinning of fibers. The dispersion, agglomeration and orientation of CNT within the PP are experimentally altered by using a surfactant and by forcing the molten material to flow through a narrow orifice (melt spinning) that promotes alignment of CNT along the flow/drawing direction. An elaborate image analysis technique is used to quantify the CNT characteristics in terms of probability distribution functions (PDF). The PDF are then introduced to the 3D RVE models which also account for the CNT-PP interfacial interactions. It is concluded that the 3D RVE models can accurately distinguish among the different cases (dispersion, distribution, geometry and alignment of CNT) as the predicted tensile modulus is in good agreement with the experimentally determined one.
Highlights
Polymer nanocomposites (PCNs) reinforced with carbon nanotube (CNT) can exhibit improved mechanical properties, high electrical and thermal conductivity, resistance against corrosion, noise damping, thermal stability over metallic materials, and many more (Godovsky, 2000; Mylvaganam and Zhang, 2007)
A comprehensive three dimensional (3D) RVE model to capture the synergistic effect of all the CNT characteristics on the composites elastic modulus and that is sensitive enough to differentiate between composites with small changes in the microstructure is absent. This study fills this gap, i.e., it investigates the ability of a multi-CNT 3D RVE model, developed combining image analysis approach and finite element method (FEM), as presented in our previous studies (Bhuiyan et al, 2012, 2013), to capture small changes in the CNT characteristics, such as dispersion, distribution, orientation, and waviness of CNT, within the polymer matrix and provides a systematic approach to understand the synergistic effect of these CNT characteristics, which can be altered by processing conditions, on the elastic properties of PCNs
The experimentally determined modulus for the bulk composites with as received and Sodium dodecylbenzene sulfonate (SDBS)-modified CNT and for the fiber composites was compared to the modulus of the corresponding composite system predicted by 3D RVE analysis models that accounted for the probability distribution functions (PDF) of the of CNT orientation, dispersion, and agglomeration
Summary
Polymer nanocomposites (PCNs) reinforced with carbon nanotube (CNT) can exhibit improved mechanical properties, high electrical and thermal conductivity, resistance against corrosion, noise damping, thermal stability over metallic materials, and many more (Godovsky, 2000; Mylvaganam and Zhang, 2007). This study fills this gap, i.e., it investigates the ability of a multi-CNT 3D RVE model, developed combining image analysis approach and finite element method (FEM), as presented in our previous studies (Bhuiyan et al, 2012, 2013), to capture small changes in the CNT characteristics, such as dispersion, distribution, orientation, and waviness of CNT, within the polymer matrix and provides a systematic approach to understand the synergistic effect of these CNT characteristics, which can be altered by processing conditions, on the elastic properties of PCNs. Such an approach has the potential to lead to composites with engineered properties.
Sign-in/Register to view highlights & summary Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a callDisclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.
