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Elastic Modulus Prediction of Polymer Nanocomposites: Production and Characterization of Cellulose Nanocrystal Reinforced Polyamide Nanocomposites
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- Author / Creator
- Demir, Eyup C.
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Polymer nanocomposites can fulfill their potential use in engineering applications as researchers and engineers gain a better understanding of nanocomposites’ modelling, production, and characterization methods. Recent polymer nanocomposite studies point out that existing modelling tools either require a significant amount of computational power or cannot capture experimental outcomes due to oversimplifications. This research mainly focuses on the development of a model for polymer nanocomposites to predict their elastic properties efficiently and accurately and to understand the parameters that have direct effects on the properties of the nanocomposites. The thesis also presents experimental work that involves development of an innovative additive manufacturing method and the detailed characterization of polymer nanocomposites.
A novel model that consists of a three-phase Mori-Tanaka model coupled with the Monte-Carlo approach is developed to predict the elastic modulus of nanocomposites. As opposed to existing models, this model defines agglomerates and utilizes a machine learning tool to identify three phases of the proposed composite system from simulated dispersion or micrograph images. Three phases of the proposed composite system are defined as agglomerates, free particles (non-agglomerated particles), and matrix. The parameters that define these three phases and other parameters such as particle loading, orientation, aspect ratio, agglomerate property are systematically investigated to perform a sensitivity study on parameters of the developed model. This sensitivity study reveals that agglomeration tendency is highly dependent on particle dispersion and critical distance defined in the model. The sensitivity study also prove that the model is sufficiently general that it can be applied to various types of polymer nanocomposites to predict their properties. The model is verified with polyamide 6 (PA6) cellulose nanocrystals (CNC) nanocomposites that are produced using spin coating method. The proposed novel model and existing conventional model predictions are compared, and it is shown that the proposed model can follow the trend of experimental results much better than the conventional ones.
Further, an innovative direct extrusion-based additive manufacturing technique is used for nanocomposite production and the experimental findings are again compared to that of model’s predictions to see the applicability of the model in 3D printed nanocomposites. This production technique can be used for nanocomposite production in prototyping or customized engineering parts at a laboratory scale. It eliminates the filament production and use in extrusion-base additive manufacturing. CNC and PA12 are used to study the proposed direct additive manufacturing technique. CNC is dispersed and PA12 is dissolved in a common solvent and then cast on the silicone baking mate for drying. The cast mixture is turned into powder and then extruded using a small pellet extruder that is designed as 3D printing head to obtain nanocomposite extrudates. The extrudates are uniaxially tested and demonstrated great enhancement in their mechanical properties. Due to promising results, a commercial 3D printer is equipped with this extruder head and dog-bone PA12 nanocomposites prepared and uniaxially tested. While elastic modulus substantially increases, yield strength shows a slight improvement. A detailed TEM analysis is performed at various CNC loadings and the retrieved TEM images are analyzed to predict the elastic modulus of PA12 nanocomposites using the proposed model. A good agreement is observed between model predictions and experimental results. The result of this work shows, for the first time that, PA12 can be 3D printed with CNC, and our direct extrusion technique can be utilized for small batch productions in research laboratories. -
- Graduation date
- Fall 2022
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- Type of Item
- Thesis
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- Degree
- Doctor of Philosophy
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- License
- This thesis is made available by the University of Alberta Library with permission of the copyright owner solely for non-commercial purposes. This thesis, or any portion thereof, may not otherwise be copied or reproduced without the written consent of the copyright owner, except to the extent permitted by Canadian copyright law.