Achieve balanced stiffness and toughness properties of nanocomposites based on poly(lactic acid)/polyolefin by using fuzzy rule-based system
Using filler and impact modifiers for balancing stiffness and toughness properties is a common strategy for modification of polymer matrices’ performance. Nanocomposites based on poly lactic acid/polyolefin elastomer/including multiwalled carbon nanotubes/carbon black nanoparticles (PLA/POE/MWCNTs/C...
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| Autores principales: | , , , |
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| Formato: | article |
| Lenguaje: | EN |
| Publicado: |
Budapest University of Technology
2022
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| Materias: | |
| Acceso en línea: | https://doaj.org/article/6418c660325e4906b64348ce36779b93 |
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| Sumario: | Using filler and impact modifiers for balancing stiffness and toughness properties is a common strategy for modification of polymer matrices’ performance. Nanocomposites based on poly lactic acid/polyolefin elastomer/including multiwalled carbon nanotubes/carbon black nanoparticles (PLA/POE/MWCNTs/CB) were produced using an internal mixer. Fuzzy rule- based system (FRBS) was applied to predict and simulate of mechanical properties of the samples. The fieldemission scanning electron microscopy (FESEM) was applied to determine the state of nanofillers distribution. The FESEM images showed that the carbon black and MWCNTs individually were well distributed. But, the simultaneous addition of nanofillers by more than 1 wt% from each one led to their agglomeration. The results illustrated that the presence of MWCNTs and carbon black separately and simultaneously led to an increase in tensile strength and Young’s modulus. The simultaneous presence of them led to an improvement in impact strength by 30%. Also, by incorporating POE into the PLA matrix, a significant increment in impact strength was obtained by 110%. The obtained surface plots from FRBS revealed that there is an interaction between nanofillers effects on the mechanical properties. Finally, a good agreement between the predicted mechanical properties using FRBS and evaluation tests led to extract accurate models with proper R2 and standard error for all responses. |
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