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Testing, Experimental Design and Numerical Analysis of Nano-Mechanical Properties in Epoxy Hybrid Systems Reinforced with Carbon Nanotubes and Graphene Nanoparticles

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Submitted:

19 November 2024

Posted:

20 November 2024

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Abstract
Hybrid nanocomposites incorporating multiple fillers are gaining significant attention due to their ability to enhance material performance, offering superior properties compared to traditional monophase systems. This study investigates hybrid epoxy-based nanocomposites reinforced with multi-walled carbon nanotubes (MWCNTs) and graphene nanosheets (GNs), introduced at two different weight concentrations of the mixed filler, i.e. 0.1 wt% and 0.5 wt%, which are, respectively, below and above the Electrical Percolation Threshold (EPT) for the two binary polymer composites that solely include one of the two nanofillers, with varying MWCNTs:GNs ratios. Mechanical properties, such as contact depth, hardness, and reduced modulus, were experimentally assessed via nanoindentation, while morphological analysis supported the mechanical results. A Design of Experiments (DoE) approach was utilized to evaluate the influence of filler concentrations on the composite's mechanical performance, and Response Surface Methodology (RSM) was applied to derive a mathematical model correlating the filler ratios with key mechanical properties. The best and worst-performing formulations, based on hardness and contact depth results, were further investigated through detailed numerical simulations using a multiphysics software. After validation considering experimental data, the simulations provided additional insights into the mechanical behavior of the hybrid composites. This work aims to contribute to the knowledge base on hybrid composites and promote the use of computational modeling techniques for optimizing the design and mechanical performance of advanced materials.
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Subject: Chemistry and Materials Science  -   Nanotechnology
Copyright: This open access article is published under a Creative Commons CC BY 4.0 license, which permit the free download, distribution, and reuse, provided that the author and preprint are cited in any reuse.
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