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    Effect of Interface Strength on Elastic and Toughness Properties of Graphene-Reinforced Si3N4 Nanocomposites
    (2025) Aslan, Betül; Bayrak, Osman
    Silicon nitride is used in advanced engineering applications. Its toughness can be improved when they are reinforced with nanoparticles, such as graphene. Although toughness improvement is relatively more achievable with the reinforcement, elastic properties of nanocomposites are generally inferior to those of monolithic ceramics. Experimental works give rich insight into the mechanical characteristics of graphene-Si3N4 nanocomposites. However, there is no consensus yet in literature on why Young’s modulus decreases upon addition of graphene into Si3N4 nanocomposites. In this study, we aimed to reveal the reason behind the deterioration of the Young’s modulus. We created and verified finite element models based on the microstructural and mechanical data provided in literature. Different void and interfacial interaction properties were tested on the models. Results revealed that graphene does not act like voids within the matrix. It rather induces randomly dispersed porosities within the interfaces. Toughness of nanocomposites improved with increase of interfacial strength. However, interfacial strength did not directly affect Young’s modulus of nanocomposites. Following the inducing of porosities within the interfaces in finite element models, it was observed that secant modulus decreased. This finding implies that optimizing porosity distribution via contact discontinuities can help achieve approximating elastic properties of graphene-Si3N4 nanocomposite models. Findings of this study will contribute to future research on nanocomposites, including fracture behavior modeling, and toughness mechanisms
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    Quantitative analysis of orientation distribution of graphene platelets in nanocomposites using TEM
    (Elsevier Sci Ltd, 2025) Bayrak, Osman; Tashkinov, Mikhail; Silberschmidt, Vadim V.; Demirci, Emrah
    Mechanical properties of nanocomposites are directly affected by their microstructures. Orientation distribution of nano-reinforcements, one of the critical microstructural parameters, is, therefore, of great importance. However, methods to quantify their orientation are limited. Many studies employ transmission electron microscopy (TEM) for qualitative characterisation of orientation distribution of graphene nanoplatelets (GNPs) in nanocomposites. However, there is no report in the literature that does it quantitatively based on TEM micrographs. In this study, a method for the use of TEM in quantitative characterisation of the orientation distribution of GNPs in nanocomposites is suggested. Materials used for this purpose were sodium alginate nanocomposites reinforced with GNPs. In order to assess the effectiveness of the suggested method, finite-element (FE) models of representative volume elements (RVEs) of the nanocomposites were developed based on the GNPs' orientation distribution data. Elastic-range tensile tests of these composites were simulated with the RVEs. The simulation results were compared with the data from experiments reported in our previous study. A strong correlation between the obtained results of numerical simulations and the experimental data was observed. Young's moduli of the nanocomposites, calculated with the simulations, were slightly higher than those from the experiments. A discrepancy of less than 4 % in the Young's moduli can be attributed to other microstructural parameters such as spatial distribution nonuniformity, wrinkling and dimensional variation of the GNPs, which were not taken into account in the FE models. Some micromechanical models were also implemented in order to assess their capability to predict the effect of GNP orientation distributions on stiffness of the nanocomposites. The Krenchel orientation factors were incorporated into the models for this purpose. This study shows that the quantitative characterisation of orientation distribution of graphene in nanocomposites is achievable through TEM analyses with the suggested methodology and can be used to underpin analysis of their properties and performance.