Multi-Scale Finite Element Analysis of Graphene/Polymer Nanocomposites Electrical Performance

Manta, Asimina; Grésil, Matthieu; Soutis, C.

doi:10.7712/100016.1936.7586

Cited by 9 publications

(14 citation statements)

References 29 publications

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“…These results suggest that fillers with high aspect ratio are able to form more stable and efficient conductive Table 7 Equations for the electrical resistivity introduced to the elements representing graphene particles in respect of the direction Electrical resistivity distributed in the RVE in respect of the direction (13) network in the volume of nanocomposite at lower volume fraction. The above conclusion is supported also by the fact that with the rise of volume fraction the deviation on the statistical sample is significantly reduced leading to more uniform sample close to uniquely defined electrical conductivity for a nanocomposite of a specific volume fraction [36]. In Fig.…”

Section: Rve Analysissupporting

confidence: 66%

“…This increasing trend is common for every case of unit cell studied, while becomes more evident with the increase of tunnelling distance. Finally, the effect of the shape of the graphene layer on the unit cell resistance is considered in respect of the direction and the tunnelling distance is depicted for all examined aspect ratios and height of barrier of 0.5 eV [36]. It could be stated that ellipse shaped graphene sheets exhibit higher local resistance in the main axis directions than the rectangle shaped ones.…”

Section: Unit Cell Analysismentioning

confidence: 99%

“…The rise of the aspect ratio leads to significant decrease in the resistance. In longitudinal direction (x-direction), the resistance saturates to a constant value in function of the ½ tunnelling distance for filler's aspect ratio being greater than 50, as it was fully studied in the [36]. Moreover, polymer and all the features controlling its response under any excitation (degree of polymerization, voids, humidity, temperature etc.)…”

Section: Unit Cell Analysismentioning

confidence: 99%

“…Finally, the effect of the graphene shape is calculated in respect of the volume fraction for the case of height of barrier being 0.5 eV [36]. The graphene shape does not affect the trend of the nanocomposite's electrical response in function of the volume fraction (V f ).…”

Section: Rve Analysismentioning

confidence: 99%

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Predictive Model of Graphene Based Polymer Nanocomposites: Electrical Performance

2016

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In this computational work, a new simulation tool on the graphene/polymer nanocomposites electrical response is developed based on the finite element method (FEM). This approach is built on the multi-scale multi-physics format, consisting of a unit cell and a representative volume element (RVE). The FE methodology is proven to be a reliable and flexible tool on the simulation of the electrical response without inducing the complexity of raw programming codes, while it is able to model any geometry, thus the response of any component. This characteristic is supported by its ability in preliminary stage to predict accurately the percolation threshold of experimental material structures and its sensitivity on the effect of different manufacturing methodologies. Especially, the percolation threshold of two material structures of the same constituents (PVDF/Graphene) prepared with different methods was predicted highlighting the effect of the material preparation on the filler distribution, percolation probability and percolation threshold. The assumption of the random filler distribution was proven to be efficient on modelling material structures obtained by solution methods, while the through-the -thickness normal particle distribution was more appropriate for nanocomposites constructed by film hot-pressing. Moreover, the parametrical analysis examine the effect of each parameter on the variables of the percolation law. These graphs could be used as a preliminary design tool for more effective material system manufacturing.

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Section: Rve Analysissupporting

confidence: 66%

Section: Unit Cell Analysismentioning

confidence: 99%

Section: Unit Cell Analysismentioning

confidence: 99%

Section: Rve Analysismentioning

confidence: 99%

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Predictive Model of Graphene Based Polymer Nanocomposites: Electrical Performance

2016

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“…Table 1 gives the measured values for electrical conductivity on GNP/epoxy composites. Nonaligned raw GNP and Triton X-100 epoxy samples were both electrical insulator, contrary to some values showed in the literature, this fact is probably due to the poor quality of GNP used during these experiments but, also confirms the theoretical result obtained by simulation where the required weight loading was 8.53% of GNP [77]. These loading of GNP was chosen to have the maximum GNP with a low viscoelasticity.…”

Section: Characterisation Of Alignment Through Fracture Surface Analysiscontrasting

confidence: 48%

Effect of pre and Post-Dispersion on Electro-Thermo-Mechanical Properties of a Graphene Enhanced Epoxy

Poutrel

Wang

et al. 2016

Appl Compos Mater

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Graphene nanoplatelet (GNP) modified epoxy nanocomposites are becoming attractive to aerospace due to possible improvements in their mechanical, electrical and thermal properties at no weight cost. The process of obtaining reliable material systems provides many challenges, especially at larger scale (a volume effect). This paper reports on the main fabrication stages of GNP-based epoxy composites, namely (i) pre-dispersion, (ii) dispersion, and (iii) post-dispersion. Each stage is developed to show the interest and potential it delivers for property enhancement. Chemical modification of GNP is presented; functionalisation by Triton X-100 shows elastic modulus improvements of the epoxy at low particle content (≤3%). The post-dispersion step as an alignment of GNP into the epoxy by an electrical field is discussed. The electrical conductivity is below the simulated percolation threshold and an improvement of the thermal diffusivity of 220% when compared to non-oriented GNP epoxy sample is achieved. The work demonstrates how the addition of functionalised graphene platelets to an epoxy resin will allow it to act as electrical and thermal conductor rather than as insulator

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Laser‐aided curing of a GnP/epoxy nanocomposite optimised by multiscale finite element analysis

Manta

Soutis

Grésil

2019

Mat Design Process Comm

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In this work, the possibility of laser‐aided curing of graphene nanoplatelet (GnP)/epoxy nanocomposites is explored by means of finite element analysis. A multiscale and multiphysics analysis is performed to identify and optimise critical manufacturing parameters—laser speed and power. The proposed model is parametrically studied, and its results are thoroughly discussed in terms of polymerisation degree and temperature field depth. Finally, the laser scanning of a specimen was conducted, and the polymerisation performance was recorded.

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Multi-Scale Finite Element Analysis of Graphene/Polymer Nanocomposites Electrical Performance

Abstract: Abstract. This paper presents a multi-scale, multi-physics finite element model (FEM)simulating

Cited by 9 publications

References 29 publications

Predictive Model of Graphene Based Polymer Nanocomposites: Electrical Performance

Predictive Model of Graphene Based Polymer Nanocomposites: Electrical Performance

Effect of pre and Post-Dispersion on Electro-Thermo-Mechanical Properties of a Graphene Enhanced Epoxy

Laser‐aided curing of a GnP/epoxy nanocomposite optimised by multiscale finite element analysis

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