Effect of fabrication process on electrical properties of polymer/multi-wall carbon nanotube nanocomposites

Hu, Ning; Masuda, Zen; Yamamoto, Go; Fukunaga, H.; Hashida, Toshiyuki; Qiu, Jinghao

doi:10.1016/j.compositesa.2008.01.002

Cited by 164 publications

(146 citation statements)

References 22 publications

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“…Because CNT type and morphology dominate the effective electrical properties, it is important to closely examine material results using MWCNTs. Only the data of Hu et al [89] in figure 20a give a percolation threshold close to the model. The MWCNT materials of Sandler et al [80] and Gojny et al [90] in figure 20 all have thresholds at lower volume fractions.…”

Section: (E) Numerical Resultsmentioning

confidence: 98%

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Estimation of the physical properties of nanocomposites by finite-element discretization and Monte Carlo simulation

Spanos

Elsbernd

Ward

et al. 2013

Phil. Trans. R. Soc. A.

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This paper reviews and enhances numerical models for determining thermal, elastic and electrical properties of carbon nanotube-reinforced polymer composites. For the determination of the effective stress–strain curve and thermal conductivity of the composite material, finite-element analysis (FEA), in conjunction with the embedded fibre method (EFM), is used. Variable nanotube geometry, alignment and waviness are taken into account. First, a random morphology of a user-defined volume fraction of nanotubes is generated, and their properties are incorporated into the polymer matrix using the EFM. Next, incremental and iterative FEA approaches are used for the determination of the nonlinear properties of the nanocomposite. For the determination of the electrical properties, a spanning network identification algorithm is used. First, a realistic nanotube morphology is generated from input parameters defined by the user. The spanning network algorithm then determines the connectivity between nanotubes in a representative volume element. Then, interconnected nanotube networks are converted to equivalent resistor circuits. Finally, Kirchhoff's current law is used in conjunction with FEA to solve for the voltages and currents in the system and thus calculate the effective electrical conductivity of the nanocomposite. The model accounts for electrical transport mechanisms such as electron hopping and simultaneously calculates percolation probability, identifies the backbone and determines the effective conductivity. Monte Carlo analysis of 500 random microstructures is performed to capture the stochastic nature of the fibre generation and to derive statistically reliable results. The models are validated by comparison with various experimental datasets reported in the recent literature.

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Section: (E) Numerical Resultsmentioning

confidence: 98%

“…Experiments chosen for effective conductivity comparison include Ramasubramaniam et al [88], Ounaies et al [82], Skákalová et al [16], Hu et al [89], Sandler et al [80] and Gojny et al [90]. These experiments provided a wide range of nanocomposite combinations for comparison.…”

Section: (E) Numerical Resultsmentioning

confidence: 99%

Estimation of the physical properties of nanocomposites by finite-element discretization and Monte Carlo simulation

Spanos

Elsbernd

Ward

et al. 2013

Phil. Trans. R. Soc. A.

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“…Shear mixing uses the shear forces induced by rotation of a propeller to break the agglomerates and obtain a homogeneous solution despite inducing difficulties for the degassing of the resin [6,56]. The variable parameters of this technique are the time, the propeller rotation speed, propeller geometry, and temperature.…”

Section: Dispersion Methodsmentioning

confidence: 99%

“…The dispersion of nanoparticles is the major step in the manufacture of graphene based nanocomposites [6]. Due to their small dimension, nanoparticles possess a high surface interaction and have a tendency to stack together and form agglomerates, decreasing the properties of the nanocomposites.…”

Section: Dispersion Methodsmentioning

confidence: 99%

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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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Electrical Properties

Grady

2011

Carbon Nanotube–Polymer Composites

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Effect of fabrication process on electrical properties of polymer/multi-wall carbon nanotube nanocomposites

Cited by 164 publications

References 22 publications

Estimation of the physical properties of nanocomposites by finite-element discretization and Monte Carlo simulation

Estimation of the physical properties of nanocomposites by finite-element discretization and Monte Carlo simulation

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

Electrical Properties

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