Enhanced thermal properties for epoxy composites with a three-dimensional graphene oxide filler

Gao, Jian; Yu, Jinhong; Wu, Xinfeng; Rao, Binqi; Song, Laifu; He, Zihai; Lu, Shaorong

doi:10.1007/s12221-015-5637-7

Cited by 26 publications

(14 citation statements)

References 47 publications

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“…Additionally, the graphene contents have great influence on the storage modulus even in very low content, which is in accordance with other research . The maximum storage modulus reached 6.3 GPa at GNS content of 1.00 wt %, which is higher than other research of epoxy composite with same GNS content . However, with the GNS content further increasing, attributing to the defects of micro pores and voids appeared at high GNS content, the storage modulus decreases to 5 GPa at GNS content of 3.00 wt %, which agrees well with the SEM result in Figure .…”

Section: Resultssupporting

confidence: 87%

Graphene nanosheets‐filled epoxy composites prepared by a fast dispersion method

Zhang

2017

J of Applied Polymer Sci

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Graphene nanosheets‐filled epoxy composites (GNS/Epoxy) were prepared at different filler loading levels from 0.25 to 3.00 wt %. A fast dispersion method as short as 5 min is employed to disperse GNS in epoxy matrix, which was enough for the homogeneous dispersion of GNS with the help of high ultrasonic frequency of 100 kHz and power of 200 W and high heat treatment temperature of 70 °C. The maximum electrical conductivity and thermal conductivity of the composites achieved 0.058 S m−1 and 0.57 W m−1 K−1, respectively, with a low electrical percolation threshold of 1.50 wt %. The electrical conductivities were further predicted by percolation theory and found to agree well with the experimental results, which indicated that the graphene nanosheets dispersed very well in the matrix even at very short processing time. The results showed that the microstructures, thermal, electrical, and mechanical properties of epoxy polymer were significantly improved by adding a low amount of graphene nanosheets. © 2017 Wiley Periodicals, Inc. J. Appl. Polym. Sci. 2017, 134, 45152.

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Section: Resultssupporting

confidence: 87%

Graphene nanosheets‐filled epoxy composites prepared by a fast dispersion method

Zhang

2017

J of Applied Polymer Sci

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“…The thermal conductivity from NEMD is dependent on the crosslink density but the EMD cannot capture the crosslinking dependence. The results also suggest that the predicted thermal conductivities are within the range of the experimental thermal conductivity values reported in the literature . However, the predicted thermal conductivities are significantly higher than those from the experiment described herein.…”

Section: Resultssupporting

confidence: 85%

“…The results also suggest that the predicted thermal conductivities are within the range of the experimental thermal conductivity values reported in the literature. [48][49][50][51][52][53][54] However, the predicted thermal conductivities are significantly higher than those from the experiment described herein. The experiments were repeated under identical conditions, which are consistent with the methods discussed in the literature.…”

Section: Resultsmentioning

confidence: 99%

Multiscale thermal modeling of cured cycloaliphatic epoxy/carbon fiber composites

Chinkanjanarot

Radue

Gowtham

et al. 2018

J of Applied Polymer Sci

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Cycloaliphatic epoxies (CEs) are commonly used for structural applications requiring improved resistance to elevated temperatures, UV radiation, and moisture relative to other epoxy materials. Accurate and efficient computational models can greatly facilitate the development of CE‐based composite materials for applications such as Aluminum Conductor Composite Core high‐voltage power lines. In this study, a new multiscale modeling method is developed for CE resins and composite materials to efficiently predict thermal properties (glass‐transition temperature, thermal expansion coefficient, and thermal conductivity). The predictions are compared to experimental data, and the results indicate that the multiscale modeling method can accurately predict thermal properties for CE‐based materials. For 85% crosslink densities, the predicted glass‐transition temperature, thermal expansion coefficient, and thermal conductivity are 279 °C, 109 ppm °C−1, 0.24 W m−1 K−1, respectively. Thus, this multiscale modeling method can be used for the future development of improved CE composite materials for thermal applications. © 2018 Wiley Periodicals, Inc. J. Appl. Polym. Sci. 2018, 135, 46371.

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“…This behavior is similar to that observed with MD modeling of the EPON862 system [45]. The results also suggest that the predicted thermal conductivities are within the range of the experimental thermal conductivity values reported in the literature [87][88][89][90][91][92][93]. However, the predicted thermal conductivities are significantly higher than those from the experiment [76] Graphene Nanoplatelet (GNP) reinforcement has been shown to improve the mechanical properties in the transverse direction of GNP/CF/Epoxy hybrid composite layers [33,36] and increase the thermal conductivity [11,14,15,22,24,[27][28][29][30][31]40].…”

supporting

confidence: 89%

Multiscale Modeling: Thermal Conductivity of Graphene/Cycloaliphatic Epoxy Composites

Chinkanjanarot¹

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Enhanced thermal properties for epoxy composites with a three-dimensional graphene oxide filler

Cited by 26 publications

References 47 publications

Graphene nanosheets‐filled epoxy composites prepared by a fast dispersion method

Graphene nanosheets‐filled epoxy composites prepared by a fast dispersion method

Multiscale thermal modeling of cured cycloaliphatic epoxy/carbon fiber composites

Multiscale Modeling: Thermal Conductivity of Graphene/Cycloaliphatic Epoxy Composites

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