Extrusion of Mica Filled Polypropylene

Fisa, B.; Vu‐Khanh, Toan; Rémillard, B.

doi:10.1177/089270578800100404

Cited by 5 publications

(5 citation statements)

References 6 publications

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“…In this case, an ADD% of 1.3% and a maximum deviation of 2.8% between experimental and correlated values were obtained finding a value of 0.048 for ϕ m . This maximum volume packing fraction is similar to those values obtained by Fisa [74] and Utracki et al [75] when studied flaky mica particles.…”

Section: Resultssupporting

confidence: 90%

“…Nevertheless, unlike Krieger-Dougherty model, MP equation does not require the knowledge of intrinsic viscosity, which is not always an easy task when particles are not spherical [69, 70]. The Maron and Piece model [67] has already been successfully utilized to model viscosity enhancements of particles with different aspect rations such as fibers [71, 72] or platelets/flakes [73, 74] and can be written as follows: …”

Section: Resultsmentioning

confidence: 99%

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Heat Transfer Capability of (Ethylene Glycol + Water)-Based Nanofluids Containing Graphene Nanoplatelets: Design and Thermophysical Profile

et al. 2017

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This research aims at studying the stability and thermophysical properties of nanofluids designed as dispersions of sulfonic acid-functionalized graphene nanoplatelets in an (ethylene glycol + water) mixture at (10:90)% mass ratio. Nanofluid preparation conditions were defined through a stability analysis based on zeta potential and dynamic light scattering (DLS) measurements. Thermal conductivity, dynamic viscosity, and density were experimentally measured in the temperature range from 283.15 to 343.15 K and nanoparticle mass concentrations of up to 0.50% by using a transient plate source, a rotational rheometer, and a vibrating-tube technique, respectively. Thermal conductivity enhancements reach up to 5% without a clear effect of temperature while rheological tests evidence a Newtonian behavior of the studied nanofluids. Different equations such as the Nan, Vogel-Fulcher-Tamman (VFT), or Maron-Pierce (MP) models were utilized to describe the temperature or nanoparticle concentration dependences of thermal conductivity and viscosity. Finally, different figures of merit based on the experimental values of thermophysical properties were also used to compare the heat transfer capability and pumping power between nanofluids and base fluid.

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Section: Resultssupporting

confidence: 90%

Section: Resultsmentioning

confidence: 99%

Heat Transfer Capability of (Ethylene Glycol + Water)-Based Nanofluids Containing Graphene Nanoplatelets: Design and Thermophysical Profile

et al. 2017

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“…Best fit curves to the data were generated, then the aspect ratio A f (or shape factor f , in the case of the Guth model) was used as the independent parameter in the model and adjusted until the model prediction coincided with the line of best fit to the data points. The modified Halpin–Tsai model incorporates the maximum packing volume fraction ϕ m of the filler as a parameter and was taken as 0.05 for RG-O platelets of nominal A f > 200 based upon the treatise of Bicerano and co-workers, as described in the Supporting Information. The results of the curve fittings are summarized in Table .…”

Section: Quantifying Dispersion: An Analysis Of Composite Models and ...mentioning

confidence: 99%

Processing–Morphology–Property Relationships and Composite Theory Analysis of Reduced Graphene Oxide/Natural Rubber Nanocomposites

et al. 2012

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Dispersion of reduced graphene oxide (RG-O) into natural rubber (NR) was found to dramatically enhance the mechanical, electrical, and thermal properties of NR. However, property improvements were strongly dependent upon the processing history and nanocomposite morphology. Cocoagulating a stable RG-O suspension with NR latex afforded a weblike morphology consisting of platelet networks between the latex particles, while two-roll mill processing broke down this structure, yielding a homogeneous and improved dispersion. The physical properties of RG-O/NR vulcanizates with both morphologies were compared over a range of loadings; it was found that the network morphology was highly beneficial for thermal and electrical conductivity properties and greatly increased stiffness but was detrimental to elongation. A detailed comparative analysis of composite models found the Guth equation gave excellent fit to modulus data of the milled samples when taking the shape factor as equal to the platelet aspect ratio quantified from transmission electron microscopy analysis.

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“…A value of m of 0.637 represents random close packing of spheres, the lower value 0.44 corresponds to rough irregular particles, while the very low value of 0.047 has been found to represent the data of flaky mica particles. 46 GNP is a flake-like particle with an aspect ratio of $700, and thus it is expected to behave somewhere between mica and rough irregular particles. To evaluate the GNP/PC behavior, we follow Kitano et al and use a modified Maron-Pierce equation, allowing the shape parameter m to become the adjustable parameter A as shown in Equation (8).…”

mentioning

confidence: 99%

Characterization of exfoliated graphite nanoplatelets/polycarbonate composites: electrical and thermal conductivity, and tensile, flexural, and rheological properties

King

Via

Morrison

et al. 2011

Journal of Composite Materials

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Exfoliated graphite nanoplatelets (GNP) can be added polymers to produce electrically conductive composites. In this study, varying amounts (2–15 wt%) GNP were added to polycarbonate (PC) and the resulting composites were tested for electrical conductivity (1/electrical resistivity), thermal conductivity, and tensile, flexural, and rheological properties. The percolation threshold was approximately 4.0 vol% (6.5 wt%) GNP. The addition of GNP to polycarbonate increased the composite electrical and thermal conductivity and tensile and flexural modulus. The 8 wt% (5.0 vol%) GNP in polycarbonate composite had a good combination of properties for electrostatic dissipative applications. The electrical resistivity and thermal conductivity were 4.0 × 107 ohm-cm and 0.37 W/m · K, respectively. The tensile modulus, ultimate tensile strength, and strain at ultimate tensile strength were 3.5 GPa, 58 MPa, and 3.5%, respectively. The flexural modulus, ultimate flexural strength, and strain at ultimate flexural strength were 3.6 GPa, 108 MPa, and 5.5%, respectively. Ductile tensile behavior is noted in pure polycarbonate and in samples containing up to 8 wt% GNP. PC and GNP/PC composites show shear-thinning behavior. Viscosity of the composite increased as the amount of GNP increased dueto a volume-filling filler effect. The viscosity of the GNP/PC composites are well described by a Kitano-modified Maron-Pierce model.

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Extrusion of Mica Filled Polypropylene

Cited by 5 publications

References 6 publications

Heat Transfer Capability of (Ethylene Glycol + Water)-Based Nanofluids Containing Graphene Nanoplatelets: Design and Thermophysical Profile

Heat Transfer Capability of (Ethylene Glycol + Water)-Based Nanofluids Containing Graphene Nanoplatelets: Design and Thermophysical Profile

Processing–Morphology–Property Relationships and Composite Theory Analysis of Reduced Graphene Oxide/Natural Rubber Nanocomposites

Characterization of exfoliated graphite nanoplatelets/polycarbonate composites: electrical and thermal conductivity, and tensile, flexural, and rheological properties

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