Quantifying dispersion of layered nanocomposites via melt rheology

Vermant, Jan; Ceccia, Simona; Dolgovskij, Michail K.; Maffettone, Pier Luca; Macosko, Christopher W.

doi:10.1122/1.2516399

Cited by 232 publications

(207 citation statements)

References 51 publications

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“…2B and C allows us to estimate dB=1.4±0.2 and dN=1.6±0.2, similar to carbon-black composites(11), but somewhat smaller than values of ~2 found for nanoclay networks (15). In addition, the Krauss fits give structure factors close to m=0.5 which is typical for 5 filled elastomers ( Fig.…”

Section: Main Textmentioning

confidence: 57%

Sensitive electromechanical sensors using viscoelastic graphene-polymer nanocomposites

Boland

Khan

Ryan

et al. 2016

Science

730

612

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Despite its widespread use in nanocomposites, the effect of embedding graphene in highly viscoelastic polymer matrices is not well-understood. We add graphene to a lightly crosslinked polysilicone, often encountered as Silly Putty, changing its electro-mechanical properties significantly. The resulting nanocomposites display unusual electromechanical behavior such as post-deformation temporal relaxation of electrical resistance and nonmonotonic changes in resistivity with strain. These phenomena are associated with the mobility of the nanosheets in the low-viscosity polymer matrix. By considering both the connectivity and mobility of the nanosheets, we develop a quantitative model that completely describes the electromechanical properties. These nanocomposites are sensitive electromechanical sensors with gauge factors >500 which can measure pulse, blood pressure and even the impact associated with the footsteps of a small spider.

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Section: Main Textmentioning

confidence: 57%

Sensitive electromechanical sensors using viscoelastic graphene-polymer nanocomposites

Boland

Khan

Ryan

et al. 2016

Science

730

612

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“…Naturally the orientation of silicate layers will determine the properties of various material functions. When studying the polypropylene-clay nanocomposites by rheological methods it was shown that the effect of flow can lead to break down of the aggregate network, and possibly, the aligning of the structural elements [30]. This resulted in time and shear history dependence of rheological properties.…”

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confidence: 99%

“…This resulted in time and shear history dependence of rheological properties. Vermant et al [30], introduced a combination of transient flow protocols in order to separate the effects of flow on the aggregate structure and on the orientation of nanoclay domains. The degree of exfoliation was determined from the low frequency viscoelastic behavior and the high frequency behavior of dynamic moduli allowed estimation of the quality of dispersion.…”

mentioning

confidence: 99%

Internal structure and linear viscoelastic properties of EVA/asphalt nanocomposites

Sureshkumar

Filippi

Polacco

et al. 2010

European Polymer Journal

106

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The effect of the addition of two different organoclays as a third component in polymer modified asphalts has been investigated. Ternary mixtures were prepared by adding clay and poly(ethylene-covinylacetate) to the asphalt, either separately, or in the form of a premixed master batch. The performed characterizations allowed the determination of how the two methods of mixing influence the interactions between asphalt and polymer and therefore the final rheological properties. In particular, it was shown that the clay had a compatibilizing effect on asphalt and polymer and that a high compatibility between clay and polymer led to a better dispersion of the polymer in the asphalt, thus influencing the final rheological properties of the studied systems.

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“…Comparing the dependencies in Figures 16.10 and 16.11 it is evident that randomization of orientation in diluted, exfoliated PA-6-based CPNC [Utracki, 2004] is about twice as fast as that of PP/PP-MA/C20A: 1 h versus > 2 h. The slower randomization in the latter system might originate in more massive intercalated stacks and/or higher matrix viscosity. In the case of intercalated nanocomposites with aggregates, the reversed and forward flows after preshearing might lead to different responses: for example, for PP with 17 wt% ethylene-propylene copolymer, 3.6 wt% PP-MA and 2.4 wt% C20A [Vermant et al, 2007]. After a short rest time, the stress overshoot obtained in forward flow was prominent, whereas no overshoot was seen in the reversed flow.…”

Section: Stress Overshoots In Shearmentioning

confidence: 99%