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Aggregate of nanoparticles: rheological and mechanical properties.

Wang Y, Wu X, Yang W, Zhai Y, Xie B, Yang M - Nanoscale Res Lett (2011)

Bottom Line: By this modified elastic model, the size of the network mesh can be estimated by the elastic modulus of the network which can be easily obtained by rheology.The stress to destroy the aggregates, i.e., the yield stress (σy), and the elastic modulus (G') of the network are found to be depended on the concentration of nano-silica (ϕ, wt.%) with the power of 4.02 and 3.83, respectively.Via this concentration dependent behavior, we can extrapolate two important mechanical parameters for the agglomerates in a dense packing state (ϕ = 1): the shear modulus and the yield stress.

View Article: PubMed Central - HTML - PubMed

Affiliation: College of Polymer Science and Engineering, State Key Laboratory of Polymer Materials Engineering, Sichuan University, Chengdu, 610065, China. ysjsanjin@163.com.

ABSTRACT
The understanding of the rheological and mechanical properties of nanoparticle aggregates is important for the application of nanofillers in nanocompoistes. In this work, we report a rheological study on the rheological and mechanical properties of nano-silica agglomerates in the form of gel network mainly constructed by hydrogen bonds. The elastic model for rubber is modified to analyze the elastic behavior of the agglomerates. By this modified elastic model, the size of the network mesh can be estimated by the elastic modulus of the network which can be easily obtained by rheology. The stress to destroy the aggregates, i.e., the yield stress (σy), and the elastic modulus (G') of the network are found to be depended on the concentration of nano-silica (ϕ, wt.%) with the power of 4.02 and 3.83, respectively. Via this concentration dependent behavior, we can extrapolate two important mechanical parameters for the agglomerates in a dense packing state (ϕ = 1): the shear modulus and the yield stress. Under large deformation (continuous shear flow), the network structure of the aggregates will experience destruction and reconstruction, which gives rise to fluctuations in the viscosity and a shear-thinning behavior.

No MeSH data available.


Related in: MedlinePlus

Shear-thinning behavior. Viscosity development at different shear rate for the sample ST-7 (a). Viscosity development curves of all samples at the same shear rate of 5 s-1 (b). The inset shows the shear rate dependence of Tf.
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Figure 5: Shear-thinning behavior. Viscosity development at different shear rate for the sample ST-7 (a). Viscosity development curves of all samples at the same shear rate of 5 s-1 (b). The inset shows the shear rate dependence of Tf.

Mentions: Firstly, a shear-thinning behavior, i.e., the shear viscosity declines with the shear rate increase, was observed for all suspensions as displayed in Figure 5a, b. In accordance with the yield behavior, this behavior is also caused by the breaking of gel network occurring when the deformation or strain overpasses the elastic deformation the network or NCAs can support. Obviously, the extent of deformation plays a key role in understanding of the shear-thinning behavior.


Aggregate of nanoparticles: rheological and mechanical properties.

Wang Y, Wu X, Yang W, Zhai Y, Xie B, Yang M - Nanoscale Res Lett (2011)

Shear-thinning behavior. Viscosity development at different shear rate for the sample ST-7 (a). Viscosity development curves of all samples at the same shear rate of 5 s-1 (b). The inset shows the shear rate dependence of Tf.
© Copyright Policy - open-access
Related In: Results  -  Collection

License
Show All Figures
getmorefigures.php?uid=PMC3211159&req=5

Figure 5: Shear-thinning behavior. Viscosity development at different shear rate for the sample ST-7 (a). Viscosity development curves of all samples at the same shear rate of 5 s-1 (b). The inset shows the shear rate dependence of Tf.
Mentions: Firstly, a shear-thinning behavior, i.e., the shear viscosity declines with the shear rate increase, was observed for all suspensions as displayed in Figure 5a, b. In accordance with the yield behavior, this behavior is also caused by the breaking of gel network occurring when the deformation or strain overpasses the elastic deformation the network or NCAs can support. Obviously, the extent of deformation plays a key role in understanding of the shear-thinning behavior.

Bottom Line: By this modified elastic model, the size of the network mesh can be estimated by the elastic modulus of the network which can be easily obtained by rheology.The stress to destroy the aggregates, i.e., the yield stress (σy), and the elastic modulus (G') of the network are found to be depended on the concentration of nano-silica (ϕ, wt.%) with the power of 4.02 and 3.83, respectively.Via this concentration dependent behavior, we can extrapolate two important mechanical parameters for the agglomerates in a dense packing state (ϕ = 1): the shear modulus and the yield stress.

View Article: PubMed Central - HTML - PubMed

Affiliation: College of Polymer Science and Engineering, State Key Laboratory of Polymer Materials Engineering, Sichuan University, Chengdu, 610065, China. ysjsanjin@163.com.

ABSTRACT
The understanding of the rheological and mechanical properties of nanoparticle aggregates is important for the application of nanofillers in nanocompoistes. In this work, we report a rheological study on the rheological and mechanical properties of nano-silica agglomerates in the form of gel network mainly constructed by hydrogen bonds. The elastic model for rubber is modified to analyze the elastic behavior of the agglomerates. By this modified elastic model, the size of the network mesh can be estimated by the elastic modulus of the network which can be easily obtained by rheology. The stress to destroy the aggregates, i.e., the yield stress (σy), and the elastic modulus (G') of the network are found to be depended on the concentration of nano-silica (ϕ, wt.%) with the power of 4.02 and 3.83, respectively. Via this concentration dependent behavior, we can extrapolate two important mechanical parameters for the agglomerates in a dense packing state (ϕ = 1): the shear modulus and the yield stress. Under large deformation (continuous shear flow), the network structure of the aggregates will experience destruction and reconstruction, which gives rise to fluctuations in the viscosity and a shear-thinning behavior.

No MeSH data available.


Related in: MedlinePlus