Development of a simulator for modelling of electrical and mechanical properties of nanocomposite materials and sensors
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A simulator is developed to estimate the electrical conductivity of polymer/nanotube composite layers as well as the change in their resistances under an applied strain. Simulation results are verified using experimental data on SU-8/Multiwall Carbon NanoTube composites. The model is based on conduction through a polymer body due to percolation between the conductive nanotubes. The simulator predicts the nanocomposite conductivity normalized by contact resistance between different filler concentrations. Several devices with different filler concentrations were fabricated on silicon substrates and studied. Experimental results agree with the performance trend that is predicted by the simulator as filler concentration and applied strains were varied independently. The simulator is capable of accounting for nanotube dimensions, polymer physical properties, conduction channel shape, and unevenly distributed forces in the polymer body.Keywords:
Percolation (cognitive psychology)
Contact resistance
Polymer nanocomposite
We evaluate the percolation threshold values for a realistic model of continuum segregated systems, where random spherical inclusions forbid the percolating objects, modeled by hardcore spherical particles surrounded by penetrable shells, to occupy large regions inside the composite. We find that the percolation threshold is generally a nonmonotonous function of segregation, and that an optimal (i.e., minimum) critical concentration exists well before maximum segregation is reached. We interpret this feature as originating from a competition between reduced available volume effects and enhanced concentrations needed to ensure percolation in the highly segregated regime. The relevance with existing segregated materials is discussed.
Percolation (cognitive psychology)
Percolation Theory
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Percolation (cognitive psychology)
Percolation Theory
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Percolation (cognitive psychology)
Volume fraction
Percolation Theory
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We report the dependence of the percolation threshold of the three-dimensional sticks systems on aspect ratio and on macroscopic anisotropy. This Monte Carlo study is the first determination of percolation thresholds for randomly oriented objects in three-space. The results show that the above dependence is determined by the excluded volume of the sticks. However, the total excluded volume of randomly oriented objects is lower than that of the same objects in parallel alignment.
Percolation (cognitive psychology)
Directed percolation
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The conductivity of two-dimensional inhomogeneous Au films near the percolation threshold is investigated in the temperature interval 0.5–55 K. The percolation threshold was approached by irradiation of initially continuous films (of thickness ∼ 10 nm) by Ar ions. It is found that near the percolation threshold (on the insulating side of the metal–insulator transition), the film exhibits one-dimensional hopping conduction. The fluctuations of hopping conductivity were also observed. It is shown that the conductivity of a two-dimensional percolation film in the vicinity of the percolation threshold can be determined by a single conducting chain. The presence of narrow insulating bridges in this chain determines the one-dimensional behavior of hopping conduction of the entire system as well as the observed resistance fluctuations. These fluctuations can be treated as a peculiar manifestation of incoherent mesoscopic effects.
Mesoscopic physics
Percolation (cognitive psychology)
Directed percolation
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Percolation (cognitive psychology)
Aspect ratio (aeronautics)
Percolation Theory
Matrix (chemical analysis)
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Percolation (cognitive psychology)
Constant (computer programming)
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Percolation (cognitive psychology)
Masterbatch
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Abstract This paper deals with the conductivity of binary polymer composites filled with an electronically conductive material. A “dynamic cluster model” is offered to describe the conductivity of such polymer composites in the highly filled region, i.e. above the percolation threshold. The model is based on the following assumptions: a modification of the basic statistical percolation equation, i.e. σ (φ−φ c ) t , where t = 1.6 to 1.9, should be applied for all systems in the highly filled region, although application is limited to the range φ = φ c + Δφ, Δφ ⟹ 0 in the strict statistical percolation approach; the most important modifications with respect to the basic equation of the statistical percolation theory are (a) the use of a constant t eff , including a constant part t 1 (resembling “ t ” in the basic statistical percolation approach) and a variable part t 2 (depending on the filler concentration φ of the specific mixture) and (b) the definition of φ c as the filler concentration where a perfect three‐dimensional network of the conductive phase has been established. This idea has been adopted from the bond‐percolation approach of Aharoni; the resulting equation should include parameters of specific polymer composites. The generalized equation σ = f (φ) is used to calculate the maximum possible conductivity of a certain mixture as well as the dependence of σ on the filler content.
Percolation (cognitive psychology)
Percolation Theory
Filler (materials)
Constant (computer programming)
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Computer‐generated pictures are presented of the connected component (''infinite cluster'') found at concentrations just above the threshold for 2D site percolation in large (400×400 site) lattices. For each case, we also show the ''backbone'' of the cluster, the smaller set of sites through which a current may flow. The simulations are contrasted with the model of conduction just above threshold due to Skal and Shklovskii and to de Gennes. That model is found to be inconsistent with the observed critical behavior of the conductivity in 2D and 3D models, but may apply to percolation in 4D and above. We show that a proper treatment of inhomogeneity on scales smaller than the coherence length is necessary to account for the observed conductivity and backbone volume just above threshold, and introduce a self‐similar model which accounts reasonably well for these properties.
Percolation (cognitive psychology)
Percolation Theory
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Citations (74)