Fractals conductivity

Hassan, A.E., J.H. Cushman, and J.W. Delleur. 1997. Monte Carlo studies of flow and transport in fractal conductivity fields comparison with stochastic perturbation theory. Water Resour. Res. 33 2519-2534. 

When the three different salts are separately blended with a less polar nylon such as nylon 12, the morphology is seen to be fractal at lower salt concentrations for the case of PANI-0.5-HDBSA containing blends (Figure 7-b and c) than the PANI-0.5-HMSA and PANI-0.5-HCSA / nylon 12 blends (Figure 6-b, c, d and Figure 8-a, respectively). TTie lower onset of electrical conductivity of PANI-0.5-HDBSA / nylon 12 compared to the PANI-0.5-HMSA and PANI-0.5-HCSA containing blends reflects the presence of the fractal conducting pathways in the PANI-0.5-HDBSA blend. 

The fractal-like organization led, therefore, to conductivity measurements at three different scales (1) the macroscopic, mm-size core of nanotube containing material, (2) a large (60 nm) bundle of nanotubes and, (3) a single microbundle, 50 nm in diameter. These measurements, though they do not allow direct insights on the electronic properties of an individual tube give, nevertheless, at a different scale and within certain limits fairly useful information on these properties. 

The fractal-like organisation of CNTs produeed by elassical earbon are diseharge suggested by Ebbesen et al. [15] lead to conductivity measurements whieh were performed at various scales. 

A characteristic feature of the carbon modifications obtained by the method developed by us is their fractal structure (Fig. 1), which manifests itself by various geometric forms. In the electrochemical cell used by us, the initiation of the benzene dehydrogenation and polycondensation process is associated with the occurrence of short local discharges at the metal electrode surface. Further development of the chain process may take place spontaneously or accompanied with individual discharges of different duration and intensity, or in arc breakdown mode. The conduction channels that appear in the dielectric medium may be due to the formation of various percolation carbon clusters. 

Numerical simulations of the data were conducted with the algorithms discussed above, with the added twist of optimizing the model to fit the data collected in the laboratory by adjusting the collision efficiency and the fractal dimension (no independent estimate of fractal dimension was made). Thus, a numerical solution was produced, then compared with the experimental data via a least squares approach. The best fit was achieved by minimizing the least squared difference between model solution and experimental data, and estimating the collision efficiency and fractal dimension in the process. The best model fit achieved for the data in Fig. 10a is plotted in Fig. 10b, and that for Fig. 11a is shown in Fig. lib. The collision efficiencies estimated were 1 x 10-4 and 2 x 10-4, and the fractal dimensions were 1.5 and 1.4, respectively. As expected, collision efficiency and fractal dimension were inversely correlated. However, the values of the estimates are, in both cases, lower than might be expected. The lower values were attributed to the following ... 

From the discussion of various simulation methods, it is clear that they will continue to play an important role in further development of aggregation theories as they have advanced the state of knowledge over the last 20 years. The major limitation of the precise methods of Molecular and Brownian Dynamics continues to be difficulty associated with treatment of aggregates with complex geometry the same topic that limits the ability to model these systems using von Smoluchowski s approach. Research needs to be conducted on the hydrodynamics of interactions between fractal aggregates of increasing complexity in order to advance the current ability to describe these types of systems. 

The reduced value of the scaling exponent, observed in Fig. 29 and Fig. 30a for filler concentrations above the percolation threshold, can be related to anomalous diffusion of charge carriers on fractal carbon black clusters. It appears above a characteristic frequency (O when the charge carriers move on parts of the fractal clusters during one period of time. Accordingly, the characteristic frequency for the cross-over of the conductivity from the plateau to the power law regime scales with the correlation length E, of the filler network. 

An explanation of the observed relaxation transition of the permittivity in carbon black filled composites above the percolation threshold is again provided by percolation theory. Two different polarization mechanisms can be considered (i) polarization of the filler clusters that are assumed to be located in a non polar medium, and (ii) polarization of the polymer matrix between conducting filler clusters. Both concepts predict a critical behavior of the characteristic frequency R similar to Eq. (18). In case (i) it holds that R= , since both transitions are related to the diffusion behavior of the charge carriers on fractal clusters and are controlled by the correlation length of the clusters. Hence, R corresponds to the anomalous diffusion transition, i.e., the cross-over frequency of the conductivity as observed in Fig. 30a. In case (ii), also referred to as random resistor-capacitor model, the polarization transition is affected by the polarization behavior of the polymer matrix and it holds that [128, 136,137]... 

In the frequency domain, Jonscher s power-law wings, when evaluated by ac-conductivity measurements, sometimes reveal a dual transport mechanism with different characteristic times. In particular, they treat anomalous diffusion as a random walk in fractal geometry [31] or as a thermally activated hopping transport mechanism [37]. 

The third relaxation process is located in the low-frequency region and the temperature interval 50°C to 100°C. The amplitude of this process essentially decreases when the frequency increases, and the maximum of the dielectric permittivity versus temperature has almost no temperature dependence (Fig 15). Finally, the low-frequency ac-conductivity ct demonstrates an S-shape dependency with increasing temperature (Fig. 16), which is typical of percolation [2,143,154]. Note in this regard that at the lowest-frequency limit of the covered frequency band the ac-conductivity can be associated with dc-conductivity cio usually measured at a fixed frequency by traditional conductometry. The dielectric relaxation process here is due to percolation of the apparent dipole moment excitation within the developed fractal structure of the connected pores [153,154,156]. This excitation is associated with the selfdiffusion of the charge carriers in the porous net. Note that as distinct from dynamic percolation in ionic microemulsions, the percolation in porous glasses appears via the transport of the excitation through the geometrical static fractal structure of the porous medium. 

---