Double networking distribution functions

For a transient double-network at deformation, the distribution functions of statistical conformation for two kinds of chains are obtained after introducing a condition of affine deformatimi, which is shown as follow ... 

In the sol-gel preparation of supported metals, a metal precursor is usually added directly to the solution prior to gelling. Regardless of whether the metal precursor participates in hydrolysis and/or condensation, it will become part of the network as the gel forms. Thus, any parameters that are important in solution chemistry (Table 1) could affect the properties of the metal upon activation. An example is the work of Zou and Gonzalez [39] cited in Section 2.I.4.3.A. When these authors used water content as a variable to change the pore size distribution of a series of Pt/Si02 catalysts, they found that the particle size distribution of reduced Pt (in the form of crystallites) is also dependent on the hydrolysis ratio. The average Pt particle size nearly doubles (from about 1.7 to 3nm) as the hydrolysis ratio increases from 10 to 60. As noted earlier, the stability of these catalysts, in terms of the resistance of Pt particles towards sintering, is a function of how well the pore diameter and particle size match. 

The complex network of pathways (nadis) through which the subtle energy moves, is clearly defined. These pathways, 72,000 in number, are distributed throughout the etheric body double, running parallel to the Central Nervous System. Their location and function are usually presented in a fairly straight-forward way. Being closely aligned to the physical body makes them not as abstract as the six nerve plexus known as chakras. 

Attraction of some chain parts, for example, some chain groups or double bonds, to the active filler surface changes significantly its number of possible conformations and configurational distribution in the layer connected to filler surface. But that influence of filler fractal surface on the polymer network, which is itself a fractal (but of different character), will decay slowly through the gradient layer, like a memory function. Because of this Liouville-Riemann differential is a very convenient approach to changes in conformational distribution. 

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