Prediction mesophase

Figure 33 The dimensions of an amphiphile are used to predict mesophase formation.

To date, there has been relatively little work reported on the mesophase pitch rheology which takes into account its liquid crystalline nature. However, several researchers have performed classical viscometric studies on pitch samples during and after their transformation to mesophase. While these results provide no information pertaining to the development of texture in mesophase pitch-based carbon fibers, this information is of empirical value in comparing pitches and predicting their spinnability, as well as predicting the approximate temperature at which an untested pitch may be melt-spun. 

The above equations have been solved to predict the commonly observed radial and line-origin textures seen in circular and non-circular mesophase pitch-based carbon fibers [39]. 

A lattice model that takes such attractions between parallel bonds into account provides a reasonable prediction of polymer melting points [13] and of their interplay with liquid-liquid demixing in polymer solutions [14]. The same factors that favor freezing do affect to a greater or lesser extent the formation of mesophases hence, there is a close relation between polymer crystallization and the formation of mesophases, which are frequently observed before polymer crystallization (see other papers in this issue). 

Alkyl derivatives of D-glucitol and D-mannitol also display a wide variety of mesophases (Fig. 3c, [11]). Predictive rules for the occurrence of various phases in relation with geometric parameters were established for 3 c... 

An artificial neural network (ANN) model was developed to predict the structure of the mesoporous materials based on the composition of their synthesis mixtures. The predictive ability of the networks was tested through comparison of the mesophase structures predicted by the model and those actually determined by XRD. Among the various ANN models available, three-layer feed-forward neural networks with one hidden layer are known to be universal approximators [11, 12]. The neural network retained in this work is described by the following set of equations that correlate the network output S (currently, the structure of the material) to the input variables U, which represent here the normalized composition of the synthesis mixture ... 

Artificial neural network, a powerful predictive tool was successfully used predict the nature of the mesophase based on the composition of the synthesis mixture. 

We turn now to the results of computer simulation. Early work in this field was based on the lattice model in which each molecule is assumed to be located at a point of a three-dimensional lattice and the variables are the orientational coordinates. Until relatively recently the computers available could not tackle a more realistic model. However, the lattice model does, to some extent, assume the answer to the problem which one is examining and is unable to make predictions about the interesting questions concerning the formation and stability of particular mesophases. 

As is known, the question of how to predict the type of mesophase on the basis of the molecular structure of liquid crystals alone is not yet solved in spite of its vital importance for low-molecular crystals. Researchers rely only on some empirical... 

According to the Onsager theory and to computer simulations of the behavior of hard sphero-cylinders [37], in the absence of additional interactions no LC ordering is predicted for rods with L/D < 4, and therefore DNA double helices with a number of base pairs N < 24 would lack the anisotropy to display mesophase behavior at any concentration (Fig. 10). 

Eq. (6.36) is, as a matter of fact, no guarantee that an anisotropic phase will really be observable. In the present state of the art, it is impossible to predict the nature and even the possible existence of a mesophase from the structural formula of a polymer. 

---