Computer-simulated chain growth

The description of a network structure is based on such parameters as chemical crosslink density and functionality, average chain length between crosslinks and length distribution of these chains, concentration of elastically active chains and structural defects like unreacted ends and elastically inactive cycles. However, many properties of a network depend not only on the above-mentioned characteristics but also on the order of the chemical crosslink connection — the network topology. So, the complete description of a network structure should include all these parameters. It is difficult to measure many of these characteristics experimentally and we must have an appropriate theory which could describe all these structural parameters on the basis of a physical model of network formation. At present, there are only two types of theoretical approaches which can describe the growth of network structures up to late post-gel stages of cure. One is based on tree-like models as developed by Dusek7 I0-26,1 The other uses computer-simulation of network structure on a lattice this model was developed by Topolkaraev, Berlin, Oshmyan 9,3l) (a review of the theoretical models may be found in Ref.7) and in this volume by Dusek). Both approaches are statistical and correlate well with experiments 6,7 9 10 13,26,31). They differ mainly mathematically. However, each of them emphasizes some different details of a network structure. 

Investigations into these topics are presented in this volume. Iron, nickel, copper, cobalt, and rhodium are among the metals studied as Fischer-Tropsch catalysts results are reported over several alloys as well as single-crystal and doped metals. Ruthenium zeolites and even meteo-ritic iron have been used to catalyze carbon monoxide hydrogenation, and these findings are also included. One chapter discusses the prediction of product distribution using a computer to simulate Fischer-Tropsch chain growth. 

Computer simulations of polymer-chain growth can be compared to experimental data in order to propose or support models of polymerization, as has been done for polylactide [126]. Physical properties, such as the dielectric constant of poly(N-vinyl carbazole), can often be correlated with structural features such as tacticity [127]. 

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