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Predicting and Measuring the Sequence Distribution of Addition Polymers

Chapter

Abstract

The sequence distribution of poly(styrene), poly(methyl methacrylate) and other addition polymers can be predicted, starting from the knowledge of polymerization reaction conditions. In many cases, the sequence distribution will be Markovian (of the first or second order), but in other cases, it cannot be described by Markovian statistics. Three examples of sequences falling in the latter class are discussed. All types of copolymers are considered: AB copolymers, ABC copolymers, ABCD copolymers. As reaction time increases, polymerization dynamics becomes less trivial. Additional parameters are required to describe how copolymer sequence varies as the reaction yield (or the reaction time) increases. Nevertheless, reaction products are conceptually simple points, and it is possible to follow their changes by drawing their trajectories in a multidimensional phase space. The task of measuring the sequence distribution is seldom trivial. Many examples of polymer sequencing using NMR spectroscopy have been collected and discussed by Randall. Mass spectrometry is also used. Often sequence distribution information must be extracted from experimental data. Flexible empirical models have been developed for this aim. Mixtures of two bernoullian chains and mixtures of two markovian chains are used. The pertubed markovianmodel features ε, a perturbation factor. Some experimental methods attempt to measure polymer sequence by partial degradation, i.e., by reducing the length of the chains until a mixture of tetramers, pentamers and hexamers is obtained. This procedure yields a new copolymer, with a new sequence distribution. The sequence of the undegraded polymer must be reconstructed from the knowledge of the sequence of the partially degraded one.

Keywords

Nuclear Magnetic Resonance Nuclear Magnetic Resonance Spectrum Bivariate Distribution Sequence Distribution Gradient Copolymer 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

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© Springer New York 2011

Authors and Affiliations

  1. 1.Institute of Chemistry and Technology of Polymers, CNRCataniaItaly

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