Statistical polymer architecture

In contrast, a continuous reactor process is controlled at steady state, thereby ensuring a homogeneous copolymer composition. Therefore, a diblock prepared in a series of CSTRs has precise block junctions and homogeneous compositions of each block. In this case, effective CCTP gives a polymer with precisely two blocks per chain, instead of the statistical multiblock architecture afforded by dual catalyst chain shuttling systems. 

Since that time, synthetic chemists have explored numerous routes to these statistically hyperbranched macromolecular structures. They are recognized to constitute the least controlled subset of structures in the major class of dendritic polymer architecture. In theory, all polymer-forming reactions can be utilized for the synthesis of hyperbranched polymers however, in practice some reactions are more suitable than others. 

A number of different types of copolymers are possible with ATRP—statistical (random), gradient, block, and graft copolymers [Matyjaszewski, 2001]. Other polymer architectures are also possible—hyperbranched, star, and brush polymers, and functionalized polymers. Statistical and gradient copolymers are discussed in Chap. 6. Functionalized polymers are discussed in Sec. 3-16b. 

Statistical, gradient, and block copolymers as well as other polymer architectures (graft, star, comb, hyperbranched) can be synthesized by NMP following the approaches described for ATRP (Secs. 3-15b-4, 3-15b-5) [Hawker et al., 2001]. Block copolymers can be synthesized via NMP using the one-pot sequential or isolated macromonomer methods. The order of addition of monomer is often important, such as styrene first for styrene-isoprene, acrylate first for acrylate-styrene and acrylate-isoprene [Benoit et al., 2000a,b Tang et al., 2003]. Different methods are available to produce block copolymers in which the two blocks are formed by different polymerization mechanisms ... 

Flory was the first to hypothesize concepts [28,52], which are now recognized to apply to statistical, or random hyperbranched polymers. However, the first purposeful experimental confirmation of dendritic topologies did not produce random hyperbranched polymers but rather the more precise, structure controlled, dendrimer architecture. This work was initiated nearly a decade before the first examples of random hyperbranched4 polymers were confirmed independently in publications by Odian/Tomalia [53] and Webster/Kim [54, 55] in 1988. At that time, Webster/Kim coined the popular term hyperbranched polymers that has been widely used to describe this type of dendritic macromolecules. 

Dendritic polymers, the fourth major architectural class of macromolecules, can be divided into three subclasses. These subclasses may be visualized according to the degree of structural perfection attained, namely (1) hyperbranched polymers (statistical structures, Chapter 7), (2) dendrigraft polymers (semi-controlled structures, reviewed in this chapter) and (3) dendrimers (controlled structures, Chapter 1). 

Dendrimer synthesis involves a repetitive building of generations through alternating chemistry steps which approximately double the mass and surface functionality with every generation as discussed earlier [1-4, 18], Random (statistical) hyperbranched polymer synthesis involves the self-condensation of multifunctional monomers, usually in a one-pot single series of covalent formation events [31], Random hyperbranched polymers and dendrimers of comparable molecular mass have the same number of branch points and terminal units, and any application requiring only these two characteristics could be satisfied by either architectural type. Since dendrimer synthesis requires many defined synthetic and process purification steps while hyperbranched synthesis may involve a one-pot synthetic step with no purification, the dendrimers will necessarily be a much more expensive material to produce. 

Each basic molecular characteristic may exhibit large interconnected variability, which is reflected in the secondary molecular characteristics. Differences in the secondary molecular characteristics may even appear within a group of polymers possessing the same overall chemical structure or architecture. For example, the particular side chains in the graft copolymers may have distinct compositions though the overall composition of macromolecules is equal. Similarly, various statistical copolymers may possess the same overall composition, while their blockiness or stereoregularity is different. It is evident that the properties of macromolecules may be extremely complex if the effects of two or even all three primary molecular characteristics are combined. 

FIGURE 16.10 The SEC chromatogram of a statistical copolymer. Each slice contains macromolecnles of nearly the same size but their molar masses may differ depending on their overall composition and architecture (blockines) hydrodynamic volume of macromolecnles depends on all molecular characteristics of polymer species. 

EG-LC was successfully applied to many polymer separations, especially those aimed at the characterization of polymer blends and statistical copolymers [20,22,23,25-29,31,32,210-216]. Sato et al. [216] have demonstrated potential of EG-LC in the separation of chemically identical polymers according to their physical architecture. 

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