Polycarbonate polymers description

A rather crude, but nevertheless efficient and successful, approach is the bond fluctuation model with potentials constructed from atomistic input (Sect. 5). Despite the lattice structure, it has been demonstrated that a rather reasonable description of many static and dynamic properties of dense polymer melts (polyethylene, polycarbonate) can be obtained. If the effective potentials are known, the implementation of the simulation method is rather straightforward, and also the simulation data analysis presents no particular problems. Indeed, a wealth of results has already been obtained, as briefly reviewed in this section. However, even this conceptually rather simple approach of coarse-graining (which historically was also the first to be tried out among the methods described in this article) suffers from severe bottlenecks - the construction of the effective potential is neither unique nor easy, and still suffers from the important defect that it lacks an intermolecular part, thus allowing only simulations at a given constant density. 

The approach developed in this paper, combining on the one side experimental techniques (dynamic mechanical analysis, dielectric relaxation, solid-state 1H, 2H and 13C NMR on nuclei at natural abundance or through specific labelling), and on the other side atomistic modelling, allows one to reach quite a detailed description of the motions involved in the solid-state transitions of amorphous polymers. Bisphenol A polycarbonate, poly(methyl methacrylate) and its maleimide and glutarimide copolymers give perfect illustrations of the level of detail that can be achieved. 

In Section I we introduce the gas-polymer-matrix model for gas sorption and transport in polymers (10, LI), which is based on the experimental evidence that even permanent gases interact with the polymeric chains, resulting in changes in the solubility and diffusion coefficients. Just as the dynamic properties of the matrix depend on gas-polymer-matrix composition, the matrix model predicts that the solubility and diffusion coefficients depend on gas concentration in the polymer. We present a mathematical description of the sorption and transport of gases in polymers (10, 11) that is based on the thermodynamic analysis of solubility (12), on the statistical mechanical model of diffusion (13), and on the theory of corresponding states (14). In Section II we use the matrix model to analyze the sorption, permeability and time-lag data for carbon dioxide in polycarbonate, and compare this analysis with the dual-mode model analysis (15). In Section III we comment on the physical implication of the gas-polymer-matrix model. 

In some studies, a statistical description of the nanotube dispersion state was obtained from TEM images. For example, Uchida et al. (56) measured the diameter distribution of SWNT bundles in poly(acrylonitrile), with and without a purification treatment involving sonication in methanol. The different bundle diameter distributions (especially the mean diameter) could explain the different composite tensile moduli. Fornes et al. (57) also determined the diameter distribution of SWNTs bundles in a polymer matrix (namely polycarbonate). To improve the contrast in the bright-field TEM images and better measure the bundle diameter, they dissolved the polymer in chloroform and studied the remaining SWNT network. 

Presently, the amount of data on transport in uniaxially oriented amorphous polymers is small in comparison with that of semicrystalline materials. The transport properties of oriented natural rubber (22), polystyrene (i3.,ii), polycarbonate (22.), and polyvinyl chloride (22,22) among others have been reported. One of the more complete descriptions of the effects of uniaxial orientation on gas transport properties of an amorphous polymer is that by Wang and Porter (34) for polystyrene. 

The fact, that macromolecular coil in diluted solution is a fractal object, allows to use the mathematical calculus of fractional differentiation and integration for its parameters description [72-74]. Within the framework of this formalism there is the possibility for exact accounting of such nonlinear phenomena as, for example, spatial correlations [74]. In the last years the methods of ftactional differentiation and integration are applied successfully for pol5mier properties description as well [75-77]. The authors [78-81] used this approach for average distance between polymer chain ends calculation of polycarbonate (PC) in two different solvents. 

The remaricable efficacy of the dual-mode sorption and transport rturdel for description of pure component data has been illustrated by plots of the linearized forms of Eq. (20.4-16) for a wide number of polymer-pen nt systems. " Typical examples of such data are shown in Fig. 20.4-10 for various gases in polycarbonate. These linearized plots are stringent tests of the ability of the proposed functional forms to describe the phenomenological data. Assink also has investigated the dual-mo model using a pulsed NMR technique and concluded the following ... 

Most polymer blends that are commercial products in the industry are partially miscible. Partially miscible polymer blends are those that exhibit some shift from their pure component glass transition temperatures. Thus, a binary miscible blend will exhibit one glass transition temperature [1], and a partially miscible blend may exhibit two distinct glass transition temperatures other than their pure component values [2,3]. Some experimental systems such as polyethylene terepthalate (PET) and poly-hydroxybenzoic (PHB), polycarbonate (PC), and styrene acrylonitrile (SAN) have been reported [4]. Very little mathematical description of partially miscible systems is available in the literature. 

FIG. 2 A chemically realistic description of a polymer chain (bisphenol-A-polycarbonate [BPA-PC] in the present example) is mapped approximately onto the bond fluctuation model by using suitable potentials for the length I of the effective bonds and the angles between them. In this example (3 1 mapping) one chemical repeat unit of BPA-PC containing n=l2 covalent bonds along the backbone of the chain is translated into three effective bonds. From Paul et al. [5]. 

Around 110 megatons (Mt) of CO2 are annually used in commercial synthesis processes, to produce urea, salicylic acid, cyclic carbonates, and polycarbonates. The largest use is for urea production, which reached around 90 Mt/yr in 1997. In addition to these applications, there are a number of promising reactions currently under study in various laboratories, reactions that differ in the extent to which CO2 is reduced during the chemical transformation. They include the synthesis of commodities and intermediates (acetic acid, methanol, carbonates, cyclic carbonates, and lactones), polymers (polyurethanes, polypyrones) and a variety of functionalized carboxylic acids (propenic acid, 3-hexen-l,6-dioic acid). A more detailed description can be found in the cited review. ... 

Thus, the theoretical description of physical aging process of amorphous polymers (on the example of their typical representative-polycarbonate) within the frameworks of fractal analysis and thermal cluster model. It has... 

The elastic-plastic tensile instability point in mild steel has received much attention and many explanations. Some polymers, such as polycarbonate, exhibit a similar phenomenon. Both steel and polycarbonate not only show an upper and lower yield point but visible striations of yielding, plastic flow or slip lines (Luder s bands) at an approximate angle of 54.7° to the load axis also occur in each for stresses equivalent to the upper yield point stress. (For a description and an example of Luder s band formation in polycarbonate, see Fig. 3.7(c)). It has been argued that this instability point (and the appearance of an upper and lower yield point) in metals is a result of the testing procedure and is related to the evolution of internal damage. That this is the case for polycarbonate will be shown in Chapter 3. For a discussion of these factors for metals, see Drucker (1962) and Kachanov (1986). 

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