Crystallization analysis fractionation and temperature rising elution fractionation are two techniques used to estimate the chemical composition distributions of semicrystalline copolymers. This study investigates the cooling rate and cocrystallization effects for both techniques with a series of ethylene/1-olefin copolymers and their blends. Ideally, both techniques should operate in the vicinity of thermodynamic equilibrium so that crystallization kinetic effects are avoided. The results show that, in fact, crystallization kinetic effects play an important role at the typical cooling rate used with both techniques. Cocrystallization is significant when fast cooling rates are used.

Crystallization analysis fractionation (CRYSTAF) is an analytical technique for determining the distribution of chain crystallizabilities of semicrystalline polymers. After only approximately a decade since it was developed, CRYSTAF has become one of the most important characterization techniques in polyolefin characterization laboratories because it provides fast and crucial information required for the proper understanding of polymerization mechanisms and structure–property relationships. In the polyolefin industry, it has been established as an indispensable tool for product development and product quality monitoring. This highlight article covers basic operation procedures, applications, and theoretical aspects of polymer fractionation with CRYSTAF. © 2005 Wiley Periodicals, Inc. J Polym Sci Part B: Polym Phys 43: 1557–1570, 2005

Crystallization analysis fractionation (Crystaf) is a polymer characterization technique for estimating the chemical composition distributions of semicrystalline copolymers. Although Crystaf has been widely used during the recent years, it is still a relatively new polymer characterization technique. More quantitative understanding of its fractionation mechanism is essential for further developments. In this work, three ethylene/ 1-hexene copolymers with different 1-hexene fractions, but similar number-average molecular weights, were analyzed by Crystaf at several cooling rates. A mathematical model was proposed to describe the effect of comonomer fraction and cooling rate on Crystaf fractionation from a fundamental point of view. The model describes the experimental Crystaf profiles of ethylene/1-hexene copolymers with different 1-hexene fractions measured at distinct cooling rates very well.

Crystallization analysis fractionation (Crystaf) is a polymer characterization technique for estimating the chemical composition distributions (CCDs) of semi-crystalline copolymers. Although Crystaf has been widely used during the recent years, it is still a relatively new polymer characterization technique. More quantitative understanding of its fractionation mechanism is essential for further developments.In this work, a series of ethylene homopolymers and ethylene/1-hexene copolymers with different molecular weight distributions (MWD) and chemical composition distributions (CCD) was analyzed by crystallization analysis fractionation (Crystaf) at several cooling rates to investigate the effect of MWD, CCD, and cooling rate on their Crystaf profiles. Using these results, we developed a mathematical model for Crystaf that considers crystallization kinetic effects ignored in all previous Crystaf models and can fit our experimental profiles very well.

their molecular weight distribution (MWD) and chemical composition distribution (CCD) that depend on copolymerization conditions and catalyst type. [ 1 ] Polymers with narrow MWDs can be synthesized with single-site-type metallocene catalysts. [ 2 ] A combination of two metallocene catalysts can be used to control polyolefi n structures with versatility and fl exibility. [3][4][5][6] Polymerization kinetics models for ethylene/1-olefi n copolymerization with two metallocenes are expressed as a system of ordinary differential or algebraic equations. These model can be used to estimate chain microstructures from a specifi c set of polymerization conditions; [ 7 ] they cannot, however, be used to determine polymerization conditions to yield desired microstructures because these models cannot be inverted.Artifi cial neural networks (ANNs) can be used to solve highly nonlinear problems. ANNs mimic the pattern recognition process of neural systems by learning from examples. Only input and output datasets are used in ANNs-phenomenological models for the process Two artifi cial neural network models (forward and inverse) are developed to describe ethylene/1olefi n copolymerization with a catalyst having two site types using training and testing datasets obtained from a polymerization kinetic model. The forward model is applied to predict the molecular weight and chemical composition distributions of the polymer from a set of polymerization conditions, such as ethylene concentration, 1-olefi n concentration, cocatalyst concentration, hydrogen concentration, and polymerization temperature. The results of the forward model agree well with those from the kinetic model. The inverse model is applied to determine the polymerization conditions to produce polymers with desired microstructures. Although the inverse model generates multiple solutions for the general case, unique solutions are obtained when one of the three key process parameters (ethylene concentration, 1-olefi n concentration, and polymerization temperature) is kept constant. The proposed model can be used as an effi cient tool to design materials from a set of polymerization conditions.