Summary
Gel permeation chromatography (GPC), also known as size exclusion chromatography (SEC), is the technique routinely used at high temperature to analyze the molar mass distribution in polyolefins. The distribution of comonomer along the molar mass distribution in a copolymer is a key microstructural feature that determines the macroscopic properties of the material, and thus, its range of possible applications and performance. The direct coupling of a modern filter‐based infrared (IR) detector to a high temperature GPC instrument, by means of a heated flow‐through cell, is here described. The analyses are carried out by recording the continuous IR absorbance chromatograms at selected bands, which show different sensitivities to the different monomer units. A slice‐by‐slice ratio of the IR bands is further calculated to determine the average chemical composition of each molar mass fraction, after GPC separation. The high sensitivity of this IR detection method allows the injection of low concentrations of sample and the use of standard GPC analysis conditions, so that chromatographic quality is not compromised even in cases where very high molar mass fractions are present. The analysis of comonomer variations along the molar mass distribution in polyolefin copolymers is discussed. Selected applications of the method to polyethylene and poly(propylene) copolymers are presented.
The introduction of single-site catalysts in the polyolefins industry opens new routes to design resins with improved performance through multicatalyst-multireactor processes. Physical combination of various polyolefin types in a secondary extrusion process is also a common practice to achieve new products with improved properties. The new resins have complex structures, especially in terms of composition distribution, and their characterization is not always an easy task. Techniques like temperature rising elution fractionation (TREF) or crystallization analysis fractionation (CRYSTAF) are currently used to characterize the composition distribution of these resins. It has been shown that certain combinations of polyolefins may result in equivocal results if only TREF or CRYSTAF is used separately for their characterization.
Summary: An infrared detector based on a set of narrow band optical filters was coupled to a high temperature Gel Permeation Chromatograph (GPC) producing continuous chromatograms of absorbance after the molar mass fractionation. A multiple linear regression (MLR) model was established to relate the measured absorbance to the average octene weight percent in industrial ethylene-octene copolymer samples. This method is compared to univariate and multivariate band ratio models. The application of these models to produce molar mass compositional distributions is also outlined.
Very powerful triple detector high-temperature gel permeation chromatography (HT-GPC) systems equipped with concentration, viscosity, and multiple angle light scattering detectors can be found in many polyolefin characterization laboratories. However, the complexity and sometimes lack of robustness of some detection methods often result in failure to achieve the required precision and long-term reliability to properly support the industrial needs. Two methods for data processing in triple detector HT-GPC of polyolefins are here described aiming at overcoming those difficulties, specifically those related to the application of multiple angle light scattering. In the first place, we propose here a data processing method using light scattering data collected at only one angle, with application of a dissymmetry correction based on an estimate of the molecular size from the hydrodynamic volume given by the universal calibration. This method (SALS DC : single-angle light scattering with dissymmetry correction) is simple and robust for molar mass distribution and averages. Long chain branching detection and quantification is another field plagued with lack of precision and inconsistency problems, due to the difficulty in collecting reliable radius of gyration (Rg) data using MALS, for calculation of the g-index. A novel approach to estimate the g-index, based on point-by-point calculation of the gpcBR index is here described and evaluated.
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