Prediction of melt rheological properties from GPC molecular weights |
| |
| Affiliation: | 1. Department of Gastrointestinal Cancer Biology, Tianjin Medical University Cancer Institute and Hospital, National Clinical Research Center for Cancer, Key Laboratory of Cancer Prevention and Therapy, Tianjin 300060, China;2. The State Key Laboratory of Molecular Oncology, Cancer Institute and Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing 100021, China;1. Friedrich-Alexander University Erlangen-Nuremberg, Institute for Material Science, Chair of Metals Science and Technology, Martensstraße 5, 91058 Erlangen, Germany;2. Technical University Dresden, Institute of Material Science, Chair of Inorganic Non-Metallic Materials, 01062 Dresden, Germany;3. Fraunhofer IKTS, Winterbergstraße 28, 01277 Dresden, Germany;1. Department of Mechanical Engineering, Materials Technology Institute, Eindhoven University of Technology, P.O. Box 513, 5600 MB, Eindhoven, The Netherlands;2. ExxonMobil Chemical Company, 5200 Bayway Drive, Baytown, TX 77520, USA |
| |
| Abstract: | The SCLAIR® solution polymerization platform produces a wide variety of ethylene-α-olefin copolymers and polyethylene homopolymers. Commercial products exhibit density and melt index values ranging from about 0.920 to 0.962 g/cm3 and 0.3–75 g/10 min respectively. Polymer molecular weight distributions can be tailored to meet a broad selection of end-use requirements. In this study, we have used a chemometric analysis approach using The Unscrambler® software to demonstrate statistical correlations between rheological properties and fundamental structural parameters for thirty-three commercial SCLAIR polyethylenes. We demonstrate that molten rheological properties such as melt index, stress exponent, zero-shear viscosity, characteristic relaxation time, cross-over modulus and frequency show good non-linear correlations with molecular weight characteristics of SCLAIR products as determined by gel permeation chromatography (GPC). We also show that, with the use of Partial Least Squares (PLS) regression techniques, most melt rheological properties can be accurately predicted on the basis of GPC data. |
| |
| Keywords: | Rheology Melt index Molecular weights Chemometric analysis |
| 本文献已被 ScienceDirect 等数据库收录！ |
|
