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Discrimination of Chain Branching Models in Addition Diene Polymerizations
The present work analyzes whether discrimination of chain branching models is possible in addition diene polymerizations, based solely on average molecular weights and monomer conversions monitored during the reaction course, as usually performed in most quality control labs of industrial plants. In...
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Published in: | Macromolecular theory and simulations 2020-03, Vol.29 (2), p.n/a |
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Main Authors: | , , |
Format: | Article |
Language: | English |
Subjects: | |
Citations: | Items that this one cites Items that cite this one |
Online Access: | Get full text |
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Summary: | The present work analyzes whether discrimination of chain branching models is possible in addition diene polymerizations, based solely on average molecular weights and monomer conversions monitored during the reaction course, as usually performed in most quality control labs of industrial plants. In addition, it is verified whether the analyzed models present enough flexibility to fit data generated with the other rival models. Three kinetic models are considered, and kinetic parameters are varied in order to represent average molecular weights and monomer conversions that are typically found in addition diene polymerizations. The results show that model discrimination is indeed feasible with few experiments, even when only average molecular weights measured through GPC analyses are available, which can be of significant practical importance at plant site for critical characterization of product properties and process performance.
The present work analyzes if discrimination of chain branching models is feasible in addition diene polymerizations, based on average molecular weights and conversions. The candidate models differ in the formation of long chain branches (LCB). The first model considers the macromonomer reincorporation step, the second one considers the transfer to polymer step, and the third model does not form LCB. |
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ISSN: | 1022-1344 1521-3919 |
DOI: | 10.1002/mats.201900043 |