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Stochastic Modeling of Poly(acrylate) Distributions Obtained by Radical Polymerization under High‐Temperature Semi‐Batch Starved‐Feed Conditions: Investigation of Model Predictions versus Experimental Data

Amin Nasresfahani, Nina Heidarzadeh, Elizabeth G. Bygott, Robin A. Hutchinson

2021Macromolecular Theory and Simulations15 citationsDOI

Abstract

Abstract Secondary reactions significantly affect acrylate polymerization rates as well as the architecture of polymer produced by high‐temperature solution radical polymerization. This impact is amplified under the semi‐batch starved‐feed policy used to keep monomer concentration low. Thus, the importance of intramolecular chain transfer (backbiting) is significantly increased, generating a tertiary radical center capable of termination, propagation, and scission. In this investigation, a comprehensive stochastic model is formulated to represent results from an experimental study designed to increase the fraction of reactive terminal double bonds (TDB) in the poly(butyl acrylate) product. Model predictions generated using three sets of literature kinetic parameters for backbiting and scission are compared. While each provides reasonable predictions of some reaction characteristics (e.g., free monomer levels, polymer molecular weights, polymer TDB content), none provide an adequate representation of all aspects of the polymerization. It is concluded that other reaction pathways might be needed to represent the system under semi‐batch conditions, thus explaining the discrepancies seen among the current parameter estimates.

Topics & Concepts

MonomerPolymerizationRadical polymerizationAcrylatePolymer chemistryPolymerChemistryIntramolecular forceMolar mass distributionMaterials scienceThermodynamicsOrganic chemistryPhysicsAdvanced Polymer Synthesis and CharacterizationPhotopolymerization techniques and applicationsPolymer Science and PVC
Stochastic Modeling of Poly(acrylate) Distributions Obtained by Radical Polymerization under High‐Temperature Semi‐Batch Starved‐Feed Conditions: Investigation of Model Predictions versus Experimental Data | Litcius