Analytical rheology of blends of linear and star polymers using a Bayesian formulation
 Sachin Shanbhag
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A Bayesian data analysis technique is presented as a general tool for inverting linear viscoelastic models of branched polymers. The proposed method takes rheological data of an unknown polymer sample as input and provides a distribution of compositions and structures consistent with the rheology, as its output. It does so by converting the inverse problem of analytical rheology into a sampling problem, using the idea of Bayesian inference. A Markov chain Monte Carlo method with delayed rejection is proposed to sample the resulting posterior distribution. As an example, the method is applied to pure linear and star polymers and linear–linear, star–star, and star–linear blends. It is able to (a) discriminate between pure and blend systems, (b) accurately predict the composition of the mixtures, in the absence of degenerate solutions, and (c) describe multiple solutions, when more than one possible combination of constituents is consistent with the rheology.
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 Title
 Analytical rheology of blends of linear and star polymers using a Bayesian formulation
 Journal

Rheologica Acta
Volume 49, Issue 4 , pp 411422
 Cover Date
 20100401
 DOI
 10.1007/s003970100443z
 Print ISSN
 00354511
 Online ISSN
 14351528
 Publisher
 SpringerVerlag
 Additional Links
 Topics
 Keywords

 Analytical rheology
 Bayesian inference
 Markov chain Monte Carlo
 Polymer blend
 Tube model
 Linear viscoelasticity
 Modeling
 Bayesian analysis
 Inverse problem
 Industry Sectors
 Authors

 Sachin Shanbhag ^{(1)}
 Author Affiliations

 1. Department of Scientific Computing, Florida State University, Tallahassee, FL, 32306, USA