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Cited by 197

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Modeling of transport of small molecules in polymer blends: Application of effective medium theory

Sax, J., Ottino, J. M.·1983·Polymer Engineering & Science

Abstract

This article concerns itself with the prediction of effective diffusion coefficients of small permeants in binary polymer blends of varying degrees of miscibility and microstructural order. Several models have been critically evaluated with the help of previously published experimental data and in terms of consistency of morphological information provided by small permeants serving as morphological probes. Completely random, two‐phase media have been modeled in terms of Effective Medium Theory with the coordination number (z) describing the average morphology. A comprehensive analysis of experimental data has shown a correlation between z and various physical situations. Near the percolation threshold, z attains a maximum value while, above it, z tends to decrease with increasing content of the conductive component. Accurate predictions of effective diffusivity can be made for volume fractions between 0.3 and 0.8 by letting z = 6. Evidence for phase inversion was studied in terms of models with ordered microstructure and transport data.

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Metadata

Authors
Sax, J., & Ottino, J. M.
Venue
Polymer Engineering & Science
Publisher
Wiley
Published
1983-02
Volume
23
Issue
3
Pages
165-176
ISSN
  • 0032-3888 (ISSN-L)
  • 1548-2634
OpenAlex
W2052317176
Cited by
197
References
1
FWCI
11.06
Type
journal article
Language
en
Open access
closed

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CoChat. (2026). Citation check: Modeling of transport of small molecules in polymer blends: Application of effective medium theory. https://app.cochat.ai/papers/10.1002/pen.760230310
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Sax, J., & Ottino, J. M. (1983). Modeling of transport of small molecules in polymer blends: Application of effective medium theory. Polymer Engineering & Science. https://doi.org/10.1002/pen.760230310

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Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).

Semantic Scholar’s data is described in: Kinney, R., et al. (2023). The Semantic Scholar Open Data Platform. arXiv:2301.10140.