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Cited by 365

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Mechanisms of Proton Conductance in Polymer Electrolyte Membranes

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Eikerling, M., Kornyshev, A. A., Kuznetsov, A. M., Ulstrup, J., Walbran, S.·2001·The Journal of Physical Chemistry B

Abstract

We provide a phenomenological description of proton conductance in polymer electrolyte membranes, based on contemporary views of proton transfer processes in condensed media and a model for heterogeneous polymer electrolyte membrane structure. The description combines the proton transfer events in a single pore with the total pore−network performance and, thereby, relates structural and kinetic characteristics of the membrane. The theory addresses specific experimentally studied issues such as the effect of the density of proton localization sites (equivalent weight) of the membrane material and the water content of the pores. The effect of the average distance between the sulfonate groups, which changes during membrane swelling, is analyzed in particular, and the factors which determine the temperature dependence of the macroscopic membrane conductance are disclosed. Numerical estimates of the specific membrane conductivity obtained from the theory agree very well with typical experimental data, thereby confirming the appropriateness of the theoretical concepts. Moreover, the versatility of the models offers a useful and transparent frame for combining the analysis of both experimental data and the results of molecular dynamics simulations.

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Metadata

Authors
Eikerling, M., Kornyshev, A. A., Kuznetsov, A. M., Ulstrup, J., & Walbran, S.
Venue
The Journal of Physical Chemistry B
Published
2001-04-07
Volume
105
Issue
17
Pages
3646-3662
ISSN
  • 1520-5207 (ISSN-L)
  • 1520-6106
OpenAlex
W1976394244
Cited by
365
References
1
FWCI
9.20
Type
journal article
Language
en
Open access
green

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Bibliographic data: Semantic Scholar, OpenAlex, Crossref.

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.