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Cited by 512

The DOI matches its Crossref record; 3 of the 3 references it cites were found in a registry or an index.

Analysis of Oxygen-Transport Diffusion Resistance in Proton-Exchange-Membrane Fuel Cells

Nonoyama, Nobuaki, Okazaki, Shinobu, Weber, Adam Z., Ikogi, Yoshihiro, Yoshida, Toshihiko·2011·Journal of The Electrochemical Society

Abstract

The quantification procedure of oxygen-transport resistances for different fuel-cell layers and phenomena is described. The total transport resistance is obtained from limiting-current measurements under conditions where oxygen diffusion is dominant (i.e., high flow rates, small cell size, humidified but subsaturated feeds, and low feed oxygen partial pressure). By systematically varying the experimental conditions, the contributions of molecular and Knudsen diffusion and permeation through the ionomer film covering the catalyst-layer agglomerates are determined. It is found that the ionomer-film resistance is dominant, especially at lower temperatures and lower Pt loadings. The calculated film properties through the ionomer hint that it is much more resistive than the bulk membrane for state-of-the-art cells.

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Metadata

Authors
Nonoyama, N., Okazaki, S., Weber, A. Z., Ikogi, Y., & Yoshida, T.
Venue
Journal of The Electrochemical Society
Published
2011
Volume
158
Issue
4
Pages
B416
ISSN
0013-4651 (ISSN-L)
OpenAlex
W1985679007
Cited by
512
References
3
FWCI
8.02
Type
journal article
Language
en
Open access
closed

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CoChat. (2026). Citation check: Analysis of Oxygen-Transport Diffusion Resistance in Proton-Exchange-Membrane Fuel Cells. https://app.cochat.ai/papers/10.1149/1.3546038
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Nonoyama, N., Okazaki, S., Weber, A. Z., Ikogi, Y., & Yoshida, T. (2011). Analysis of Oxygen-Transport Diffusion Resistance in Proton-Exchange-Membrane Fuel Cells. Journal of The Electrochemical Society. https://doi.org/10.1149/1.3546038

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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.