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Record W4235910172 · doi:10.1149/ma2014-01/18/810

Estimation of Leakage Current in Proton Exchange Membrane Fuel Cells

2014· article· en· W4235910172 on OpenAlexaff
Seyed Mohammad Rezaei Niya, Ryan K. Phillips, Mina Hoorfar

Bibliographic record

VenueECS Meeting Abstracts · 2014
Typearticle
Languageen
FieldEngineering
TopicFuel Cells and Related Materials
Canadian institutionsUniversity of British Columbia
Fundersnot available
KeywordsProton exchange membrane fuel cellCathodeAnodeHydrogenAnalytical Chemistry (journal)Polarization (electrochemistry)Leakage (economics)ChemistryNafionGaseous diffusionMaterials scienceMembraneCurrent densityElectrodeChromatographyElectrochemistryPhysicsOrganic chemistry

Abstract

fetched live from OpenAlex

Although the membrane of the proton exchange membrane (PEM) fuel cell is considered to be hydrogen impermeable and electrically insulated, there is still current leakage inside the fuel cell which is often assumed to be around 0.01 A.cm-2 in PEM fuel cell modeling literature [1]. Unlike other types of fuel cells [2, 3], this current leakage has not been measured directly for PEM. The reactants (hydrogen and oxygen) crossover across the membrane, however, has been studied [4, 5]. It has been shown that the oxygen crossover is considerably less than that of hydrogen [5]. In this study, the amount of leakage current in a PEM fuel cell is estimated based on polarization curves and impedance measurements obtained for a 5-cm2 cell containing Nafion 212. The measurements were conducted with four different gas diffusion layers (GDLs) with different PTFE and MPL loadings. Although the polarization curves are different for different GDLs, it is expected to have the same amount of the leakage current as the same membrane has been used. Considering the Tafel equation [1] for the anode and cathode, the activation loss in a PEM fuel cell can be presented as ηact = RT/(nF)*(1/αA+1/αC ) * ln(j+jleak ) - RT/ ( nF )*ln(j0,A 1/αA j0,C 1/αC ) where ηact , j, jleak , R, T, n, F, αA , αC , j0,A and j0,C are the activation loss, current density, leakage current density, universal gas constant, temperature, number of electrons transferred due to the reaction, Faraday constant, anode and cathode charge transfer coefficients and anode and cathode exchange current densities, respectively. As the above equation shows, it is expected to have a linear relation between ηact and ln(j+jleak ). To find the leakage current, the activation loss has to be determined from the polarization curves. By assuming negligible mass transport loss in the low current density region, the total overpotential can be determined based on the difference between the theoretical cell voltage (1.23 V) and the measured voltage. As a result, the activation loss can be calculated by subtracting the losses due to the contact resistance and proton transfer in the membrane (i.e., ohmic loss) from the total overpotential. This ohmic loss can be estimated from the high frequency resistance in the Nyquist plot as the intersection of the plot with the real impedance axis [6]. This loss can be considered as an ordinary resistance [6]. Thus, the corresponding overpotential becomes a linear function of the current density. To subtract this loss, it is necessary to rotate the polarization curve counter-clockwise with the same angle of the ohmic-loss line, as it is shown in Figure 1. Then, leakage current (jleak ) can be determined from the best linear fit to the ηact versus ln(j+jleak ) graph. Using this methodology, the leakage current of the cell operated with the same membrane but four different GDLs are calculated and presented in Table 1. The polarization curves are shown in Figure 2. Although the polarization curves and Nyquist plots are different, the leakage currents are the same since the same membrane was utilized. References R. O’hayre, S. Cha, W. Colella and F.B. Prinz, Fuel Cell Fundamentals, Second ed., John Wiley & Sons (2009). J.P. Meyers and J. Newman, J. Electrochem. Soc., 149, A729 (2002) D.J.L. Brett, A. Atkinson, N.P. Brandon and S.J. Skinner, Chem. Soc. Rev., 37, 1568 (2008) S.S. Kocha, J.D. Yang and J.S. Yi, AIChE Journal, 52, 1916 (2006) B.T. Huang, Y. Chatillon, C. Bonnet, F. Lapicque, S. Leclerc and M. Hinaje, Fuel Cells, 12, 335 (2012) S.M. Rezaei Niya, M. Hoorfar, Submitted to Electrochimica Acta.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.002
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.002
Threshold uncertainty score0.007

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.002
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0020.002
Science and technology studies0.0000.000
Scholarly communication0.0010.002
Open science0.0010.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0000.001

Machine scores (provisional)

The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.

Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.

Opus teacher head0.010
GPT teacher head0.219
Teacher spread0.209 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designBench or experimental
Domainnot available
GenreEmpirical

How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".

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Citations2
Published2014
Admission routes1
Has abstractyes

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