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Record W2305323828 · doi:10.1149/ma2014-01/11/565

Characterization of the Porous Transport Layer (PTL)

2014· article· en· W2305323828 on OpenAlexaff
Ryan K. Phillips, Seyed Mohammad Rezaei Niya, 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 cellPorosityPolarization (electrochemistry)Water transportMass transportContact angleMaterials scienceChemistryGaseous diffusionChemical engineeringComposite materialAnalytical Chemistry (journal)MembraneFuel cellsWater flowChromatographyEnvironmental engineering

Abstract

fetched live from OpenAlex

Water management and effective reactant transport play a vital role in enhancing performance of the proton exchange membrane (PEM) fuel cell. The porous transport layer (PTL) consists of a macro porous gas diffusion layer (GDL) coated with a micro porous layer (MPL). These layers decrease mass transport losses by removing excess water and improving reactant gas distribution. A well-known manufacturing technique to decrease mass transport losses is to increase hydrophobicity of the PTL by loading Teflon to the GDL and MPL. A polarization curve will demonstrate the influence of this increase in hydrophobicity, especially in the region of high current densities where water production is high and the cell requires more reactants. In this region, mass transport losses cause a drastic loss in power. To study the mass transport effects, three PTLs were characterized with ex-situ and in-situmethods. Two different GDLs (GDL1 and GDL2) and two different Teflon loadings in the MPL (MPLT18 and MPLT50) were compared. Two ex-situproperties that are known to influence fuel cell performance are the contact angle (CA) of water in the PTL and the pore size distribution (PSD) of the GDL and MPL. Low surface tension liquid techniques were used to find the CA of the PTL and PSD of the MPL [1]. These results are reported in Table 1. Figure 1 shows the SEM images of the three PTLs (MPL side). Two in-situ techniques that are used to characterize fuel cells are impedance measurements and, more commonly, polarization curves. As mentioned, a polarization curve shows the effect of mass transport losses in the high current density region; a more effective PTL will lead to a higher maximum power. The polarizations curves in Figure 2 demonstrate this power trend in the PTLs tested. While this is the primary technique used for fuel cell testing, it lacks information about the fuel cell performance at its true optimum level. Impedance testing is considered a valuable tool in analyzing the resistance of fuel cells in its different regions of current density [2-6]. Figure 3 shows the impedance as a function of frequency in the form of a Bode magnitude diagram for the PTLs tested. In order to evaluate the total resistance of the cell (activation, ohmic, and mass transport resistances), the low frequency impedance at 1 Hz was used. These total resistance values are plotted as a function of current density in Figure 2. Evaluating the point of minimum resistance for each PTL reveals that this point is well below the maximum power, as displayed in Figure 2. This point appears to be of high importance and warrants further research. The results also show that as the hydrophobicity of the PTL increases (higher CA/ lower PS), the performance of the cell increases. Not only did GDL1 MPLT50 produce the best performance, its total resistance only increased slightly from its minimum until 1 A/cm2, supporting the robustness of this PTL. The results from this study can be applied to the characterization of any fuel cell system for its optimum performance. References R.K. Phillips, et al., World Hydrogen Energy Conference, 763, (2012) S.M. Rezaei Niya, M. Hoorfar, J. Power Sources, 240, 281, (2013) X. Yuan, et al, J. Hydrogen Energy, 32, 4365, (2007) L. Omati, et al., J. Hydrogen Energy, 36, 8053, (2011) D. Malevich, et al., J. Electrochem. Soc., 156, B216, (2009) Y. Tang, et al., J. Electrochem. Soc., 153, A2036, (2006)

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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)0.000
metaresearch head score (Gemma)0.000
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 score0.001
Threshold uncertainty score0.004

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.000

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 head0.007
GPT teacher head0.178
Teacher spread0.172 · 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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Citations0
Published2014
Admission routes1
Has abstractyes

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