Characterization of the Porous Transport Layer (PTL)
Bibliographic record
Abstract
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 imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".