Characterization of the Porous Transport Layer (PTL)
Notice bibliographique
Résumé
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)
Récupéré en direct depuis OpenAlex et désinversé. Les résumés ne sont pas conservés dans cette base de données : les index inversés représentent 8,6 Go des 9,3 Go de texte de la base, et le serveur dispose de 13 Go libres.
Comment cette classification a été obtenuedéplier
Prédiction machine sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Le volet Gemma est une étiquette directe du modèle pour chaque travail de la base, lue sur la notice réduite au titre. Le volet Codex est un classifieur appris des 10 348 étiquettes directes de Codex et calibré sur les taux pondérés de l'échantillon; les champs sans appui suffisant ne portent aucun appel Codex. Le mode candidate est l'union des deux volets; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont pas des étiquettes humaines.
Scores du classifieur distillé par catégorie (deux têtes)
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,000 | 0,000 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,000 | 0,000 |
| Bibliométrie | 0,000 | 0,000 |
| Études des sciences et des technologies | 0,000 | 0,000 |
| Communication savante | 0,000 | 0,000 |
| Science ouverte | 0,000 | 0,000 |
| Intégrité de la recherche | 0,000 | 0,000 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,001 | 0,000 |
Scores machine (provisoires)
Les deux têtes enseignantes du modèle étudiant, lues sur ce travail. Un score ordonne la base pour la relecture; il n'affirme jamais une catégorie, et le statut de validation accompagne chaque rangée tel quel.
Scores de référence d'un modèle non mature (critères de maturité non atteints, 7 itérations). Un score ordonne; il n'affirme jamais une catégorie.
score_only:v0-immature-baseline · tel quel depuis la passe de notation : score_only signifie que le nombre peut ordonner les travaux, et qu'aucune étiquette de catégorie n'en découleClassification
machine, non validéePrédiction automatique; un appel candidat d’une seule source (Gemma direct ou Codex distillé), pas un consensus.
Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».