Investigation of Charge-Transfer and Mass-Transport Resistances in PEMFCs with Microporous Layer Using Electrochemical Impedance Spectroscopy
Bibliographic record
Abstract
The influence of a microporous layer (MPL) on polarization and electrochemical impedance behavior of proton exchange membrane fuel cells (PEMFCs) was investigated. Commercial carbon backing electrode with MPL applied on one of its sides were employed for both the anode and cathode. The ohmic, charge-transfer, and mass-transport resistances at various current densities were obtained by deconvolution of electrochemical impedance spectroscopy data. PEMFCs with an MPL showed higher performance and lower variability in the charge-transfer and mass-transport regions of the polarization curve (current density above ) within a batch of identically built cells. For cells with and without MPL, the charge-transfer resistance decreased while mass-transport resistance increased with an increase in current density. The difference in charge-transfer resistance for cells with and without MPLs was found to be statistically insignificant due to the large variability in data for cells without MPLs. Cells with MPLs demonstrated lower mass-transport resistance compared to cells without MPL. The time constant for the mass-transport process probed at a low-frequency regime of the impedance was obtained from the Warburg impedance. Among the various possible oxygen-transport processes, the estimated time constant for oxygen transport in the porous transport layer (PTL) was found to be within an order of magnitude of the Warburg-impedance-derived value. Accordingly, it was assessed that the presence of an MPL helped reduce the water saturation in the PTL, thereby improving the oxygen transport to the cathode catalyst layer.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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".