Numerical and Experimental Analyses on Deviated Concentration Loss with Alkaline Anion-Exchange Membrane Fuel Cells
Bibliographic record
Abstract
The polarization curves of low-temperature fuel cells, such as polymer electrolyte membrane fuel cells, typically have three distinct regimes dictated by certain limiting factors. These kinetic, ohmic, and concentration-loss regimes have overpotentials that are dictated by electrochemical activation, ohmic loss, and reactant concentration, respectively. However, a peculiar polarization curve with a deviated concentration loss regime was recently presented for alkaline anion-exchange membrane fuel cells (AEMFCs), and the cause of this deviation remains unknown to date. In this work, a 2D, steady-state H 2 /O 2 model was developed to simulate transport and reactions in an AEMFC in order to explore the origin and mechanism of deviations in the polarization curve. According to the model, the charge transfer and hydroxyl ion transport resistances in the cathode catalyst layer were rate-limiting factors and contributed to the deviated concentration loss above ∼450 mA cm –2, which was observed in baseline simulations. Improving water management in the cathode catalyst layer was found to be an effective strategy for mitigating the deviated concentration loss and enhancing cell performance, potentially enabling high-performance AEMFCs in the future.
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 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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| 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".