Self-Humidifying Proton Exchange Membranes for Fuel Cell Applications: Advances and Challenges
Bibliographic record
Abstract
Polymer electrolyte fuel cells (PEFCs) provide efficient and carbon-free power by converting the hydrogen chemical energy. The PEFCs can reach their greatest performance in humidified condition, as proton exchange membranes (PEMs) should be humidified for their proton transportation function. Thus, external humidifiers are commonly employed to increase the water content of reactants. However, being burdened with external humidifiers can make the control of PEFCs complicated and costly, in particular for transportation application. To overcome this issue, self-humidifying PEMs have been introduced, with which PEFC can be fed by dry reactants. In fact, internal humidification is accomplished by produced water from the recombination of permeated hydrogen and oxygen gases on the incorporated platinum catalysts within the PEM. While the water production agent remains constant, there is a broad range of additives that are utilized to retain the generated water and facilitate the proton conduction path in the PEM. This review paper has classified the aforementioned additives in three categories: inorganic materials, proton-conductive materials, and carbon-based additives. Moreover, synthesis methods, preparation procedures, and characterization tests are overviewed. Eventually, self-humidifying PEMs endowed with platinum and different additives are compared from performance and stability perspectives, such as water uptake, proton conductivity, fuel cell performance, gas cross-over, and the overall durability. In addition, their challenges and possible solutions are reviewed. Considering the concerns regarding the long-term durability of such PEMs, it seems that further investigations can be beneficial to confirm their reliability for prolonged PEFC operation.
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How this classification was reachedexpand
Direct model labels (unvalidated)
Per-model category and study-design labels from the labeling rounds. They are machine output, unvalidated, and the disagreement between models ships as data. No study design here is MEDLINE-validated yet.
| Model arm | Categories | Study design | Confidence |
|---|---|---|---|
| gemma | no category Domain: not available · Genre: Review About the Canadian research system: no · About a Canadian topic: no | Not applicable | low |
| gpt | no category Domain: not available · Genre: Review About the Canadian research system: no · About a Canadian topic: no | Systematic review | high |
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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.003 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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, unvalidatedLabeled directly by 2 models reading the full record.
The models disagree on parts of this classification; every voice is preserved in the section at the end of the page.
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".