Understanding of Nafion Membrane Additive Behaviors in Proton Exchange Membrane Fuel Cell Conditioning
Bibliographic record
Abstract
Durability and cost are the two major factors limiting the large-scale implementation of fuel cell technology for use in commercial, residential, or transportation applications. The conditioning cost is usually negligible for making proton exchange membrane fuel cells (PEMFCs) at R&D or demo stage with several tens of stacks each year. However, with industry's focus shifting from component development to commercial high-volume manufacturing, the conditioning process requires significant additional capital investments and operating costs, thus becomes one of the bottlenecks for PEMFC manufacturing, particularly at a high production volume (>1000 stack/year). To understand the mechanisms behind PEMFC conditioning, and to potentially reduce conditioning time or even to eliminate the conditioning process, the conditioning behaviors of commercial Nafion™ XL100 and Nafion® NRE 211 membranes were studied. The potential effects of the membrane additive on fuel cell conditioning were diagnosed using in situ electrochemical impedance spectroscopy (EIS). It was found that the membrane additive led to the significant variation of the charge transfer resistance in EIS during conditioning, which affected the conditioning behavior of the membrane electrode assembly (MEA).
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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 | 0.001 |
| 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 | 0.001 |
| Open science | 0.000 | 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".