Effects of 1, 2, 4-Triazole Additive on PEM Fuel Cell Conditioning
Bibliographic record
Abstract
Melt processing is one of the essential technologies for the mass production of polymer electrolyte membranes (PEM) at low cost. Azoles have been widely used in PEM to improve their conductivity at a relatively low humidity and recently as bifunctional additives in a melt blowing processing for PEM mass production. In this work, we attempted to assess the effect of 1, 2, 4-triazole additive in membranes and in catalyst layers on PEM fuel cell conditioning. Various characterization tools including electrochemical impedance spectroscopy (EIS), cyclic voltammetry (CV) and conditioning with constant current were applied to diagnose the temporary electrochemical reaction effect and the permanent performance loss caused by the triazole additives. It was found that triazole additives in membranes could migrate into the catalyst layers and significantly affect the open circuit voltage (OCV) and the conditioning. The effect could be partially or completely removed/cleaned either through longer conditioning time or via CV cycling, which depends on the amount of additives remaining in the membrane. The findings provide valuable scientific insights on the relevance of post treatment steps during membrane production and overcoming fuel cell contamination issues due to residual additive in the membranes and understanding the quality control needed for fuel cell membranes by melt blowing processing.
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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 | 0.000 |
| 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".