Evaluation of materials for bipolar plates in simulated PEM fuel-cell cathodic environments
Bibliographic record
Abstract
The bipolar plates in proton exchange membrane fuel cells (PEMFC) are exposed to an oxidizing environment on the cathodic side, and therefore are susceptible to corrosion. Corrosion resistant materials are needed for the bipolar plates in order to improve the lifespan of fuel cells. This article described a study in which a molybdenum (Mo) coating was deposited over austenitic stainless steel 316 and carbon steel as substrates in order to evaluate the resulting surfaces with respect to their corrosion resistance in simulated anodic and cathodic PEMFC environments. The molybdenum oxide films were characterized by scanning electron microscopy (SEM) and Raman spectroscopy. The article presented the experiment and discussed the results of the corrosion behaviour of coated stainless steel. In general, the electrochemical characterization of bare materials and coated steel consisted of slow potentiodynamic polarization curves followed by a constant potential polarization test. The test medium was 0.5M sulfuric acid with additional introduction of oxygen to simulate the cathodic environment. All tests were performed at ambient temperature and at 50 degrees Celsius. The potentiostat used was a Gamry instrument. It was concluded that it is possible to deposit Mo-oxides on steel without using another alloying metal. The preferred substrate for corrosion prevention was found to be an alloy with high chromium content. 24 refs., 4 figs.
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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 | 0.000 | 0.000 |
| 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".