An Electrochemical Investigation of Potential Metallic Bipolar Plate Materials for PEM Fuel Cells
Bibliographic record
Abstract
Increasing attention is being paid to the use of metallic materials as a replacement for non-porous graphite in bipolar plates (BPs) for polymer exchange membrane (PEM) fuel cells. The ideal BP material should demonstrate high values of electrical conductivity, thermal conductivity, corrosion resistance and compressive strength and low values of hydrogen/gas permeability and density. Although metallic materials demonstrate many of those properties, their corrosion resistance can be inadequate, which in turn can lead to unacceptable values of contact resistivity. In this study the corrosion properties of SS316L, SS347, SS410, Al6061 alloy, A36 steel and Ti were investigated in simulated PEMFC anode and cathode environments. SS316L, SS347 and Ti exhibited better corrosion resistance than the other metals. The three metals had similar anodic and cathodic corrosion current densities; the corrosion current was negative in the simulated anode conditions and positive in the simulated cathode conditions. These negative currents arose because of the reaction 2H++2enrH2 on the metal electrode and 2H2Or4H++O2+4en on the platinum electrode. This did not cause corrosion of the metaln surface because the negative currents provide cathodic protection. For the Al6061 alloy, and the A36 steel, the cathodic corrosion current density was much larger than the anodic current density. However, for SS410, the anodic current density is larger. Although SS316L, SS347 and Ti had the better corrosion resistance, they still corroded and metal ions would migrate to membrane andn therefore degrade both the membrane and the fuel cell performance.
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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 | 0.000 |
| 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 | 0.000 |
| 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".