Zr<sub>2</sub>N<sub>2</sub>O Coating-Improved Corrosion Resistance for the Anodic Dissolution Induced by Cathodic Transient Potential
Bibliographic record
Abstract
Developing a corrosion-resistant and electrically conductive coating on metallic bipolar plates is essential to mitigate the performance degradation induced by the high cathodic transient potentials (CTPs) in the start-up/shut-down (SU/SD) processes of polymer electrolyte membrane fuel cells (PEMFCs). Herein, a zirconium oxynitride (Zr 2 N 2 O) coating prepared by atomic layer deposition was used to improve the corrosion resistance of 304 stainless steel (304 SS) toward anodic dissolution at various CTPs. Triangular potential pulses were applied to the specimens to simulate potential variations at the cathode side of the PEMFCs at SU/SD stages. Results show that the Zr 2 N 2 O coating can provide effective protection at a CTP as positive as 1.1 V versus Ag/AgCl. At all CTPs examined, the peak current density ( i peak ) extracted from the pulse test of the coated specimen (Zr 2 N 2 O/SS) is 2 orders of magnitude lower than that of uncoated 304 SS, indicating that the presence of the Zr 2 N 2 O coating remarkably increases the corrosion resistance for the anodic dissolution induced by CTPs. More importantly, upon increasing the CTPs, 304 SS experiences severe intergranular corrosion after 4050 pulses, whereas Zr 2 N 2 O/SS shows slight pitting corrosion. The quite low i peak and the mitigated corrosion morphologies of Zr 2 N 2 O/SS confirm that incorporating oxygen into the protective coating for achieving a high oxidation resistance is a feasible way to restrain the anodic dissolution caused by high CTPs. Analysis of the electron energy level diagrams of the passive film suggests a protective coating with a wider valence band contributing to the improved corrosion resistance toward the transpassive dissolution.
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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 | 0.000 | 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".