Controlled Oxygen Incorporation in TiN Coatings via Heat Treatment for Applications in PEMFC Metallic Bipolar Plates
Bibliographic record
Abstract
Improving the corrosion resistance while maintaining good electrical conductivity is of vital importance for the application of stainless steel in bipolar plates of polymer electrolyte membrane fuel cells (PEMFCs). Transition nitride coatings on steel surfaces, such as TiN, is considered as a possible solution. However, most coatings still fail to exhibit good corrosion resistance and high electrical conductivity simultaneously, especially after corrosion testing. This study prepares TiN on 316L stainless steel (SS) and conducts heat treatment on the TiN deposited samples at different temperatures. The corrosion behaviours of the prepared samples are investigated under the simulated working environments of fuel cell. Our results demonstrate that heat treatment at appropriate temperatures is an effective approach to improve the corrosion resistance of TiN coatings while maintaining a considerable electrical conductivity. The interfacial contact resistance (ICR) test results indicate that high temperature (450 °C) heat treatment has detrimental effect on the electrical conductivity of samples due to the formation of a thick oxide dominated layer, while samples heat treated at 300 °C only form graded layers with suitable oxide amount which endows the coated specimens with a very low ICR value both before and after corrosion tests. This suggests that the heat treatment of TiN coatings under suitable conditions is a feasible strategy to simultaneously achieve an enhanced corrosion resistance and a good electrical conductivity of the TiN coated samples for bipolar plates in PEMFCs.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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".