Diamond‐Like‐Carbon Coated Metallic Bipolar Plates for PEM Fuel Cells: An Assessment of Coating Thickness Effect
Bibliographic record
Abstract
ABSTRACT Metallic bipolar plates are key to low‐cost compact stack design for proton exchange membrane (PEM) fuel cells, but suffer serious surface corrosion and require surface protective coatings such as diamond‐like‐carbon (DLC). In this study, the impact of DLC coating thickness on the corrosion characteristics of SS316L bipolar plates is investigated in an ex‐situ environment simulating the operating environment in PEM fuel cells, and three coating thicknesses of 47, 100, and 197 nm are evaluated for their surface morphology, composition, corrosion resistance, wettability, and interfacial contact resistance. x‐Ray photoelectron spectroscopy measurements reveal the sp 2 (graphite‐like) to sp 3 (diamond‐like) hybridization ratio, while scanning electron microscopy results show dense coating layer structures. Corrosion resistance is assessed using potentiostatic and potentiodynamic polarizations and electrochemical impedance spectroscopy (EIS) in a simulated fuel cell environment with 1 M H 2 SO 4 at 80°C. It is found that all coated samples meet the US Department of Energy (DOE) targets for corrosion resistance, and the 100 nm thick DLC coating shows the best performance, with the lowest corrosion current density (0.110 µA/cm 2 ), highest corrosion potential (17.31 mV), and highest resistance to charge transfer (5.36·10 6 Ω cm 2 ); and maintain hydrophobicity (contact angle of 92° and surface free energy of 17.00 mJ/m 2 ) after 5 h of potentiostatic polarization, suggesting excellent potential for water management and corrosion resistance. Further, these coated samples meet the DOE targets for interfacial contact resistance of < 10 mΩ cm 2 . Therefore, the 100 nm thick DLC coating demonstrates superior anticorrosion performance required for PEM fuel cell applications.
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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".