Impact of Surface Pretreatment on the Corrosion Resistance and Adhesion of Thin Film Coating on SS316L Bipolar Plates for Proton-Exchange Membrane Fuel Cell Applications
Bibliographic record
Abstract
Coated SS316L is a potential alternative to the graphite bipolar plates (BPPs) used in proton-exchange membrane fuel cells (PEMFCs) owing to their low manufacturing cost and machinability. Due to their susceptibility to corrosion and passivation, which increases PEMFC ohmic resistance, protective and conductive coatings on SS316L have been developed. However, coating adhesion is one of the challenges in the harsh acidic environment of PEMFCs, affecting the performance and durability of BPPs. This study compares mechanical polishing and the frequently adopted chemical etchants for SS316L: Adler’s, V2A, and Carpenter’s etchant with different etching durations and their impact on the wettability, adhesion, and corrosion resistance of a Nb-coated SS316L substrate. Contact angle measurements and laser microscopy revealed that all etching treatments increased the hydrophobicity and surface roughness of SS316L substrates. Ex situ potentiodynamic and potentiostatic polarization tests and interfacial contact resistance analysis revealed high corrosion resistance, interfacial conductivity, and adhesion of the Nb-coated SS316L substrate pretreated with V2A (7 min) and Adler’s (3 min) etchant. Increased hydrophobicity (contact angle = 101°) and surface roughness (Ra = 74 nm) achieved using V2A etchant led to the lowest corrosion rate (3.3 µA.cm−2) and interfacial resistance (15.4 mΩ.cm2). This study established pretreatment with V2A etchant (a solution of HNO3, HCl, and DI water (1:9:23 mole ratio)) as a promising approach for improving the longevity, electrochemical stability, and efficiency of the coated SS316L BPPs for PEMFC application.
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".