Effect of anode material on hydrogen diffusion into the substrate duringZn-Ni electroplating process
Bibliographic record
Abstract
Zinc-Nickel (Zn-Ni) coating is an emerging replacement for Cadmium (Cd) plating in the aerospace industry to protect the components manufactured from high strength steels such as landing gears from corrosion. As per industrial standard, Zn-Ni plated components require postplating baking to avoid hydrogen embrittlement (HE) of the substrate due to the diffusion of hydrogen (H) atoms into the substrate metal during this plating process. Brush electroplating is widely used in the aerospace industry for onsite repair of the locally damaged coating in service however, after this plating process an on-site localized post-plating baking is not viable. Therefore, it is important to investigate the different plating parameters in order to minimize the diffusion of H atoms into the substrate metal during Zn-Ni brush electroplating process. With this motive, the effect of using platinum (Pt) and graphite (Gr) as anode materials on the diffusion of H atoms into the substrate metal during Zn-Ni electroplating process was investigated in this work using Devanathan-Stachurski double cell. It was observed that under similar plating conditions, the diffusion of H atoms into the substrate was comparatively lower when Zn-Ni plating was carried out using Pt anode than with the Gr anode. Large amount of H atoms diffused into the substrate through the coating even after the completion of the coating process in both cases. Also, lower plating efficiency was obtained when Pt anode was used.
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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 | 0.001 |
| 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".