Pitting Corrosion and Mechanical Properties of Direct Current and Pulsed Reverse Current Electrodeposited Nickel-Tungsten Coatings
Bibliographic record
Abstract
The electrochemical corrosion and mechanical properties of direct current and pulsed reverse current electrodeposited nickel and nickel-tungsten were investigated by using cyclic polarization measurement and nano-indentation techniques. Direct and pulsed reverse current electrodeposited nickel-tungsten coatings revealed a significant higher resistance to pitting corrosion when compared to direct and pulsed reverse current deposited nickel. Furthermore, pulsed reverse current electrodeposited nickel-tungsten displayed the most noble corrosion potential and higher corrosion resistance compared to direct current electrodeposited nickel-tungsten. This was attributed to the more nano- crystalline structure of the pulsed-reverse current deposited coatings when compared to that of the direct current electrodeposited nickel-tungsten. The average modulus for both direct and pulsed reverse current deposited nickel-tungsten were found to be similar but the average hardness of direct current deposited nickel-tungsten was slightly higher than that of pulsed reverse current deposited nickel-tungsten. This was attributed to the higher tungsten content (35 wt.%) in the direct current deposited nickel-tungsten coating compared to that (25 wt.%) in the pulsed reverse current deposited nickel-tungsten and is supported by our energy dispersive X-ray spectroscopy results.
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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".