Influence of bath composition on the electrodeposition of amorphous <scp>Ni‐Mo</scp> alloys using potassium‐sodium tartrate as complexing agent
Bibliographic record
Abstract
Abstract Currently, sodium citrate is the most used complexing agent for electrodeposition of Ni‐Mo coatings, especially for studying the effect of bath temperature and current density on the proprieties of Ni‐Mo coatings. This study evaluates the influence of the concentrations of nickel sulphate and sodium molybdate on the electrodeposition of Ni‐Mo alloys in baths using tartrate as a complexing agent. Electrodepositions were carried out in a two‐electrode cell. A 3 2 complete factorial design of experiments along with response surface methodology (RSM) was used for the optimization of bath variables for electrodeposition of Ni‐Mo alloys. The coatings obtained were characterized in terms of chemical composition and surface morphology. The chemical composition of the coatings was most influenced by the sodium molybdate concentration. Ni‐Mo coatings of good quality were obtained in potassium‐sodium tartrate baths with Mo content in the range 18‐44 wt%; some results present even higher Mo content than coatings obtained from citrate baths. The coating with the highest Mo content was deposited from the bath with a concentration ratio of nickel sulphate to sodium molybdate of 1:1. Surface morphology varied according to chemical composition, and microcracks were observed for the coating with 41 wt% Mo, for which scanning electron microscopy (SEM) images also show the presence of nodules. The most homogeneous coating surface was obtained at a concentration ratio of nickel sulphate to sodium molybdate of 10:3. X‐ray diffraction analyses have shown that all coatings obtained in this work are amorphous.
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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) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 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".