Correlating the Layer Properties of Ni-alumina Composite Coatings and the Mechanism of Codeposition
Bibliographic record
Abstract
Ni-Al2O3 composite coatings electrodeposited from Watt’s electrolyte include Al2O3 nano-and submicron particles. The effect of particle size and concentration of the particles in the electrolyte were investigated on the morphology and incorporation value of particles into the deposits. The influence of alumina particles on the electrodeposition behavior of Ni was also studied by means of electrochemical impedance spectroscopy. The results achieved from the impedance measurements and the correlation with the layer characterization could help to better understand the codeposition mechanism derived from the impact of different particle characteristics including size and concentration on the nature of the double-layer. The increase in the concentration of particles from 1 to 20 g/l resulted in an increase of the double-layer capacity and decrease of the charge transfer resistance, while the addition of submicron particles had a higher influence on the characteristics of the double-layer compared to the nano particles. Although the alumina particles with submicron size could stimulate the incorporation of particles faster than those with nano size, the strengthening performance of the layers not only depended on the incorporation value of the particles, but also on the microstructure of the deposits.
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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".