Tribological Properties of Functionally Graded Ni-Al<sub>2</sub>O<sub>3</sub>Nanocomposite Coating
Bibliographic record
Abstract
The main challenge in producing metal matrix composite coatings is achieving a homogeneous distribution of the second phase particles and avoiding the particle agglomeration. Despite all the works done up to now, the effect of the particles’ distribution in the matrix of composite coatings on the tribological properties has rarely been reported. The primary concern of the present study is to investigate the tribological behavior of functionally graded nickel-Al 2 O 3 nanocomposite coating produced at different duty cycles in 3.5%wt NaCl solution. Before starting the tribocorrosion test, the open circuit potential variations are small and reached stable values which followed by a negative shift during and after the tribocorrosion test. SEM and EBSD images showed that by decreasing the duty cycle the width of the wear track decreased because of more incorporated particles and changing of the (001) structure to compact random texture. Although the EIS results show the corrosion resistance decreased by increasing the embedded particles, higher hardness and compact structure result in better tribocorrosion properties of samples prepared at lower duty cycle. The adhesive wear was the dominant mechanism for coatings plated at high duty cycle and it changed to the abrasive wear in sample prepared at lower duty cycle.
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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".