Friction and Wear Studies of Uncoated and TiZrN Coated Brass Substrates
Bibliographic record
Abstract
Objectives: Investigation of enhancement in tribological properties of TiZrN coated brass substrate. Methods/Statistical Analysis: The magnetron sputtering was used to develop TiZrN coating on brass substrate by varying titanium (Ti) target power. X-ray diffraction (XRD) and Scanning Electron Microscopy (SEM) was used to do structural characterization of TiZrN coatings. Tribological properties of TiZrN coatings such as friction and wear were investigated by a pin on disc tribometer. Findings: The evolution of well intense (200) and (311) peaks of TiZrN coatings was observed with rise in power of titanium target. Increase of titanium power has a negligible effect on average crystallite size of TiZrN coatings and average crystallite size is around 4-5nm. TiZrN coatings are uniform, smooth and crack free as observed from SEM images for all samples. Tribological properties of TiZrN coatings were examined with testing parameters such as load and sliding distance. Application/Improvements: This coating may be useful for applications where low friction and wear is required such as gears, bearings, and electrical applications. Keywords: Friction, Sputtering, TiZrN, Tribology, Wear
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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".