Friction and Wear Studies of Uncoated and TiZrN Coated Brass Substrates
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it