Wear behaviour of nanostructured and conventional 8 wt-%Y<sub>2</sub>O<sub>3</sub>–ZrO<sub>2</sub> coatings against Si<sub>3</sub>N<sub>4</sub> ball
Bibliographic record
Abstract
The aim of the present paper is to investigate and compare the wear and tribological behaviour of two types of yttria–partially stabilised zirconia coatings, i.e.nanostructured and conventional zirconia. The coatings, 8 wt-%Y2O3–ZrO2, were produced using an air plasma spraying (APS) technique. Substrates used were made from AISI 304 stainless steel. To perform the wear tests, a pin on disc wear testing machine, using a 10 mm silicon nitride (Si3N4) ball as the pin, was employed. Coatings produced were characterised before and after being subjected to wear testing, using optical microscopy, scanning electron microscopy, energy dispersive X-ray spectrometry and X-ray diffraction. Regarding the wear tests, effects of various parameters, such as wear distance, substrate temperature, disc rotating speed (sliding velocity) and applied normal load, were investigated. Results obtained (weight loss, wear rate, coefficient of friction and worn surface microstructure) revealed that under the wear conditions applied, the nanostructured zirconia coating exhibited a better wear resistance and tribological properties than the conventional one. Also it was observed that the difference in wear resistance between both coatings tested is a function of wear testing parameters such as wear distance, substrate temperature, disc rotating speed and applied normal load.
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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) | 0.000 | 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 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".