Wear Resistance of Nanostructured Thermal Barrier Coatings
Bibliographic record
Abstract
Abstract For more than two decades researchers have been working on thermal barrier coatings to improve the performance of diesel engines. However, these coatings have still not achieved widespread application in conventional diesel engines. The original motivation for this work was the improvement of fuel economy, since even a few percent improvement would result in huge savings in the transportation industries, but the coatings also effect exhaust emissions, component wear, and the sensitivity of engines to fuel quality. Wear at high temperatures, where conventional lubricants are not effective, is a serious problem in low heat rejection engines. Ceramic materials such as thermal barrier coatings in cylinder liners must have an acceptable wear rate and coefficient of friction. In this work we compare the wear behaviour of nanostructured thermal spray zirconia coatings with conventional zirconia coatings. First, process parameters that allowed the nanoparticles present in the feedstock powder to be retained in the coating were found. Then pin on disc wear tests of the two types of coatings were carried out at room temperature. The coating containing retained nanoparticles exhibited a lower coefficient of friction and less wear loss under discontinuous testing than the conventional coating.
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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".