Erosion Resistance of Titania Co-Doped Yttria Stabilized Zirconia
Bibliographic record
Abstract
The standard yttria-stabilized zirconia (YSZ) has been used as thermal barrier coatings (TBCs) in the hot sections of gas turbine engines for several decades. To achieve further improvement to the thermal insulation capability of current TBCs, doping of alternative oxides to zirconia or co-doping of oxides to YSZ has been employed. In our previous study, it has been shown that doping of 7YSZ with titania (TiO2) reduces thermal conductivity of 7YSZ substantially. As TBCs are susceptible to various failure mechanisms, in this study the erosion resistance of TiYSZ at high impingement speed and angle is evaluated along with measurements of hardness (H) and elastic modulus (E). Specimens with 5 different TiO2 doping amounts (5%, 7.5%, 10%, 12.5% and 15%) are fabricated using plasma spraying and high temperature sintering. The erosion test results show that sample with 5% TiO2 (5TiYSZ) suffers the most erosion damage at high impingement angle due to brittle fracture while 10-15TiYSZ samples exhibit less brittle erosion damage which leads to lower erosion rates under the same test condition. When comparing the erosion rates (defined as the loss of sample mass per mass unit of abrasive particles) to the hardness values, they were found to follow the same trend. The addition of TiO2 (10–15 wt%) had the effect of reducing the erosion rate of 7YSZ at high impingement angle.
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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".