Mechanical Properties of Titania-Doped Yttria Stabilized Zirconia (TiYSZ) for Use as Thermal Barrier Coating (TBC)
Bibliographic record
Abstract
Representative samples of yttria stabilized zirconia (7YSZ) co-doped with varying concentrations of TiO2 were fabricated using plasma spraying. Samples were sintered in order to minimize porosity and to simulate the bulk material properties. After sintering, porosity levels of less than 1.25% were achieved. Both as-sprayed and sintered samples with 5, 10 and 15 wt% TiO2 addition levels were microstructurally characterized using SEM, XRD and optical image analysis methods. Vickers hardness, Young’s modulus, and fracture toughness were measured using nano and macroindentation methods. Microstructural analysis revealed that sintering of the TiO2 doped samples was required to achieve a homogeneous composition distribution, with TiO2 predominantly residing in solid solution within the ZrO2 matrix. Sintering for 325 hs at 1200 °C resulted in sufficient diffusion of TiO2 into the 7YSZ. The addition of TiO2 stabilized more tetragonal phase as revealed by XRD measurement. Sintering also showed significant improvements in fracture toughness in all co-doped samples. Fracture toughness values calculated using load-independent equations provided a clear trend in fracture toughness improvement with TiO2 addition. Ferroelastic toughening of the tetragonal phase was believed to have played an effect. There was also a reduction in monoclinic phase content with TiO2 addition, which may have limited microcrack formation and consequently increased the fracture toughness. With the addition of 10 wt% TiO2, the fracture toughness was improved by over 50%; however, this improvement started to decline at 15 wt% TiO2 addition. Volumetric porosity measurements also revealed significant improvements in fracture toughness with respect to lowering the porosity content as observed in all sintered samples.
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.
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".