Thermal Properties and Phase Analysis of Titania Doped Yttria-Zirconia Ceramics for Use as High Temperature Thermal Barrier Coatings (TBCs)
Bibliographic record
Abstract
Titania doped YSZ ceramic samples were subjected to calorimetric, thermal and microstructural analyses to assess the value of titania as a dopant for use as thermal barrier coating in modern gas turbine engines. The primary objective of titania addition was to effectively stabilize the tetragonal phase at operating temperatures while lowering the thermal conductivity. Powder blends with 5, 10, and 15 wt% titania added to standard 7YSZ powder were sintered at 1200°C for 325hrs after plasma spraying. Basic physical properties related to the thermal conductivity of the material such as bulk densities and Young’s modulus were determined. Phase analysis of all samples was performed using x-ray diffraction techniques so the percentage of monoclinic, tetragonal, and cubic phases could be determined. For all titania samples, it was shown that the composition was predominantly tetragonal with slightly decreasing amounts of monoclinic phases present with increasing titania content. The results of calorimetric analysis showed a marginal decrease in the specific heat capacities of the sintered titania doped samples. Similarly, thermal diffusivities were lowered by the addition of titania, though only slightly, since it has been previously shown that diffusivity is more strongly linked to sample porosity. Using these results, the experimental thermal conductivities for all titania doped samples were determined and compared to theoretical conductivities based solely on the mechanical properties of the ceramic. The results showed a decrease in thermal conductivity with the addition of titania, though with higher values than that predicted from the theoretical model. Experimental thermal conductivities were also shown to decrease with temperature initially, while increasing slightly at higher temperatures, which is most probably due to the radiation effect.
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How this classification was reachedexpand
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) | 0.001 | 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 teacher head, 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".