The Effect of Nb, Ta, and Ti on the Oxidation of a New Polycrystalline Ni-Based Superalloy
Bibliographic record
Abstract
Abstract The effect of variations in Nb, Ta, and Ti concentrations in exchange for Al on the oxidation resistance of a new polycrystalline Ni-based superalloy (C19) was studied in air at 800 °C for up to 1000 h. An external scale of Ti-doped Cr2O3 and a sub-scale of discontinuous Al2O3 intrusions formed on the surface of all the studied alloys. Contrary to other reports, increasing the Nb concentration improved the oxidation performance and may have promoted the formation of a CrTaO4 layer, thereby reducing oxygen ingress. The addition of Ta also significantly improved oxidation resistance and reduced the depth of the Al2O3 intrusions. Increasing the Ti concentration did not significantly affect the oxidation performance, potentially due to the relatively low Ti concentrations investigated. Several of the studied alloys with modified Ta and Ti concentrations showed regions of continuous Al2O3 scale formation, suggesting that the compositions are in a transition regime between Cr2O3-forming and Al2O3-forming behaviour. The findings suggested that part of the Ti content in C19 could potentially be replaced with Nb, Ta and/or other elements to further enhance oxidation resistance and other desirable properties. Overall, the insights gained could serve as a guide to optimise the composition of C19 and similar alloys for enhanced oxidation resistance.
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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".