Characterisation of transient oxide formation on NiCrAlY after heat treatment in vacuum
Bibliographic record
Abstract
To better understand transient oxide formation on the surface of standalone NiCrAlY in a vacuum, plasma sprayed NiCrAlY samples were subjected to a series of heat treatments at temperatures ranging from 1000 to 1100°C and for two different holding times. The morphology, composition and type of transient oxide(s) formed after heat treatment were characterised using scanning electron microscopy, energy dispersive X-ray spectroscopy and X-ray diffraction (XRD). All samples exhibited the formation of a top layer of discrete or island like Ni rich oxide in cellular shaped alumina. The alumina layer, although difficult to be detected in cross-section, was very dense and covered most of the coating surface. The transisent oxide formation on the heat treated surfaces was further analysed using XRD and α-Al2O3 was detected on all heat treated samples in addition to NiO and possible spinel. Cr2O3 seemed to be present on the samples heat treated at 1000 and 1050°C but not on the sample heat treated at 1100°C. Increased Al surface content, in comparison to the as sprayed sample, was found on all heat treated samples. High Al content, corresponding to the extent of alumina formation on heat treated samples, was observed for samples heat treated for a longer time as more oxygen diffusion took place. The coating surface Al content increased with heat treatment from 1000 to 1050°C and reduced from 1050 to 1100°C.
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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".