In situ and ex situ characterization of microstructure evolution of a γ-γ’ coating on CMSX-4 Plus superalloy during thermal cycling: Insights into Pt diffusion and phase transformations
Bibliographic record
Abstract
The microstructure evolution of a Pt-rich γ-γ’ coating deposited on a third generation CMSX-4 Plus nickel-based superalloy was investigated during relatively short thermal cycles (from 300 °C to 1100 °C with a dwell time of 5 min at 1100 °C) using both ex situ and in situ characterization techniques. Ex situ analyses, including Scanning Electron Microscopy (SEM) and Energy-Dispersive X-Ray Spectroscopy (EDS) and postmortem X-Ray Diffraction (XRD), demonstrated that Pt and Al interdiffusion induced by thermal cycling leads to significant microstructure changes in the coating. Lattice parameters of γ and γ’ phases were revealed to correlate with Pt content. In a novel approach, in situ XRD using laboratory equipment was employed to monitor microstructural evolution during 250 thermal cycles. This unique in situ analysis highlighted the microstructural differences at low and high temperatures within each cycle. For the first time, it was observed that partial dissolution of γ’ precipitates occurs at high temperatures, altering the local chemical composition of both γ and γ’ phases. • Diffusion of Pt and Al drives the evolution of γ-γ’ coating microstructure during thermal cycling. • A direct correlation between γ and γ’ lattice parameters and their respective Pt content was established by XRD and EDS. • First in situ observation of Pt-rich γ-γ’ coating microstructure during cycling. • X-ray diffraction reveals γ' phase dissolution and reprecipitation during cycling. • X-ray diffraction highlights reversible Pt enrichment in γ phase at high temperatures.
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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".