Modeling oxygen permeability through topcoat and thermally grown oxide in dense Yb <sub>2</sub> Si <sub>2</sub> O <sub>7</sub> environmental barrier coatings
Bibliographic record
Abstract
Abstract The oxygen permeability of ytterbium disilicate (YbDS) topcoat (TC) and silicon dioxide (SiO 2 ) thermally grown oxide (TGO) is evaluated. The primary goal is to elucidate the oxidation mechanisms in environmental barrier coatings (EBCs). For this purpose, oxidant diffusion is investigated using physics‐based and numerical modeling. The oxygen permeability constants are systematically evaluated and quantified in terms of thermodynamics using defect reactions and the parabolic rate constant (kp), respectively. Dry oxygen and wet oxygen conditions as well as different temperatures, partial pressures, and topcoat modifiers are investigated. The results offer evidence that the oxygen permeability constant for the YbDS topcoat is an order of magnitude higher than for the TGO. As such, the TGO hinders the oxidant diffusion stronger, proving to be the diffusion rate‐controlling layer. Moreover, water vapor strongly increases the oxidant permeation with defect reactions playing a key role. It is suggested that the mass transfer through the topcoat is primarily by outward ytterbium ion diffusion and inward oxygen ion movement, with the latter being dominant, particularly in wet environments. The effect of topcoat modifiers on oxidant permeation is composition sensitive and seems to be related to their interaction with oxygen ions and their mobility.
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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 | 0.001 | 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".