High‐Temperature Neutron Diffraction of the AlN–Al <sub>2</sub> O <sub>3</sub> –Y <sub>2</sub> O <sub>3</sub> System
Bibliographic record
Abstract
The importance of aluminum nitride (AlN) stems from its application in microelectronics as a substrate material due to high thermal conductivity, high electrical resistance, mechanical strength and hardness, thermal durability, and chemical stability. Yttria (Y 2 O 3 ) is the best additive for AlN sintering. AlN densifies by a liquid‐phase mechanism, where the surface oxide, Al 2 O 3 , reacts with Y 2 O 3 to form an Y‐Al‐O‐N liquid that promotes particle rearrangement and densification. Construction of the phase relations in this multicomponent system is essential for optimizing the properties of AlN. The ternary phase diagram of the AlN–Al 2 O 3 –Y 2 O 3 was developed by Gibbs energy minimization using interpolation procedures based on modeling the binary subsystems. This paper aims at testing the resultant understanding experimentally at selected compositions using in situ high‐temperature neutron diffractometry. These experimental results agree with the thermodynamic calculations of AlN–Al 2 O 3 –Y 2 O 3 . The ternary phase diagram has been constructed for the first time in this work. High‐temperature neutron diffractometry has permitted real time measurement of the reactions involved in this ternary system, especially to determine the temperature range for each reaction, which would have been difficult to establish by other means.
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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".