A comparative study on nitridation mechanism and microstructural development of porous reaction bonded silicon nitride in the presence of CaO, MgO and Al<sub>2</sub>O<sub>3</sub>
Bibliographic record
Abstract
During the in-situ nitridation of Si in the presence of CaO, MgO and Al2O3 formation of reaction bonded silicon nitride ceramics (RBSN) having various microstructures indicated the possibility of different nitridation mechanisms operating. Experimental evidence suggested that, whereas the morphology of pores was controlled by nitridation of Si(g) in CaO-RBSN and SiO(g) in Al2O3-RBSN, a combination of these two reactions occurred during the nitridation of the MgO-RBSN. By inhibiting the growth of whiskers and maximizing the α/β ratio, the CaO addition led to the formation of matte grains creating clean spherical cavities with d < 40 µm and flexural strength of 0.8 MPa. In contrast, when using Al2O3 additions, a microstructure with a very low α/β ratio, fine inter-particle pores and 2.4 MPa flexural strength, reinforced with interlocking whiskers, was produced. The highest porosity (85%) and the lowest strength (0.3 MPa) occurred in the MgO-RBSN, which was composed of both matte grains and fine whiskers. Local supersaturation and low content of β-nuclei led to the formation of anisotropic β-grains with a bimodal microstructure in heat-treated CaO-RBSN while a unimodal microstructure was observed in heat-treated MgO-RBSN. No porosity loss or β-grain growth occurred in the heat-treated Al2O3-RBSN.
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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".