Laminated Si<sub>3</sub>N<sub>4</sub>/SiC Composites with Self-Sealed Structure
Bibliographic record
Abstract
Self-sealed laminar Si3N4/SiC composites, with different cross-section shapes and various thickness ratios of Si3N4 to SiC, have been fabricated. The laminates consist alternately of thicker Si3N4 layers ranging from 100 to 500µm and thinner SiC layers ranging from 6 to 15µm after sintering. Preliminary results indicate that SiC thin layer forms during sintering according to the reaction Si3N4 + 3C ® 3SiC + 2N2, which is confirmed by X-ray diffraction. An excellent physical and chemical compatibility between Si3N4 and SiC layers was observed. The self-sealed Si3N4/SiC composites not only demonstrate a superb resistance to delamination, usually associated with the plate-form ones, but also show a high damage-tolerance behavior. The laminated Si3N4/ SiC composite with a layer thickness ratio of Si3N4 to SiC of approximately 40 gives the highest value of work of fracture (WOF) of approximately 406 kJ/m3, whereas the highest toughness of 21 MPam1/2 was achieved at the layer thickness ratio of 50. The effects of the relative thickness of Si3N4 and SiC layers on the densification of the laminates are examined and fracture behavior and microstructure of the Si3N4/SiC laminates discussed.
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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) | 0.002 | 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".