Toughening of ZrB2-based composites with in-situ synthesized ZrC from ZrO2 and graphite precursors
Bibliographic record
Abstract
ZrB2 matrix composites toughened with in-situ formed ZrC were fabricated by spark plasma sintering (SPS) utilizing ZrB2/ZrO2/graphite powder mixtures at 1900 °C for 7 min under 40 MPa load. Different amounts of graphite nano-flakes (3, 6, and 9 wt%) were added to ZrB2–20 vol% ZrO2 aiming in-situ formation of ZrC reinforcement. Clean ZrB2/ZrC interfaces were observed in the as-sintered microstructure, especially in the composite containing 6 wt% graphite. The reduction of ZrO2 with graphite, which formed ZrC, was progressed over a zirconium oxycarbide (ZrCxOy) intermediate. The hardness of the SPSed ZrB2-based composites was decreased with increasing the graphite content. However, the fracture toughness of the composites showed a reverse trend. By adjusting the graphite content to 6 wt%, excellent control of microstructure could be achieved for the prepared composites with a higher amount of ZrC and lower content of other unfavorable phases. It was finally found that achieving the optimum microstructure of the composite could improve its mechanical performance, and provides a hardness of 17.3 GPa and fracture toughness of 5.0 MPa m1/2.
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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) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 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".