Influence of CNT–GNP hybrid reinforcements on microstructure and tribological properties of ZrB2–SiC composites consolidated via spark plasma sintering
Bibliographic record
Abstract
Present study investigates the microstructure evolution and tribological behavior of ZrB 2 –SiC composites reinforced with different carbonaceous reinforcements (CNTs and GNPs) for use at extremely high temperatures, synthesized via spark plasma sintering. While ZrB 2 has excellent thermal and mechanical properties, its poor sinterability and wear resistance are limitations. To overcome these, CNTs and GNPs were incorporated along with SiC using SPS. The ZrB 2 -SiC-CNT-GNP (ZSCG) composite showed improved densification and hardness (33.0 GPa) due to enhanced load transfer and crack deflection. Under 10 N load, ZSCG exhibited the lowest value of coefficient of friction (COF: 0.51) and lower wear volume (0.0521 mm 3 ), owing to tribolayer formation and carbon-based lubrication. Raman and SEM-EDS confirmed graphitization and uniform reinforcements dispersion, indicating strong potential for aerospace and extreme-environment applications.
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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".