Fracture of Graphene-Ceramic Composites With Defect
Bibliographic record
Abstract
Abstract Utilized in various protective barriers, electronics, energy devices, and aerostructures, silicon carbide (SiC) is celebrated for its superb thermo-chemo-mechanical properties. Particularly, modifying SiC with various additives such as graphene-based inclusions has recently proved to be a practical way to attain damage-tolerant SiC ceramic matrix composites with various multifunctionalities. Nonetheless, the presence of defect in the aforementioned hybrid material could have noticeable impact on their mechanical properties including fracture performance. Such problem received less attention so far due to the difficulties in tracking defects in experiments and incapability of the traditional modeling and computational platforms. In that regard, the fracture of hybrid graphene-SiC system with defect is examined in this work. To this end, phase field model of brittle fracture is developed to address damage mechanics and crack propagation across the defective graphene-SiC hybrid materials. The numerical results reveal that the location and density of defects have adverse influence on the resistance to complete fracture of the above-mentioned hybrid materials. Basically, the resistance to complete fracture decreases for the graphene-ceramic composite with defect closer to the initial notch location. Further, resistance to complete fracture is lowered remarkably as the density of defect increases inside the ceramic matrix; thus, defect is anticipated to be considered as a design parameter to achieve reliable and multifunctional graphene-ceramic composites with enhanced fracture properties.
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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 | 0.001 | 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".