Investigation of the sintering behavior of SiC-5TiB2 composites reinforced by graphene quantum dots
Bibliographic record
Abstract
The purpose of this research is to fabricate and investigate the properties of SiC-5TiB2 nano composites reinforced with graphene quantum dot nanoparticles by a pressureless sintering method. In this way, SiC, TiB2, and graphene quantum dots were used in nanometer dimensions. First, before performing any laboratory operation, the thermodynamic behavior of the system was checked using HSC software. The graphene quantum dots reinforcement amount was 0.6 wt%, and the sinter temperature was defined as 2000, 2050, 2100, 2150, and 2200 °C. After weighing the initial powders, the grinding process was carried out in an ethanol-based wet environmental and a polymer chamber, using zirconia balls, for two hours at a speed of 200 rpm. The sintering process was also carried out at certain temperatures in an argon atmosphere for two hours. Then, XRD, FESEM, and Raman analyses were performed, and density, microhardness, and fracture toughness tests were used for further investigations. The microstructure of the samples was also investigated to investigate the fracture toughness mechanisms. The results show that the sample sintered at 2150 °C with a relative density of 96.26%, and a hardness of 28.65 GPa and a fracture toughness of 4.1 MPa.m1/2 is the best case.
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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".