High-temperature spark plasma sintering of h-BN composites reinforced with carbon nanotubes, carbon fibers, and graphene nanoplates
Bibliographic record
Abstract
In this study, two h-BN-based composites reinforced with carbon fibers (CF) and carbon fibers/carbon nanotubes (CNTs)/graphene nanoplates (GNPs) have been produced successfully through a high-temperature spark plasma sintering. 1 Wt.% short carbon fibers (length of 5 mm) with 0.1 Wt.% of CNTs and also 0.1 Wt.% of GNPs as hybrid composite were mixed through a simple mixing method including a high energy sonicating and stirring on the hot plate in ethanol media until drying. Moreover, h-BN/1 Wt. % CF composite was mixed with a similar method to compare impacts of CNTs and GNPs addition on the mechanical properties and microstructure of h-BN/CF composite. The high-temperature spark plasma sintering processes were performed at vacuum conditions of almost 20-25 MPa with a starting pressure of 10 and a final applied pressure of 50 MPa at a maximum temperature of 1900˚C. Both prepared samples showed near full densification of higher than 98.1 % of the theoretical density determined by Archimedes’ principle. Investigation of the crystalline phases by XRD represented only related peaks to h-BN. The FESEM images indicated an almost uniform distribution of reinforcement in the h-BN matrix. Furthermore, the polished surface of the provided samples showed only the pulled-out carbon fibers effects while the fracture surfaces confirmed the presence of CF and it’s tunneling effects. The obtained mechanical properties revealed 273±12 MPa of bending strength, 1.32±0.1 GPa of Vickers hardness, and 4.79±0.2 MPa.m0.5 fracture toughness for the prepared hybrid composite.
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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".