Synthesis and characterization of mechanical properties of boron–carbon-based superhard composites
Bibliographic record
Abstract
Abstract In this work, we investigated a modern combined processing technique for the synthesis of lightweight superhard composites based on boron–carbon. We used traditional B4C with precipitates of free graphite and Al powder as initial materials. In the first stage, the composites were fabricated by the self-propagating high-temperature synthesis (SHS) with the subsequent hot pressing of the compound. Further, by the disintegration and attrition milling, the ultrafine-grained powder was obtained. We used HCl and HNO3 acids for the chemical leaching of the powder to remove various impure compounds. At the last stage, a solid composite was obtained by the spark plasma sintering (SPS) method under nitrogen pressure. The main feature of this approach is to implement different synthesis techniques and chemical leaching to eliminate soft phases and to obtain superhard compounds from low-cost materials. The phases were studied by X-ray diffraction and scanning electron microscopy with energy-dispersive spectroscopy. The composites compacted by the SPS method contained superhard compounds such as B13C2, B11.7C3.3, and c-BN. The fabricated composite has an ultrafine-grained microstructure. Using a Berkovich indenter, the following nanohardness results were achieved: B13C2 ~ 43 GPa, c-BN ~ 65 GPa (all in Vickers scale) along with a modulus of elasticity ranging between ~ 400 GPa and ~ 450 GPa.
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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 | 0.001 | 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".