Effects of carbon nano-additives on characteristics of TiC ceramics prepared by field-assisted sintering
Bibliographic record
Abstract
Five carbonaceous nano-additives (graphite, graphene, carbon black, carbon nanotubes, and diamond) had different impacts on the sinterability, microstructural evolution, and properties of titanium carbide. In this research, the sintering by spark plasma was employed to produce the monolithic TiC and carbon-doped ceramics under the sintering parameters of 1900 ºC, 10 min, 40 MPa. The carbon black additive had the best performance in densifying the TiC, thanks to its fine particle size, as well as its high chemical reactivity with TiO2 surface oxide. By contrast, the incorporation of nano-diamonds resulted in a considerable decline in the relative density of TiC owing to the graphitization phenomenon, together with the gas production at high temperatures. Although carbon precipitation from the TiC matrix occurred in all samples, some of the added carbonaceous phases promoted this phenomenon, while the others hindered it to some extent. Amongst the introduced additives, carbon black had the most contribution to grain refining, so that a roughly halved average grain size was attained in comparison with the undoped specimen. The highest values of hardness (3233 HV0.1 kg), thermal conductivity (25.1 W/mK), and flexural strength (658 MPa) secured for the ceramic incorporated by 5 wt% nano carbon black.
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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".