Synthesizability improvement of B4C ceramics by optimizing the process temperature and atmosphere
Bibliographic record
Abstract
In this research, the effects of synthesis temperature, holding time, and furnace atmosphere on the synthesizability of B4C ceramics using glucose and boric acid as the starting materials were scrutinized. Three temperatures of 1300, 1400, and 1500 °C were selected as synthesis temperatures. The synthesis process was carried out in a tubular furnace for 4 h in Ar atmosphere. To scrutinize the interactive effect of synthesis temperature and holding time, three samples were synthesized at 1500, 1400, and 1300 °C for 4, 8, and 12 h, respectively. Moreover, two types of controlled atmospheres, traditional Ar and an innovative CO/CO2 setup, were considered to optimize the synthesis process. X-ray diffraction (XRD) patterns were employed to determine the optimum synthesis temperature and atmosphere based on the detection of B4C peaks as the desired product and undesirable hydrocarbon and carbon byproducts. The results showed that B4C synthesized at 1500 °C for 4 h in Ar atmosphere contained the least byproduct impurities, so this temperature was chosen as the optimal choice. However, the sample fabricated at 1400 °C for 8 h is a good choice in cases where lower manufacturing temperatures are desired. The efficiency of the innovative setup was similar to the traditional one; therefore, considering the economic aspects, the CO/CO2 atmosphere was chosen as an acceptable option for B4C synthesis.
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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".