Effect of B <sub>2</sub> O <sub>3</sub> /SiO <sub>2</sub> molar ratio and B <sub>2</sub> O <sub>3</sub> content on viscosity and structure of B <sub>2</sub> O <sub>3</sub> –SiO <sub>2</sub> –Al <sub>2</sub> O <sub>3</sub> –Na <sub>2</sub> O–TiO <sub>2</sub> glass lubricant
Bibliographic record
Abstract
Abstract Glass lubricant with a suitable viscosity is essential for producing high‐quality titanium products by the hot extrusion process. In this study, a novel lubricant of B 2 O 3 –SiO 2 –Al 2 O 3 –Na 2 O–TiO 2 (BSANT) glass was proposed, where the effects of B 2 O 3 /SiO 2 molar ratio (B/Si) and B 2 O 3 content on viscosity and structure were investigated. The results showed that the BSANT glass viscosity was gradually decreased, and the viscous activation energy was decreased by 20.24% with an increased B/Si ratio from 0.415 to 0.719. This was attributed to the decreased amounts of Q 3 , [BO 4 ], [AlO 4 ], and bridging oxygen (BO) in the network structure, which in turn resulted in a decreased network degree of polymerization. When B 2 O 3 content was increased from 18.70 to 33.12 mol.%, the melt viscosity value at 950 ℃ was decreased by 64.51%, and the viscous activation energy was reduced by 15.82%. Boron anomalies were also observed in glass melts with the B 2 O 3 content from 28.28 to 33.12 mol.%. The mechanisms responsible for this phenomenon were thoroughly discussed in the study. Additionally, a comparison and discussion of the applications of glass lubricants in metal hot working were presented. A glass lubricant with a specific composition was recommended for use in hot extrusion process within the temperature range of 950–1100°C due to its relatively stable viscosity properties.
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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".