Effect of BaO 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–BaO glass lubricant
Bibliographic record
Abstract
Abstract Glass lubricant with a suitable viscosity is critical for the efficient production of high‐quality titanium alloy in hot extrusion process. In this study, a new glass lubricant, (30.93‐0.309 x )B 2 O 3 ‐(48.45‐0.485 x )SiO 2 ‐(7.22‐0.072 x )Al 2 O 3 ‐(13.4‐0.134 x )Na 2 O‐ x BaO (BSANB), was proposed for titanium alloy hot extrusion. The effect of BaO content varying from 0 wt.% to 15 wt.% on its viscosity was investigated, and the mechanism of viscosity change was clarified through melt structure analysis by using molecular dynamics (MD) simulation, X‐ray photoelectron spectroscopy (XPS), and Raman spectroscopy. The glass viscosity was measured in the temperature range of 950°C to 1100°C, and the atomic self‐diffusion coefficient of the glass melt was calculated. The results demonstrated that the BaO content has a significant influence on the viscosity of the BSANB glass at the hot extrusion temperature range. The viscosity value at 950°C was decreased by 52.87%, from 103.5 Pa·s to 48.8 Pa·s, when BaO content was increased from 0 wt.% to 15 wt.%. MD simulation results of the BSANB glass show that the self‐diffusion coefficient of Si atoms was lower than that of Al and B atoms, indicating that Si‐O bonds were more stable in the melt. The diffusion coefficient of Si atom was directly related to the rheological behavior of the melt, which can be reflected by the melt viscosity. The results of MD simulation, XPS and Raman spectral fitting at 950°C showed an increase in the amounts of Q 1 , Q 0 and [BO 4 ], while the amounts of Q 2 , Q 3 , Q 4 and [AlO 4 ] were decreased when BaO content was increased from 0 wt.% to 15 wt.%. This demonstrated that the melt network was depolymerized with an increased BaO content, which determined the melt viscosity change. In addition, the mechanism responsible for glass viscosity reduction due to temperature increase was also discussed.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.004 | 0.001 |
| Meta-epidemiology (narrow) | 0.004 | 0.003 |
| Meta-epidemiology (broad) | 0.006 | 0.005 |
| Bibliometrics | 0.001 | 0.004 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.003 | 0.001 |
| Research integrity | 0.001 | 0.006 |
| Insufficient payload (model declined to judge) | 0.000 | 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; both teacher heads agree on what is shown here.
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".