Structural characterization of PbO–B2O3–SiO2 glasses
Bibliographic record
Abstract
The effects of silica on the density, boron–oxygen speciation and thermal properties of glasses from the system: PbO–B2O3–SiO2 (PbO concentration: 30, 40, 50 and 60 mol% and silica concentration: 5, 10, 20 and 30 mol%) was studied by 11 B MAS NMR and DSC techniques. The incorporation of silica in the borate network steadily increases glass density, decreases the glass transition temperature and increases the thermal stability of glasses against crystallization. SiO2 at low concentrations of up to 20 mol% increases the three dimensional network connectivity by promoting the conversion of BO3 into [BO4]– units, however at higher silica contents of 30 mol%, the formation of [BO4]– was suppressed and nonbridging oxygens were rapidly generated in SiO4 and BO3 units. The average number of NBOs per BO3 unit increases with silica concentration and this was indicated in the NMR spectra of glass series with 60 mol% PbO which exhibited a shift in the centre of gravity of the BO3resonance peak towards more positive ppm values (de-shielding) at a silica concentration of 30 mol%. DSC studies indicated phase separation in the glasses which suggested that the mixing of Pb2+ ions, BO3, [BO4]– and SiO4 units was not completely random.
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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".