Effect of Nitrogen on Properties of Na <sub>2</sub> O–CaO–SrO–ZnO–SiO <sub>2</sub> Glasses
Bibliographic record
Abstract
Glasses in the Na 2 O–CaO–SrO–ZnO–SiO 2 system have previously been investigated for suitability as a reagent in Al‐free glass polyalkenoate cements (GPCs). These materials have many properties that offer potential in orthopedics. However, their applicability has been limited, to date, because of their poor strength. This study was undertaken with the aim of increasing the mechanical properties of a series of these Zn‐based GPC glasses by doping with nitrogen to give overall compositions of: 10Na 2 O–10CaO–20SrO–20ZnO–(40−3 x )SiO 2 – x Si 3 N 4 ( x is the no. of moles of Si 3 N 4 ). The density, glass‐transition temperature, hardness, and elastic modulus of each glass were found to increase fairly linearly with nitrogen content. Indentation fracture resistance also increases with nitrogen content according to a power law relationship. These increases are consistent with the incorporation of N into the glass structure in threefold coordination with silicon resulting in extra cross‐linking of the glass network. This was confirmed using 29 Si MAS‐NMR which showed that an increasing number of Q 2 units and some Q 3 units with extra bridging anions are formed as nitrogen content increases at the expense of Q 1 units. A small proportion of Zn ions are found to be in tetrahedral coordination in the base oxide glass and the proportion of these increases with the presence of nitrogen.
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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 | 0.001 |
| 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".