Incorporating Germanium Oxide into the Glass Phase of Novel Zinc/Magnesium-Based GPCs Designed for Bone Void Filling: Evaluating Their Physical and Mechanical Properties
Bibliographic record
Abstract
The structural role of Germanium (Ge), when substituting for Zinc (Zn) up to 8 mol % in the 0.48SiO2–0.12CaO–0.36ZnO–0.04MgO glass series, was investigated with respect to both the glass chemistry and also the properties of glass polyalkenoate cements (GPCs) manufactured from them. The Network connectivity (NC) of the glass was calculated to increase from 1.83 to 2.42 with the addition of GeO2 (0–8 mol %). Differential thermal analysis (DTA) results confirmed an increase in the glass transition temperature (Tg) of the glass series with GeO2 content. X-ray photoelectron spectroscopy (XPS) showed an increase in the ratio of bridging oxygens (BO) to non-bridging oxygens (NBO) with the addition of GeO2, supporting the NC and DTA results. 29Si magic angle spinning nuclear magnetic resonance spectroscopy (29Si MAS-NMR) determined a chemical shift from −80.3 to −83.7 ppm as the GeO2 concentration increased. These ionomeric glasses were subsequently used as the basic components in a series of GPCs by mixing them with aqueous polyacrylic acid (PAA). The handling properties of the GPCs resulting were evaluated with respect to the increasing concentration of GeO2 in the glass phase. It was found that the working times of these GPCs increased from 3 to 15 min, while their setting times increased from 4 to 18 min, facilitating the injectability of the Zn/Mg-GPCs through a 16-gauge needle. These Ge-Zn/Mg-GPCs were found to be injectable up to 96% within 12 min. Zn/Mg-GPCs containing GeO2 show promise as injectable cements for use in bone void filling.
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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) | 0.000 | 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; 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".