Glass Polyalkenoate Cements Designed for Cranioplasty Applications: An Evaluation of Their Physical and Mechanical Properties
Bibliographic record
Abstract
Glass polyalkenoate cements (GPCs) have potential for skeletal cementation. Unfortunately, commercial GPCs all contain, and subsequently release, aluminum ions, which have been implicated in degenerative brain disease. The purpose of this research was to create a series of aluminum-free GPCs constructed from silicate (SiO₂), calcium (CaO), zinc (ZnO) and sodium (Na₂O)-containing glasses mixed with poly-acrylic acid (PAA) and to evaluate the potential of these cements for cranioplasty applications. Three glasses were formulated based on the SiO₂-CaO-ZnO-Na₂O parent glass (KBT01) with 0.03 mol % (KBT02) and 0.06 mol % (KBT03) germanium (GeO₂) substituted for ZnO. Each glass was then mixed with 50 wt % of a patented SiO₂-CaO-ZnO-strontium (SrO) glass composition and the resultant mixtures were subsequently reacted with aqueous PAA (50 wt % addition) to produce three GPCs. The incorporation of Ge in the glass phase was found to result in decreased working (142 s to 112 s) and setting (807 s to 448 s) times for the cements manufactured from them, likely due to the increase in crosslink formation between the Ge-containing glasses and the PAA. Compressive (σc) and biaxial flexural (σf) strengths of the cements were examined at 1, 7 and 30 days post mixing and were found to increase with both maturation and Ge content. The bonding strength of a titanium cylinder (Ti) attached to bone by the cements increased from 0.2 MPa, when placed, to 0.6 MPa, after 14 days maturation. The results of this research indicate that Germano-Silicate based GPCs have suitable handling and mechanical properties for cranioplasty fixation.
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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 | 0.001 | 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".