Bioactivity in silica/poly(γ-glutamic acid) sol–gel hybrids through calcium chelation
Bibliographic record
Abstract
Bioactive glasses and inorganic/organic hybrids have great potential as biomedical implant materials. Sol-gel hybrids with interpenetrating networks of silica and biodegradable polymers can combine the bioactive properties of a glass with the toughness of a polymer. However, traditional calcium sources such as calcium nitrate and calcium chloride are unsuitable for hybrids. In this study calcium was incorporated by chelation to the polymer component. The calcium salt form of poly(γ-glutamic acid) (γCaPGA) was synthesized for use as both a calcium source and as the biodegradable toughening component of the hybrids. Hybrids of 40wt.% γCaPGA were successfully formed and had fine scale integration of Ca and Si ions, according to secondary ion mass spectrometry imaging, indicating a homogeneous distribution of organic and inorganic components. (29)Si magic angle spinning nuclear magnetic resonance data demonstrated that the network connectivity was unaltered with changing polymer molecular weight, as there was no perturbation to the overall Si speciation and silica network formation. Upon immersion in simulated body fluid a hydroxycarbonate apatite surface layer formed on the hybrids within 1week. The polymer molecular weight (Mw 30-120kDa) affected the mechanical properties of the resulting hybrids, but all hybrids had large strains to failure, >26%, and compressive strengths, in excess of 300MPa. The large strain to failure values showed that γCaPGA hybrids exhibited non-brittle behaviour whilst also incorporating calcium. Thus calcium incorporation by chelation to the polymer component is justified as a novel approach in hybrids for biomedical materials.
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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".