Investigating the effect of <scp>T</scp>i<scp>O</scp><sub>2</sub> on the structure and biocompatibility of bioactive glass
Bibliographic record
Abstract
Abstract Titanium (Ti4+) containing materials have been widely used in medical applications due to its associated bioactivity in vivo. This study investigates the replacement of Si4+ with Ti4+ within the system SiO2‐Na2O‐CaO‐P2O5 to determine its influence on glass structure. This strategy was conducted in order to control the glass solubility to further improve the cellular response. Ti4+ incorporation was found to have little influence on the glass transition temperature (Tg = 520 ± 8°C) and magic angle spinning‐nuclear magnetic resonance (MAS‐NMR) shifts (−80 ppm) up to additions of 18 wt %. However, at 30 wt % the Tg increased to 600°C and MAS‐NMR spectra shifted to −88 ppm. There was also an associated reduction in glass solubility as a function of Ti4+ incorporation as determined by inductively coupled plasma optical emission spectroscopy where Si4+ (1649–44 mg/L) and Na+ (892–36 mg/L) levels greatly reduced while Ca2+ (3–5 mg/L) and (2–7 mg/L) levels remained relatively unchanged. MC3T3 osteoblasts were used for cell culture testing and it was determined that the Ti4+ glasses increased cell viability and also facilitated greater osteoblast adhesion and proliferation to the glass surface compared to the control glass. © 2015 Wiley Periodicals, Inc. J Biomed Mater Res Part B: Appl Biomater, 104B: 1703–1712, 2016.
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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".