The impact of gallium content on degradation, bioactivity, and antibacterial potency of zinc borate bioactive glass
Bibliographic record
Abstract
Abstract Zinc borate glasses with increasing gallium content (0, 2.5, 5, 10, and 15 Wt % Ga) were synthesized and their degradation, bioactivity in simulated body fluid (SBF), and antibacterial properties were investigated. ICP measurements showed that increased gallium content in the glass resulted in increased gallium ion release and decreased release of other ions. Degradability declined with the addition of gallium, indicating the formation of more symmetric BO3 units with three bridging oxygens and asymmetric BO3 units with two bridging oxygens in the glass network as the gallium content in the series increased. The formation of amorphous CaP on the glass surface after 24 h of incubation in SBF was confirmed by SEM, XRD, and FTIR analyses. Finally, antibacterial evaluation of the glasses using the agar disc‐diffusion method demonstrated that the addition of gallium increased the antibacterial potency of the glasses against P. aeruginosa (Gram‐negative) while decreasing it against S. epidermidis (Gram‐positive); considering the ion release trends, this indicates that the gallium ion is responsible for the glasses’ antibacterial behavior against P. aeruginosa while the zinc ion controls the antibacterial activity against S. epidermidis. The statistical significance of the observed trends in the measurements were confirmed by applying the Kruskal‐Wallis H Test. © 2017 Wiley Periodicals, Inc. J Biomed Mater Res Part B: Appl Biomater, 106B: 367–376, 2018.
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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".