In vitro evaluation of the enhancement of glass ionomer cement features by using chitosan and nanodiamond
Bibliographic record
Abstract
This study investigated the influence of chitosan and nanodiamond incorporation on the surface, optical, and mechanical properties of glass ionomer cement. Total 56 samples (5 mm diameter and 2 mm thickness) were prepared and divided into 4 groups according to the incorporation of chitosan and nanodiamond on Fuji II glass ionomer cement: Control group: no incorporation; 10%CH group: incorporation of 10% chitosan; 10%ND group: incorporation of 10% of nanodiamond; 5%CH-5%ND group: incorporation of 5% chitosan and 5% nanodiamond (n=14). Analyses of color stability, surface roughness, fluorescence intensity, microhardness, morphology, and chemical composition were investigated. Additionally, water sorption, hygroscopic expansion, contact angle, surface free energy, and total free energy of interaction were also assessed. After the initial readings, the samples were individually stored in red wine solution for 28 days. Data were subjected to ANOVA followed by Tukey´s test (α=.05). Aging in wine solution altered the optical, mechanical, and surface properties of glass ionomer cement regardless of the incorporation of the compound (P<.05). 10% chitosan-incorporated glass ionomer cement promoted higher color alteration, surface roughness, and water sorption after aging (P<.05). 10% nanodiamond-incorporated glass ionomer cement showed higher microhardness compared to the other groups before aging (P<.05), however there were no differences among them after aging (P>.05). In general, no differences between the 5% chitosan- and 5% nanodiamond-incorporated glass ionomer cement and control groups were noted on the evaluated analyses (P>.05). Thus, the incorporation of 5% chitosan and 5% nanodiamond is a satisfactory alternative for maintain the surface, optical, and mechanical properties of the glass ionomer cement.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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 teacher head, 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".