Color Stability of Glass Ionomer Cement after Reinforced with Two Different Nanoparticles
Bibliographic record
Abstract
Aim. This study aimed to compare the staining characteristics of a commercially available restorative glass ionomer cement to a formulation reinforced by the addition of carbon nanotubes and another formulation reinforced by the addition of silver nanoparticles to the powder of the same cement. Methodology. Twenty samples each of a control glass ionomer cement (PULPDENT® Glass Fill®, Pulpdent Corp. Watertown, MA, USA), control cement reinforced with 0.0006 gm (0.03% by weight) of carbon nanotubes (Sigma Aldrich, St Louis MO, USA), and control cement reinforced with 0.2 gm (10% by weight) of silver nanoparticles (Nanocyl™, Nanocyl SA, Sambreville, Belgium) were immersed in a staining solution. Color evaluations were carried out after 1 h, 24 h, and 1 week. Color change values were calculated. Results. The results indicated that carbon nanotube reinforced specimens exhibited less color stability when compared to controlled glass ionomer cement specimens; however, both samples had significantly greater color stability than silver nanoparticle reinforced glass ionomer samples. Conclusion. It can be concluded within the limitations of this study that carbon nanotube reinforced glass ionomer cements have better color stability than silver nanoparticle reinforced glass ionomer cements.
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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 | 0.001 |
| 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".