Effect of Different Concentrations of Silver Nanoparticles on the Quality of the Chemical Bond of Glass Ionomer Cement Dentine in Primary Teeth
Bibliographic record
Abstract
Background: The nanotechnologies have been applied for dental restorative materials manufacturing such as glass ionomer cement, composites, tooth regeneration, and endodontic sealers. The study aimed to investigate the chemical bond of conventional glass ionomer cement and to evaluate the addition of different concentrations of silver nanoparticles (AgNPs) on the quality of the chemical bond of glass ionomer cement to primary dentin. Methods: Silver nanoparticle (AgNP) powder was added in concentrations of 0.2, 0.4, and 0.6% to the conventional powder of GIC Fuji II. Then, the powder was added to the liquid and mixed with the recommended powder/liquid ratio of 3.6:1 g. The Fourier-transform infrared spectra (FTIR) of teeth with 0.2%, 0.4%, and 0.6% w/w of silver nanoparticles in GIC fills and the control tooth were obtained. The conventional glass ionomer was used as a control group. The control and the plain silver tooth were subjected to FTIR analysis using an ATR–FTIR spectrophotometer (ThermoFisher Scientific, Waltham, MA, United States) with zinc selenide (ZnSe) ATR crystal (attenuated total reflection) and OPUS v7.5 software. All spectra were recorded in the range of 500–3,500 cm −1 in the transmission mode with an ATR module. Results: The AgNPs added at 0.2, 0.4, and 0.6% concentration to GIC provided some information in the context of bond interaction with the dentin. Various bond peaks were seen for calcium, carbonate, phosphate, and amide. In our study, only the amide and phosphate were generated. The amide peaks were almost similar to the control, 0.2%, 0.4%, and 0.6%, with the peaks in the range of 1250–1650 cm −1 . There was a clear shift in the phosphate peak from the control, 0.2, and 0.4%, which was about 1050 cm −1 , whereas for 0.6%, there was a clear shift from 1050 cm −1 to 880 cm −1 . Conclusion: GIC supplemented with AgNPs showed that a concentration above 0.4% of AgNPs altered the bond quality in dentin interaction. In conclusion, adding AgNPs at a minimal level improves the mechanical properties and maintains the same bond quality as GIC.
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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".