Comparative Analysis of Color Stability and Its Impact on Artificial Aging: An In Vitro Study of Bioactive Chitosan, Titanium, Zirconia, and Hydroxyapatite Nanoparticle-Reinforced Glass Ionomer Cement Compared With Conventional Glass Ionomer Cement
Bibliographic record
Abstract
Background Discoloration affects glass ionomer cement (GIC) color stability due to its brittle nature and microporosity. To counter this, incorporating alternative materials is essential for maintaining color stability. Aim This study aims to determine the color stability and gloss of GIC modified with bioactive chitosan, titanium, zirconia, and hydroxyapatite nanoparticles before and after artificial aging. Materials and methods The study was conducted at Saveetha Research Centre, Saveetha Dental College and Hospitals, Saveetha Institute of Medical and Technical Sciences, Saveetha University, located in Chennai, India. Green-mediated chitosan, titanium, zirconia, and hydroxyapatite (Ch-Ti-Zr-HA) nanoparticles were synthesized using the one-pot synthesis technique. Forty-eight disc-shaped specimens were prepared by incorporating the obtained nanoparticles (nanocomposite) into the GIC, with a diameter of 5 mm and thickness of 2 mm. The specimens were prepared in different concentrations (3%, 5%, and 10%) designated as group I, group II, and group III, respectively. Group IV, serving as the control, consisted of conventional GIC without any modifications. Following preparation, scanning electron microscopy (SEM) and energy-dispersive X-ray (EDX) microanalysis confirmed sample elements, and the specimens were submerged in distilled water for a duration of 24 hours prior to the commencement of testing. Subsequently, the specimens underwent artificial aging (thermocycling), between temperatures of 5°C and 55°C, for a total of 30,000 cycles, with a 30-second dwell time. Color change and gloss characteristics were assessed both after 24 hours and following thermocycling using a spectrophotometer and glossometer, respectively. The average color change parameter (ΔE) was measured using Adobe Photoshop. The data obtained were subjected to statistical analysis using an unpaired t-test. Results Significant color stability variations were observed post thermocycling (P = 0.001). Group 2 (5%) exhibited minimal delta E difference (0.508 ± 0.105), indicating superior color stability, while group 4 (control) had maximum difference (1.15 ± 0.187), indicating lower stability. Gloss tests confirmed GIC's polishability, where there were significant differences among all the groups. Conclusion It can be concluded that 5% nanoparticle-modified GIC has better color stability and gloss than conventional GIC. Further studies are needed to analyze the color stability and gloss through dentifrices and other beverages.
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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) | 0.001 | 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".