Assessing the Influence of Thermocycling on Compressive Strength, Flexural Strength, and Microhardness in Green-Mediated Nanocomposite-Enhanced Glass Ionomer Cement Compared to Traditional Glass Ionomer Cement
Bibliographic record
Abstract
Background and objective Glass ionomer cement (GIC), also known as polyalkenoate cement, has been extensively used in dentistry for both luting and restorative purposes. Despite being the first choice for aesthetic restorations due to their chemical bonding ability to teeth, GICs have faced challenges such as low mechanical properties, abrasion resistance, and sensitivity to moisture, leading to the search for improved materials. This study aims to assess the effects of thermocycling on the compressive, flexural strength, and microhardness of green-mediated nanocomposite-modified GIC in comparison to traditional GIC. Methodology Green-mediated nanoparticles, consisting of chitosan, titanium, zirconia, and hydroxyapatite (Ch-Ti-Zr-HA), were synthesized using a one-pot synthesis technique to form nanocomposites. These nanocomposites were then incorporated into GIC specimens in varying concentrations (3%, 5%, and 10%), denoted as Group I, Group II, and Group III, respectively. Group IV served as the control, consisting of conventional GIC. To assess the performance of the novel restorative materials over an extended period, compressive strength, flexural strength, and microhardness were measured before and after thermocycling using a universal material testing machine. Furthermore, scanning electron microscopy (SEM) analysis was carried out following the thermocycling process. The collected data were subjected to statistical analysis through one-way analysis of variance (ANOVA) and paired t-tests. Results The findings demonstrated that, in comparison to the control group, both the mean compressive strength and flexural strength, as well as hardness, were notably higher for the 10% and 5% nanocomposite-modified GIC specimens before and after thermocycling (P < 0.05). Notably, there was no notable difference observed between the 5% and 10% concentrations (P > 0.05). These results suggest that incorporating green-mediated nanocomposites (Ch-Ti-Zr-HA) modified GIC at either 5% or 10% concentration levels leads to improved mechanical properties, indicating their potential as promising alternatives in dental restorative materials. Conclusions Based on our findings, it can be inferred that the 10% and 5% concentrations of green-mediated (Ch-Ti-Zr-HA) modified GIC exhibit superior compressive and flexural strength compared to conventional GIC. Additionally, analysis of the scanning electron microscope (SEM) morphology revealed that green-mediated GIC displays smoother surface characteristics in contrast to conventional GIC. These results underscore the potential advantages of utilizing green-mediated nanocomposite-modified GIC in dental applications, suggesting enhanced mechanical properties and surface quality over conventional.
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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) | 0.001 | 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".