Surface Modification of ZrO<sub>2</sub> Nanoparticles and Its Effects on the Properties of Dental Resin Composites
Bibliographic record
Abstract
Abstract Endowing dental resin composites (DRCs) with suitable radiopacity is necessary for clinical diagnosis during treatment of caries. To reach this effect, ZrO2 nanoparticles were introduced into the DRCs in this work after modification with two different methods, one modified with 3-methacryloxypropyltrimethoxysilane (ZrO2@γ-MPS) and the other coated with mesoporous SiO2 and then modified with γ-MPS (ZrO2@m-SiO2@γ-MPS). These ZrO2 nanoparticles were used as functional additives, and the SiO2 nanoparticles were used as the fundamental filler in the preparation of DRCs to study the effects of the surface modification of ZrO2 nanoparticles and the amount of particles added (0, 3, 5, and 7 wt %) on the properties of the DRCs. The DRC containing ZrO2@m-SiO2@γ-MPS showed a higher transmittance, which may be attributed to the fact that the SiO2 coating reduced the refractive index of the nanoparticles and thus decreased the scattering of light within the DRC. The silane polymer film on the surface of ZrO2@γ-MPS nanoparticles acted as a lubricant and decreased the viscosity of DRC, but the DRC containing ZrO2@m-SiO2@γ-MPS showed a higher viscosity due to the presence of a mesoporous structure. The radiopacity value of the DRCs containing the functional additives was close to 1 mm Al, much higher than that of the DRC filled with SiO2 nanoparticles alone (0.74 ± 0.07 mm Al), meeting the requirement of ISO 4049:2009. The surface modification of ZrO2 nanoparticles had no significant influence on the mechanical properties, radiopacity, and cytotoxicity of DRCs (p > 0.05). This study provides useful insight into the design and development of radiopaque DRCs with excellent physical and mechanical properties.
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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".