Exploring the Physicochemical, Mechanical, and Photocatalytic Antibacterial Properties of a Methacrylate-Based Dental Material Loaded with ZnO Nanoparticles
Bibliographic record
Abstract
While resin-based materials meet the many requirements of a restorative material, they lack adequate, long-lasting antimicrobial power. This study investigated a zinc oxide nanoparticle (ZnO NP)-loaded resin-blend (RB) toward a new antimicrobial photodynamic therapy (aPDT)-based approach for managing dental caries. The results confirmed that up to 20 wt% ZnO NPs could be added without compromising the degree of conversion (DC) of the original blend. The DC achieved for the 20 wt% ZnO NP blend has been the highest reported. The effects on flexural strength (FS), shear bond strength to dentin (SBS), water sorption (WS), solubility (SL), and viability of Streptococcus mutans under 1.35 J/cm2 blue light or dark conditions were limited to ≤20 wt% ZnO NP loading. The addition of up to 20 wt% ZnO NPs had a minimal impact on FS or SBS, while a reduction in the bacteria count was observed. The maximum loading resulted in an increase in SL. Furthermore, 28-day aging in 37 °C water increased the FS for all groups, while it sustained the reduction in bacteria count for the 20 wt% resin blends. Overall, the ZnO NP-loaded resin-based restorative material presents significant potential for use in aPDT.
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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".