Influence of TiO2 and ZrO2 Nanoparticles on Adhesive Bond Strength and Viscosity of Dentin Polymer: A Physical and Chemical Evaluation
Bibliographic record
Abstract
The present study aimed to formulate an experimental adhesive (EA) and reinforce it with 5 wt.% titanium dioxide (TiO2) or zirconium oxide (ZrO2) to yield 5% TiO2 and 5% ZrO2 adhesives, respectively, and then analyze the impact of this reinforcement on various mechanical properties of the adhesives. The EA contained a blend of monomers such as bisphenol A glycol dimethacrylate (BisGMA), triethylene glycol dimethacrylate (TEGDMA), 2-hydroxyethyl methacrylate (HEMA), and ethyl 4-dimethylamino benzoate and camphorquinone. The EA included ethyl 4-dimethylamino benzoate and camphorquinone photo-initiators, and diphenyliodonium hexafluorophosphate (DPIHP) was also included to act as an electron initiator. The TiO2 and ZrO2 nanoparticles were incorporated into the EA post-synthesis. To characterize the filler nanoparticles, scanning electron microscopy (SEM) and line-energy dispersive X-ray (EDX) spectroscopy were performed. The adhesives were characterized by analyzing their rheological properties, shear-bond strength (SBS), and interfacial failure types. Further, the resin–dentin interface was also analyzed via SEM. The TiO2 nanoparticles were spherically shaped on the SEM micrographs, while the ZrO2 nanoparticles were seen as non-uniformly shaped agglomerates. The EDX mapping demonstrated the presence of Ti and oxygen for TiO2 and Zr and oxygen for the ZrO2 nanoparticles. Both 5% TiO2 and 5% ZrO2 adhesives revealed decreased viscosity as compared with the EA. The 5% TiO2 adhesive demonstrated higher SBS values for both non-thermocycled (NTC) and thermocycled samples (NTC: 25.35 ± 1.53, TC: 23.89 ± 1.95 MPa), followed by the 5% ZrO2 adhesive group (NTC: 23.10 ± 2.22, TC: 20.72 ± 1.32 MPa). The bulk of the failures (>70%) were of adhesive type in all groups. The SEM analysis of the resin–dentin interface revealed the development of a hybrid layer and resin tags (of variable depth) for the EA and 5% TiO2 groups. However, for the 5% ZrO2 group, the hybrid layer and resin tag establishment appeared compromised. Reinforcement of the EA with TiO2 or ZrO2 caused an increase in the adhesive’s SBS (with the 5% TiO2 group demonstrating the highest values) in comparison with the EA (without nanoparticles). However, both nanoparticle-containing adhesives revealed decreased viscosity compared with the EA (without nanoparticles). Further studies investigating the impact of diverse filler concentrations on the properties of adhesives are suggested.
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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".