Synergistic Effect of Bioactive Inorganic Fillers in Enhancing Properties of Dentin Adhesives—A Review
Bibliographic record
Abstract
Dentin adhesives (DAs) play a critical role in the clinical success of dental resin composite (DRC) restorations. A strong bond between the adhesive and dentin improves the longevity of the restoration, but it is strongly dependent on the various properties of DAs. The current review was aimed at summarizing the information present in the literature regarding the improvement of the properties of DAs noticed after the addition of bioactive inorganic fillers. From our search, we were able to find evidence of multiple bioactive inorganic fillers (bioactive glass, hydroxyapatite, amorphous calcium phosphate, graphene oxide, calcium chloride, zinc chloride, silica, and niobium pentoxide) in the literature that have been used to improve the different properties of DAs. These improvements can be seen in the form of improved hardness, higher modulus of elasticity, enhanced bond, flexural, and ultimate tensile strength, improved fracture toughness, reduced nanoleakage, remineralization of the adhesive-dentin interface, improved resin tag formation, greater radiopacity, antibacterial effect, and improved DC (observed for some fillers). Most of the studies dealing with the subject area are in vitro. Future in situ and in vivo studies are recommended to positively attest to the results of laboratory findings.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.004 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".