Physicochemical and biological properties of dental materials and formulations with silica nanoparticles: A narrative review
Bibliographic record
Abstract
OBJECTIVE: Silica nanoparticles (SNPs) have been extensively studied and used in different dental applications to promote improved physicochemical properties, high substance loading efficiency, in addition to sustained delivery of substances for therapeutic or preventive purposes. Therefore, this study aimed to review the SNPs applications in nanomaterials and nanoformulations in dentistry, discussing their effect on physicochemical properties, biocompatibility and ability to nanocarry bioactive substances. DATA RESOURCES: Literature searches were conducted on PubMed, Web of Science, and Scopus databases to identify studies examining the physicochemical and biological properties of dental materials and formulations containing SNPs. Data extraction was performed by one reviewer and verified by another STUDY SELECTION: A total of 50 were reviewed. In vitro studies reveal that SNPs improved the general properties of dental materials and formulations, such as microhardness, fracture toughness, flexural strength, elastic modulus and surface roughness, in addition to acting as efficient nanocarriers of substances, such as antimicrobial, osteogenic and remineralizing substances, and showed biocompatibility CONCLUSIONS: SNPs are biocompatible, improve properties of dental materials and serve as effective carriers for bioactive substances CLINICAL SIGNIFICANCE: Overall, SNPs are a promising drug delivery system that can improve dental materials biological and physicochemical and aesthetic properties, increasing their longevity and clinical performance. However, more studies are needed to elucidate SNPs short- and long-term effects in the oral cavity, mainly on in vivo and clinical studies, to prove their effectiveness and safety.
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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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.005 | 0.004 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".