Review of nano‐technology applications in <scp>resin‐based</scp> restorative materials
Bibliographic record
Abstract
OBJECTIVE: Nanotechnology has progressed significantly and particles as small as 3 nm are being employed in resin-based restorative materials to improve clinical performance. The goal of this review is to report the progress of nanotechnology in Restorative Dentistry by reviewing the advantages, limitations, and applications of resin-based restorative materials with nanoparticles. MATERIALS AND METHODS: A literature review was conducted using PubMed/Medline, Scopus and Embase databases. In vitro, in vivo and in situ research studies published in English between 1999 and 2020, and which focused on the analysis of resin-based restorative materials containing nanoparticles were included. RESULTS: A total of 140 studies were included in this review. Studies reported the effect of incorporating different types of nanoparticles on adhesive systems or resin composites. Mechanical, physical, and anti-bacterial properties were described. The clinical performance of resin-based restorative materials with nanoparticles was also reported. CONCLUSIONS: The high surface area of nanoparticles exponentially increases the bioactivity of materials using bioactive nanofillers. However, the tendency of nanoparticles to agglomerate, the chemical instability of the developed materials and the decline of rheological properties when high ratios of nanoparticles are employed are some of the obstacles to overcome in the near future. CLINICAL SIGNIFICANCE: In spite of the recent advancements of nanotechnology in resin-based restorative materials, some challenges need to be overcome before new nano-based restorative materials are considered permanent solutions to clinical problems.
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) | 0.002 | 0.001 |
| Bibliometrics | 0.006 | 0.007 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.002 |
| 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".