Dental Resin Composites Reinforced by Rough Core–Shell SiO<sub>2</sub> Nanoparticles with a Controllable Mesoporous Structure
Bibliographic record
Abstract
A porous structure within filler particles may improve interfacial bonding between the resin matrix and fillers for the preparation of dental resin composites (DRCs). In this study, rough core–shell SiO 2 (rSiO 2 ) nanoparticles with controllable mesoporous structures were synthesized via an oil–water biphase reaction system and characterized by transmission electron microscopy (TEM), scanning electron microscopy (SEM), and N 2 adsorption–desorption measurements. The influence of the mesoporous shell thickness of rSiO 2 and mass ratio between rSiO 2 and smooth SiO 2 (sSiO 2 ) on the physical and mechanical properties of DRCs was studied. The rSiO 2 with a thin mesoporous shell could form a strong physical interlocking with the resin matrix, which improved the mechanical properties with the exception of flexural modulus. The mechanical properties were further optimized by mixing rSiO 2 and sSiO 2 . The flexural strength and compressive strength of the DRC at a mass ratio of 5:5 increased by 24.3% and 16.8%, respectively, compared with the DRC filled with sSiO 2 alone. There is no statistically significant difference in the flexural modulus between these two DRCs ( p > 0.05). The DRCs in this study showed excellent biocompatibility on the human dental pulp cells (HDPCs) as demonstrated by the cytotoxicity tests. The use of rSiO 2 provides a promising approach to develop strong, durable, and biocompatible DRCs.
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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".