RETRACTED: Influence of carbon and graphene oxide nanoparticle on the adhesive properties of dentin bonding polymer: A SEM, EDX, FTIR study
Post-publication record
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Bibliographic record
Abstract
Objective:This study was aimed at including 2.5 wt.% of carbon nanoparticles (CNPs) and graphene oxide NPs (GNPs) in a control adhesive (CA) and then investigate the effect of this inclusion on their mechanical properties and its adhesion to root dentin. Materials and methods:Scanning electron microscopy and energy dispersive X-ray (SEM-EDX) mapping were conducted to investigate the structural features and elemental distribution of CNPs and GNPs, respectively. These NPs were further characterized by Raman spectroscopy. The adhesives were characterized by evaluating their push-out bond strength (PBS), rheological properties, degree of conversion (DC) investigation, and failure type analysis. Results:The SEM micrographs revealed that the CNPs were irregular and hexagonal, whereas the GNPs were flake-shaped. EDX analysis showed that carbon (C), oxygen (O), and zirconia (Zr) were found in the CNPs, while the GNPs were composed of C and O. The Raman spectra for CNPs and GNPs revealed their characteristic bands (CNPs-D band: 1334 cm−1, GNPs-D band: 1341 cm−1, CNPs-G band: 1650 cm−1, and GNPs-G band: 1607 cm−1). The testing verified that the highest bond strength to root dentin were detected for GNP-reinforced adhesive (33.20 ± 3.55 MPa), trailed closely by CNP-reinforced adhesive (30.48 ± 3.10 MPa), while, the CA displayed lowest values (25.11 ± 3.60 MPa). The inter-group comparisons of the NP-reinforced adhesives with the CA revealed statistically significant results (p < 0.01). Failures of adhesive nature were most common in within the adhesives and root dentin. The rheological assessment results demonstrated a reduced viscosity for all the adhesives observed at advanced angular frequencies. All the adhesives verified suitable dentin interaction shown by hybrid layer and appropriate resin tag development. A reduced DC was perceived for both NP-reinforced adhesives, compared to the CA. Conclusion:The present study’s findings have demonstrated that 2.5% GNP adhesive revealed the highest, suitable root dentin interaction, and acceptable rheological properties. Nevertheless, a reduced DC was observed (matched with the CA). Prospective studies probing the influence of diverse concentrations of filler NPs on the adhesive’s mechanical properties to root dentin are recommended.
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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) | 0.002 | 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".