Assessment of the Physical Properties of an Experimental Adhesive Dentin Bonding Agent with Carbon Nanoparticles
Bibliographic record
Abstract
The present study was aimed at reinforcing the control adhesive (CA) with two concentrations (2.5% & 5%) of carbon nanoparticles (CNPs) and evaluating the impact of these additions on the adhesive’s properties. Scanning electron microscopy (SEM) and energy dispersive X-Ray (EDX) spectroscopy were utilized to examine the morphological characteristics and elemental mapping of the filler CNPs. To investigate the adhesive’s properties, rheological assessment, shear bond strength (SBS) testing, analysis of the adhesive–dentin interface, degree of conversion (DC) analysis, and failure mode investigations were carried out. The SEM micrographs of CNPs verified roughly hexagonal-shaped cylindrical particles. The EDX plotting established the presence of carbon (C), oxygen (O), and zirconia (Zr). Upon rheological assessment, a gradual reduction in the viscosity was observed for all the adhesives at higher angular frequencies. The SBS testing revealed the highest values for 2.5% CNP adhesive group (25.15 ± 3.08 MPa) followed by 5% CNP adhesive group (24.25 ± 3.05 MPa). Adhesive type interfacial failures were most commonly found in this study. The 5% CNP containing adhesive revealed thicker resin tags and a uniform hybrid layer without any gaps (compared with 2.5% CNP adhesive and CA). The reinforcement of the CA with 2.5% and 5% CNPs augmented the adhesive’s bond strength. Nevertheless, a diminished viscosity (at higher angular frequencies) and reduced DC were observed for the two CNP reinforced adhesives. CNP reinforced dentin adhesives are effective in enhancing the adhesive bond integrity of resin to dentin.
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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".