Assessment of the Physical Properties of an Experimental Adhesive Dentin Bonding Agent with Carbon Nanoparticles
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
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.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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) | 0.000 | 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 it