Influence of Different Conditioning Treatments on the Bond Integrity of Root Dentin to rGO Infiltrated Dentin Adhesive. SEM, EDX, FTIR and MicroRaman Study
Why this work is in the frame
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Bibliographic record
Abstract
The present study aimed to synthesize and equate the mechanical properties and dentin interaction of two adhesives; experimental adhesive (EA) and 5 wt.% reduced graphene oxide rGO) containing adhesive. Scanning electron microscopy (SEM)-Energy-dispersive X-ray spectroscopy (EDX), Micro-Raman spectroscopy, push-out bond strength test, and Fourier Transform Infrared (FTIR) spectroscopy were employed to study nano-bond strength, degree of conversion (DC), and adhesive-dentin interaction. The EA was prepared, and rGO particles were added to produce two adhesive groups, EA-rGO-0% (control) and rGO-5%. The canals of sixty roots were shaped and prepared, and fiber posts were cemented. The specimens were further alienated into groups based on the root canal disinfection technique, including 2.5% sodium hypochlorite (NaOCl), Photodynamic therapy (PDT), and ER-CR-YSGG laser (ECYL). The rGO nanoparticles were flake-shaped, and EDX confirmed the presence of carbon (C). Micro-Raman spectroscopy revealed distinct peaks for graphene. Push-out bond strength test demonstrated highest values for the EA-rGO-0% group after NaOCl and PDT conditioning whereas, rGO-5% showed higher values after ECYL conditioning. EA-rGO-0% presented greater DC than rGO-5% adhesive. The rGO-5% adhesive demonstrated comparable push-out bond strength and rheological properties to the controls. The rGO-5% demonstrated acceptable DC (although lower than control group), appropriate dentin interaction, and resin tag establishment.
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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