Influence of Different Conditioning Treatments on the Bond Integrity of Root Dentin to rGO Infiltrated Dentin Adhesive. SEM, EDX, FTIR and MicroRaman Study
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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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".