Synergistic effect of graphene oxide/calcium phosphate nanofiller in a dentin adhesive on its dentin bond integrity and degree of conversion. A scanning electron microscopy, energy dispersive X‐ray spectroscopy, Fourier transform infrared, <scp>micro‐Raman</scp>, and bond strength study
Bibliographic record
Abstract
The objective was to formulate and analyze a dentin adhesive incorporated with graphene oxide (GO) nanoparticle and calcium phosphate (CaP) composite. Methods comprising of scanning electron microscopy (SEM)-energy dispersive X-ray spectroscopy (EDX), micro-Raman spectroscopy, shear bond strength (SBS), and Fourier transform infrared (FTIR) spectroscopy were used to characterize nanoparticle composite, dentin bond toughness, degree of conversion (DC), and adhesive-dentin interaction. Postsynthesis of GO nanoparticles, they were functionalized with CaP using standard process. The GO-CaP composite was not added to experimental adhesive (negative control group, GO-CaP-0%), and added at 2.5 and 5 wt% to yield GO-CaP-2.5% and GO-CaP 5% groups, respectively. Teeth were set to form bonded samples utilizing adhesives in three groups for SBS testing, with and without thermocycling. The homogenous diffusion of GO-CaP composite was verified in the adhesive. Resin tags having standard penetrations were observed on SEM micrographs. The EDX analysis confirmed the occurrence of calcium, phosphorus, and carbon ions in the composite containing adhesives. The SBS test revealed highest mean values for GO-CaP-5% followed by GO-CaP-2.5%. The FTIR spectra verified the presence of apatite peaks and the micro-Raman spectra showed characteristic D and G bands for GO nanoparticles. GO-CaP composite in dentin adhesive may improve its bond strength. The addition of 5 wt% resulted in a bond strength that was superior to all other groups. GO-CaP-5% group demonstrated lower DC (to control), uniform distribution of GO and CaP composite within adhesive, appropriate dentin interaction, and resin tag formation.
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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".