Dentin Bond Integrity of Hydroxyapatite Containing Resin Adhesive Enhanced with Graphene Oxide Nano-Particles—An SEM, EDX, Micro-Raman, and Microtensile Bond Strength Study
Bibliographic record
Abstract
The aim was to synthesize and characterize an adhesive incorporating HA and GO nanoparticles. Techniques including scanning electron microscopy (SEM) and energy dispersive X-ray spectroscopy (EDX), micro-tensile bond strength (μTBS), and micro-Raman spectroscopy were employed to investigate bond durability, presence of nanoparticles inside adhesive, and dentin interaction. Control experimental adhesive (CEA) was synthesized with 5 wt% HA. GO particles were fabricated and added to CEA at 0.5 wt% (HA-GO-0.5%) and 2 wt% GO (HA-GO-2%). Teeth were prepared to produce bonded specimens using the three adhesive bonding agents for assessment of μTBS, with and without thermocycling (TC). The adhesives were applied twice on the dentin with a micro-brush followed by air thinning and photo-polymerization. The HA and GO nanoparticles demonstrated uniform dispersion inside adhesive. Resin tags with varying depths were observed on SEM micrographs. The EDX mapping revealed the presence of carbon (C), calcium (Ca), and phosphorus (P) in the two GO adhesives. For both TC and NTC samples, HA-GO-2% had higher μTBS and durability, followed by HA-GO-0.5%. The representative micro-Raman spectra demonstrated D and G bands for nano-GO particles containing adhesives. HA-GO-2% group demonstrated uniform diffusion in adhesive, higher μTBS, adequate durability, and comparable resin tag development to controls.
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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".