Influence of Hydroxyapatite Nanospheres in Dentin Adhesive on the Dentin Bond Integrity and Degree of Conversion: A Scanning Electron Microscopy (SEM), Raman, Fourier Transform-Infrared (FTIR), and Microtensile Study
Bibliographic record
Abstract
An experimental adhesive incorporated with different nano-hydroxyapatite (n-HA) particle concentrations was synthesized and analyzed for dentin interaction, micro-tensile bond strength (μTBS), and degree of conversion (DC). n-HA powder (5 wt % and 10 wt %) were added in adhesive to yield three groups; gp-1: control experimental adhesive (CEA, 0 wt % HA), gp-2: 5 wt % n-HA (HAA-5%), and gp-3: 10 wt % n-HA (HAA-10%). The morphology of n-HA spheres was evaluated using Scanning Electron Microscopy (SEM). Their interaction in the adhesives was identified with SEM, Energy-Dispersive X-ray (EDX), and Micro-Raman spectroscopy. Teeth were sectioned, divided in study groups, and assessed for μTBS and failure mode. Employing Fourier Transform-Infrared (FTIR) spectroscopy, the DC of the adhesives was assessed. EDX mapping revealed the occurrence of oxygen, calcium, and phosphorus in the HAA-5% and HAA-10% groups. HAA-5% had the greatest μTBS values followed by HAA-10%. The presence of apatite was shown by FTIR spectra and Micro-Raman demonstrated phosphate and carbonate groups for n-HA spheres. The highest DC was observed for the CEA group followed by HAA-5%. n-HA spheres exhibited dentin interaction and formed a hybrid layer with resin tags. HAA-5% demonstrated superior μTBS compared with HAA-10% and control adhesive. The DC for HAA-5% was comparable to control adhesive.
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.
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".