Ultra‐morphological characteristics of dentin surfaces after different preparations and treatments
Bibliographic record
Abstract
Using transmission electron microscopy (TEM), this study investigated the characteristics of smear layers after preparation of dentin surfaces using different methods. In addition, this study analyzed, using scanning electron microscopy (SEM), the interactions of smear layers with the various acidic agents used subsequently to treat the dentin surface. Mid-coronal dentin surfaces were prepared according to one of five instrumentation methods: fracture; grinding with carbide bur; grinding with regular-grit diamond bur; grinding with superfine-grit diamond bur; or grinding with 600-grit silicon carbide (SiC) abrasive paper. Then, each prepared dentin surface was further divided into control (no further treatment) or treatment with one of the following agents: phosphoric acid solution (PAS); phosphoric acid gel (PAG); Clearfil SE Bond 2 Primer (SE2); or Scotchbond Universal Adhesive (SBU). Longitudinal ultra-thin sections were made for TEM observations. Dentin discs, representing each surface preparation and treatment method described above, were created, and the morphology was examined by SEM. The TEM results revealed differences in thickness and denseness of the smear layer, according to the different methods of instrumentation used to prepare the dentin surface. The two forms of phosphoric acid completely removed the smear layer in all groups. Agglomerated silica particles were observed on the top of the collagen layer after etching with PAG. For all dentin surfaces, except for that prepared with SiC, SE2 demonstrated better etching efficacy on different smear layers, whereas SBU provided only superficial interaction.
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How this classification was reachedexpand
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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".