Effects of Potassium Oxalate on Knoop Hardness of Etch-and-Rinse Adhesives
Bibliographic record
Abstract
The objective of this study was to determine whether the hardness of etch-and-rinse adhesives may be affected by the pretreatment of acid-etched dentin with potassium oxalate desensitizer. Unerupted human third molars were cut into crown segments by removing the occlusal enamel and roots. The pulp chamber of these crown segments was connected to a syringe barrel filled with phosphate-buffered saline so that the moisture of dentin was maintained during the bonding procedures. Three etch-and-rinse adhesives-two two-step systems (Adper Single Bond 2 [SB], One-Step [OS]) and one three-step system (Adper Scotchbond Multi-Purpose [MP])-were applied to acid-etched dentin that had been treated (experimental groups) or not (control groups) with potassium oxalate (BisBlock). The Knoop hardness (KHN) of adhesives was taken at different sites of the outer surface of the adhesive-bonded dentin. The KHN of the three tested adhesives applied to acid-etched dentin treated with potassium oxalate was significantly lower than that exhibited by the respective controls (not treated with oxalate; p<0.05). Regardless of the adhesive, the treatment with potassium oxalate reduced the adhesives' KHN (p<0.05), with the OS system exhibiting the lowest KHN compared with the MP and SB systems.
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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 | 0.001 |
| 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) | 0.002 | 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".