Analysis of Physical and Mechanical Properties of Universal Composites under Different Types of Polishing before and after Acid Challenge
Bibliographic record
Abstract
This study aimed to evaluate in vitro the degree of surface smoothness provided by two different polishing techniques and the effect of acid challenge on the alteration of surface roughness (Ra), microhardness (Knoop), and color ( Δ E 00 ) of three nanoparticulate composites, simulating 1 year of exposure to hydrochloric acid (HCl). Eighty specimens for each composite were divided into four groups ( n = 240), being control without polishing, control with wear, WPC (wear + polishing with Cosmedent Kit), and WPB (wear + BisCover LV liquid polish). Repeated measures ANOVA was applied for Ra and Knoop Microhardness. For the color ( Δ E) three‐way ANOVA was applied. In cases of statistically significant the Tukey posttest was applied ( α = 0.05). Both types of polishing tested resulted in a surface smoothness below the critical value established by the studies (Ra ≥ 0.2 μ m), even after immersion. The microhardness of all composite resins decreased after the challenges. The specimens immersed in HCl showed a lower microhardness (42.2 Kgf/mm 2 ) when compared to the specimens immersed in artificial saliva (44.7 Kgf/mm 2 ). Regarding the color change, the composites presented values compatible with clinical acceptability, with a statistically significant difference only between the control group and the other types of polishing for the Z350 XT resin ( Δ E 00 = 3.78). It was concluded that both mechanical and chemical polishing produced a satisfactory surface smoothness, even after immersions in artificial saliva and HCl. The microhardness of the composites was affected by the challenges and the composites tested were within clinical acceptability with regard to color change.
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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) | 0.001 | 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".