Effect of Indenter Load on Vickers Microhardness and Indentation Depth of One Resin Composite
Bibliographic record
Abstract
The load and size of the indentation may affect the hardness value. This study investigated the effect of the indentation size on the microhardness of one resin-based composite (RBC). Metal molds 4 mm deep and 12 mm in diameter were filled with Tetric EvoCeram Bulk Fill (Ivoclar) and light-cured for 10 s using a broad-spectrum LED curing light. The Vickers microhardness and the degree of conversion (DC) at the top and bottom surfaces were measured 24 h later before and after polishing the RBC. The microhardness measurements were made using 50, 100, 300 and 1000-gf loads with the same 8 s dwell time. The DC was measured in the same region using mid FT-IR spectroscopy. Repeated measures analysis of variance tests were used to determine if the surface (top vs. bottom) or the indenter loads had a significant effect on the VH, or if polishing affected the VH and the DC (alpha = 0.05). It was found that the indenter load did not affect the Vickers hardness of the unpolished top surface (p = 0.759), the polished top surface (p = 0.374), or the polished bottom surface (p = 0.083) of the Tetric EvoCeram Bulk Fill. Increasing the indenter load did increase the VH of the unpolished surface at the bottom (p < 0.0001). Polishing increased the DC by 13.5% at the top and by 46.7% at the bottom surface.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".