Knoop Microhardness Mapping Used to Compare the Efficacy of LED, QTH and PAC Curing Lights
Bibliographic record
Abstract
This study used a hardness mapping technique to compare the ability of seven curing lights to polymerize five composites. Six curing lights (Sapphire [plasma-arc: PAC], Bluephase16i [light emitting diode: LED], LEDemetron II [LED], SmartLite IQ [LED], Allegro [LED] and UltraLume-5 [Polywave LED]) were compared to an Optilux 501 (halogen: QTH) light. Five resin composites (Vit-1-escence, Tetric Evoceram, Filtek Z250, 4 Seasons and Solitaire 2) were polymerized at 4 mm and 8 mm from the end of the light guide. Four composites were light cured for the following times using these lights: Sapphire (5 seconds), Bluephase16i (5 seconds), LEDemetron II (5 seconds), SmartLite IQ (10 seconds), UltraLume-5 (10 seconds), Allegro (10 seconds) and Optilux 501 (20 seconds). Solitaire 2 required double these irradiation times. On each specimen, the Knoop microhardness (KHN) was measured at 49 locations across a 3 x 3 mm grid to determine the ability of each light to cure each brand of composite. The PAC light delivered the broadest spectrum of wavelengths, the greatest irradiance and hardness values that were 4.7 to 18.1 KHN(50gf) harder than the other lights. The ability of the lights to cure these five composites was ranked from highest to lowest: Sapphire, Optilux 501, Allegro, UltraLume-5, SmartLite IQ, LEDemetron II and Bluephase16i (ANOVA with REGWQ multiple comparison adjustment, p < 0.01).
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 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 | 0.001 |
| 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".