The Effect of Multiple Antireflective Coatings and Center Thickness on Resistance of Polycarbonate Spectacle Lenses to Penetration by Pointed Missiles
Bibliographic record
Abstract
PURPOSE: Previous work has shown that the impact resistance to blunt missiles is affected by coatings applied to either CR-39 or polycarbonate lenses. We investigated the effects of multiple antireflection (minimum angle of resolution [MAR]) coatings on the resistance of polycarbonate lenses to puncture on impact by sharp, high-speed missiles. METHODS: Four groups of surfaced plano polycarbonate lenses were investigated. Two groups had a scratch-resistant (SR) coating applied to both surfaces. One of these groups had a 2-mm center thickness and the other had a 3-mm center thickness. The other two groups of 2-mm and 3-mm thick lenses had a MAR coating applied over the SR coating. The lenses were impacted by a missile consisting of an industrial sewing machine needle mounted in a cylindrical aluminum carrier. RESULTS: The sharp missiles were able to pierce the lenses at speeds between 29.6 m/s and 46.2 m/s. Impact resistance was lowest for the thinner lenses and lenses with a MAR coating. The effect of the MAR and lens thickness was subadditive. CONCLUSIONS: We have confirmed previous observations that polycarbonate lenses are more susceptible to penetration by sharp, high-speed missiles than blunt missiles. We have also found that reducing lens center thickness and applying a MAR coating further reduces the penetration resistance. Therefore, the use of 2-mm center thickness and MAR-coated polycarbonate lenses should be discouraged for industrial eye protectors where sharp missile hazards are possible.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 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".