Microlens Arrays above Interlaced Plasmonic Pixels for Optical Security Devices with High‐Resolution Multicolor Motion Effects
Bibliographic record
Abstract
Abstract Optical security devices based on high‐density microlens arrays on top of printed microimages or interlaced patterns can display high‐resolution image frames with attractive motion effects. However, the effects are so far restricted to a single color due to challenges in printing multiple colors with sufficiently high resolution and precision. This article introduces a viable solution based on microlens arrays above plasmonic colors to enable high‐resolution multicolor image frames with multidirectional motion effects. Plasmonic color pixels comprised of metalized nanowell arrays are implemented to print 2D interlaced patterns with super‐high resolution and nanoscale precision. Using a 2D microlens array of 25 µm lens pitch, 50 color image frames are obtained from a plasmonic color pattern printed with resolution of 10 160 dots per inch. The image frames show multiple colors, which significantly boost the color performance of microlens‐based security devices. The display resolution of image frames reaches 1016 pixels per inch, which is at least six times higher than conventional lenticular images. The color image frames also show excellent motion effects when moving the view point along any arbitrary direction. The presented devices can be advantageous for commercial applications in security, authentication, and product branding.
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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 | 0.000 |
| 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".