Parametric analysis of thin multifunctional elastomeric optical sheets
Bibliographic record
Abstract
Abstract Flexible optical sheets are thin large-area polymer light guide structures that can be used to create innovative passive light-harvesting and illumination systems. The optically transparent micro-patterned polymer sheet is designed to be draped over arbitrary surfaces or hung like a curtain. The light guidance sheet is fabricated by bonding two or more micro-patterned layers with different indices of optical refraction. By imprinting micro-optical elements on the constituent layers, it is possible to have portions of the optical sheet act as a light concentrator, near ‘lossless’ transmitter, or diffuser. However, the performance and efficiency of the flexible optical sheet depends on the overall curvature (κ) of the optical sheet and the relative orientation of incident light source. To illustrate this concept, the impact of key design parameters on the controlled guidance of light through a two-layer polydimethylsiloxane (PDMS) concentrator-transmitter-diffuser optical sheet is investigated using ray tracing simulation software. The analysis initially considers a flat (κ=0) PDMS optical sheet exposed to a collimated light source. The impact of sheet curvature (κ>0) on both system efficiency and illumination uniformity is then briefly explored. Critical design guidelines for creating multifunctional monolithic optical sheets are also summarized.
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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 | 0.001 | 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".