Shaping LED Beams with Radially Distributed Waveguide‐Encoded Lattices
Bibliographic record
Abstract
Abstract Slim, flexible polymer films imprinted with a radial distribution of cylindrical waveguides precisely control the shape and trajectory of light emitting diode (LED) beams. These radially distributed waveguide‐encoded lattices (RDWEL) are generated when a large, converging population comprising thousands of self‐trapped incandescent beams induces the corresponding array of waveguides in a photopolymerizable fluid. The waveguides are multimoded and impart a seamless field of view (FOV) of 70°, an enhancement of 320%, to the polymer film. A divergent LED beam incident on the plane‐faced RDWEL efficiently couples into its constituent waveguides and, depending on their orientation, is either focused or increases in divergence. In the RDWELDIV configuration, where waveguides diverge along the propagation axis, the LED beam suffers a 45% increase in divergence. When the same film is flipped to the RDWELCONV geometry, where waveguides converge along the propagation axis, the beam focuses to an effective focal length of ≈2 mm. These findings represent a new approach based on wave‐guided beam steering to precisely tailor LED beams. By changing parameters such as the FOV, lattice geometry, refractive index contrast, it would be possible to systematically tailor the shape and propagation of LED beams. This is not possible with existing technologies.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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) | 0.001 | 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".