Color-changing and color-tunable photonic bandgap fiber textiles
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
We present the fabrication and use of plastic Photonic Band Gap Bragg fibers in photonic textiles for applications in interactive cloths, sensing fabrics, signage and art. In their cross section Bragg fibers feature periodic sequence of layers of two distinct plastics. Under ambient illumination the fibers appear colored due to optical interference in their microstructure. Importantly, no dyes or colorants are used in fabrication of such fibers, thus making the fibers resistant to color fading. Additionally, Bragg fibers guide light in the low refractive index core by photonic bandgap effect, while uniformly emitting a portion of guided color without the need of mechanical perturbations such as surface corrugation or microbending, thus making such fibers mechanically superior to the standard light emitting fibers. Intensity of side emission is controlled by varying the number of layers in a Bragg reflector. Under white light illumination, emitted color is very stable over time as it is defined by the fiber geometry rather than by spectral content of the light source. Moreover, Bragg fibers can be designed to reflect one color when side illuminated, and to emit another color while transmitting the light. By controlling the relative intensities of the ambient and guided light the overall fiber color can be varied, thus enabling passive color changing textiles. Additionally, by stretching a PBG Bragg fiber, its guided and reflected colors change proportionally to the amount of stretching, thus enabling visually interactive and sensing textiles responsive to the mechanical influence. Finally, we argue that plastic Bragg fibers offer economical solution demanded by textile applications.
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.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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 it