Azobenzene Elastomers for Mechanically Tunable Diffraction Gratings
Bibliographic record
Abstract
Three azobenzene-containing side-chain liquid crystalline polymers (SCLCPs) were grafted onto a styrene−butadiene−styrene (SBS) triblock copolymer to yield photoactive thermoplastic elastomers. The SCLCPs used were a polymethacrylate and two polyacrylates having different glass and phase transition temperatures. We have investigated the stretching-induced orientation of azobenzene mesogens, the orientation erasure by UV irradiation, and the consequences on the formation of diffraction gratings on stretched films. The results show that a combination of high degree of orientation of trans -azobenzene in nonirradiated areas with an efficient photoisomerization leading to disordered cis -azobenzene in irradiated areas is necessary for an efficient diffraction grating. However, this is not the only mechanism responsible for the formation of grating. Changes in the anisotropic morphology of stretched SBS may occur in these elastomers as a result of the photoisomerization process, which contributes to the formation of grating in films under strain and accounts for the stable diffraction grating remained in the relaxed state. These azobenzene elastomers can be used to record gratings that display reversible changes in diffraction angle and efficiency and the diffraction efficiency of which also depends strongly on the polarization of the probe light as well as the alignment of the fringes with respect to the strain of the film.
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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) | 0.001 | 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".