Water Resistant Cellulose Acetate Based Photopolymer for Recording of Volume Phase Holograms
Bibliographic record
Abstract
The development of environmentally robust photosensitive materials for holographic recording is crucial for applications such as outdoor LED light redirection, holographic displays and holographic sensors. Despite the progress in holographic recording materials development, their sensitivity to humidity remains a challenge and protection from the environment is required. One approach to solving this challenge is to select substrate such as cellulose acetate, which is water resistant. This work reports the development of a cellulose-based photopolymer with sensitivity of 3.5 cm2/mJ and refractive index modulation of 2.5 × 10−3 achieved in the transmission mode of recording. The suitability for holographic recording was demonstrated by recording gratings with the spatial frequency of 800 linepairs/mm. The intensity dependence of the diffraction efficiency of gratings recorded in 70 μm thick layers was studied and it was observed that the optimum recording intensity was 10 mW/cm2. The robustness of the structures was studied after immersing the layer in water for one hour. It was observed that the diffraction efficiency and the surface characteristics measured before and after exposure to water remain unchanged. Finally, the surface hardness was characterized and was shown to be comparable to that of glass and significantly higher than the one of PVA-based acrylamide photopolymer.
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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".