New photopolymer materials for holographic data storage
Bibliographic record
Abstract
Photopolymer materials have significant advantages making them suitable for applications as holographic data storage media. They provide phase holograms, which are very attractive since they can achieve very high diffraction efficiency. It is often desirable to provide an in-situ recording of holographic recording with different lasers sources. Diffractive optical elements (DOEs) attract great interest and holographic photopolymer dispersed liquid crystals (H-PDLC) are emerging as very promising for these applications. Recently, such materials have been sensitized to be sensitive in the near infrared region of light. The characterization of these materials is an important challenge that can result in a better understanding of the holographic grating formation photochemical process. These steps are important in order to reach the fabrication of more efficient DOEs. Since, these materials have self-developing properties (via free radical photopolymerization), the growth curve of the diffraction efficiency can be monitored as it is being recorded. We are interested in (and we actually study) holographic recording characteristics of our photopolymers, which include diffraction efficiency (in transmission and reflection modes), modulation transfer function, sensitivity, recording linearity, shrinkage, stability, etc. The corresponding details will be reported at the conference meeting.
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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 | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.002 |
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".