Photoactive/Passive Molecular Glass Blends: An Efficient Strategy to Optimize Azomaterials for Surface Relief Grating Inscription
Bibliographic record
Abstract
Irradiation of azomaterials causes various photophysical and photomechanical effects that can be exploited for the preparation of functional materials such as surface relief gratings (SRGs). Herein, we develop and apply an efficient strategy to optimize the SRG inscription process by decoupling, for the first time, the important effects of the azo content and glass transition temperature ( T g ). We prepare blends of a photoactive molecular glass functionalized with the azo Disperse Red 1 (gDR1) with a series of analogous photopassive molecular glasses. Blends with 10 and 40 mol % of gDR1 are completely miscible, present very similar optical properties, and cover a wide range of T g from below to well above ambient temperature. SRG inscription experiments show that the diffraction efficiency (DE), residual DE, and initial inscription rate reach a maximum when T g is 25–40 °C above ambient temperature for low to high azo content, respectively. Indeed, for a fixed 40 mol % azo content, choosing the optimal T g enables doubling the SRG inscription rate and increasing DE 6-fold. Moreover, a higher azo content enables higher DE for a similar T g . Spectroscopy measurements indicate that the photo-orientation of DR1 and its thermal stability are maximal with T g around 70 °C, independent of the azo content. We conclude that the SRG potential of azomaterials depends on their capability to photo-orient but that the matrix rigidity eventually limits the inscription kinetics, leading to an optimal T g that depends on the azo content. This study exposes clear material design guidelines to optimize the SRG inscription process and the photoactivity of azomaterials.
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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) | 0.001 | 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".