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 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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| 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 teacher head, 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".