Adsorption-Induced Optical Modulation in ZIF Thin Film Stacks with Distinct Order for Photonic Crystal Applications
Bibliographic record
Abstract
Due to their high chemical and thermal stability, zeolitic imidazolate frameworks (ZIFs) are interesting materials for various applications. To adapt ZIFs for new applications, such as optics, it is necessary to achieve precise shaping and control over film formation. Thin films are ideal for this purpose, as several of them are commonly used in optical devices. Here, we present a variety of different ZIF thin films that are accessible by applying a ZIF-8 seeding layer approach. High-quality ZIF-8 thin films are used as a seeding layer for the rapid production of other ZIF layers. All ZIFs are provided in high optical quality and have been characterized with respect to their optical properties. The presented thin films can also be fabricated as stacked layers with the desired order of different ZIFs to form photonic crystal structures, such as Bragg-stacks. Depending on the system, the fabrication of the stacked layers takes only a few hours. Finally, the thin films are loaded with guest molecules to influence their optical properties. Subsequent selective loading of the pores in ZIF 8/ZIF 90 stacks was achieved by using guest molecules of different polarity.
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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".