Monolithic, Hierarchical Surface Reliefs by Holographic Photofluidization of Azopolymer Arrays: Direct Visualization of Polymeric Flows
Bibliographic record
Abstract
Abstract Hierarchically ordered, monolithic surface reliefs have attracted a great deal of interest due to their applications in advanced photonics and interface sciences. While many impressive achievements in fabrication of such surface reliefs have been made over the last decade, all established methods are still restricted by a number of factors, such as limited control of structural features, inherently induced structural defects, impractically low throughput, and technical barriers caused by mechanical contact. Herein, a deterministic and scalable fabrication of hierarchically ordered, monolithic surface reliefs by holographic photofluidization of azopolymer line arrays is demonstrated. In particular, it is shown that the structural features of monolithic surface reliefs including shapes and modulation heights can be deterministically tunable by adjusting the polarization and irradiation time of the holographic interference pattern. Moreover, by a direct visualization of azopolymeric flow according to the light polarization, a long‐standing question about the origin of surface‐relief‐grating formation on azopolymer film is addressed in terms of polymeric flows. Finally, as proof of concept for the practical application of the obtained hierarchical surface reliefs, dependence of wetting properties on modulating height is demonstrated.
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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".