Azopolymer‐Based Nanoimprint Lithography: Recent Developments in Methodology and Applications
Bibliographic record
Abstract
Nanofabrication on soft polymeric surfaces is an essential process in many fields, for example, chip manufacturing, microfluidics, high efficiency solar cells, and anticounterfeiting. In order to achieve these applications, various nanofabrication methods have been explored. Among them, nanoimprint lithography (NIL) has drawn worldwide attention because of its cheap and fast processability. In this minireview, an overview of azopolymer-based NIL is provided. Since their discovery, azopolymers have demonstrated versatile photoresponsive characteristics due to their unique physical and chemical properties that originate from the photoisomerization of azobenzene chromophores. As such, two aspects are reported in this minireview. On the one hand, various azopolymers showing photofluidization and photoswitchable glass transition temperatures have been developed, thus facilitating methodological advancements in NIL. On the other hand, these on-demand NIL methods provide greater opportunities for azopolymer-based applications, such as templating of optics, directional photo-manipulation of nanopatterns, and micro photo-actuators. Also the challenges are discussed that remain in this field.
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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) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".