High-resolution fabrication of nanopatterns by multistep iterative miniaturization of hot-embossed prestressed polymer films and constrained shrinking
Bibliographic record
Abstract
The fabrication of nanostructures and nanopatterns is of crucial importance in microelectronics, nanofluidics, and the manufacture of biomedical devices and biosensors. However, the creation of nanopatterns by means of conventional nanofabrication techniques such as electron beam lithography is expensive and time-consuming. Here, we develop a multistep miniaturization approach using prestressed polymer films to generate nanopatterns from microscale patterns without the need of complex nanolithography methods. Prestressed polymer films have been used as a miniaturization technique to fabricate features with a smaller size than the initial imprinted features. However, the height of the imprinted features is significantly reduced after the thermal shrinking of the prestressed films due to the shape memory effect of the polymer, and as a result, the topographical features tend to disappear after shrinking. We have developed a miniaturization approach that controls the material flow and maintains the shrunken patterns by applying mechanical constraints during the shrinking process. The combination of hot embossing and constrained shrinking makes it possible to reduce the size of the initial imprinted features even to the nanoscale. The developed multistep miniaturization approach allows using the shrunken pattern as a master for a subsequent miniaturization cycle. Well-defined patterns as small as 100 nm are fabricated, showing a 10-fold reduction in size from the original master. The developed approach also allows the transfer of the shrunken polymeric patterns to a silicon substrate, which can be used as a functional substrate for many applications or directly as a master for nanoimprint lithography.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".