Deep UV patterning of acrylic masters for molding biomimetic dry adhesives
Bibliographic record
Abstract
We present a novel fabrication method for the production of biomimetic dry adhesives that allows enormous variation in fiber shapes and sizes. The technology is based on deep-UV patterning of commercial acrylic with semi-collimated light available from germicidal lamps, and combined careful processing conditions, material selection and novel developer choices to produce relatively high-aspect-ratio fibers with overhanging caps on large areas. These acrylic fibers are used as a master mold for subsequent silicone rubber negative mold casting. Because the bulk acrylic demonstrates little inherent adhesion to silicone rubbers, the master molds created in this process do not require any surface treatments to achieve high-yield demolding of interlocked structures. Multiple polymers can be cast from silicone rubber negative molds and this process could be used to structure smart materials on areas over multiple square feet. Using direct photopatterning of acrylic allows many of the desired structures for biomimetic dry adhesives to be produced with relative ease compared to silicon-based molding processes, including angled fibers and hierarchical structures. Optimized fiber shapes for a variety of polymers can be produced using this process, and adhesion measurements on a well-characterized polyurethane, ST-1060, are used to determine the effect of fiber geometry on adhesion performance.
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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 | 0.001 |
| 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".