Bibliographic record
Abstract
Although bioinspired dry adhesives are nearly a decade old, there are to date no available commercial products based on these materials. Although commercialization is a while off, great strides have been made with respect to physical modeling of actual gecko adhesion, synthetic fabrication methods, and introduction of capabilities like self-cleaning, directional behavior, and superhydrophobic behavior into synthetic variations. Despite the large number of fabrication methods available, there are still specific difficulties in manufacturing these materials that limit their use in commercial applications. In this paper, we describe how a simple manufacturing technology can be adapted to create relatively high-strength adhesives at low costs on large areas. Our focus is on determining how larger diameter, easily manufactured fiber shapes can be best designed to adhere to smooth surfaces. This manufacturing method has been used to successfully produce adhesives from a variety of materials, and we demonstrate how it can be adapted to form the microscale mushroom-shaped fibers necessary for strong adhesion without needing vacuum casting. Additionally, we present practical lessons learned in what makes an effective dry adhesive for industrial applications, where the expected surfaces to be encountered are mostly flat and rigid in comparison to the adhesive material. Finally, the importance of tip roughness due to manufacturing methods is demonstrated to be a significant source of adhesion reduction which must be accounted for when designing these materials.
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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 | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
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".