Adhesion Circle: A New Approach To Better Characterize Directional Gecko-Inspired Dry Adhesives
Bibliographic record
Abstract
The number of different designs of directional gecko-inspired adhesives has proliferated over the past 15 years, but some basic characterization tools are still nonstandardized, which can make direct comparisons of different adhesives in the literature difficult. By far the most common type of test for directional adhesives, the load-drag-pull (LDP) test is useful but can miss substantial information on the exact behavior of gecko-inspired adhesives in a variety of loading conditions. Other test techniques, including angled approaches and pull-offs, have been employed by a few groups but they are not as widely adopted; peel tests can be employed but require a larger amount of adhesive material to use in the test, which is not always practical given some current manufacturing constraints. Very few tests have looked at the effect of off-main axis loads on the performance of directional adhesives, however, and this quality of performance may be very important in applications where direct control over displacements or angle of pull-off in pitch and yaw of the peeling interface may not be practical or possible. To address this overlooked area of characterization, we introduce a new test concept for anisotropic adhesives, the adhesion circle, and also compare how the radial normal adhesion performance is altered depending on whether the pull-off comes after a displacement drag or when pulled at a constant angle from vertical after a preload. Testing directional adhesive designs made with different geometries shows that unexpected behaviors at pull-off angles not in the direction of the strong-weak axis can sometimes be seen. The complete adhesion circle tests should help better design directional adhesives for scaled up performance, and can be completed with relatively simple hardware that is typically used in most current directional adhesive tests.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".