Adhesion and friction in gecko toe attachment and detachment
Bibliographic record
Abstract
Geckos can run rapidly on walls and ceilings, requiring high friction forces (on walls) and adhesion forces (on ceilings), with typical step intervals of approximately 20 ms. The rapid switching between gecko foot attachment and detachment is analyzed theoretically based on a tape model that incorporates the adhesion and friction forces originating from the van der Waals forces between the submicron-sized spatulae and the substrate, which are controlled by the (macroscopic) actions of the gecko toes. The pulling force of a spatula along its shaft with an angle between theta 0 and 90 degrees to the substrate, has a "normal adhesion force" contribution, produced at the spatula-substrate bifurcation zone, and a "lateral friction force" contribution from the part of spatula still in contact with the substrate. High net friction and adhesion forces on the whole gecko are obtained by rolling down and gripping the toes inward to realize small pulling angles between the large number of spatulae in contact with the substrate. To detach, the high adhesion/friction is rapidly reduced to a very low value by rolling the toes upward and backward, which, mediated by the lever function of the setal shaft, peels the spatulae off perpendicularly from the substrates. By these mechanisms, both the adhesion and friction forces of geckos can be changed over three orders of magnitude, allowing for the swift attachment and detachment during gecko motion. The results have obvious implications for the fabrication of dry adhesives and robotic systems inspired by the gecko's locomotion mechanism.
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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) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".