Hierarchical modelling of attachment and detachment mechanisms of gecko toe adhesion
Bibliographic record
Abstract
The mechanics of reversible adhesion of the gecko is investigated in terms of the attachment and detachment mechanisms of the hierarchical microstructures on its toe. At the bottom of the hierarchy, we show that a spatula pad of tiny thickness can be well absorbed onto a substrate with a large surface area and a highly constrained decohesion process zone, both of which are beneficial for robust attachment. With different peeling angles, the peeling strength of a spatula pad for attachment can be 10 times larger than that for detachment. At the intermediate level of hierarchy, we show that a seta can achieve a stress level similar to that in the spatula pad by uniformly distributing adhesion forces; as a consequence, the 10 times difference in the peel-off force of a single spatula pad for attachment and detachment is magnified up to a 100 times difference in adhesion energy at the level of seta. At the top of the hierarchy, the attachment process of a gecko toe is modelled as a pad under displacement-controlled pulling, leading to an adhesive force much larger than the gecko's body weight, while the associated detachment process is modelled as a pad under peeling, resulting in a negligible peel-off force. The present work reveals, in a more systematic way than previous studies in the literature, that the hierarchical microstructures on the gecko's toe can indeed provide the gecko with robust adhesion for attachment and reversible adhesion for easy detachment at the same time.
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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 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| 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) | 0.003 | 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".