The Crowding Model as a Tool to Understand and Fabricate Gecko-Inspired Dry Adhesives
Bibliographic record
Abstract
A model based on geometrical considerations of pillars in a square lattice is analyzed to predict its compression behavior under an applied normal load. Specifically, the “crowding model” analyzes the point at which tilting pillars become crowded onto neighboring pillars, which limits the achievable tilt angle under an applied normal load, which in turn limits their adhesion and friction forces. The crowding model is applied to the setal arrays of the tokay gecko. Good agreement is found between the predictions of the crowding model (a critical tilt angle of θc = 12.6° to the substrate corresponding to a vertical compression of Δz =49 μm of the setae within the setal array) and experimental data for the compression of tokay gecko setal arrays. The model is also used as a criterion to predict the number density of setae in a tokay gecko setal array based on the lateral inter-pillar spacing distance, s, between tetrads of setae and the effective diameter, d, of the tetrad. The model predicts a packing density of ∼14,200 setae/mm2, which is again in good agreement with the measured value of ∼14,400 setae/mm2. The crowding model can be used as a tool to determine the optimum geometrical parameters, including the diameter and the spacing distance between pillars, to fabricate dry adhesives inspired by the gecko.
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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) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 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".