The Crowding Model as a Tool to Understand and Fabricate Gecko-Inspired Dry Adhesives
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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 | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it