Gecko adhesion pad: a smart surface?
Bibliographic record
Abstract
Recently, it has been shown that humidity can increase the adhesion of the spatula pads that form the outermost (adhesive) surface of the tokay gecko feet by 50% relative to the main adhesion mechanism (i.e. van der Waals adhesive forces), although the mechanism by which the enhancement is realized is still not well understood. A change in the surface hydrophobicity of a gecko setal array is observed when the array, which supports the spatulae, is exposed to a water drop for more than 20 min, suggesting a change in the hydrophilic-lyophilic balance (HLB), and therefore of the conformation of the surface proteins. A surface force apparatus (SFA) was used to quantify these changes, i.e. in the adhesion and friction forces, while shearing the setal array against a silica surface under (i) dry conditions, (ii) 100% humidity and (iii) when fully immersed in water. The adhesion increased in the humid environment but greatly diminished in water. Although the adhesion forces changed significantly, the friction forces remained unaffected, indicating that the friction between these highly textured surfaces is 'load-controlled' rather than 'adhesion-controlled'. These results demonstrate that the gecko adhesive pads have the ability to exploit environmental conditions to maximize their adhesion and stabilize their friction forces. Future designs of synthetic dry adhesives inspired by the gecko can potentially include similar 'smart' surfaces that adapt to their environment.
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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 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".