Harnessing\nTunable Scanning Probe Techniques to Measure\nShear Enhanced Adhesion of Gecko-Inspired Fibrillar Arrays
Bibliographic record
Abstract
The hierarchical arrays of mesoscale\nto nanoscale fibrillar structures\non a gecko’s foot enable the animal to climb surfaces of varying\nroughness. Adhesion force between the fibrillar structures and various\nsurfaces is maximized after the gecko drags its foot in one direction,\nwhich has also been demonstrated to improve the adhesion forces of\nartificial fibrillar arrays. Essential conditions that influence the\nmagnitude of these interactions include the lateral distance traveled\nand velocity between the contacting surfaces, as well as the velocity\nat which the two surfaces are subsequently separated. These parameters\nhave, however, not been systematically investigated to assess the\nadhesion properties of artificial adhesives. We introduce a systematic\nstudy that investigates these conditions using a scanning probe microscope\nto measure the adhesion forces of artificial adhesives through a process\nthat mimics the mechanism by which a gecko climbs. The measured adhesion\nresponse was different for arrays of shorter and longer fibrils. These\nresults from 9000 independent measurements also provide further insight\ninto the dynamics of the interactions between fibrillar arrays and\ncontacting surfaces. These studies establish scanning probe microscopy\ntechniques as a versatile approach for measuring a variety of adhesion\nproperties of artificial fibrillar adhesives.
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How this classification was reachedexpand
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 | 0.000 | 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) | 0.011 | 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 teacher head, 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".