Harnessing Tunable Scanning Probe Techniques to Measure Shear Enhanced Adhesion of Gecko-Inspired Fibrillar Arrays
Bibliographic record
Abstract
The hierarchical arrays of mesoscale to nanoscale fibrillar structures on a gecko's foot enable the animal to climb surfaces of varying roughness. Adhesion force between the fibrillar structures and various surfaces is maximized after the gecko drags its foot in one direction, which has also been demonstrated to improve the adhesion forces of artificial fibrillar arrays. Essential conditions that influence the magnitude of these interactions include the lateral distance traveled and velocity between the contacting surfaces, as well as the velocity at which the two surfaces are subsequently separated. These parameters have, however, not been systematically investigated to assess the adhesion properties of artificial adhesives. We introduce a systematic study that investigates these conditions using a scanning probe microscope to measure the adhesion forces of artificial adhesives through a process that mimics the mechanism by which a gecko climbs. The measured adhesion response was different for arrays of shorter and longer fibrils. These results from 9000 independent measurements also provide further insight into the dynamics of the interactions between fibrillar arrays and contacting surfaces. These studies establish scanning probe microscopy techniques as a versatile approach for measuring a variety of adhesion properties of artificial fibrillar adhesives.
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.
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 | 0.000 |
| Open science | 0.000 | 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".