Improved Bio-inspired Artificial Gecko Adhesive by Using Hierarchical Fibrillar Structures
Bibliographic record
Abstract
Geckos are well known for being rapid climbers that have long existed in nature. The reversible and reusable adhesive on their feet intrigues scientists to explore a bio-mimetic adhesive, which inherits the adhesion properties of the gecko’s adhesives. Recent advances in electron microscopy reveal the secret of gecko’s climbing ability: there are hierarchical fibrillar structures branching from the skin of their climbing feet. Sizes of these hierarchical fibrils range from micrometer to nanometer. These fibrils are arranged to closely resemble a tree, and these tree like structures form a fibril forest on the skin of the climbing feet. Nano-fibrils in close proximity with the contacting surfaces interact with the substrate through intermolecular forces. Slender micro-fibrils extend the nano-fibrils, which are located at their open ends, to reach recesses of the contacting surfaces. The special arrangement of the fibrillar arrays enables quick attachment and detachment of the feet from surfaces of different materials and varying roughness. Inspired by the gecko’s adhesive, artificial fibrillar adhesives have been sought developing for more than a decade. Early attempts were focused on making use of the intermolecular interaction by nano-fibrillar arrays. These artificial fibrillar adhesives have achieved great performance on flat surfaces but not as good when they were used on relatively rough surfaces. Recent attempts of preparing a hierarchical fibrillar structure, which contains fibrils in different length scales, have rare success on improving adhesion performance. Evidence of extra compliancy provided by the hierarchical structure is also not clear. This thesis provides evidence that there is a correlation between structure compliancy and adhesion performance of a hierarchical fibrillar adhesive. Improved compliancy and adhesion forces are observed on a hierarchical fibrillar structure with achievements of several milestones, which include developing methods for preparing and characterizing hierarchical fibrillar structures. Experimental results also reveal the interaction of fibrillar arrays with the contacting surfaces. Information obtained is valuable for future development and application of such artificial fibrillar adhesive.
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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) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| 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) | 0.000 | 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 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".