Design of gecko-inspired fibrillar surfaces with strong attachment and easy-removal properties: a numerical analysis of peel-zone
Bibliographic record
Abstract
Despite successful fabrication of gecko-inspired fibrillar surfaces with strong adhesion forces, how to achieve an easy-removal property becomes a major concern that may restrict the wide applications of these bio-inspired surfaces. Research on how geckos detach rapidly has inspired the design of novel adhesive surfaces with strong and reversible adhesion capabilities, which relies on further fundamental understanding of the peeling mechanisms. Recent studies showed that the peel-zone plays an important role in the peeling off of adhesive tapes or fibrillar surfaces. In this study, a numerical method was developed to evaluate peel-zone deformation and the resulting mechanical behaviour due to the deformations of fibrillar surfaces detaching from a smooth rigid substrate. The effect of the geometrical parameters of pillars and the stiffness of backing layer on the peel-zone and peel strength, and the strong attachment and easy-removal properties have been analysed to establish a design map for bio-inspired fibrillar surfaces, which shows that the optimized strong attachment and easy-removal properties can vary by over three orders of magnitude. The adhesion and peeling design map established provides new insights into the design and development of novel gecko-inspired fibrillar surfaces.
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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 | 0.001 |
| 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 | 0.000 |
| 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".