Controllable biomimetic adhesion using embedded phase change material
Bibliographic record
Abstract
In many cases, such as in the instance of climbing robots or temporary adhesives, there is the need to be able to dynamically control the level of adhesion a biomimetic dry adhesive can provide. In this study, the effect of changing the backing layer stiffness of a dry adhesive is examined. Embedding a phase change material within the backing of a synthetic dry adhesive sheet allows the stiffness to be tailored at different points of a preload and adhesion cycle. Larger contact areas and more equal load sharing between adhesive fibres can be achieved by increasing the backing layer stiffness after initial deformation when the adhesive backing is loaded in its softened state. Adhesion behaviour is examined when the backing layer is maintained in solid and softened phases during complete load cycles and for load cycles under the condition of contact with the softened phase backing followed by pull-off during the solid phase. Absolute adhesion force is increased for trials in which a soft backing layer hardens prior to pull-off. This effect is due to the increased contact area made between the rounded probe and the softened material during preloading and the more equal load sharing condition during pull-off when the backing layer becomes stiff again.
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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 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 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".