Benefit of Backing‐Layer Compliance in Fibrillar Adhesive Patches—Resistance to Peel Propagation in the Presence of Interfacial Misalignment
Bibliographic record
Abstract
Abstract Investigations of backing‐layer effects in bioinspired fibrillar adhesives have shown that increased compliance is detrimental to the strength of fibril arrays under normal loading due to an increase in severity of a circumferential load concentration. In this work, the impact of misalignment on the performance of fibrillar adhesive patches contacting smooth flat surfaces is examined, demonstrating that the conditions for circumferential detachment are extremely limited. For an array of fibrils on a backing layer of varying thickness, normal adhesion tests are performed against a flat surface that maintains a fixed angle of misalignment with respect to the adhesive surface. In the aligned state the detachment is circumferential and the detachment force is highest for the thinnest, least compliant backing layer. However, for misalignment angles on the order of just 0.1°, peel‐like detachments are observed. The thickest backing layer, being 210% more compliant than the thinnest, yields a 43% increase in the adhesive strength at a misalignment angle of 0.4°. This suggests that out‐with conditions of precise alignment, backing‐layer compliance is beneficial to strength under normal loading. A mechanical model is presented, revealing the mechanism behind enhanced resistance to peel propagation is deformation of the backing layer at the detachment front which reduces differential stretching of fibrils.
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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 | 0.000 | 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".