Theoretical limits in detachment strength for axisymmetric bi-material\n adhesives
Bibliographic record
Abstract
Dry adhesives rely on short-ranged intermolecular bonds, hence requiring a\nlow elastic modulus to conform to the surface roughness of the adhered\nmaterial. Under external loads, however, soft adhesives accumulate strain\nenergy, which release drives the propagation of interfacial flaws prompting\ndetachment. The ideal adhesive is then soft but rigid. The solution to this\ncontroversial requirement is a bi-material adhesive having a soft tip, for\nsurface conformation, and a rigid backing, for reduced strain energy release,\nhence, better adhesive strength. This design strategy is widely observed in\nnature across multiple species. However, the detachment mechanisms of these\nadhesives are poorly understood and quantitative analysis of their adhesive\nstrength is still missing. Based on linear elastic fracture mechanics, we\nanalyze the strength of axisymmetric bi-material adhesives. We observed two\nmain detachment mechanisms, namely (i) center crack propagation and (ii) edge\ncrack propagation. If the soft tip is sufficiently thin, mechanism (i)\ndominates and provides stable crack propagation, thereby toughening the\ninterface. We ultimately provide the maximum theoretical strength of these\nadhesives obtaining closed form estimates for the detachment stress independent\nof the crack size, rendering the interface flaw tolerant.\n
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".