Optimal Load Sharing in Bioinspired Fibrillar Adhesives: Asymptotic Solution
Bibliographic record
Abstract
Abstract We propose here an asymptotic solution defining the optimal compliance distribution for a fibrillar adhesive to obtain maximum theoretical strength. This condition corresponds to that of equal load sharing (ELS) among fibrils, i.e., all the fibrils are carrying the same load at detachment; hence, they all detach simultaneously. We model the array of fibrils as a continuum of linear elastic material that cannot laterally transmit load (analogous to a Winkler soil). Ultimately, we obtain the continuum distribution of fibril's compliance in the closed-form solution and compare it with previously obtained data for a discrete model for fibrillar adhesives. The results show improving accuracy for an incremental number of fibrils and smaller center-to-center spacing. Surprisingly, the approximation introduced by the asymptotic model shows reduced sensitivity of the adhesive strength with respect to misalignment and improved adhesive strength for large misalignment angles.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 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 | 0.001 |
| 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".