Rough Fibrils Provide a Toughening Mechanism in Biological Fibers
Bibliographic record
Abstract
Spider silk is a fascinating natural composite material. Its combination of strength and toughness is unrivalled in nature, and as a result, it has gained considerable interest from the medical, physics, and materials communities. Most of this attention has focused on the one to tens of nanometer scale: predominantly the primary (peptide sequences) and secondary (β sheets, helices, and amorphous domains) structure, with some insights into tertiary structure (the arrangement of these secondary structures) to describe the origins of the mechanical and biological performance. Starting with spider silk, and relating our findings to collagen fibrils, we describe toughening mechanisms at the hundreds of nanometer scale, namely, the fibril morphology and its consequences for mechanical behavior and the dissipation of energy. Under normal conditions, this morphology creates a nonslip fibril kinematics, restricting shearing between fibrils, yet allowing controlled local slipping under high shear stress, dissipating energy without bulk fracturing. This mechanism provides a relatively simple target for biomimicry and, thus, can potentially be used to increase fracture resistance in synthetic materials.
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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 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".