Shear-Induced β-Crystallite Unfolding in Condensed Phase Nanodroplets Promotes Fiber Formation in a Biological Adhesive
Bibliographic record
Abstract
Natural materials provide an increasingly important role model for the development and processing of next-generation polymers. The velvet worm Euperipatoides rowelli hunts using a projectile, mechanoresponsive adhesive slime that rapidly and reversibly transitions into stiff glassy polymer fibers following shearing and drying. However, the molecular mechanism underlying this mechanoresponsive behavior is still unclear. Previous work showed the slime to be an emulsion of nanoscale charge-stabilized condensed droplets comprised primarily of large phosphorylated proteins, which under mechanical shear coalesce and self-organize into nano- and microfibrils that can be drawn into macroscopic fibers. Here, we utilize wide-angle X-ray diffraction and vibrational spectroscopy coupled with in situ shear deformation to explore the contribution of protein conformation and mechanical forces to the fiber formation process. Although previously believed to be unstructured, our findings indicate that the main phosphorylated protein component possesses a significant β-crystalline structure in the storage phase and that shear-induced partial unfolding of the protein is a key first step in the rapid self-organization of nanodroplets into fibers. The insights gained here have relevance for sustainable production of advanced polymeric 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 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 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".