Hagfish slime threads as a biomimetic model for high performance protein fibres
Bibliographic record
Abstract
Textile manufacturing is one of the largest industries in the world, and synthetic fibres represent two-thirds of the global textile market. Synthetic fibres are manufactured from petroleum-based feedstocks, which are becoming increasingly expensive as demand for finite petroleum reserves continues to rise. For the last three decades, spider silks have been held up as a model that could inspire the production of protein fibres exhibiting high performance and ecological sustainability, but unfortunately, artificial spider silks have yet to fulfil this promise. Previous work on the biomechanics of protein fibres from the slime of hagfishes suggests that these fibres might be a superior biomimetic model to spider silks. Based on the fact that the proteins within these 'slime threads' adopt conformations that are similar to those in spider silks when they are stretched, we hypothesized that draw processing of slime threads should yield fibres that are comparable to spider dragline silk in their mechanical performance. Here we show that draw-processed slime threads are indeed exceptionally strong and tough. We also show that post-drawing steps such as annealing, dehydration and covalent cross-linking can dramatically improve the long-term dimensional stability of the threads. The data presented here suggest that hagfish slime threads are a model that should be pursued in the quest to produce fibres that are ecologically sustainable and economically viable.
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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".