Mussel Byssus Structure‐Function and Fabrication as Inspiration for Biotechnological Production of Advanced Materials
Bibliographic record
Abstract
Biotechnology offers an exciting avenue toward the sustainable production of high performance proteinaceous polymeric materials. In particular, the mussel byssus-a high performance adhesive bio-fiber used by mussels to cling on hard surfaces-has become a veritable archetype for bio-inspired self-healing fibers, tough coatings, and versatile wet adhesives. However, successful translation of mussel-inspired design principles into man-made materials hinges upon elucidating structure-function relationships and biological fabrication processes. This review provides a detailed survey of the state-of-the-art understanding of the biochemical structure-function relationships defining byssus performance with a particular focus on structural hierarchy and metal coordination-based cross-linking. The efforts to mimic the byssus in man-made materials are then discussed. While there has been a strong push to mimic the byssus via synthetic chemistry taking a reductionist approach, herein the focus is specifically on recent progress of biotechnology-based strategies that more closely approximate the biochemical complexity of the natural material. As an outlook, an overview of recent research toward understanding the natural byssus assembly process is provided, as processing remains a critical factor in achieving native-like properties.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".