Bioinspired Fabrication and Characterization of a Synthetic Fish Skin for the Protection of Soft Materials
Bibliographic record
Abstract
The scaled skin of fish is a high-performance natural armor that represents a source of inspiration for novel engineering designs. In this paper, we present a biomimetic fish skin material, fabricated with a design and components that are simple, that achieves many of the advantageous attributes of natural materials, including the unique combination of flexibility and mechanical robustness. The bioinspired fish skin material is designed to replicate the structural, mechanical, and functional aspects of a natural teleost fish skin comprised of leptoid-like scales, similar to that of the striped red mullet Mullus surmuletus. The man-made fish skin material consists of a low-modulus elastic mesh or "dermis" layer that holds rigid, plastic scales. The mechanics of the synthetic material is characterized under in-plane, bending, and indentation modes of deformation and is successfully described by theoretical deformation models that have been developed. This combined experimental and modeling approach elucidates the critical mechanisms by which the composite material achieves its unique properties and provides design rules that allow for the engineering of scaled skins. Such artificial scaled skins that are flexible, lightweight, transparent, and robust under mechanical deformation may thus have potential as thin protective coatings for soft 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".