From vesicles to materials: bioinspired strategies for fabricating hierarchically structured soft matter
Bibliographic record
Abstract
Certain organisms including species of mollusks, polychaetes, onychophorans and arthropods produce exceptional polymeric materials outside their bodies under ambient conditions using concentrated fluid protein precursors. While much is understood about the structure-function relationships that define the properties of such materials, comparatively less is understood about how such materials are fabricated and specifically, how their defining hierarchical structures are achieved via bottom-up assembly. Yet this information holds great potential for inspiring sustainable manufacture of advanced polymeric materials with controlled multi-scale structure. In the present perspective, we first examine recent work elucidating the formation of the tough adhesive fibres of the mussel byssus via secretion of vesicles filled with condensed liquid protein phases (coacervates and liquid crystals)-highlighting which design principles are relevant for bio-inspiration. In the second part of the perspective, we examine the potential of recent advances in drops and additive manufacturing as a bioinspired platform for mimicking such processes to produce hierarchically structured materials. This article is part of the theme issue 'Bio-derived and bioinspired sustainable advanced materials for emerging technologies (part 1)'.
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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 | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".