Self-aggregation of water-dispersible nanocollagen helices
Bibliographic record
Abstract
Inspired by nature, collagen is an outstanding polypeptide utilized to exploit its bioactivity and material design for healthcare technologies. In this study, we describe the self-aggregation of water-dispersible nanocollagen helices upon solidification to fabricate different forms of natural collagen materials. Chemically extracted native collagen fibrils are uniform anisotropic nanoparticles with an average diameter of about 50 nm and a high aspect ratio. The as-prepared collagen nanofibrils are soluble in sodium acetate-acetic acid buffer and are dispersible in water, thus generating collagen liquids that are used as distinct biopolymer precursors for materials development. Our interesting findings indicate that water-dispersible collagen-derived alcogels undergo critical point drying to self-arrange hierarchical nanofibrils into helix bundles in collagen sponge-like aerogels. Notably, using lyophilization to remove water in the biopolymer dispersion, a full regeneration of solidified fibers is achieved, producing collagen aerogels with lightweight characteristics similar to natural cottons. The self-aggregation of water-dispersible collagen occurs under freeze-drying conditions to turn individual nanofibrils into sheets with layered structures in the aerogel networks. The development of transparent, water resistant collagen bioplastic-like membranes was achieved by supramolecular self-assembly of water-dispersible collagen nanofibrils. Our efforts present a reliable concept in soft matter for creating promising collagen examples of liquids, hydrogels, aerogels, and membranes to increase utilization value of native collagen for biomedicine, pharmaceuticals, cosmetics, and nutrients.
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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".