Mistletoe- and mussel-inspired fabrication of hierarchically structured protein-cellulose scaffolds from biomolecular condensates
Bibliographic record
Abstract
Nature's ability to produce hierarchical materials via biomolecular self-assembly can inspire bioinspired avenues to advanced materials using biorenewable components and water as a solvent. Recent advances have shown that biomolecular condensates are important precursor phases for fabricating biological materials like silk, mussel byssus, and velvet worm slime. Here, we leverage recent findings on the role of malleable biomolecular phases from both animal and plant systems to develop a synergistic mussel- and mistletoe-inspired approach for fabricating protein-cellulose composite scaffolds possessing tunable hierarchical structure. We demonstrate that recombinant mussel foot protein-1 (rMfp-1), undergoes controlled phase separation in solution when mixed with surface-functionalized anionic cellulose nanorods, forming condensates with characteristic core-shell morphology. Using a facile approach based on freeze-drying of suspensions, we produce freestanding protein-cellulose composite scaffolds possessing tunable porous structures with potential as scaffolds for tissue engineering. Through a cross-disciplinary approach combining various spectroscopic and imaging modalities, we gain mechanistic insights into the role of intermolecular interactions and physical processes in guiding this process. These findings highlight that hierarchically structured materials can be fabricated simply via multi-component phase separation. This work establishes a framework for understanding and controlling bio-inspired material fabrication, offering a strategy to engineer materials with tunable structure and properties that bridge biomaterials research and emerging directions in synthetic biology
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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".