Strong and Sustainable Supramolecular Nanofiber Assembling in Acoustic Flow Field
Bibliographic record
Abstract
Abstract Hierarchical assembly of polysaccharides into nanofiber is at the core of generating advanced biomimetic nanomaterials. However, the artificial synthesis of supramolecular nanofiber from polysaccharides remains an open challenge due to their complicated structure, irregular, and strong interaction. Herein, by mimicking the assembly of natural macromolecules in an out‐of‐equilibrium state, supramolecular nanofiber is successfully fabricated from natural polysaccharides through regular and strong interaction, and a high‐energy and oriented flow field. The high energy of ultrasound can surmount the energy landscape of dynamically stable electrostatic interaction among polysaccharides, while the acoustic‐oriented streaming overcomes the disordered arrangement of macromolecules, thus inducing the orderly arrangement of polysaccharide chains to form kinetically stable nanofibers. The kinetically trapped assembly and the resulting structural evolution can be monitored by scattering and imaging experiments, while the microscopic mechanism can be confirmed by theoretical simulation. Mechanically strong, water‐resistant, and humidity stimulus‐responsive bioplastic film can be fabricated from the supramolecular nanofibers. The discoveries provide critical insights into the assembly of polysaccharides into supramolecular nanofibers and open up many possibilities to prepare advanced nanomaterials from natural polysaccharides.
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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) | 0.001 | 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".