Controllable and Reversible Assembly of Nanofiber from Natural Macromolecules via Protonation and Deprotonation
Bibliographic record
Abstract
Nanofiber is the critical building block for many biological systems to perform various functions. Artificial assembly of molecules into nanofibers in a controllable and reversible manner will create "smart" functions to mimic those of their natural analogues and fabricate new functional materials, but remains an open challenge especially for nature macromolecules. Herein, the controllable and reversible assembly of nanofiber (CSNF) from natural macromolecules with oppositely charged groups are successfully realized by protonation and deprotonation of charged groups. By controlling the electrostatic interaction via protonation and deprotonation, the size and morphology of the assembled nanostructures can be precisely controlled. A strong electrostatic interaction contributes to large nanofiber with high strength, while poor electrostatic interaction produces finer nanofiber or nanoparticle. And especially, the assembly, disassembly, and reassembly of the nanofiber occurs reversibly through protonation and deprotonation, thereby paving a new way for precisely controlling the assembly process and structure of nanofiber. The reversible assembly allows the nanostructure to dynamically reorganize in response to subtle perturbation of environment. The as-prepared CSNF is mechanical strong and can be used as a nano building block to fabricate high-strength film, wire, and straw. This study offers many opportunities for the biomimetic synthesis of new functional 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) | 0.001 | 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".