Helical Rosette Nanotubes with Tunable Stability and Hierarchy
Bibliographic record
Abstract
The design of nanostructured materials with tunable dimensions and properties that maintain their structural integrity under physiological conditions is a major challenge in biomedical engineering and nanomedicine. Helical rosette nanotubes (HRN) are a new class of materials produced through a hierarchical self-assembly process of low molecular weight synthetic organic modules in water. Here, we describe a synthetic strategy to tune their stability and hierarchy by preorganization of the self-assembling units, control of net charge per unit of nanotube surface area, amphiphilicity, and number of H-bonds per self-assembling module, and through peripheral steric (de)compression. Using these criteria, HRNs with tunable stability and hierarchical architecture were produced from self-assembling modules that (a) persist as individual molecules in solution, (b) self-assemble into HRN but denature at high temperature (<85 degrees C), (c) self-assemble into HRN whose structural integrity persists even in boiling water (>95 degrees C), and (d) self-assemble into well-dispersed short nanotubes, long nanotubes, ribbons, or superhelices. Given the biocompatibility, synthetic accessibility, and chemical and physical tunability of these materials, numerous applications in biomedical engineering, materials science, and nanoscience and technology are envisioned.
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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".