Geometric Transformations Afforded by Rotational Freedom in Aramid Amphiphile Nanostructures
Bibliographic record
Abstract
Molecular self-assembly in water leads to nanostructure geometries that can be tuned owing to the highly dynamic nature of amphiphiles. There is growing interest in strongly interacting amphiphiles with suppressed dynamics, as they exhibit ultrastability in extreme environments. However, such amphiphiles tend to assume a limited range of geometries upon self-assembly due to the specific spatial packing induced by their strong intermolecular interactions. To overcome this limitation while maintaining structural robustness, we incorporate rotational freedom into the aramid amphiphile molecular design by introducing a diacetylene moiety between two aramid units, resulting in diacetylene aramid amphiphiles (D-AAs). This design strategy enables rotations along the carbon–carbon sp hybridized bonds of an otherwise fixed aramid domain. We show that varying concentrations and equilibration temperatures of D-AA in water lead to self-assembly into four different nanoribbon geometries: short, extended, helical, and twisted nanoribbons, all while maintaining robust structure with thermodynamic stability. We use advanced microscopy, X-ray scattering, spectroscopic techniques, and two-dimensional (2D) NMR to understand the relationship between conformational freedom within strongly interacting amphiphiles and their self-assembly pathways.
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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".