Cyclic Peptide–Polymer Complexes and Their Self‐Assembly
Bibliographic record
Abstract
Abstract Pepped up polymers: The synthesis and properties of novel chiral cyclic peptides designed to complex with suitable polymers through hydrogen bonding are described. A substituted cyclic peptide self‐assembles into supramolecular nanotubes and develops noncovalent interactions with poly(vinyl alcohol) (PVA) by means of its carboxyl side chains. magnified image The efficient synthesis of novel chiral cyclic peptides cyclo[NHCHX‐CHCHCH2CO(NHCH2CHCHCH2CO)2] designed to develop hydrogen‐bonding interactions with suitable polymers is described. Complexation of a carboxylic acid derivatized cyclic peptide 2 (X=CH2OCOCH2CH2CO2H) capable of self‐assembling as “endless” tubes, with poly(vinyl alcohol) (PVA) led to a vast weak‐interaction network, in which the cyclopeptide developed extensive hydrogen‐bonding interactions with the hydroxyl groups of PVA through not only the carboxylic acid, but also its ester carbonyl and amide groups. In aqueous solution, the peptide/PVA complexes self‐assemble into long‐grain ricelike aggregates compatible with the stacking of cyclic peptides through intercycle hydrogen bonds. Upon casting on silicon wafer, the anisotropic aggregates can coalesce to form filaments tens of micrometers long. The study demonstrates that complexing functionalized cyclic peptides with polymers through hydrogen bonding is a useful approach for using polymers to mediate the self‐assembly and self‐organization of cyclic peptides.
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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".