Molecular Basis for Water-Promoted Supramolecular Chirality Inversion in Helical Rosette Nanotubes
Bibliographic record
Abstract
Helical rosette nanotubes (RNTs) are obtained through the self-assembly of the GwedgeC motif, a self-complementary DNA base analogue featuring the complementary hydrogen bonding arrays of both guanine and cytosine. The first step of this process is the formation of a 6-membered supermacrocycle (rosette) maintained by 18 hydrogen bonds, which then self-organizes into a helical stack defining a supramolecular sextuple helix whose chirality and three-dimensional organization arise from the chirality, chemical structure, and conformational organization of the GwedgeC motif. Because a chiral GwedgeC motif is predisposed to express itself asymmetrically upon self-assembly, there is a natural tendency for it to form one chiral RNT over its mirror image. Here we describe the synthesis and characterization of a chiral GwedgeC motif that self-assembles into helical RNTs in methanol, but undergoes mirror image supramolecular chirality inversion upon the addition of very small amounts of water (<1% v/v). Extensive physical and computational studies established that the mirror-image RNTs obtained, referred to as chiromers, result from thermodynamic (in water) and kinetic (in methanol) self-assembly processes involving two conformational isomers of the parent GwedgeC motif. Although derived from conformational states, the chiromers are thermodynamically stable supramolecular species, they display dominant/recessive behavior, they memorize and amplify their chirality in an achiral environment, they change their chirality in response to solvent and temperature, and they catalytically transfer their chirality. On the basis of these studies, a detailed mechanism for supramolecular chirality inversion triggered by specific molecular interactions between water molecules and the GwedgeC motif is proposed.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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".