Supramolecular Chirality Transfer toward Chiral Aggregation: Asymmetric Hierarchical Self‐Assembly
Bibliographic record
Abstract
Self-assembly, as a typical bottom-up strategy for the fabrication of functional materials, has been applied to fabricate chiral materials with subtle chiral nanostructures. The chiral nanostructures exhibit great potential in asymmetric catalysis, chiral sensing, chiral electronics, photonics, and even the realization of several biological functions. According to existing studies, the supramolecular chirality transfer process combined with hierarchical self-assembly plays a vital role in the fabrication of multiscale chiral structures. This progress report focuses on the hierarchical self-assembly of chiral or achiral molecules that aggregate with asymmetric spatial structures such as twisted bands, helices, and superhelices in different environments. Herein, recent studies on the chirality transfer induced self-assembly based on a variety of supramolecular interactions are summarized. In addition, the influence of different environments and the states of systems including solutions, condensed states, gel systems, interfaces on the asymmetric hierarchical self-assembly, and the expression of chirality are explored. Moreover, both the driving forces that facilitate chiral bias and the supramolecular interactions that play an important role in the expression, transfer, and amplification of the chiral sense are correspondingly discussed.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
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".