Simple Preparation of Sophisticated Multiresponsive Core‐Crystalline Micelles via an Approach Reminiscent of the Division of Labor in Microorganisms
Bibliographic record
Abstract
Division of labor, where two identical cells differentiate to carry out distinctive function, represents a major development in evolution. Despite obvious advantages, such behavior remains elusive in supramolecular assembly, due to the difficulty to force a population of identical building blocks to spontaneously split and perform different tasks as they assemble. Here, this differentiation is achieved using a crystallization‐driven self‐assembly approach. A seeded growth on a suspension of aggregated seed crystallites is performed and nanoflower‐like mesostructures with 3D central sections surrounded by rays of 2D petals are prepared. This morphological divergence brings remarkably distinct responses to specific stimuli: high‐temperature annealing induces the formation of hollow petals, while, astonishingly, a simple solvent transfer leads to the perforation of their thick central sections, without affecting their overall morphological integrity. This work demonstrates how an artificial division of labor can be promoted to fabricate highly complex structures via supramolecular self‐assembly.
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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 | 0.001 |
| 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".