Driving forces and molecular interactions in the self-assembly of block copolymers to form fiber-like micelles
Bibliographic record
Abstract
One-dimensional (1D) nanoscale objects abundant in nature commonly possess hierarchical structures and are generally constructed via bottom-up self-assembly strategies. The unique high aspect ratio morphology of the assembled nanofibrillar materials, such as collagen, cellulose, and silk, together with highly ordered architectures, endows a range of remarkable functionalities in nature. Inspired by this hierarchical building principle, block copolymers (BCPs) have been developed and employed to engineer man-made functional 1D nanostructures and as models to study the self-assembly process. The rapid development of advanced polymerization techniques allows for the precise design of BCPs and the resulting assemblies with intensive studies on distinct structure–property–function relationships. In this Review, we summarize and discuss the formation of fiber-like micelles from the perspectives of fundamental driving forces and molecular interactions involved in the solution self-assembly process. Three main formation mechanisms are highlighted, including covalent bonding, volume exclusion, and crystallization, which are involved in the corresponding domains of coronal, interfacial, and core segments of BCPs. Two spatiotemporal levels of fiber-like assemblies are discussed. In addition, the emerging applications and a general guidance for the rational design of advanced BCPs are proposed in light of the unique traits of fiber-like micelles.
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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 | 0.001 |
| 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".