Competitive Self-Assembly Kinetics as a Route To Control the Morphology of Core-Crystalline Cylindrical Micelles
Bibliographic record
Abstract
Nucleated self-assembly in selective solvents of core-crystalline block copolymers (BCPs) is a special case of living supramolecular polymerization, leading to rodlike micelles of controlled and uniform length. For the crystallization-driven self-assembly of PFS-containing BCPs (PFS = polyferrocenyldimethylsilane), the formation of block comicelles by sequential addition of different BCPs is well-established. But there are only a few examples of living copolymerization, the simultaneous addition of pairs of BCPs with different corona-forming chains. At present, relatively little is known about the competitive kinetics of different BCPs crystallizing on a common seed. Here we report a systematic study of the competitive seeded growth kinetics of pairs of linear PFS-containing BCPs and show that one can manipulate the kinetics to control the morphology of the comicelles. We found that the seeded-growth kinetics of the individual BCP unimer dominates the coassembly behavior and thus the morphology of the corona. Patchy comicelles with microphase-segregated corona chains are formed when the epitaxial growth rates of the two different BCPs on the common seed are similar. In contrast, factors that lead to dissimilar growth rates (long corona-forming blocks or introduction of charges on corona-forming chains) promote large-scale separation of the corona blocks, leading to block comicelles. Because the termini of the comicelles remain living, they can further direct the growth of unimers, resulting in hierarchical block comicelles with patchy blocks and single-component (homo) blocks. Furthermore, the patchy comicelles can be loaded with either gold or platinum nanoparticles, generating organic-inorganic hybrid materials with potential application in catalysis.
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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".