Scalable Fiber-like Micelles and Block Co-micelles by Polymerization-Induced Crystallization-Driven Self-Assembly
Bibliographic record
Abstract
Self-assembled 1D block copolymer nanoparticles (micelles) are of interest for a range of applications. However, morphologically pure samples are often challenging to access, and precise dimensional control is not possible. Moreover, the development of synthetic protocols that operate on a commercially viable scale has been a major challenge. Herein, we describe the preparation 1D fiber-like micelles with crystalline cores at high concentrations by a one-pot process termed polymerization-induced crystallization-driven self-assembly (PI-CDSA). We also demonstrate the formation of uniform fibers by living PI-CDSA, a process in which block copolymer synthesis, self-assembly, and seeded growth are combined. We have demonstrated that the method is successful for block copolymers that possess the same composition as that of the seed (homoepitaxial growth) and also where the coronal chemistries differ to give segmented 1D fibers known as block co-micelles. We have also shown that heteroepitaxial growth allows the formation of scaled-up block co-micelles where the composition of both the core and corona was varied. These proof-of-concept experiments indicate that PI-CDSA is a promising, scalable route to a variety of polydisperse or uniform 1D nanoparticles based on block copolymers with different crystalline core chemistries and, therefore, functions.
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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 | 0.000 |
| 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".