Self-Seeding in One Dimension: A Route to Uniform Fiber-like Nanostructures from Block Copolymers with a Crystallizable Core-Forming Block
Bibliographic record
Abstract
One-dimensional micelles formed by the self-assembly of crystalline-coil poly(ferrocenyldimethylsilane) (PFS) block copolymers exhibit self-seeding behavior when solutions of short micelle fragments are heated above a certain temperature and then cooled back to room temperature. In this process, a fraction of the fragments (the least crystalline fragments) dissolves at elevated temperature, but the dissolved polymer crystallizes onto the ends of the remaining seed fragments upon cooling. This process yields longer nanostructures (up to 1 μm) with uniform width (ca. 15 nm) and a narrow length distribution. In this paper, we describe a systematic investigation of factors that affect the self-seeding behavior of PFS block copolymer micelle fragments. For PI(1000)-PFS(50) (the subscripts refer to the number average degree of polymerization) in decane, these factors include the presence of a good solvent (THF) for PFS and the effect of annealing the fragments prior to the self-seeding experiments. THF promoted the dissolution of the micelle fragments, while preannealing improved their stability. We also extended our experiments to other PFS block copolymers with different corona-forming blocks. These included PI(637)-PFS(53) in decane, PFS(60)-PDMS(660) in decane (PDMS = polydimethylsiloxane), and PFS(30)-P2VP(300) in 2-propanol (P2VP = poly(2-vinylpyridine)). The most remarkable result of these experiments is our finding that the corona-forming chain plays an important role in affecting how the PFS chains crystallize in the core of the micelles and, subsequently, the range of temperatures over which the micelle fragments dissolve. Our results also show that self-seeding is a versatile approach to generate uniform PFS fiber-like nanostructures, and in principle, the method should be extendable to a wide variety of crystalline-coil block copolymers.
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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) | 0.000 | 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 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".