Factors affecting the growth and fragmentation of polyferrocenylsilane diblock copolymer micelles
Bibliographic record
Abstract
Polyferrocenylsilane (PFS) diblock copolymers self-assemble in selective solvents to form one-dimensional micelles for a broad range of polymer compositions and experimental conditions, driven by the crystallization of the PFS block that forms the micelle core. The most striking feature of these micelles is that they remain active for further growth. They can be extended in length when additional polymer, dissolved in a good solvent, is added to a solution of the pre-existing micelles. This thesis describes several studies investigating the factors that affect the growth and fragmentation of PFS diblock copolymer micelles in solution, with a particular emphasis on polyisoprene-PFS (PI-PFS) diblock copolymers. The goal of my research was trying to provide deeper understanding of this crystallization-driven self-assembly (CDSA) process.\nIn an attempt to understand the growth kinetics of the PI-PFS cylindrical micelles, I added tiny amount of short micelle seeds into supersaturated solution of the same polymer, and followed the micelle growth by light scattering. The data analysis showed that the increase of micelle length could be described by an expression with two exponential decay terms. In another attempt to examine the factors that may affect the growth behavior of the PI-PFS micelles, I found that PI-PFS long micelles underwent fragmentation when they were subjected to external stimuli, e.g. addition of polar solvent, or heating. During the course of studying the effect of heating on the micelles, I developed a new approach to generate cylindrical micelles with controllable and uniform length, a one-dimensional analogue of self-seeding of crystalline polymers. I carried out a systematic study to investigate the self-seeding behavior of PFS block copolymers.
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 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".