Effects of Blockiness and Polydispersity on the Phase Behaviour of non-Markovian Random Block Copolymers
Bibliographic record
Abstract
A model of non-Markovian random block copolymers is developed and used to study the effects of blockiness and compositional polydispersity on the phase behaviour of random block copolymers. The model approximates a random copolymer as a series of segments with equal lengths, while each segment is composed of sequences of different monomers drawn randomly from a distribution. The phase behaviour of the model random copolymers is first examined using the random phase approximation (RPA) to study the effects of blockiness and polydispersity on the order-disorder transition. It is observed that the critical point is inversely proportional to the blockiness. Compositional polydispersity is found to facilitate phase separation, and could induce macrophase separation. Next, the model is implemented into self-consistent field theory (SCFT) in order to elucidate the full phase behaviour of symmetric (A/B)-A random copolymers. Finally, the model is applied to the particular case of poly(styrenesulfonate-b-methylbutylene) (PSS-PMB) to study the effects of blockiness on the phase behaviour. In particular, the stability and structure of the `swollen gyroid' morphology predicted by previous Monte Carlo simulations is examined.
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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 | 0.001 | 0.001 |
| 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 | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".