Effect of Molecular Architecture and Composition on the Aggregation Pathways of POEGMA Random Copolymers in Water
Bibliographic record
Abstract
Understanding of the temperature-induced phase transition of poly(oligo(ethylene glycol) methyl ether methacrylate) (POEGMA) random copolymers with varied composition remains largely incomplete. Upon heating they can form either macroscopically phase-separated aggregates or micelles. We examined the effect of polymer architecture by rationally designing and synthesizing various POEGMA copolymer structures via atom transfer radical polymerization using OEGMA monomers of different EO lengths. Micelle formation occurred for copolymers with a small fraction of long side chains counterbalanced by an appropriate number of short side chains, while macroscopic phase separation occurred for other copolymer compositions. In some copolymer compositions and architectures, micelle formation followed by macroscopic phase separation occurred, and the temperature of these phase transitions could be tailored accordingly. This new strategy allows the control over the microstructure and specific transition temperatures enabling, for instance, the preparation of nanocarriers for encapsulating hydrophobic compounds.
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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".