Core–shell structure and segregation effects in composite droplet polymer blends
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Abstract Core–shell morphology formation within the dispersed phase was studied for composite droplet polymer‐blend systems comprising a high‐density polyethylene matrix, polystyrene shell and different molecular weights of poly(methyl methacrylate) core material. The blends were prepared in the melt using an internal mixer, and the morphology was analyzed by electron microscopy. Changing the viscoelastic properties of the core in the dispersed phase dramatically affects PS‐PMMA segregation within the dispersed composite droplet itself. A high‐molecular‐weight‐PMMA core contains a large quantity of occluded PS inclusions, while the low‐molecular‐weight PMMA results in a perfectly segregated PS shell and PMMA core. These phenomena were attributed to the viscosity of the PMMA. Using the latter system, a direct microscopic study of the shell formation process demonstrates unambiguously that under conditions of perfect segregation, the onset of complete shell formation corresponds to a shell thickness that is close to two times the radius of gyration of polystyrene. Thus, the thinnest possible shell in such a system possesses a molecular‐scale thickness. The system with the high‐molecular‐weight‐PMMA core demonstrates an onset of complete shell formation that is displaced to higher concentrations due to the poor segregation effect. By counterbalancing the effects of viscosity ratio and interfacial effects on the composite droplet size, it is possible to generate perfectly segregated core–shell dispersed‐phase morphologies of almost identical size with a controlled shell thickness ranging from 40 to 300 nm.
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.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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 it