Controlling the Morphology of Immiscible Cocontinuous Polymer Blends via Silica Nanoparticles Jammed at the Interface
Bibliographic record
Abstract
Cocontinuous polymer blends have wide applications. They can form conductive plastics with improved mechanical properties. When one phase is extracted, they yield porous polymer sheets, which can be used as filters or membrane supports. However, the cocontinuous morphology is intrinsically unstable due to coarsening during static annealing. In this study, silica nanoparticles, ∼100 nm diameter, with different wetting properties were melt compounded in polyethylene/poly(ethylene oxide) blends. Calculated wetting coefficients of these particles match well with their phase contact angles and their locations in the blends. We demonstrated that a monolayer of particles jamming at interfaces can effectively suppress coarsening and stabilize the cocontinuous morphology. We also correlated the wettability of individual particles at interface to their coarsening suppression ability and found that the most hydrophobic silica nanoparticle is the most effective to arrest coarsening. Moreover, during annealing, we used the rheological dynamic time sweep, a facial but sensitive method, to relate the morphology change with particle dispersion on the interface. We further corroborated these measurements by scanning electron microscopy and confocal microscopy imaging.
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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".