Particle-size dependent stability of co-continuous polymer blends
Bibliographic record
Abstract
The properties of polymer blend nanocomposites are typically associated with spatiotemporal distribution of nanoparticles within a polymer blend system. Here, we present in situ high-temperature confocal rheology studies to assess the effect of particle size on the extent of particle agglomeration, particle migration, and subsequently their influence on the coarsening dynamics of polymer blends filled with pristine silica particles. We investigate co-continuous polypropylene-poly(ethylene-co-vinyl acetate) blends filled with five different silica particles with a diameter ranging from 5 to 490 nm. While particle size does not play a role when particles are thermodynamically driven to their preferred polymer phase, a striking effect is achieved when particles are kinetically trapped at the interface. We find that the interparticle interaction largely driven by size dependent long-range repulsive forces governs their extent of agglomeration, severely affecting their ability to stabilize co-continuous morphology. Strikingly, the largest (490 nm) particles are more effective in suppressing coarsening than 5 nm particles, while 140 and 250 nm particles are found to be the most effective. We demonstrate that kinetic trapping of primary particles of either size is influenced by the interplay of interfacial folding during melt blending and Laplacian pressure exerted at the interface. These results extend our fundamental understanding of the stabilization of co-continuous morphology in polymer blends by particles.
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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".