Decoupling the interplay of polymer properties and particle size in stability of co-continuous blend composites
Bibliographic record
Abstract
Interfacially localizing particles in co-continuous polymer blends requires a complex interplay between the properties of polymers, such as interfacial tension between them, Γ, viscosity, η, viscosity ratio between them, and particle properties, such as particle size and particle surface chemistry. Here, we investigate the formation and coarsening dynamics of four co-continuous blend composites based on polypropylene, PP (or linear low-density polyethylene), and poly(ethylene-co-vinyl acetate), EVA filled with pristine silica of two sizes (140 and 250 nm). By choosing polymer blend components with different viscosities and interfacial tensions and particles with varying size and size distributions, we were able to elucidate their relative contributions in the stabilization of co-continuous polymer microstructures. By utilizing confocal rheology, we show that the evolution of storage modulus during coarsening of polymer blend composites is primarily dependent on the strength of the initial interfacial particle network. Our findings indicate that the initial domain size and kinetic control of interfacial particle localization in co-continuous polymer blends are determined by the Γ/η ratio of the neat blend. However, this relationship does not hold in low viscosity systems. When polymer blend viscosity is lower, it reduces the kinetic barrier at the interface, leading to a higher proportion of particles localizing in the favorable EVA phase. We also find that the smaller particles have a higher propensity for interfacial localization. These findings provide insight into the success of kinetic particle trapping at the interface of co-continuous blends and the resulting composite properties based on the choice of component properties.
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How this classification was reachedexpand
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) | 0.000 | 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 teacher head, 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".