Granular polymer nanocomposites
Bibliographic record
Abstract
Contrary to classical theories, nanoparticle dispersion in polymer melt has been shown to decrease the bulk viscosity, and to increase the membrane permeability and selectivity when incorporated into certain amorphous polymer glasses. However, the effects of particle concentration, particle size, and polymer configuration at particle interfaces are not well understood. To elucidate how the particle size, chain length, and mixture composition influence polymer-chain packing and, thus, free volume---which is known to primarily influence rheological and permeation properties of polymer nanocomposites---the volume of acrylic spheres (representing nanoparticles) mixed with aluminum ball chains (representing polymer chains) was measured, and the partial molar sphere volume at small but finite sphere volume fractions was calculated. The results show that the sphere radius with respect to the minimum chain loop size is the primary dimensionless parameter that affects mixture free volume. Moreover, free volume is maximal---up to twice the intrinsic inclusion volume per particle---when the sphere radius and the minimum chain loop size are comparable, which is because of the increase in sphere-chain interactions, whereas sphere-sphere interactions decrease the mixture free volume when particles are large. It was further determined that, in the presence of nanoparticles, free volume and polymer chain architecture play a determinative role in influencing the glass transition temperature of polymer nanocomposites. The reason for the decrease in the glass transition temperature of polymer nanocomposites is known to be the repulsive chain-nanoparticle interactions. However, in the absence of enthalpic interactions, it is still elusive how and why the glass transition temperature declines with nanoparticle loading. To examine the nanoparticle influence on chain relaxation dynamics and, thus, nanocomposite glass transition temperature, the relaxation time (the time to reach the close-packed, jammed state) of granular chain-sphere mixtures was measured by systematically changing the sphere size, chain length, and mixture composition. Measuring the compaction dynamics reveals that spherical inclusions profoundly influence the chain relaxation time when the characteristic nanoparticle separation and nanoparticle size are comparable to the chain loop size. This study can shed light on polymer architecture in the presence of nanoparticles, especially when chains are very long and, thus, beyond the capability of current computer simulations. This macroscopic, granular model can also be used to optimize the design of polymer nanocomposites by a judicious choice of nanoparticle size, chain length, and mixture composition for industrial and biomedical applications.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.085 | 0.011 |
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".