Morphology of Polymer Brushes Infiltrated by Attractive Nanoinclusions of Various Sizes
Bibliographic record
Abstract
Addition of nanoparticles can control the morphologies of grafted polymer layers that are important in a variety of natural and artificial systems. We study the morphologies of grafted polymer layers interacting attractively with nanoparticle inclusions, as a function of particle size and the interaction strength, using self-consistent field theory and Langevin dynamics simulations. We find that the addition of nanoparticles causes distinctive changes in the layer morphology. For sufficiently strong interaction/binding, increasing the concentration of nanoparticles causes a compression of the polymer layer into a compact, low height state, followed by a subsequent rebound and swelling at sufficiently high concentrations. For nanoparticles of small size, the compression of the layer is sharp and occurs over a narrow range of nanoparticle concentrations. The transition region widens as the nanoparticle size increases. The transition is initiated via a dense layer of tightly bound monomers and nanoparticles near the grafting surface, with a low density region above it. For nanoparticles much larger than the characteristic graft spacing in the brush, the behavior is reversed: the nanoparticles penetrate only the dilute region near the top of the polymer layer without causing the layer to collapse.
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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".