Laser-Light-Scattering Study of Internal Motions of Polymer Chains Grafted on Spherical Latex Particles
Bibliographic record
Abstract
Using atom-transfer radical polymerization, we have prepared core−shell particles by grafting thermally sensitive poly( N -isopropylacrylamide) (PNIPAM) chains on a spherical polystyrene latex core (with a radius of ∼287 nm) via the “grafting-from” approach. As the temperature increases from 25 °C to 35 °C, the PNIPAM shell shrinks from a thickness of 625 nm to 110 nm and the chain density near the core increases from 7.3 × 10 -3 g/cm 3 to 7.2 × 10 -2 g/cm 3 . Using such core−shell particles, we have, for the first time, been able to study dynamics of long chains anchored on a particle in dilute dispersion by laser-light scattering. Our results showed that, besides the translational diffusion of the particle as a whole, there also exists an additional slow relaxation mode that is only observable at larger scattering vectors ( q ),which are presumably related to internal motions of the shell. In the fully swollen state, the relaxation rate of the slow motion (〈Γ〉 slow ) is insensitive to the observation length (1/ q ); however, its contribution to the scattering intensity ( A slow ) increases as q increases. In the shrunken state, 〈Γ〉 slow slightly decreases as q increases. 〈Γ〉 slow can be scaled to the shell thickness (〈 L 〉 brush ) as 〈Γ〉 slow ∝, with α = −2.5 ± 0.2, which is smaller than the predicated value of 3.
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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".