Structure and Dynamics in Dense Suspensions of Micellar Nanocolloids
Bibliographic record
Abstract
We present an experimental description of the dynamics of a dense colloidal suspension of model hairy nanoparticles using dynamic light scattering. These colloids were obtained by cross-linking the poly(2-cinnamoylethyl methacrylate) cores of polystyrene-block-poly(2-cinnamoylethyl methacrylate), copolymer micelles. Because of their small size and the small spacing of their liquidlike structure, as detected with small-angle neutron scattering, these particles enable the systematic investigation of the low-scattering wavevector ( q ) fraction of the dynamic structure factor ( S ( q, t )) away from the peak, which relates to the osmotic modulus of the suspension. The two relaxation processes contributing to S ( q, t ) are the fast cooperative diffusion of the concentration fluctuations (hair interactions) and the self-diffusion of the slightly polydisperse cores (incoherent contribution). The former speeds up and loses intensity with increasing concentration, analogous to linear and hyperstar polymer solutions, whereas the latter slows down and exhibits virtually increasing intensity with concentration as a consequence of the correlation hole at low q 's, much like the hard sphere colloids. The study of these hairy particles contributes to the overall picture of the dynamic response of concentrated colloidal suspensions sterically stabilized by grafted macromolecules.
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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) | 0.000 | 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 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".