From near hard spheres to colloidal surfboards
Bibliographic record
Abstract
This work revisits the synthesis of the colloidal particles most commonly used for making model near hard suspensions or as building blocks of model colloidal gels, i.e. sterically stabilised poly(methyl methacrylate) (PMMA) particles. The synthesis of these particles is notoriously hard to control and generally the problems are ascribed to the difficulty in synthesising the graft stabiliser (PMMA- g -PHSA). In the present work, it is shown that for improving the reliability of the synthesis as a whole, control over the polycondensation of the 12-polyhydroxystearic acid is the key. By changing the catalyst and performing the polycondensation in the melt, the chain length of the 12-polyhydroxystearic acid is better controlled, as confirmed by 1 H-NMR spectroscopy. Control over the graft copolymer now enables us to make small variations of near hard sphere colloids, for example spherical PMMA particles with essentially the same core size and different stabilising layer thicknesses can now be readily produced, imparting controlled particle softness. The PMMA spheres can be further employed to create, in gram scale quantities, colloidal building blocks having geometrical and/or chemical anisotropy by using a range of mechanical deformation methods. The versatility of the latter methods is demonstrated for polystyrene latex particles as well.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.002 |
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".