Selective Depletion Interactions in Mixtures of Rough and Smooth Silica Spheres
Bibliographic record
Abstract
The depletion interaction as induced between colloids by the addition of a polymer depletant is one of the few ways in which short-ranged attractions between particles can be controlled. Due to these tunable interactions, colloid-polymer mixtures have contributed to a better understanding of the role of attractions both in equilibrium phenomena such as phase transitions and liquid surfaces as well as in systems out of equilibrium such as gelation and the glass transition. It is known that, by simple geometric effects, surface roughness decreases the strength of the depletion interaction. In this study, we demonstrate both by Monte Carlo simulations and experiments that it is possible to generate enough difference in attraction strength to induce phase separation in smooth particles but not in rough particles. Roughness was induced by coating smooth particles with smaller spherical colloids. We indicate how effective potentials can be obtained through simulations and how the interplay between gravity and the depletion interaction with a flat container wall can be used to obtain a simple measure of the interaction strengths as a function of roughness.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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 teacher head, 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".