Variations of the Depletion Zones around Inclusions Explain the Complexity of Brush-Induced Depletion Interactions
Bibliographic record
Abstract
Depletion forces are relevant in a variety of contexts such as the phase behavior of colloid-polymer or colloid-depletant mixtures and clustering of inclusions in mobile brushes. They arise from the tendency to minimize the volume of the depletion zone formed around colloidal particles or inclusions. In comparison to depletion interactions widely studied for colloidal particles or polymers in a suspension of spherical depletants, depletion interactions between nonspherical inclusions in mobile polymer brushes display complex behaviors. When the brush is shorter than the inclusion height, the inclusions in brushes experience apparent attraction; yet, such attraction is reduced or even becomes repulsive when the brush is overgrown beyond the inclusion height. Here we use the self-consistent field theory (SCFT) to calculate the depletion zones around two cylindrical inclusions and offer a clear explanation of how these complex behaviors arise. In tall brushes, the changes of the depletion zone volume with varying intercylinder separation are opposite in sign at the upper and lower parts of cylinders. Consequently, in tall brushes, cylinders even shorter than the size of a correlation blob experience repulsion, but long cylinders attract each other. Our study reveals that brush-induced depletion interactions are decided by the complex interplay among the sizes of brushes, inclusions, and correlation blobs.
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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 | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| 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".