Depletion, melting and reentrant solidification in mixtures of soft and hard colloids
Bibliographic record
Abstract
We present extensive experimental and theoretical investigations on the structure, phase behavior, dynamics and rheology of model soft-hard colloidal mixtures realized with large, multiarm star polymers as the soft component and smaller, compact stars as the hard one. The number and length of the arms in star polymers control their softness, whereas the size ratio, the overall density and the composition are additional parameters varied for the mixtures. A coarse-grained theoretical strategy is employed to predict the structure of the systems as well as their ergodicity properties on the basis of mode coupling theory, for comparison with rheological measurements on the samples. We discovered that dynamically arrested star-polymer solutions recover their ergodicity upon addition of colloidal additives. At the same time the system displays demixing instability, and the binodal of the latter meets the glass line in a way that leads, upon addition of a sufficient amount of colloidal particles, to an arrested phase separation and reentrant solidification. We present evidence for a subsequent solid-to-solid transition well within the region of arrested phase separation, attributed to a hard-sphere-mixture type of glass, due to osmotic shrinkage of the stars at high colloidal particle concentrations. We systematically investigated the interplay of star functionality and size ratio with glass melting and demixing, and rationalized our findings by the depletion of the big stars due to the smaller colloids. This new depletion potential in which, contrary to the classic colloid-polymer case, the hard component depletes the soft one, has unique and novel characteristics and allows the calculation of phase diagrams for such mixtures. This work covers a broad range of soft-hard colloidal mixture compositions in which the soft component exceeds the hard one in size and provides general guidelines for controlling the properties of such complex mixtures.
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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".