Effect of softness on glass melting and re-entrant solidification in mixtures of soft and hard colloids
Bibliographic record
Abstract
We present a systematic investigation of the structure and dynamic properties of model soft-hard colloidal mixtures. Results of a coarse-grained theoretical model are contrasted with rheological data, where the soft and hard colloids are mimicked by large star polymers with high functionality as the soft component and smaller stars with ultrahigh functionality as the hard one. Previous work by us revealed the recovery of the ergodicity of glassy soft star solutions and subsequent arrested phase separation and re-entrant solid transition upon progressive addition of small hard depletants. Here, we use different components to show that a small variation in softness has a significant impact on the state diagram of such mixtures. In particular, we establish that rendering the soft component more penetrable and modifying the size ratio bring about a remarkable shift in both the phase separation region and the glass-melting line so that the region of restored ergodicity can be notably enhanced and extended to much higher star polymer concentrations than for pure systems. We further rationalize our findings by analyzing the features of the depletion interaction induced by the smaller component that result from the interplay between the size ratio and the softness of the large component. These results demonstrate the great sensitivity of the phase behavior of entropic mixtures to small changes in the molecular architecture of the soft stars and point to the importance of accounting for details of the internal microstructure of soft colloidal particles for tailoring the flow properties of soft composites.
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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 | 0.001 | 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".