Liquid–liquid crystalline phase separation of filamentous colloids and semiflexible polymers: experiments, theory and simulations
Bibliographic record
Abstract
Liquid-liquid crystalline phase separation (LLCPS) is the process by which an initially homogenous single-phase solution composed of a solvent-most frequently water- and a solute-typically rigid or semiflexible macromolecules, polymers, supramolecular aggregates, or filamentous colloids-demixes into two (or more) distinct phases in which one phase is depleted by the solute and features properties of isotropic solutions, whereas the other is enriched by the solute and exhibits liquid crystalline anisotropic properties. Differently from the more common liquid-liquid phase separation (LLPS) of flexible macromolecules, which is a trade-off between entropy and enthalpy, LLCPS is mostly an entropy-controlled process in which the morphology, composition and properties of the new phases depend primarily on kinetics and thermodynamic factors and, unexpectedly, on the history followed to reach a specific point in the phase diagram. This review aims to comprehensively discuss the process of LLCPS from experimental, theoretical, and simulation standpoints. We discuss the main systems and experimental approaches followed over the past decades to induce and control LLCPS, then we delve into the main theoretical and modeling approaches available to rationalize this process, and finally, we expand on how numerical simulations can significantly enrich the understanding of LLCPS. A final section touches on possible applications and the significance of LLCPS beyond pure physics, that is, in the broader context of biology, nanotechnology, and everyday life.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| 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".