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Generic multicomponent mixtures are multistable

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arxiv 2405.01138 v3 pith:JOD7IXKJ submitted 2024-05-02 cond-mat.soft physics.bio-ph

classification cond-mat.softphysics.bio-ph
keywords mixturescomponentsdropletscondensatesmulticomponentnumbercellscoexisting
verification ladder T0 review T1 audit T2 compute T3 formal
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Liquid mixtures of many interacting components often exhibit numerous coexisting types of droplets. An exciting example is the cytosol of biological cells, where diverse droplets, called condensates, are essential for cellular function. However, how much their formation is constrained by thermodynamics is currently unclear. Linear stability analysis predicts that homogeneous mixtures become more robust to fluctuations as the number of components increases, suggesting that droplets do not form easily in multicomponent mixtures. In contrast, we show through numerical simulations and analytical scaling laws that the number of coexisting phases typically increases with the number of components in equilibrium. The combination of both results suggests that generic multicomponent mixtures can maintain many metastable states with various droplets, generalizing the nucleation-and-growth regime of binary mixtures. Our theory also indicates why cells exhibit much fewer condensates than components and how they could exploit multistability to independently form various condensates.

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Cited by 2 Pith papers

Reviewed papers in the Pith corpus that reference this work. Sorted by Pith novelty score. Full citation record

  1. Metastable phase separation and information retrieval in multicomponent mixtures

    cond-mat.stat-mech 2025-09 conditional novelty 7.0 of 10

    Metastable phase-separated states in multicomponent liquids can store and retrieve compositional information, as shown in a Hopfield-liquid model with matching simulations.

  2. Combinatorial decision-making driven by multicomponent surface condensates

    physics.bio-ph 2025-09 conditional novelty 6.0 of 10

    Multicomponent surface condensates can be trained to classify input compositions, with hidden species enabling nonlinear boundaries and reservoir-level tuning enabling task reprogramming.

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